Original Research

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

METHODS

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

 

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

 

RESULTS

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

DISCUSSION

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

 

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

 

CONCLUSION

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

 

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

METHODS

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

 

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

 

RESULTS

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

DISCUSSION

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

 

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

 

CONCLUSION

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

 

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

METHODS

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

 

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

 

RESULTS

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

DISCUSSION

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

 

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

 

CONCLUSION

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

 

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

METHODS

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

 

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

RESULTS

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

 

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

DISCUSSION

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

 

CONCLUSION

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

 

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

METHODS

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

 

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

RESULTS

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

 

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

DISCUSSION

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

 

CONCLUSION

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

 

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

METHODS

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

 

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

RESULTS

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

 

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

DISCUSSION

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

 

CONCLUSION

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

 

In 2014, the United States spent $3 trillion on healthcare; hospitalization consumed 32% of these expenditures.¹ Today, Medicare patients account for over 50% of hospital days and over 30% of all hospital discharges in the United States.² Despite this staggering financial burden, hospitalization of older adults often results in poor patient outcomes.^3-6 The exponential growth of the hospitalist movement, from 350 hospitalists nationwide in 1995 to over 44,000 in 2014, has become the key strategy for providing care to hospitalized geriatric patients.^7-10 Most of these hospitalists have not received geriatric training.^11-15

There is growing evidence that a geriatric approach, emphasizing multidisciplinary management of the complex needs of older patients, leads to improved outcomes. Geriatric Evaluation and Management Units (GEMUs), such as Acute Care for Elderly (ACE) models, have demonstrated significant decreases in functional decline, institutionalization, and death in randomized controlled trials.^16,17 Multidisciplinary, nonunit based efforts, such as the mobile acute care of elderly (MACE), proactive consultation models (Sennour/Counsell), and the Hospital Elder Life Program (HELP), have demonstrated success in preventing adverse events and decreasing length of stay (LOS).^17-20

However, these models have not been systematically implemented due to challenges in generalizability and replicability in diverse settings. To address this concern, an alternative approach must be developed to widely “generalize” geriatric expertise throughout hospitals, regardless of their location, size, and resources. This initiative will require systematic integration of evidence-based decision support tools for the standardization of clinical management in hospitalized older adults.²¹

The 1998 Assessing Care of Vulnerable Elders (ACOVE) project developed a standardized tool to measure and evaluate the quality of care by using a comprehensive set of quality indicators (QIs) to improve the care of “vulnerable elders” (VEs) at a high risk for functional and cognitive decline and death.^22-24 The latest systematic review concludes that, although many studies have used ACOVE as an assessment tool of quality, there has been a dearth of studies investigating the ACOVE QIs as an intervention to improve patient care.²⁵

Our study investigated the role of ACOVE as an intervention by using the QIs as a standardized checklist in the acute care setting. We selected the 4 most commonly encountered QIs in the hospital setting, namely venous thrombosis prophylaxis (VTE), indwelling bladder catheter, mobilization, and delirium evaluation, in order to test the feasibility and impact of systematically implementing these ACOVE QIs as a therapeutic intervention for all hospitalized older adults.

METHODS

This study (IRB #13-644B) was conducted using a prospective intervention with a nonequivalent control group design comprised of retrospective chart data from May 1, 2014, to June 30, 2015. Process and outcome variables were extracted from electronic medical records ([EMR], Sunrise Clinical Manager [SCM]) of 2,396 patients, with 530 patients in the intervention unit and 1,866 on the control units, at a large academic tertiary center operating in the greater New York metropolitan area. Our study investigated the role of ACOVE as an intervention to improve patient care by using selected QIs as a standardized checklist tool in the acute care setting. Of the original 30 hospital-specific QIs, our study focused on the care of older adults admitted to the medicine service.²⁶ We selected commonly encountered QIs, with the objective of testing the feasibility and impact of implementing the ACOVE QIs as an intervention to improve care of hospitalized older adults. This intervention consisted of applying the checklist tool, constructed with 4 selected ACOVE QIs and administered daily during interdisciplinary rounds, namely: 2 general “medical” indicators, VTE prophylaxis and indwelling bladder catheters, and 2 “geriatric”-focused indicators, mobilization and delirium evaluation.

 

Subject matter experts (hospitalists, geriatricians, researchers, administrators, and nurses) reviewed the ACOVE QIs and agreed upon the adaptation of the QIs from a quality measure assessment into a feasible and acceptable intervention checklist tool (Table 1). The checklist was reviewed during daily interdisciplinary rounds for all patients 75 years and older. While ACOVE defined vulnerable elders by using the Vulnerable Elder Screen (VES), we wanted to apply this intervention more broadly to all hospitalized older adults who are most at risk for poor outcomes.²⁷ Patients admitted to the intensive care unit, inpatient psychiatry, inpatient leukemia/lymphoma, and surgical services were excluded.

Daily interdisciplinary rounds are held on every one of the five 40-bed medical units; they last approximately 1 hour, and consist of a lead hospitalist, nurse manager, nurse practitioners, case managers, and the nursing staff. During interdisciplinary rounds, nurses present the case to the team members who then discuss the care plan. These 5 medical units did not differ in terms of patient characteristics or staffing patterns; the intervention unit was chosen simply for logistical reasons, in that the principal investigator (PI) had been assigned to this unit prior to study start-up.

Prior to the intervention, LS held an education session for staff on the intervention unit staff (who participated on interdisciplinary rounds) to explain the concept of the ACOVE QI initiative and describe the four QIs selected for the study. Three subsequent educational sessions were held during the first week of the intervention, with new incoming staff receiving a brief individual educational session. The staff demonstrated significant knowledge improvement after session completion (pre/post mean score 70.6% vs 90.0%; P < .0001).

The Clinical Information System for the Health System EMR, The Eclipsys SCM, has alerts with different levels of severity from “soft” (user must acknowledge a recommendation) to “hard” (requires an action in order to proceed).

To measure compliance of the quality indicators, we collected the following variables:

QI 1: VTE prophylaxis

Through SCM, we collected type of VTE prophylaxis ordered (pharmacologic and/or mechanical) as well as start and stop dates for all agents. International normalized ratio levels were checked for patients receiving warfarin. Days of compliance were calculated.

QI 2: Indwelling Bladder Catheters

SCM data were collected on catheter entry and discontinuation dates, the presence of an indication, and order renewal for bladder catheter at least every 3 days.

QI 3: Mobilization

Ambulation status prior to admission was extracted from nursing documentation completed on admission to the medical ward. Patients documented as bedfast were categorized as nonambulatory prior to admission. Nursing documentation of activity level and amount of feet ambulated per nursing shift were collected. In addition, hospital day of physical therapy (PT) order and hospital days with PT performed were charted. Compliance with QI 3 in patients documented as ambulatory prior to hospital admission was recorded as present if there was a PT order within 48 hours of admission.

QI 4: Delirium Evaluation

During daily rounds, the hospitalist (PI) questioned nurses about delirium evaluation, using the first feature of the Confusion Assessment Method (CAM) as well as the “single question in delirium,” namely, “Is there evidence of an acute change in mental status from the patient’s baseline?” and “Do you think [name of patient] has been more confused lately?”^28,29 Because EMR does not contain a specified field for delirium screening and documentation, and patients are not routinely included in rounds, documentation with QI 4 was recorded using the “key words” method as described in the work by Puelle et al.³⁰ To extract SCM key words, nursing documentation of the “cognitive/perceptual/neurological exam” section of the EMR on admission and on all subsequent documentation (once per shift) was retrieved to identify acute changes in mental status (eg, “altered mental status, delirium/delirious, alert and oriented X 3, confused/confusion, disoriented, lethargy/lethargic”).³⁰ In addition, nurses were asked to activate an SCM parameter, “Acute Confusion” SCM parameter, in the nursing documentation section, which includes potential risk factors for confusion.

In addition to QI compliance, we collected LOS, discharge disposition, and 30-day readmission data.

Generalized linear mixed models (GLMM) for binary clustered (ie, hierarchical) data were used to estimate compliance rates (ie, nurse adherence) for each group (intervention group or control group) in the postintervention period, along with their corresponding 95% confidence intervals. GLMM was used to account for the hierarchical structure of the data: nursing units within a hospital. In order to calculate the Charlson Comorbidity Index, we extracted past medical history from the EMR.³¹

Subjects (N = 2,396) were included in the comparison of the intervention group vs control group for each of the following 4 ACOVE QI compliance measures: DVT, mobilization, bladder catheter, and delirium.

 

RESULTS

Of the 2,396 patient admissions, 530 were in the intervention unit and 1,866 were in the control unit. In the intervention group, the average age was 84.65 years, 75.58% were white and 47.21% were married. There was no difference in patient demographics between groups (Table 2).

 

QI 1: VTE Prophylaxis

Compliance with VTE prophylaxis was met in 78.3% of the intervention subjects and 76.5% of the controls (P < .4371) (Table 3). Of note, the rate of VTE prophylaxis was 57% in the intervention vs 39% in the control group (P < .0056), in the 554 patients for whom compliance was not met. Mechanical prophylaxis was used in 35.6% of intervention subjects vs 30.6 in the control (P = .048). Patients who received no form of prophylaxis were 0.5% in the intervention and 3% in the control (P = .027).

QI 2: Indwelling Bladder Catheters

Out of 2,396 subjects, 406 had an indwelling bladder catheter (16.9%). Compliance with the catheter was met in 72.2% of the intervention group vs 54.4% in the control group (P = .1061). An indication for indwelling bladder catheters was documented in 100% of the subjects. The average number of catheter days was 5.16 in the intervention vs 5.88 in the control (P < .2284). There was statistical significance in catheter compliance in the longer stay (>15 days) subjects, decreasing to 23.32% in the control group while staying constant in the intervention group 71.5% (P = .0006).

QI 3: Mobilization

Of the 2,396 patients, 1,991 (83.1%) were reported as ambulatory prior to admission. In the intervention vs control group, 74 (14%) vs 297 (15.7%), respectively, were nonambulatory. Overall compliance with Q3 was 62.9% in the intervention vs 48.2% in the control (P < .0001). More specifically, the average time to PT order in the intervention group was 1.83 days vs 2.22 days in the control group (P < .0051) and the time to PT evaluation was 2.14 days vs 2.42 days, respectively (P < .0108). In the intervention group, 84 patients (15.8%) did not have a PT consult vs 511 (27%) in the control group (P < .0001). The average times per subject in which the nurses documented the approximate number of feet ambulated was 6.48 in the intervention group vs 0.11 in the control group.

QI 4: Delirium Evaluation

In terms of nursing documentation indicating the presence of an acute confusional state, the intervention group had 148 out of 530 nursing notes (27.9%) vs 405 out of 1,866 in the control group (21.7%; P = .0027). However, utilization of the “acute confusion” parameter with documentation of a risk factor did not differ between the groups (5.8% in the intervention group vs 5.6% in the control group, P < .94).

LOS, Discharge Disposition, and 30-Day Readmissions

LOS did not differ between intervention and control groups (6.37 days vs 6.27 days, respectively), with a median of 5 days (P = .877). Discharge disposition in the 2 groups included the following: home/home with services (71.32% vs 68.7%), skilled nursing facility/assisted living/long-term care (24.34 versus 25.83), inpatient hospice/home hospice (2.64 vs 2.25), and expired (1.13 vs 1.77; P < .3282). In addition, 30-day readmissions did not differ (21% vs 20%, respectively, P = .41).

DISCUSSION

Our goal was to explore an evidence-based, standardized approach to improve the care of hospitalized older adults. This approach leverages existing automated EMR alert functions with an additional level of decision support for VEs, integrated into daily multidisciplinary rounds. The use of a daily checklist-based tool offers a cost-effective and practical pathway to distribute the burden of compliance responsibility amongst team members.

As we anticipated and similar to study findings in hospitalized medicine, geriatric trauma, and primary care, compliance with general care QIs was better than geriatric-focused QIs.^27,32 Wenger et al³³ demonstrated significant improvements with screening for falls and incontinence; however, screening for cognitive impairment did not improve in the outpatient setting by imbedding ACOVE QIs into routine physician practice.

Increased compliance with VTE prophylaxis and indwelling bladder catheters may be explained by national financial incentives for widespread implementation of EMR alert systems. Conversely, mobilization, delirium assessment, and management in hospitalized older adults don’t benefit from similar incentives.

VTE Prophylaxis

The American College of Chest Physicians (ACCP) supports the use of VTE prophylaxis, especially in hospitalized older adults with decreased mobility.³⁴ While greater adoption of EMR has already increased adherence, our intervention resulted in an even higher rate of compliance with the use of pharmacologic VTE prophylaxis.³⁵ In the future, validated scores for risk of thrombosis and bleeding may be integrated into our QI-based checklist.

 

Indwelling Bladder Catheters

The potential harms of catheters have been described for over 50 years, yet remain frequently used.^36,37 Previous studies have shown success in decreasing catheter days with computer-based and multidisciplinary protocols.^36-39

Our health system’s EMR has built-in “soft” and “hard” alerts for indwelling bladder catheters, so we did not expect intervention-associated changes in compliance.

Mobilization

Hospitalization in older adults frequently results in functional decline.^4,5,40 In response, the mobilization QI recommends an ambulation plan within 48 hours for those patients who were ambulatory prior to admission; it does not specifically define the components of the plan.²⁶ There are several multicomponent interventions that have demonstrated improvement in functional decline, yet they require skilled providers.^41,42 Our intervention implemented specific ambulation plan components: daily ambulation and documentation reminders and early PT evaluation.

While functional status measures have existed for decades, most are primarily geared to assess community-residing individuals and not designed to measure changes in function during hospitalization.^43,44 Furthermore, performance-based hospital measures are difficult to integrate into the daily nursing workflow as they are time consuming.^45,46 In practice, nurses routinely use free text to document functional status in the hospital setting, rendering comparative analysis problematic. Yet, we demonstrated that nurses were more engaged in reporting mobilization (increased documentation of ambulation distance and a decrease in time to PT). Future research should focus on the development of a standardized tool, integrated into the EMR, to accurately measure function in the acute care setting.

Delirium Evaluation

Delirium evaluation remains one of the most difficult clinical challenges for healthcare providers in hospitalized individuals, and our study reiterated these concerns. Previous research has consistently demonstrated that the diagnosis of delirium is missed by up to 75% of clinicians.^47,48 Indeed, our study, which exclusively examined nursing documentation of the delirium evaluation QI, found that both groups showed strikingly low compliance rates. This may have been due to the fact that we only evaluated nursing documentation of suspected or definite diagnosis of delirium and a documented attempt to attribute the altered mental state to a potential etiology.³¹ By utilizing the concept of “key words,” as developed by Puelle et al.³⁰, we were able to demonstrate a statistically significant improvement in nursing delirium documentation in the intervention group. This result should be interpreted with caution, as this approach is not validated. Furthermore, our operational definition of delirium compliance (ie, nurse documentation of delirium, requiring the launching of a separate parameter) may have been simply too cumbersome to readily integrate into the daily workflow. Future research should study the efficacy of a sensitive EMR-integrated screening tool that facilitates recognition, by all team members, of acute changes in cognition.

Although a number of QI improved for the intervention group, acute care utilization measures such as LOS, discharge disposition, and 30-day readmissions did not differ between groups. It may well be that improving quality for this very frail, vulnerable population may simply not result in decreased utilization. Our ability to further decrease LOS and readmission rates may be limited due to restriction of range in this complex patient population (eg, median LOS value of 5 days).

Limitations

Although our study had a large sample size, data were only collected from a single-center and thus require further exploration in different settings to ensure generalizability. In addition, QI observance was based on the medical record, which was problematic for some indicators, notably delirium identification. While prior literature highlights the difficulty in identifying delirium, especially during clinical practice without specialized training, our compliance was strikingly low.⁴⁷ While validated measures such as CAM may have been included as part of the assessment, there is currently no EMR documentation of such measures and therefore, these data could not be obtained.

CONCLUSION

In summary, our study demonstrates the successful integration of the established ACOVE QIs as an intervention, rather than as an assessment method, for improving care of hospitalized older patients. By utilizing a checklist-based tool at the bedside allows the multidisciplinary team to implement evidence-based practices with the ultimate goal of standardizing care, not only for VEs, but potentially for other high-risk populations with multimorbidity.⁴⁹ This innovative approach provides a much-needed direction to healthcare providers in the ever increasing stressful conditions of today’s acute care environment and for the ultimate benefit and safety of our older patients.

Disclosure

The authors declare no conflicts of interest. This study was supported by New York State Empire Clinical Research Investigators Program (ECRIP). The sponsor had no role in the conception, study design, data collection, data analysis, interpretation of data, manuscript preparation, or the decision to submit the manuscript for publication.

 

 

In 2014, the United States spent $3 trillion on healthcare; hospitalization consumed 32% of these expenditures.¹ Today, Medicare patients account for over 50% of hospital days and over 30% of all hospital discharges in the United States.² Despite this staggering financial burden, hospitalization of older adults often results in poor patient outcomes.^3-6 The exponential growth of the hospitalist movement, from 350 hospitalists nationwide in 1995 to over 44,000 in 2014, has become the key strategy for providing care to hospitalized geriatric patients.^7-10 Most of these hospitalists have not received geriatric training.^11-15

There is growing evidence that a geriatric approach, emphasizing multidisciplinary management of the complex needs of older patients, leads to improved outcomes. Geriatric Evaluation and Management Units (GEMUs), such as Acute Care for Elderly (ACE) models, have demonstrated significant decreases in functional decline, institutionalization, and death in randomized controlled trials.^16,17 Multidisciplinary, nonunit based efforts, such as the mobile acute care of elderly (MACE), proactive consultation models (Sennour/Counsell), and the Hospital Elder Life Program (HELP), have demonstrated success in preventing adverse events and decreasing length of stay (LOS).^17-20

However, these models have not been systematically implemented due to challenges in generalizability and replicability in diverse settings. To address this concern, an alternative approach must be developed to widely “generalize” geriatric expertise throughout hospitals, regardless of their location, size, and resources. This initiative will require systematic integration of evidence-based decision support tools for the standardization of clinical management in hospitalized older adults.²¹

The 1998 Assessing Care of Vulnerable Elders (ACOVE) project developed a standardized tool to measure and evaluate the quality of care by using a comprehensive set of quality indicators (QIs) to improve the care of “vulnerable elders” (VEs) at a high risk for functional and cognitive decline and death.^22-24 The latest systematic review concludes that, although many studies have used ACOVE as an assessment tool of quality, there has been a dearth of studies investigating the ACOVE QIs as an intervention to improve patient care.²⁵

Our study investigated the role of ACOVE as an intervention by using the QIs as a standardized checklist in the acute care setting. We selected the 4 most commonly encountered QIs in the hospital setting, namely venous thrombosis prophylaxis (VTE), indwelling bladder catheter, mobilization, and delirium evaluation, in order to test the feasibility and impact of systematically implementing these ACOVE QIs as a therapeutic intervention for all hospitalized older adults.

METHODS

This study (IRB #13-644B) was conducted using a prospective intervention with a nonequivalent control group design comprised of retrospective chart data from May 1, 2014, to June 30, 2015. Process and outcome variables were extracted from electronic medical records ([EMR], Sunrise Clinical Manager [SCM]) of 2,396 patients, with 530 patients in the intervention unit and 1,866 on the control units, at a large academic tertiary center operating in the greater New York metropolitan area. Our study investigated the role of ACOVE as an intervention to improve patient care by using selected QIs as a standardized checklist tool in the acute care setting. Of the original 30 hospital-specific QIs, our study focused on the care of older adults admitted to the medicine service.²⁶ We selected commonly encountered QIs, with the objective of testing the feasibility and impact of implementing the ACOVE QIs as an intervention to improve care of hospitalized older adults. This intervention consisted of applying the checklist tool, constructed with 4 selected ACOVE QIs and administered daily during interdisciplinary rounds, namely: 2 general “medical” indicators, VTE prophylaxis and indwelling bladder catheters, and 2 “geriatric”-focused indicators, mobilization and delirium evaluation.

 

Subject matter experts (hospitalists, geriatricians, researchers, administrators, and nurses) reviewed the ACOVE QIs and agreed upon the adaptation of the QIs from a quality measure assessment into a feasible and acceptable intervention checklist tool (Table 1). The checklist was reviewed during daily interdisciplinary rounds for all patients 75 years and older. While ACOVE defined vulnerable elders by using the Vulnerable Elder Screen (VES), we wanted to apply this intervention more broadly to all hospitalized older adults who are most at risk for poor outcomes.²⁷ Patients admitted to the intensive care unit, inpatient psychiatry, inpatient leukemia/lymphoma, and surgical services were excluded.

Daily interdisciplinary rounds are held on every one of the five 40-bed medical units; they last approximately 1 hour, and consist of a lead hospitalist, nurse manager, nurse practitioners, case managers, and the nursing staff. During interdisciplinary rounds, nurses present the case to the team members who then discuss the care plan. These 5 medical units did not differ in terms of patient characteristics or staffing patterns; the intervention unit was chosen simply for logistical reasons, in that the principal investigator (PI) had been assigned to this unit prior to study start-up.

Prior to the intervention, LS held an education session for staff on the intervention unit staff (who participated on interdisciplinary rounds) to explain the concept of the ACOVE QI initiative and describe the four QIs selected for the study. Three subsequent educational sessions were held during the first week of the intervention, with new incoming staff receiving a brief individual educational session. The staff demonstrated significant knowledge improvement after session completion (pre/post mean score 70.6% vs 90.0%; P < .0001).

The Clinical Information System for the Health System EMR, The Eclipsys SCM, has alerts with different levels of severity from “soft” (user must acknowledge a recommendation) to “hard” (requires an action in order to proceed).

To measure compliance of the quality indicators, we collected the following variables:

QI 1: VTE prophylaxis

Through SCM, we collected type of VTE prophylaxis ordered (pharmacologic and/or mechanical) as well as start and stop dates for all agents. International normalized ratio levels were checked for patients receiving warfarin. Days of compliance were calculated.

QI 2: Indwelling Bladder Catheters

SCM data were collected on catheter entry and discontinuation dates, the presence of an indication, and order renewal for bladder catheter at least every 3 days.

QI 3: Mobilization

Ambulation status prior to admission was extracted from nursing documentation completed on admission to the medical ward. Patients documented as bedfast were categorized as nonambulatory prior to admission. Nursing documentation of activity level and amount of feet ambulated per nursing shift were collected. In addition, hospital day of physical therapy (PT) order and hospital days with PT performed were charted. Compliance with QI 3 in patients documented as ambulatory prior to hospital admission was recorded as present if there was a PT order within 48 hours of admission.

QI 4: Delirium Evaluation

During daily rounds, the hospitalist (PI) questioned nurses about delirium evaluation, using the first feature of the Confusion Assessment Method (CAM) as well as the “single question in delirium,” namely, “Is there evidence of an acute change in mental status from the patient’s baseline?” and “Do you think [name of patient] has been more confused lately?”^28,29 Because EMR does not contain a specified field for delirium screening and documentation, and patients are not routinely included in rounds, documentation with QI 4 was recorded using the “key words” method as described in the work by Puelle et al.³⁰ To extract SCM key words, nursing documentation of the “cognitive/perceptual/neurological exam” section of the EMR on admission and on all subsequent documentation (once per shift) was retrieved to identify acute changes in mental status (eg, “altered mental status, delirium/delirious, alert and oriented X 3, confused/confusion, disoriented, lethargy/lethargic”).³⁰ In addition, nurses were asked to activate an SCM parameter, “Acute Confusion” SCM parameter, in the nursing documentation section, which includes potential risk factors for confusion.

In addition to QI compliance, we collected LOS, discharge disposition, and 30-day readmission data.

Generalized linear mixed models (GLMM) for binary clustered (ie, hierarchical) data were used to estimate compliance rates (ie, nurse adherence) for each group (intervention group or control group) in the postintervention period, along with their corresponding 95% confidence intervals. GLMM was used to account for the hierarchical structure of the data: nursing units within a hospital. In order to calculate the Charlson Comorbidity Index, we extracted past medical history from the EMR.³¹

Subjects (N = 2,396) were included in the comparison of the intervention group vs control group for each of the following 4 ACOVE QI compliance measures: DVT, mobilization, bladder catheter, and delirium.

 

RESULTS

Of the 2,396 patient admissions, 530 were in the intervention unit and 1,866 were in the control unit. In the intervention group, the average age was 84.65 years, 75.58% were white and 47.21% were married. There was no difference in patient demographics between groups (Table 2).

 

QI 1: VTE Prophylaxis

Compliance with VTE prophylaxis was met in 78.3% of the intervention subjects and 76.5% of the controls (P < .4371) (Table 3). Of note, the rate of VTE prophylaxis was 57% in the intervention vs 39% in the control group (P < .0056), in the 554 patients for whom compliance was not met. Mechanical prophylaxis was used in 35.6% of intervention subjects vs 30.6 in the control (P = .048). Patients who received no form of prophylaxis were 0.5% in the intervention and 3% in the control (P = .027).

QI 2: Indwelling Bladder Catheters

Out of 2,396 subjects, 406 had an indwelling bladder catheter (16.9%). Compliance with the catheter was met in 72.2% of the intervention group vs 54.4% in the control group (P = .1061). An indication for indwelling bladder catheters was documented in 100% of the subjects. The average number of catheter days was 5.16 in the intervention vs 5.88 in the control (P < .2284). There was statistical significance in catheter compliance in the longer stay (>15 days) subjects, decreasing to 23.32% in the control group while staying constant in the intervention group 71.5% (P = .0006).

QI 3: Mobilization

Of the 2,396 patients, 1,991 (83.1%) were reported as ambulatory prior to admission. In the intervention vs control group, 74 (14%) vs 297 (15.7%), respectively, were nonambulatory. Overall compliance with Q3 was 62.9% in the intervention vs 48.2% in the control (P < .0001). More specifically, the average time to PT order in the intervention group was 1.83 days vs 2.22 days in the control group (P < .0051) and the time to PT evaluation was 2.14 days vs 2.42 days, respectively (P < .0108). In the intervention group, 84 patients (15.8%) did not have a PT consult vs 511 (27%) in the control group (P < .0001). The average times per subject in which the nurses documented the approximate number of feet ambulated was 6.48 in the intervention group vs 0.11 in the control group.

QI 4: Delirium Evaluation

In terms of nursing documentation indicating the presence of an acute confusional state, the intervention group had 148 out of 530 nursing notes (27.9%) vs 405 out of 1,866 in the control group (21.7%; P = .0027). However, utilization of the “acute confusion” parameter with documentation of a risk factor did not differ between the groups (5.8% in the intervention group vs 5.6% in the control group, P < .94).

LOS, Discharge Disposition, and 30-Day Readmissions

LOS did not differ between intervention and control groups (6.37 days vs 6.27 days, respectively), with a median of 5 days (P = .877). Discharge disposition in the 2 groups included the following: home/home with services (71.32% vs 68.7%), skilled nursing facility/assisted living/long-term care (24.34 versus 25.83), inpatient hospice/home hospice (2.64 vs 2.25), and expired (1.13 vs 1.77; P < .3282). In addition, 30-day readmissions did not differ (21% vs 20%, respectively, P = .41).

DISCUSSION

Our goal was to explore an evidence-based, standardized approach to improve the care of hospitalized older adults. This approach leverages existing automated EMR alert functions with an additional level of decision support for VEs, integrated into daily multidisciplinary rounds. The use of a daily checklist-based tool offers a cost-effective and practical pathway to distribute the burden of compliance responsibility amongst team members.

As we anticipated and similar to study findings in hospitalized medicine, geriatric trauma, and primary care, compliance with general care QIs was better than geriatric-focused QIs.^27,32 Wenger et al³³ demonstrated significant improvements with screening for falls and incontinence; however, screening for cognitive impairment did not improve in the outpatient setting by imbedding ACOVE QIs into routine physician practice.

Increased compliance with VTE prophylaxis and indwelling bladder catheters may be explained by national financial incentives for widespread implementation of EMR alert systems. Conversely, mobilization, delirium assessment, and management in hospitalized older adults don’t benefit from similar incentives.

VTE Prophylaxis

The American College of Chest Physicians (ACCP) supports the use of VTE prophylaxis, especially in hospitalized older adults with decreased mobility.³⁴ While greater adoption of EMR has already increased adherence, our intervention resulted in an even higher rate of compliance with the use of pharmacologic VTE prophylaxis.³⁵ In the future, validated scores for risk of thrombosis and bleeding may be integrated into our QI-based checklist.

 

Indwelling Bladder Catheters

The potential harms of catheters have been described for over 50 years, yet remain frequently used.^36,37 Previous studies have shown success in decreasing catheter days with computer-based and multidisciplinary protocols.^36-39

Our health system’s EMR has built-in “soft” and “hard” alerts for indwelling bladder catheters, so we did not expect intervention-associated changes in compliance.

Mobilization

Hospitalization in older adults frequently results in functional decline.^4,5,40 In response, the mobilization QI recommends an ambulation plan within 48 hours for those patients who were ambulatory prior to admission; it does not specifically define the components of the plan.²⁶ There are several multicomponent interventions that have demonstrated improvement in functional decline, yet they require skilled providers.^41,42 Our intervention implemented specific ambulation plan components: daily ambulation and documentation reminders and early PT evaluation.

While functional status measures have existed for decades, most are primarily geared to assess community-residing individuals and not designed to measure changes in function during hospitalization.^43,44 Furthermore, performance-based hospital measures are difficult to integrate into the daily nursing workflow as they are time consuming.^45,46 In practice, nurses routinely use free text to document functional status in the hospital setting, rendering comparative analysis problematic. Yet, we demonstrated that nurses were more engaged in reporting mobilization (increased documentation of ambulation distance and a decrease in time to PT). Future research should focus on the development of a standardized tool, integrated into the EMR, to accurately measure function in the acute care setting.

Delirium Evaluation

Delirium evaluation remains one of the most difficult clinical challenges for healthcare providers in hospitalized individuals, and our study reiterated these concerns. Previous research has consistently demonstrated that the diagnosis of delirium is missed by up to 75% of clinicians.^47,48 Indeed, our study, which exclusively examined nursing documentation of the delirium evaluation QI, found that both groups showed strikingly low compliance rates. This may have been due to the fact that we only evaluated nursing documentation of suspected or definite diagnosis of delirium and a documented attempt to attribute the altered mental state to a potential etiology.³¹ By utilizing the concept of “key words,” as developed by Puelle et al.³⁰, we were able to demonstrate a statistically significant improvement in nursing delirium documentation in the intervention group. This result should be interpreted with caution, as this approach is not validated. Furthermore, our operational definition of delirium compliance (ie, nurse documentation of delirium, requiring the launching of a separate parameter) may have been simply too cumbersome to readily integrate into the daily workflow. Future research should study the efficacy of a sensitive EMR-integrated screening tool that facilitates recognition, by all team members, of acute changes in cognition.

Although a number of QI improved for the intervention group, acute care utilization measures such as LOS, discharge disposition, and 30-day readmissions did not differ between groups. It may well be that improving quality for this very frail, vulnerable population may simply not result in decreased utilization. Our ability to further decrease LOS and readmission rates may be limited due to restriction of range in this complex patient population (eg, median LOS value of 5 days).

Limitations

Although our study had a large sample size, data were only collected from a single-center and thus require further exploration in different settings to ensure generalizability. In addition, QI observance was based on the medical record, which was problematic for some indicators, notably delirium identification. While prior literature highlights the difficulty in identifying delirium, especially during clinical practice without specialized training, our compliance was strikingly low.⁴⁷ While validated measures such as CAM may have been included as part of the assessment, there is currently no EMR documentation of such measures and therefore, these data could not be obtained.

CONCLUSION

In summary, our study demonstrates the successful integration of the established ACOVE QIs as an intervention, rather than as an assessment method, for improving care of hospitalized older patients. By utilizing a checklist-based tool at the bedside allows the multidisciplinary team to implement evidence-based practices with the ultimate goal of standardizing care, not only for VEs, but potentially for other high-risk populations with multimorbidity.⁴⁹ This innovative approach provides a much-needed direction to healthcare providers in the ever increasing stressful conditions of today’s acute care environment and for the ultimate benefit and safety of our older patients.

Disclosure

The authors declare no conflicts of interest. This study was supported by New York State Empire Clinical Research Investigators Program (ECRIP). The sponsor had no role in the conception, study design, data collection, data analysis, interpretation of data, manuscript preparation, or the decision to submit the manuscript for publication.

 

 

In 2014, the United States spent $3 trillion on healthcare; hospitalization consumed 32% of these expenditures.¹ Today, Medicare patients account for over 50% of hospital days and over 30% of all hospital discharges in the United States.² Despite this staggering financial burden, hospitalization of older adults often results in poor patient outcomes.^3-6 The exponential growth of the hospitalist movement, from 350 hospitalists nationwide in 1995 to over 44,000 in 2014, has become the key strategy for providing care to hospitalized geriatric patients.^7-10 Most of these hospitalists have not received geriatric training.^11-15

There is growing evidence that a geriatric approach, emphasizing multidisciplinary management of the complex needs of older patients, leads to improved outcomes. Geriatric Evaluation and Management Units (GEMUs), such as Acute Care for Elderly (ACE) models, have demonstrated significant decreases in functional decline, institutionalization, and death in randomized controlled trials.^16,17 Multidisciplinary, nonunit based efforts, such as the mobile acute care of elderly (MACE), proactive consultation models (Sennour/Counsell), and the Hospital Elder Life Program (HELP), have demonstrated success in preventing adverse events and decreasing length of stay (LOS).^17-20

However, these models have not been systematically implemented due to challenges in generalizability and replicability in diverse settings. To address this concern, an alternative approach must be developed to widely “generalize” geriatric expertise throughout hospitals, regardless of their location, size, and resources. This initiative will require systematic integration of evidence-based decision support tools for the standardization of clinical management in hospitalized older adults.²¹

The 1998 Assessing Care of Vulnerable Elders (ACOVE) project developed a standardized tool to measure and evaluate the quality of care by using a comprehensive set of quality indicators (QIs) to improve the care of “vulnerable elders” (VEs) at a high risk for functional and cognitive decline and death.^22-24 The latest systematic review concludes that, although many studies have used ACOVE as an assessment tool of quality, there has been a dearth of studies investigating the ACOVE QIs as an intervention to improve patient care.²⁵

Our study investigated the role of ACOVE as an intervention by using the QIs as a standardized checklist in the acute care setting. We selected the 4 most commonly encountered QIs in the hospital setting, namely venous thrombosis prophylaxis (VTE), indwelling bladder catheter, mobilization, and delirium evaluation, in order to test the feasibility and impact of systematically implementing these ACOVE QIs as a therapeutic intervention for all hospitalized older adults.

METHODS

This study (IRB #13-644B) was conducted using a prospective intervention with a nonequivalent control group design comprised of retrospective chart data from May 1, 2014, to June 30, 2015. Process and outcome variables were extracted from electronic medical records ([EMR], Sunrise Clinical Manager [SCM]) of 2,396 patients, with 530 patients in the intervention unit and 1,866 on the control units, at a large academic tertiary center operating in the greater New York metropolitan area. Our study investigated the role of ACOVE as an intervention to improve patient care by using selected QIs as a standardized checklist tool in the acute care setting. Of the original 30 hospital-specific QIs, our study focused on the care of older adults admitted to the medicine service.²⁶ We selected commonly encountered QIs, with the objective of testing the feasibility and impact of implementing the ACOVE QIs as an intervention to improve care of hospitalized older adults. This intervention consisted of applying the checklist tool, constructed with 4 selected ACOVE QIs and administered daily during interdisciplinary rounds, namely: 2 general “medical” indicators, VTE prophylaxis and indwelling bladder catheters, and 2 “geriatric”-focused indicators, mobilization and delirium evaluation.

 

Subject matter experts (hospitalists, geriatricians, researchers, administrators, and nurses) reviewed the ACOVE QIs and agreed upon the adaptation of the QIs from a quality measure assessment into a feasible and acceptable intervention checklist tool (Table 1). The checklist was reviewed during daily interdisciplinary rounds for all patients 75 years and older. While ACOVE defined vulnerable elders by using the Vulnerable Elder Screen (VES), we wanted to apply this intervention more broadly to all hospitalized older adults who are most at risk for poor outcomes.²⁷ Patients admitted to the intensive care unit, inpatient psychiatry, inpatient leukemia/lymphoma, and surgical services were excluded.

Daily interdisciplinary rounds are held on every one of the five 40-bed medical units; they last approximately 1 hour, and consist of a lead hospitalist, nurse manager, nurse practitioners, case managers, and the nursing staff. During interdisciplinary rounds, nurses present the case to the team members who then discuss the care plan. These 5 medical units did not differ in terms of patient characteristics or staffing patterns; the intervention unit was chosen simply for logistical reasons, in that the principal investigator (PI) had been assigned to this unit prior to study start-up.

Prior to the intervention, LS held an education session for staff on the intervention unit staff (who participated on interdisciplinary rounds) to explain the concept of the ACOVE QI initiative and describe the four QIs selected for the study. Three subsequent educational sessions were held during the first week of the intervention, with new incoming staff receiving a brief individual educational session. The staff demonstrated significant knowledge improvement after session completion (pre/post mean score 70.6% vs 90.0%; P < .0001).

The Clinical Information System for the Health System EMR, The Eclipsys SCM, has alerts with different levels of severity from “soft” (user must acknowledge a recommendation) to “hard” (requires an action in order to proceed).

To measure compliance of the quality indicators, we collected the following variables:

QI 1: VTE prophylaxis

Through SCM, we collected type of VTE prophylaxis ordered (pharmacologic and/or mechanical) as well as start and stop dates for all agents. International normalized ratio levels were checked for patients receiving warfarin. Days of compliance were calculated.

QI 2: Indwelling Bladder Catheters

SCM data were collected on catheter entry and discontinuation dates, the presence of an indication, and order renewal for bladder catheter at least every 3 days.

QI 3: Mobilization

Ambulation status prior to admission was extracted from nursing documentation completed on admission to the medical ward. Patients documented as bedfast were categorized as nonambulatory prior to admission. Nursing documentation of activity level and amount of feet ambulated per nursing shift were collected. In addition, hospital day of physical therapy (PT) order and hospital days with PT performed were charted. Compliance with QI 3 in patients documented as ambulatory prior to hospital admission was recorded as present if there was a PT order within 48 hours of admission.

QI 4: Delirium Evaluation

During daily rounds, the hospitalist (PI) questioned nurses about delirium evaluation, using the first feature of the Confusion Assessment Method (CAM) as well as the “single question in delirium,” namely, “Is there evidence of an acute change in mental status from the patient’s baseline?” and “Do you think [name of patient] has been more confused lately?”^28,29 Because EMR does not contain a specified field for delirium screening and documentation, and patients are not routinely included in rounds, documentation with QI 4 was recorded using the “key words” method as described in the work by Puelle et al.³⁰ To extract SCM key words, nursing documentation of the “cognitive/perceptual/neurological exam” section of the EMR on admission and on all subsequent documentation (once per shift) was retrieved to identify acute changes in mental status (eg, “altered mental status, delirium/delirious, alert and oriented X 3, confused/confusion, disoriented, lethargy/lethargic”).³⁰ In addition, nurses were asked to activate an SCM parameter, “Acute Confusion” SCM parameter, in the nursing documentation section, which includes potential risk factors for confusion.

In addition to QI compliance, we collected LOS, discharge disposition, and 30-day readmission data.

Generalized linear mixed models (GLMM) for binary clustered (ie, hierarchical) data were used to estimate compliance rates (ie, nurse adherence) for each group (intervention group or control group) in the postintervention period, along with their corresponding 95% confidence intervals. GLMM was used to account for the hierarchical structure of the data: nursing units within a hospital. In order to calculate the Charlson Comorbidity Index, we extracted past medical history from the EMR.³¹

Subjects (N = 2,396) were included in the comparison of the intervention group vs control group for each of the following 4 ACOVE QI compliance measures: DVT, mobilization, bladder catheter, and delirium.

 

RESULTS

Of the 2,396 patient admissions, 530 were in the intervention unit and 1,866 were in the control unit. In the intervention group, the average age was 84.65 years, 75.58% were white and 47.21% were married. There was no difference in patient demographics between groups (Table 2).

 

QI 1: VTE Prophylaxis

Compliance with VTE prophylaxis was met in 78.3% of the intervention subjects and 76.5% of the controls (P < .4371) (Table 3). Of note, the rate of VTE prophylaxis was 57% in the intervention vs 39% in the control group (P < .0056), in the 554 patients for whom compliance was not met. Mechanical prophylaxis was used in 35.6% of intervention subjects vs 30.6 in the control (P = .048). Patients who received no form of prophylaxis were 0.5% in the intervention and 3% in the control (P = .027).

QI 2: Indwelling Bladder Catheters

Out of 2,396 subjects, 406 had an indwelling bladder catheter (16.9%). Compliance with the catheter was met in 72.2% of the intervention group vs 54.4% in the control group (P = .1061). An indication for indwelling bladder catheters was documented in 100% of the subjects. The average number of catheter days was 5.16 in the intervention vs 5.88 in the control (P < .2284). There was statistical significance in catheter compliance in the longer stay (>15 days) subjects, decreasing to 23.32% in the control group while staying constant in the intervention group 71.5% (P = .0006).

QI 3: Mobilization

Of the 2,396 patients, 1,991 (83.1%) were reported as ambulatory prior to admission. In the intervention vs control group, 74 (14%) vs 297 (15.7%), respectively, were nonambulatory. Overall compliance with Q3 was 62.9% in the intervention vs 48.2% in the control (P < .0001). More specifically, the average time to PT order in the intervention group was 1.83 days vs 2.22 days in the control group (P < .0051) and the time to PT evaluation was 2.14 days vs 2.42 days, respectively (P < .0108). In the intervention group, 84 patients (15.8%) did not have a PT consult vs 511 (27%) in the control group (P < .0001). The average times per subject in which the nurses documented the approximate number of feet ambulated was 6.48 in the intervention group vs 0.11 in the control group.

QI 4: Delirium Evaluation

In terms of nursing documentation indicating the presence of an acute confusional state, the intervention group had 148 out of 530 nursing notes (27.9%) vs 405 out of 1,866 in the control group (21.7%; P = .0027). However, utilization of the “acute confusion” parameter with documentation of a risk factor did not differ between the groups (5.8% in the intervention group vs 5.6% in the control group, P < .94).

LOS, Discharge Disposition, and 30-Day Readmissions

LOS did not differ between intervention and control groups (6.37 days vs 6.27 days, respectively), with a median of 5 days (P = .877). Discharge disposition in the 2 groups included the following: home/home with services (71.32% vs 68.7%), skilled nursing facility/assisted living/long-term care (24.34 versus 25.83), inpatient hospice/home hospice (2.64 vs 2.25), and expired (1.13 vs 1.77; P < .3282). In addition, 30-day readmissions did not differ (21% vs 20%, respectively, P = .41).

DISCUSSION

Our goal was to explore an evidence-based, standardized approach to improve the care of hospitalized older adults. This approach leverages existing automated EMR alert functions with an additional level of decision support for VEs, integrated into daily multidisciplinary rounds. The use of a daily checklist-based tool offers a cost-effective and practical pathway to distribute the burden of compliance responsibility amongst team members.

As we anticipated and similar to study findings in hospitalized medicine, geriatric trauma, and primary care, compliance with general care QIs was better than geriatric-focused QIs.^27,32 Wenger et al³³ demonstrated significant improvements with screening for falls and incontinence; however, screening for cognitive impairment did not improve in the outpatient setting by imbedding ACOVE QIs into routine physician practice.

Increased compliance with VTE prophylaxis and indwelling bladder catheters may be explained by national financial incentives for widespread implementation of EMR alert systems. Conversely, mobilization, delirium assessment, and management in hospitalized older adults don’t benefit from similar incentives.

VTE Prophylaxis

The American College of Chest Physicians (ACCP) supports the use of VTE prophylaxis, especially in hospitalized older adults with decreased mobility.³⁴ While greater adoption of EMR has already increased adherence, our intervention resulted in an even higher rate of compliance with the use of pharmacologic VTE prophylaxis.³⁵ In the future, validated scores for risk of thrombosis and bleeding may be integrated into our QI-based checklist.

 

Indwelling Bladder Catheters

The potential harms of catheters have been described for over 50 years, yet remain frequently used.^36,37 Previous studies have shown success in decreasing catheter days with computer-based and multidisciplinary protocols.^36-39

Our health system’s EMR has built-in “soft” and “hard” alerts for indwelling bladder catheters, so we did not expect intervention-associated changes in compliance.

Mobilization

Hospitalization in older adults frequently results in functional decline.^4,5,40 In response, the mobilization QI recommends an ambulation plan within 48 hours for those patients who were ambulatory prior to admission; it does not specifically define the components of the plan.²⁶ There are several multicomponent interventions that have demonstrated improvement in functional decline, yet they require skilled providers.^41,42 Our intervention implemented specific ambulation plan components: daily ambulation and documentation reminders and early PT evaluation.

While functional status measures have existed for decades, most are primarily geared to assess community-residing individuals and not designed to measure changes in function during hospitalization.^43,44 Furthermore, performance-based hospital measures are difficult to integrate into the daily nursing workflow as they are time consuming.^45,46 In practice, nurses routinely use free text to document functional status in the hospital setting, rendering comparative analysis problematic. Yet, we demonstrated that nurses were more engaged in reporting mobilization (increased documentation of ambulation distance and a decrease in time to PT). Future research should focus on the development of a standardized tool, integrated into the EMR, to accurately measure function in the acute care setting.

Delirium Evaluation

Delirium evaluation remains one of the most difficult clinical challenges for healthcare providers in hospitalized individuals, and our study reiterated these concerns. Previous research has consistently demonstrated that the diagnosis of delirium is missed by up to 75% of clinicians.^47,48 Indeed, our study, which exclusively examined nursing documentation of the delirium evaluation QI, found that both groups showed strikingly low compliance rates. This may have been due to the fact that we only evaluated nursing documentation of suspected or definite diagnosis of delirium and a documented attempt to attribute the altered mental state to a potential etiology.³¹ By utilizing the concept of “key words,” as developed by Puelle et al.³⁰, we were able to demonstrate a statistically significant improvement in nursing delirium documentation in the intervention group. This result should be interpreted with caution, as this approach is not validated. Furthermore, our operational definition of delirium compliance (ie, nurse documentation of delirium, requiring the launching of a separate parameter) may have been simply too cumbersome to readily integrate into the daily workflow. Future research should study the efficacy of a sensitive EMR-integrated screening tool that facilitates recognition, by all team members, of acute changes in cognition.

Although a number of QI improved for the intervention group, acute care utilization measures such as LOS, discharge disposition, and 30-day readmissions did not differ between groups. It may well be that improving quality for this very frail, vulnerable population may simply not result in decreased utilization. Our ability to further decrease LOS and readmission rates may be limited due to restriction of range in this complex patient population (eg, median LOS value of 5 days).

Limitations

Although our study had a large sample size, data were only collected from a single-center and thus require further exploration in different settings to ensure generalizability. In addition, QI observance was based on the medical record, which was problematic for some indicators, notably delirium identification. While prior literature highlights the difficulty in identifying delirium, especially during clinical practice without specialized training, our compliance was strikingly low.⁴⁷ While validated measures such as CAM may have been included as part of the assessment, there is currently no EMR documentation of such measures and therefore, these data could not be obtained.

CONCLUSION

In summary, our study demonstrates the successful integration of the established ACOVE QIs as an intervention, rather than as an assessment method, for improving care of hospitalized older patients. By utilizing a checklist-based tool at the bedside allows the multidisciplinary team to implement evidence-based practices with the ultimate goal of standardizing care, not only for VEs, but potentially for other high-risk populations with multimorbidity.⁴⁹ This innovative approach provides a much-needed direction to healthcare providers in the ever increasing stressful conditions of today’s acute care environment and for the ultimate benefit and safety of our older patients.

Disclosure

The authors declare no conflicts of interest. This study was supported by New York State Empire Clinical Research Investigators Program (ECRIP). The sponsor had no role in the conception, study design, data collection, data analysis, interpretation of data, manuscript preparation, or the decision to submit the manuscript for publication.

 

 

Fever in hospitalized patients is a nonspecific finding with many potential causes. Blood cultures (BC) are commonly obtained prior to commencing parenteral antibiotics in febrile patients. However, as many as 35% to 50% of positive BCs represent a contamination with organisms inoculated from the skin into culture bottles at the time of sample collection.^1-3 Such results represent false-positive BCs that can lead to unnecessary investigations and treatment.

Recently, Coburn et al. reviewed the severity of chills (graded on an ordinal scale) as the most useful predictor of true bacteremia (positive likelihood ratio [LR], 4.7; 95% confidence interval [CI], 3.0–7.2),^4-6 and the lack of the systemic inflammatory response syndrome (SIRS) criteria as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 We have also previously reported normal food consumption as a negative indicator of true bacteremia, with a 98.3% negative predictive value.⁸ Henderson’s Basic Principles of Nursing Care emphasizes the importance of evaluating whether a patient can eat and drink adequately,⁹ and the evaluation of a patient’s food consumption is a routine nursing staff practice, which is treated as vital sign in Japan, in contrast to nursing practices in the United States.

However, these data were the result of a single-center retrospective study using the nursing staff’s assessment of food consumption, and they cannot be generalized to larger patient populations. Therefore, the aim of this prospective, multicenter study was to measure the accuracy of food consumption and shaking chills as predictive factors for true bacteremia.

METHODS

Study Design

This was a prospective multicenter observational study (UMIN ID: R000013768) involving 3 hospitals in Tokyo, Japan, that enrolled consecutive patients who had BCs obtained. This study was approved by the ethical committee at Juntendo University Nerima Hospital and each of the participating centers, and the study was conducted in accordance with the Declaration of Helsinki 1971, as revised in 1983. We evaluated 2,792 consecutive hospitalized patients (mean age, 68.9 ± 17.1 years; 55.3% men) who had BCs obtained between April 2013 and August 2014, inclusive. The indication for BC acquisition was at the discretion of the treating physician. The study protocol and the indication for BCs are described in detail elsewhere.⁸ We excluded patients with anorexia-inducing conditions such as gastrointestinal disease, including gastrointestinal bleeding, enterocolitis, gastric ulceration, peritonitis, appendicitis, cholangitis, pancreatitis, diverticulitis, and ischemic colitis. We also excluded patients receiving chemotherapy for malignancy. In this study, true bacteremia was defined as identical organisms isolated from 2 sets of blood cultures (a set refers to one aerobic bottle and one anaerobic bottle). Moreover, even if only one set of blood cultures was acquired, when the identified pathogen could account for the clinical presentation, we also defined this as true bacteremia. Briefly, contaminants were defined as organisms common to skin flora, including Bacillus species, coagulase-negative Staphylococcus, Corynebacterium species, and Micrococcus species, without isolation of an identical organism with the same antibiotic susceptibilities from another potentially infected site in a patient with incompatible clinical features and no risk factors for infection with the isolated organism. Single BCs that were positive for organisms that were unlikely to explain the patient’s symptoms were also considered as contaminants. Patients with contaminated BCs were excluded from the analyses.

 

Structure of Reliability Study Procedures

Nurses in the 3 different hospitals performed daily independent food consumption ratings during each patient’s stay. Interrater reliability assessments were conducted in the morning or afternoon, and none of the raters had access to the other nurses’ scores at any time. The study nurses performed simultaneous ratings during these assessments (one interacted with and rated the patient while the other observed and rated the same patient).

Prediction Variables of True Bacteremia


1. Food consumption. Assessment of food consumption has been previously described in detail.⁸ Briefly, we characterized the patients’ oral intake based on the meal taken immediately prior to the BCs. For example, if a fever developed at 2 pm, lunch consumption was evaluated. If a fever developed at 2 am, dinner consumption was evaluated. We categorized the patients into 3 groups: low food consumption (<50% consumed), moderate food consumption (>50% to <80% consumed), and high food consumption (>80% consumed). To simplify our prediction rule, we subsequently divided food consumption into just 2 groups: high food consumption, referred to as the “normal food consumption group,” and the combination of low and moderate food consumption, referred to as the “poor food consumption group.”

2. Chills. As done previously, the physician evaluated the patient for a history of chills at the time of BCs and classified the patients into 1 of 4 grades^4,5: “no chills,” the absence of any chills; “mild chills,” feeling cold, equivalent to needing an outer jacket; “moderate chills,” feeling very cold, equivalent to needing a thick blanket; and “shaking chills,” feeling extremely cold with rigors and generalized bodily shaking, even under a thick blanket. To distinguish between those patients who had shaking chills and those who did not, we divided the patients into 2 groups: the “shaking chills group” and the combination of none, mild, and moderate chills, referred to as the “negative shaking chills group.”

3. Other predictive variables. We considered the following additional predictive variables: age, gender, axillary body temperature (BT), heart rate (HR), systolic blood pressure (SBP), respiratory rate (RR), white blood cell count (WBC), and serum C-reactive protein level (CRP). These predictive variables were obtained immediately prior to the BCs. We defined SIRS based on standard criteria (HR >90 beats/m, RR >20/m, BT <36°C or >38°C, and a WBC <4 × 10³ WBC/μL or >12 × 10³ WBC/μL). Patients were subcategorized by age into 2 groups (≤69 years and >70 years). CRP levels were dichotomized as >10.0 mg/dL or ≤10.0 mg/dL. We reviewed the patients’ charts to determine whether they had received antibiotics. In the case of walk-in patients, we interviewed the patients regarding whether they had visited a clinic; if they had, they were questioned as to whether any antibiotic therapy had been prescribed.

Statistical Analysis

Continuous variables are presented as the mean with the associated standard deviation (SD). All potential variables predictive of true bacteremia are shown in Table 1. The variables were dichotomized by clinically meaningful thresholds and used as potential risk-adjusted variables. We calculated the sensitivity and specificity and positive and negative predictive value for each criterion. Multiple logistic regression analysis was used to select components that were significantly associated with true bacteremia (the level of statistical significance determined with maximum likelihood methods was set at P < .05). To visualize and quantify other aspects in the prediction of true bacteremia, a recursive partitioning analysis (RPA) was used to make a decision tree model for true bacteremia. This nonparametric regression method produces a classification tree following a series of nonsequential top-down binary splits. The tree-building process starts by considering a set of predictive variables and selects the variable that produces 2 subsets of participants with the greatest purity. Two factors are considered when splitting a node into its daughter nodes: the goodness of the split and the amount of impurity in the daughter nodes. The splitting process is repeated until further partitioning is no longer possible and the terminal nodes have been reached. Details on this method are discussed in Monte Carlo Calibration of Distributions of Partition Statistics (www.jmp.com).

Probability was considered significant at a value of P < .05. All statistical tests were 2-tailed. Statistical analyses were conducted by a physician (KI) and an independent statistician (JM) with the use of the SPSS^® v.16.0 software package (SPSS Inc., Chicago, IL) and JMP^® version 8.0.2 (SAS Institute, Cary, NC).

RESULTS

Patients Characteristics

Two thousand seven hundred and ninety-two patients met the inclusion criteria for our study, from which 849 were excluded (see Figure 1 for flow diagram). Among the remaining 1,943 patients, there were 317 patients with positive BCs, of which 221 patients (69.7%) were considered to have true-positive BCs and 96 (30.3%) were considered to have contaminated BCs. After excluding these 96 patients, 221 patients with true bacteremia (true bacteremic group) were compared with 1,626 nonbacteremic patients (nonbacteremic group; Figure 1). The baseline characteristics of the subjects are shown in Table 1. The mean BT was 38.4 ± 1.2°C in the true bacteremic group and 37.9 ± 1.0°C in the nonbacteremic group. The mean serum CRP level was 11.6 ± 9.6 mg/dL in the true bacteremic group and 7.3 ± 6.9 mg/dL in the nonbacteremic group. In the true bacteremic group, there were 6 afebrile patients, and 27 patients without leukocytosis. The pathogens identified from the true-positive BCs were Escherichia coli (n = 59, 26.7%), including extended-spectrum beta-lactamase producing species, Staphylococcus aureus (n = 36, 16.3%), including methicillin-resistant Staphylococcus aureus, and Klebsiella pneumoniae (n = 22, 10.0%; Supplemental Table 1).

 

The underlying clinical diagnoses in the true bacteremic group included urinary tract infection (UTI), pneumonia, abscess, catheter-related bloodstream infection (CRBSI), cellulitis, osteomyelitis, infective endocarditis (IE), chorioamnionitis, iatrogenic infection at hemodialysis puncture sites, bacterial meningitis, septic arthritis, and infection of unknown cause (Supplemental Table 2).

Interrater Reliability Testing of Food Consumption

Patients were evaluated during their hospital stays. The interrater reliability of the evaluation of food consumption was very high across all participating hospitals (Supplemental Table 3). To assess the reliability of the evaluations of food consumption, patients (separate from this main study) were selected randomly and evaluated independently by 2 nurses in 3 different hospitals. The kappa scores of agreement between the nurses at the 3 different hospitals were 0.83 (95% CI, 0.63-0.88), 0.90 (95% CI, 0.80-0.99), and 0.80 (95% CI, 0.67-0.99), respectively. The interrater reliability of food consumption evaluation by the nurses was very high at all participating hospitals.

Food Consumption

The low, moderate, and high food consumption groups consisted of 964 (52.1%), 306 (16.6%), and 577 (31.2%) patients, respectively (Table 1). Of these, 174 (18.0%), 33 (10.8%), and 14 (2.4%) patients, respectively, had true bacteremia. The presence of poor food consumption had a sensitivity of 93.7% (95% CI, 89.4%-97.9%), specificity of 34.6% (95% CI, 33.0%-36.2%), and a positive LR of 1.43 (95% CI, 1.37-1.50) for predicting true bacteremia. Conversely, the absence of poor food consumption (ie, normal food consumption) had a negative LR of 0.18 (95% CI, 0.17-0.19).

Chills

The no, mild, moderate, and shaking chills groups consisted of 1,514 (82.0%), 148 (8.0%), 53 (2.9%), and 132 (7.1%) patients, respectively (Table 1). Of these, 136 (9.0%), 25 (16.9%), 8 (15.1%), and 52 (39.4%) patients, respectively, had true bacteremia. The presence of shaking chills had a sensitivity of 23.5% (95% CI, 22.5%-24.6%), a specificity of 95.1% (95% CI, 90.7%-99.4%), and a positive LR of 4.78 (95% CI, 4.56–5.00) for predicting true bacteremia. Conversely, the absence of shaking chills had a negative LR of 0.80 (95% CI, 0.77-0.84).

Prediction Model for True Bacteremia

The components identified as significantly related to true bacteremia by multiple logistic regression analysis are indicated in Table 2. The significant predictors of true bacteremia were shaking chills (odds ratio [OR], 5.6; 95% CI, 3.6-8.6; P < .01), SBP <90 mmHg (OR, 3.1; 95% CI, 1.6-5.7; P < 01), CRP levels >10.0 mg/dL (OR, 2.2; 95% CI, 1.6-3.1; P < .01), BT <36°C or >38°C (OR, 1.8; 95% CI, 1.3-2.6; P < .01), WBC <4 × 10³/μL or >12 × 10³/μL (OR, 1.6; 95% CI, 1.2-2.3; P = .003), HR >90 bpm (OR, 1.5; 95% CI, 1.1-2.1; P = .021), and female (OR, 1.4; 95% CI, 1.0-1.9; P = .036). An RPA to create an ideal prediction model for patients with true bacteremia or nonbacteremia is shown in Figure 2. The original group consisted of 1,847 patients, including 221 patients with true bacteremia. The pretest probability of true bacteremia was 2.4% (14/577) for those with normal food consumption (Group 1) and 2.4% (13/552) for those with both normal food consumption and the absence of shaking chills (Group 2). Conversely, the pretest probability of true bacteremia was 16.3% (207/1270) for those with poor food consumption and 47.7% (51/107) for those with both poor food consumption and shaking chills. The patients with true bacteremia with normal food consumption and without shaking chills consisted of 4 cases of CRBSI and UTI, 2 cases of osteomyelitis, 1 case of IE, 1 case of chorioamnionitis, and 1 case for which the focus was unknown (Supplemental Table 4).

DISCUSSION

In this observational study, we evaluated if a simple algorithm using food consumption and shaking chills was useful for assessing whether a patient had true bacteremia. A 2-item screening checklist (nursing assessment of food consumption and shaking chills) had excellent statistical properties as a brief screening instrument for true bacteremia.

We have prospectively validated that food consumption, as assessed by nurses, is a reliable predictor of true bacteremia.⁸ A previous single-center retrospective study showed similar findings, but these could not be generalized across all institutions because of the limited nature of the study. In this multicenter study, we used 2 statistical methods to reduce selection bias. First, we performed a kappa analysis across the hospitals to evaluate the interrater reliability of the evaluation of food consumption. Second, we used an RPA (Figure 2), also known as a decision tree model. RPA is a step-by-step process by which a decision tree is constructed by either splitting or not splitting each node on the tree into 2 daughter nodes.¹⁰ By using this method, we successfully generated an ideal approach to predict true bacteremia using food consumption and shaking chills. After adjusting for food consumption and shaking chills, groups 1 to 2 had sequentially decreasing diagnoses of true bacteremia, varying from 221 patients to only 13 patients.

Appetite is influenced by many factors that are integrated by the brain, most importantly within the hypothalamus. Signals that impinge on the hypothalamic center include neural afferents, hormones, cytokines, and metabolites.¹¹ These factors elicit “sickness behavior,” which includes a decrease in food-motivated behavior.¹² Furthermore, exposure to pathogenic bacteria increases serotonin, which has been shown to decrease metabolism in amphid neurons by transcriptional and post-transcriptional mechanisms.¹³ Therefore, nonbacteremic patients retain their appetites. Shaking chills are a well-known predictor of true bacteremia.^4,5 Several cytokines, including tumor necrosis factor-alpha and interleukins 6 and 10, may be related to shaking chills.¹⁴ Coburn et al. reviewed that shaking chills appear to be useful for identifying true bacteremia (positive LR, 4.7; 95% CI, 3.0-7.2),^5,6 similar to our study. In our study, the pretest probability of true bacteremia was the same whether shaking chills was included or not (ie, 2.4% for normal food consumption and 2.4% for normal food consumption plus absence of shaking chills). This would seem to imply that the assessment of shaking chills does not appear to add anything over food assessment alone when trying to rule out bacteremia. Rather, shaking chills seem more important for ruling in bacteremia rather than ruling it out. Moreover, the recent retrospective study revealed that age >60 years (OR = 2.75, 95% CI, 1.23-6.48, P = .015), female sex (OR = 2.21, 95% CI, 1.07- 4.67, P = .038), heart rate >90 bpm (OR = 5.18, 95% CI, 2.25-12.48, P < .001) and neutrophil percentage >80% (OR = 3.61, 95% CI, 1.71- 8.00, P = .001) were independent risk factors for true bacteremia.¹⁵ Conversely, the lack of the SIRS criteria was reported as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 However, the evaluation of SIRS criteria requires the acquisition of laboratory data. To our knowledge, no previous prospective studies have evaluated food consumption in terms of a risk prediction for true bacteremia. This extremely simple model can enable a physician to make a rapid bedside estimation of the risk of true bacteremia.

The strengths of this study include its relatively large sample size, multicenter design, uniformity of data collection across sites, and completeness of data collection from study participants. All of these factors allowed for a robust analysis.

However, there are several limitations of this study. First, the physicians or nurses asked the patients about the presence of shaking chills when they obtained the BCs. It may be difficult for patients, especially elderly patients, to provide this information promptly and accurately. Some patients did not call the nurse when they had shaking chills, and the chills were not witnessed by a healthcare provider. However, we used a more specific definition for shaking chills: a feeling of being extremely cold with rigors and generalized bodily shaking, even under a thick blanket. Second, this algorithm is not applicable to patients with immunosuppressed states because none of the hospitals involved in this study perform bone marrow or organ transplantation. Third, although we included patients with dementia in our cohort, we did not specifically evaluate performance of the algorithm in patients with this medical condition. It is possible that the algorithm would not perform well in this subset of patients owing to their unreliable appetite and food intake. Fourth, some medications may affect appetite, leading to reduced food consumption. Although we have not considered the details of medications in this study, we found that the pretest probability of true bacteremia was low for those patients with normal food consumption regardless of whether the medication affected their appetites or not. However, the question of whether medications truly affect patients’ appetites concurrently with bacteremia would need to be specifically addressed in a future study.

 

CONCLUSION

In conclusion, we have established a simple algorithm to identify patients with suspected true bacteremia who require the acquisition of blood cultures. This extremely simple model can enable physicians to make a rapid bedside estimation of the risk of true bacteremia.

Acknowledgment

The authors thank Drs. H. Honda and S. Saint, and Ms. A. Okada for their helpful discussions with regard to this study; Ms. M. Takigawa for the collection of data; and Ms. T. Oguri for providing infectious disease consultation on the pathogenicity of the identified organisms.

Disclosure

This work was supported by JSPS KAKENHI Grant Number 15K19294 (to TK) and 20590840 (to KI) from the Japan Society for the Promotion of Science. The authors report no potential conflicts of interest relevant to this article.

Fever in hospitalized patients is a nonspecific finding with many potential causes. Blood cultures (BC) are commonly obtained prior to commencing parenteral antibiotics in febrile patients. However, as many as 35% to 50% of positive BCs represent a contamination with organisms inoculated from the skin into culture bottles at the time of sample collection.^1-3 Such results represent false-positive BCs that can lead to unnecessary investigations and treatment.

Recently, Coburn et al. reviewed the severity of chills (graded on an ordinal scale) as the most useful predictor of true bacteremia (positive likelihood ratio [LR], 4.7; 95% confidence interval [CI], 3.0–7.2),^4-6 and the lack of the systemic inflammatory response syndrome (SIRS) criteria as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 We have also previously reported normal food consumption as a negative indicator of true bacteremia, with a 98.3% negative predictive value.⁸ Henderson’s Basic Principles of Nursing Care emphasizes the importance of evaluating whether a patient can eat and drink adequately,⁹ and the evaluation of a patient’s food consumption is a routine nursing staff practice, which is treated as vital sign in Japan, in contrast to nursing practices in the United States.

However, these data were the result of a single-center retrospective study using the nursing staff’s assessment of food consumption, and they cannot be generalized to larger patient populations. Therefore, the aim of this prospective, multicenter study was to measure the accuracy of food consumption and shaking chills as predictive factors for true bacteremia.

METHODS

Study Design

This was a prospective multicenter observational study (UMIN ID: R000013768) involving 3 hospitals in Tokyo, Japan, that enrolled consecutive patients who had BCs obtained. This study was approved by the ethical committee at Juntendo University Nerima Hospital and each of the participating centers, and the study was conducted in accordance with the Declaration of Helsinki 1971, as revised in 1983. We evaluated 2,792 consecutive hospitalized patients (mean age, 68.9 ± 17.1 years; 55.3% men) who had BCs obtained between April 2013 and August 2014, inclusive. The indication for BC acquisition was at the discretion of the treating physician. The study protocol and the indication for BCs are described in detail elsewhere.⁸ We excluded patients with anorexia-inducing conditions such as gastrointestinal disease, including gastrointestinal bleeding, enterocolitis, gastric ulceration, peritonitis, appendicitis, cholangitis, pancreatitis, diverticulitis, and ischemic colitis. We also excluded patients receiving chemotherapy for malignancy. In this study, true bacteremia was defined as identical organisms isolated from 2 sets of blood cultures (a set refers to one aerobic bottle and one anaerobic bottle). Moreover, even if only one set of blood cultures was acquired, when the identified pathogen could account for the clinical presentation, we also defined this as true bacteremia. Briefly, contaminants were defined as organisms common to skin flora, including Bacillus species, coagulase-negative Staphylococcus, Corynebacterium species, and Micrococcus species, without isolation of an identical organism with the same antibiotic susceptibilities from another potentially infected site in a patient with incompatible clinical features and no risk factors for infection with the isolated organism. Single BCs that were positive for organisms that were unlikely to explain the patient’s symptoms were also considered as contaminants. Patients with contaminated BCs were excluded from the analyses.

 

Structure of Reliability Study Procedures

Nurses in the 3 different hospitals performed daily independent food consumption ratings during each patient’s stay. Interrater reliability assessments were conducted in the morning or afternoon, and none of the raters had access to the other nurses’ scores at any time. The study nurses performed simultaneous ratings during these assessments (one interacted with and rated the patient while the other observed and rated the same patient).

Prediction Variables of True Bacteremia


1. Food consumption. Assessment of food consumption has been previously described in detail.⁸ Briefly, we characterized the patients’ oral intake based on the meal taken immediately prior to the BCs. For example, if a fever developed at 2 pm, lunch consumption was evaluated. If a fever developed at 2 am, dinner consumption was evaluated. We categorized the patients into 3 groups: low food consumption (<50% consumed), moderate food consumption (>50% to <80% consumed), and high food consumption (>80% consumed). To simplify our prediction rule, we subsequently divided food consumption into just 2 groups: high food consumption, referred to as the “normal food consumption group,” and the combination of low and moderate food consumption, referred to as the “poor food consumption group.”

2. Chills. As done previously, the physician evaluated the patient for a history of chills at the time of BCs and classified the patients into 1 of 4 grades^4,5: “no chills,” the absence of any chills; “mild chills,” feeling cold, equivalent to needing an outer jacket; “moderate chills,” feeling very cold, equivalent to needing a thick blanket; and “shaking chills,” feeling extremely cold with rigors and generalized bodily shaking, even under a thick blanket. To distinguish between those patients who had shaking chills and those who did not, we divided the patients into 2 groups: the “shaking chills group” and the combination of none, mild, and moderate chills, referred to as the “negative shaking chills group.”

3. Other predictive variables. We considered the following additional predictive variables: age, gender, axillary body temperature (BT), heart rate (HR), systolic blood pressure (SBP), respiratory rate (RR), white blood cell count (WBC), and serum C-reactive protein level (CRP). These predictive variables were obtained immediately prior to the BCs. We defined SIRS based on standard criteria (HR >90 beats/m, RR >20/m, BT <36°C or >38°C, and a WBC <4 × 10³ WBC/μL or >12 × 10³ WBC/μL). Patients were subcategorized by age into 2 groups (≤69 years and >70 years). CRP levels were dichotomized as >10.0 mg/dL or ≤10.0 mg/dL. We reviewed the patients’ charts to determine whether they had received antibiotics. In the case of walk-in patients, we interviewed the patients regarding whether they had visited a clinic; if they had, they were questioned as to whether any antibiotic therapy had been prescribed.

Statistical Analysis

Continuous variables are presented as the mean with the associated standard deviation (SD). All potential variables predictive of true bacteremia are shown in Table 1. The variables were dichotomized by clinically meaningful thresholds and used as potential risk-adjusted variables. We calculated the sensitivity and specificity and positive and negative predictive value for each criterion. Multiple logistic regression analysis was used to select components that were significantly associated with true bacteremia (the level of statistical significance determined with maximum likelihood methods was set at P < .05). To visualize and quantify other aspects in the prediction of true bacteremia, a recursive partitioning analysis (RPA) was used to make a decision tree model for true bacteremia. This nonparametric regression method produces a classification tree following a series of nonsequential top-down binary splits. The tree-building process starts by considering a set of predictive variables and selects the variable that produces 2 subsets of participants with the greatest purity. Two factors are considered when splitting a node into its daughter nodes: the goodness of the split and the amount of impurity in the daughter nodes. The splitting process is repeated until further partitioning is no longer possible and the terminal nodes have been reached. Details on this method are discussed in Monte Carlo Calibration of Distributions of Partition Statistics (www.jmp.com).

Probability was considered significant at a value of P < .05. All statistical tests were 2-tailed. Statistical analyses were conducted by a physician (KI) and an independent statistician (JM) with the use of the SPSS^® v.16.0 software package (SPSS Inc., Chicago, IL) and JMP^® version 8.0.2 (SAS Institute, Cary, NC).

RESULTS

Patients Characteristics

Two thousand seven hundred and ninety-two patients met the inclusion criteria for our study, from which 849 were excluded (see Figure 1 for flow diagram). Among the remaining 1,943 patients, there were 317 patients with positive BCs, of which 221 patients (69.7%) were considered to have true-positive BCs and 96 (30.3%) were considered to have contaminated BCs. After excluding these 96 patients, 221 patients with true bacteremia (true bacteremic group) were compared with 1,626 nonbacteremic patients (nonbacteremic group; Figure 1). The baseline characteristics of the subjects are shown in Table 1. The mean BT was 38.4 ± 1.2°C in the true bacteremic group and 37.9 ± 1.0°C in the nonbacteremic group. The mean serum CRP level was 11.6 ± 9.6 mg/dL in the true bacteremic group and 7.3 ± 6.9 mg/dL in the nonbacteremic group. In the true bacteremic group, there were 6 afebrile patients, and 27 patients without leukocytosis. The pathogens identified from the true-positive BCs were Escherichia coli (n = 59, 26.7%), including extended-spectrum beta-lactamase producing species, Staphylococcus aureus (n = 36, 16.3%), including methicillin-resistant Staphylococcus aureus, and Klebsiella pneumoniae (n = 22, 10.0%; Supplemental Table 1).

 

The underlying clinical diagnoses in the true bacteremic group included urinary tract infection (UTI), pneumonia, abscess, catheter-related bloodstream infection (CRBSI), cellulitis, osteomyelitis, infective endocarditis (IE), chorioamnionitis, iatrogenic infection at hemodialysis puncture sites, bacterial meningitis, septic arthritis, and infection of unknown cause (Supplemental Table 2).

Interrater Reliability Testing of Food Consumption

Patients were evaluated during their hospital stays. The interrater reliability of the evaluation of food consumption was very high across all participating hospitals (Supplemental Table 3). To assess the reliability of the evaluations of food consumption, patients (separate from this main study) were selected randomly and evaluated independently by 2 nurses in 3 different hospitals. The kappa scores of agreement between the nurses at the 3 different hospitals were 0.83 (95% CI, 0.63-0.88), 0.90 (95% CI, 0.80-0.99), and 0.80 (95% CI, 0.67-0.99), respectively. The interrater reliability of food consumption evaluation by the nurses was very high at all participating hospitals.

Food Consumption

The low, moderate, and high food consumption groups consisted of 964 (52.1%), 306 (16.6%), and 577 (31.2%) patients, respectively (Table 1). Of these, 174 (18.0%), 33 (10.8%), and 14 (2.4%) patients, respectively, had true bacteremia. The presence of poor food consumption had a sensitivity of 93.7% (95% CI, 89.4%-97.9%), specificity of 34.6% (95% CI, 33.0%-36.2%), and a positive LR of 1.43 (95% CI, 1.37-1.50) for predicting true bacteremia. Conversely, the absence of poor food consumption (ie, normal food consumption) had a negative LR of 0.18 (95% CI, 0.17-0.19).

Chills

The no, mild, moderate, and shaking chills groups consisted of 1,514 (82.0%), 148 (8.0%), 53 (2.9%), and 132 (7.1%) patients, respectively (Table 1). Of these, 136 (9.0%), 25 (16.9%), 8 (15.1%), and 52 (39.4%) patients, respectively, had true bacteremia. The presence of shaking chills had a sensitivity of 23.5% (95% CI, 22.5%-24.6%), a specificity of 95.1% (95% CI, 90.7%-99.4%), and a positive LR of 4.78 (95% CI, 4.56–5.00) for predicting true bacteremia. Conversely, the absence of shaking chills had a negative LR of 0.80 (95% CI, 0.77-0.84).

Prediction Model for True Bacteremia

The components identified as significantly related to true bacteremia by multiple logistic regression analysis are indicated in Table 2. The significant predictors of true bacteremia were shaking chills (odds ratio [OR], 5.6; 95% CI, 3.6-8.6; P < .01), SBP <90 mmHg (OR, 3.1; 95% CI, 1.6-5.7; P < 01), CRP levels >10.0 mg/dL (OR, 2.2; 95% CI, 1.6-3.1; P < .01), BT <36°C or >38°C (OR, 1.8; 95% CI, 1.3-2.6; P < .01), WBC <4 × 10³/μL or >12 × 10³/μL (OR, 1.6; 95% CI, 1.2-2.3; P = .003), HR >90 bpm (OR, 1.5; 95% CI, 1.1-2.1; P = .021), and female (OR, 1.4; 95% CI, 1.0-1.9; P = .036). An RPA to create an ideal prediction model for patients with true bacteremia or nonbacteremia is shown in Figure 2. The original group consisted of 1,847 patients, including 221 patients with true bacteremia. The pretest probability of true bacteremia was 2.4% (14/577) for those with normal food consumption (Group 1) and 2.4% (13/552) for those with both normal food consumption and the absence of shaking chills (Group 2). Conversely, the pretest probability of true bacteremia was 16.3% (207/1270) for those with poor food consumption and 47.7% (51/107) for those with both poor food consumption and shaking chills. The patients with true bacteremia with normal food consumption and without shaking chills consisted of 4 cases of CRBSI and UTI, 2 cases of osteomyelitis, 1 case of IE, 1 case of chorioamnionitis, and 1 case for which the focus was unknown (Supplemental Table 4).

DISCUSSION

In this observational study, we evaluated if a simple algorithm using food consumption and shaking chills was useful for assessing whether a patient had true bacteremia. A 2-item screening checklist (nursing assessment of food consumption and shaking chills) had excellent statistical properties as a brief screening instrument for true bacteremia.

We have prospectively validated that food consumption, as assessed by nurses, is a reliable predictor of true bacteremia.⁸ A previous single-center retrospective study showed similar findings, but these could not be generalized across all institutions because of the limited nature of the study. In this multicenter study, we used 2 statistical methods to reduce selection bias. First, we performed a kappa analysis across the hospitals to evaluate the interrater reliability of the evaluation of food consumption. Second, we used an RPA (Figure 2), also known as a decision tree model. RPA is a step-by-step process by which a decision tree is constructed by either splitting or not splitting each node on the tree into 2 daughter nodes.¹⁰ By using this method, we successfully generated an ideal approach to predict true bacteremia using food consumption and shaking chills. After adjusting for food consumption and shaking chills, groups 1 to 2 had sequentially decreasing diagnoses of true bacteremia, varying from 221 patients to only 13 patients.

Appetite is influenced by many factors that are integrated by the brain, most importantly within the hypothalamus. Signals that impinge on the hypothalamic center include neural afferents, hormones, cytokines, and metabolites.¹¹ These factors elicit “sickness behavior,” which includes a decrease in food-motivated behavior.¹² Furthermore, exposure to pathogenic bacteria increases serotonin, which has been shown to decrease metabolism in amphid neurons by transcriptional and post-transcriptional mechanisms.¹³ Therefore, nonbacteremic patients retain their appetites. Shaking chills are a well-known predictor of true bacteremia.^4,5 Several cytokines, including tumor necrosis factor-alpha and interleukins 6 and 10, may be related to shaking chills.¹⁴ Coburn et al. reviewed that shaking chills appear to be useful for identifying true bacteremia (positive LR, 4.7; 95% CI, 3.0-7.2),^5,6 similar to our study. In our study, the pretest probability of true bacteremia was the same whether shaking chills was included or not (ie, 2.4% for normal food consumption and 2.4% for normal food consumption plus absence of shaking chills). This would seem to imply that the assessment of shaking chills does not appear to add anything over food assessment alone when trying to rule out bacteremia. Rather, shaking chills seem more important for ruling in bacteremia rather than ruling it out. Moreover, the recent retrospective study revealed that age >60 years (OR = 2.75, 95% CI, 1.23-6.48, P = .015), female sex (OR = 2.21, 95% CI, 1.07- 4.67, P = .038), heart rate >90 bpm (OR = 5.18, 95% CI, 2.25-12.48, P < .001) and neutrophil percentage >80% (OR = 3.61, 95% CI, 1.71- 8.00, P = .001) were independent risk factors for true bacteremia.¹⁵ Conversely, the lack of the SIRS criteria was reported as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 However, the evaluation of SIRS criteria requires the acquisition of laboratory data. To our knowledge, no previous prospective studies have evaluated food consumption in terms of a risk prediction for true bacteremia. This extremely simple model can enable a physician to make a rapid bedside estimation of the risk of true bacteremia.

The strengths of this study include its relatively large sample size, multicenter design, uniformity of data collection across sites, and completeness of data collection from study participants. All of these factors allowed for a robust analysis.

However, there are several limitations of this study. First, the physicians or nurses asked the patients about the presence of shaking chills when they obtained the BCs. It may be difficult for patients, especially elderly patients, to provide this information promptly and accurately. Some patients did not call the nurse when they had shaking chills, and the chills were not witnessed by a healthcare provider. However, we used a more specific definition for shaking chills: a feeling of being extremely cold with rigors and generalized bodily shaking, even under a thick blanket. Second, this algorithm is not applicable to patients with immunosuppressed states because none of the hospitals involved in this study perform bone marrow or organ transplantation. Third, although we included patients with dementia in our cohort, we did not specifically evaluate performance of the algorithm in patients with this medical condition. It is possible that the algorithm would not perform well in this subset of patients owing to their unreliable appetite and food intake. Fourth, some medications may affect appetite, leading to reduced food consumption. Although we have not considered the details of medications in this study, we found that the pretest probability of true bacteremia was low for those patients with normal food consumption regardless of whether the medication affected their appetites or not. However, the question of whether medications truly affect patients’ appetites concurrently with bacteremia would need to be specifically addressed in a future study.

 

CONCLUSION

In conclusion, we have established a simple algorithm to identify patients with suspected true bacteremia who require the acquisition of blood cultures. This extremely simple model can enable physicians to make a rapid bedside estimation of the risk of true bacteremia.

Acknowledgment

The authors thank Drs. H. Honda and S. Saint, and Ms. A. Okada for their helpful discussions with regard to this study; Ms. M. Takigawa for the collection of data; and Ms. T. Oguri for providing infectious disease consultation on the pathogenicity of the identified organisms.

Disclosure

This work was supported by JSPS KAKENHI Grant Number 15K19294 (to TK) and 20590840 (to KI) from the Japan Society for the Promotion of Science. The authors report no potential conflicts of interest relevant to this article.

Fever in hospitalized patients is a nonspecific finding with many potential causes. Blood cultures (BC) are commonly obtained prior to commencing parenteral antibiotics in febrile patients. However, as many as 35% to 50% of positive BCs represent a contamination with organisms inoculated from the skin into culture bottles at the time of sample collection.^1-3 Such results represent false-positive BCs that can lead to unnecessary investigations and treatment.

Recently, Coburn et al. reviewed the severity of chills (graded on an ordinal scale) as the most useful predictor of true bacteremia (positive likelihood ratio [LR], 4.7; 95% confidence interval [CI], 3.0–7.2),^4-6 and the lack of the systemic inflammatory response syndrome (SIRS) criteria as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 We have also previously reported normal food consumption as a negative indicator of true bacteremia, with a 98.3% negative predictive value.⁸ Henderson’s Basic Principles of Nursing Care emphasizes the importance of evaluating whether a patient can eat and drink adequately,⁹ and the evaluation of a patient’s food consumption is a routine nursing staff practice, which is treated as vital sign in Japan, in contrast to nursing practices in the United States.

However, these data were the result of a single-center retrospective study using the nursing staff’s assessment of food consumption, and they cannot be generalized to larger patient populations. Therefore, the aim of this prospective, multicenter study was to measure the accuracy of food consumption and shaking chills as predictive factors for true bacteremia.

METHODS

Study Design

This was a prospective multicenter observational study (UMIN ID: R000013768) involving 3 hospitals in Tokyo, Japan, that enrolled consecutive patients who had BCs obtained. This study was approved by the ethical committee at Juntendo University Nerima Hospital and each of the participating centers, and the study was conducted in accordance with the Declaration of Helsinki 1971, as revised in 1983. We evaluated 2,792 consecutive hospitalized patients (mean age, 68.9 ± 17.1 years; 55.3% men) who had BCs obtained between April 2013 and August 2014, inclusive. The indication for BC acquisition was at the discretion of the treating physician. The study protocol and the indication for BCs are described in detail elsewhere.⁸ We excluded patients with anorexia-inducing conditions such as gastrointestinal disease, including gastrointestinal bleeding, enterocolitis, gastric ulceration, peritonitis, appendicitis, cholangitis, pancreatitis, diverticulitis, and ischemic colitis. We also excluded patients receiving chemotherapy for malignancy. In this study, true bacteremia was defined as identical organisms isolated from 2 sets of blood cultures (a set refers to one aerobic bottle and one anaerobic bottle). Moreover, even if only one set of blood cultures was acquired, when the identified pathogen could account for the clinical presentation, we also defined this as true bacteremia. Briefly, contaminants were defined as organisms common to skin flora, including Bacillus species, coagulase-negative Staphylococcus, Corynebacterium species, and Micrococcus species, without isolation of an identical organism with the same antibiotic susceptibilities from another potentially infected site in a patient with incompatible clinical features and no risk factors for infection with the isolated organism. Single BCs that were positive for organisms that were unlikely to explain the patient’s symptoms were also considered as contaminants. Patients with contaminated BCs were excluded from the analyses.

 

Structure of Reliability Study Procedures

Nurses in the 3 different hospitals performed daily independent food consumption ratings during each patient’s stay. Interrater reliability assessments were conducted in the morning or afternoon, and none of the raters had access to the other nurses’ scores at any time. The study nurses performed simultaneous ratings during these assessments (one interacted with and rated the patient while the other observed and rated the same patient).

Prediction Variables of True Bacteremia


1. Food consumption. Assessment of food consumption has been previously described in detail.⁸ Briefly, we characterized the patients’ oral intake based on the meal taken immediately prior to the BCs. For example, if a fever developed at 2 pm, lunch consumption was evaluated. If a fever developed at 2 am, dinner consumption was evaluated. We categorized the patients into 3 groups: low food consumption (<50% consumed), moderate food consumption (>50% to <80% consumed), and high food consumption (>80% consumed). To simplify our prediction rule, we subsequently divided food consumption into just 2 groups: high food consumption, referred to as the “normal food consumption group,” and the combination of low and moderate food consumption, referred to as the “poor food consumption group.”

2. Chills. As done previously, the physician evaluated the patient for a history of chills at the time of BCs and classified the patients into 1 of 4 grades^4,5: “no chills,” the absence of any chills; “mild chills,” feeling cold, equivalent to needing an outer jacket; “moderate chills,” feeling very cold, equivalent to needing a thick blanket; and “shaking chills,” feeling extremely cold with rigors and generalized bodily shaking, even under a thick blanket. To distinguish between those patients who had shaking chills and those who did not, we divided the patients into 2 groups: the “shaking chills group” and the combination of none, mild, and moderate chills, referred to as the “negative shaking chills group.”

3. Other predictive variables. We considered the following additional predictive variables: age, gender, axillary body temperature (BT), heart rate (HR), systolic blood pressure (SBP), respiratory rate (RR), white blood cell count (WBC), and serum C-reactive protein level (CRP). These predictive variables were obtained immediately prior to the BCs. We defined SIRS based on standard criteria (HR >90 beats/m, RR >20/m, BT <36°C or >38°C, and a WBC <4 × 10³ WBC/μL or >12 × 10³ WBC/μL). Patients were subcategorized by age into 2 groups (≤69 years and >70 years). CRP levels were dichotomized as >10.0 mg/dL or ≤10.0 mg/dL. We reviewed the patients’ charts to determine whether they had received antibiotics. In the case of walk-in patients, we interviewed the patients regarding whether they had visited a clinic; if they had, they were questioned as to whether any antibiotic therapy had been prescribed.

Statistical Analysis

Continuous variables are presented as the mean with the associated standard deviation (SD). All potential variables predictive of true bacteremia are shown in Table 1. The variables were dichotomized by clinically meaningful thresholds and used as potential risk-adjusted variables. We calculated the sensitivity and specificity and positive and negative predictive value for each criterion. Multiple logistic regression analysis was used to select components that were significantly associated with true bacteremia (the level of statistical significance determined with maximum likelihood methods was set at P < .05). To visualize and quantify other aspects in the prediction of true bacteremia, a recursive partitioning analysis (RPA) was used to make a decision tree model for true bacteremia. This nonparametric regression method produces a classification tree following a series of nonsequential top-down binary splits. The tree-building process starts by considering a set of predictive variables and selects the variable that produces 2 subsets of participants with the greatest purity. Two factors are considered when splitting a node into its daughter nodes: the goodness of the split and the amount of impurity in the daughter nodes. The splitting process is repeated until further partitioning is no longer possible and the terminal nodes have been reached. Details on this method are discussed in Monte Carlo Calibration of Distributions of Partition Statistics (www.jmp.com).

Probability was considered significant at a value of P < .05. All statistical tests were 2-tailed. Statistical analyses were conducted by a physician (KI) and an independent statistician (JM) with the use of the SPSS^® v.16.0 software package (SPSS Inc., Chicago, IL) and JMP^® version 8.0.2 (SAS Institute, Cary, NC).

RESULTS

Patients Characteristics

Two thousand seven hundred and ninety-two patients met the inclusion criteria for our study, from which 849 were excluded (see Figure 1 for flow diagram). Among the remaining 1,943 patients, there were 317 patients with positive BCs, of which 221 patients (69.7%) were considered to have true-positive BCs and 96 (30.3%) were considered to have contaminated BCs. After excluding these 96 patients, 221 patients with true bacteremia (true bacteremic group) were compared with 1,626 nonbacteremic patients (nonbacteremic group; Figure 1). The baseline characteristics of the subjects are shown in Table 1. The mean BT was 38.4 ± 1.2°C in the true bacteremic group and 37.9 ± 1.0°C in the nonbacteremic group. The mean serum CRP level was 11.6 ± 9.6 mg/dL in the true bacteremic group and 7.3 ± 6.9 mg/dL in the nonbacteremic group. In the true bacteremic group, there were 6 afebrile patients, and 27 patients without leukocytosis. The pathogens identified from the true-positive BCs were Escherichia coli (n = 59, 26.7%), including extended-spectrum beta-lactamase producing species, Staphylococcus aureus (n = 36, 16.3%), including methicillin-resistant Staphylococcus aureus, and Klebsiella pneumoniae (n = 22, 10.0%; Supplemental Table 1).

 

The underlying clinical diagnoses in the true bacteremic group included urinary tract infection (UTI), pneumonia, abscess, catheter-related bloodstream infection (CRBSI), cellulitis, osteomyelitis, infective endocarditis (IE), chorioamnionitis, iatrogenic infection at hemodialysis puncture sites, bacterial meningitis, septic arthritis, and infection of unknown cause (Supplemental Table 2).

Interrater Reliability Testing of Food Consumption

Patients were evaluated during their hospital stays. The interrater reliability of the evaluation of food consumption was very high across all participating hospitals (Supplemental Table 3). To assess the reliability of the evaluations of food consumption, patients (separate from this main study) were selected randomly and evaluated independently by 2 nurses in 3 different hospitals. The kappa scores of agreement between the nurses at the 3 different hospitals were 0.83 (95% CI, 0.63-0.88), 0.90 (95% CI, 0.80-0.99), and 0.80 (95% CI, 0.67-0.99), respectively. The interrater reliability of food consumption evaluation by the nurses was very high at all participating hospitals.

Food Consumption

The low, moderate, and high food consumption groups consisted of 964 (52.1%), 306 (16.6%), and 577 (31.2%) patients, respectively (Table 1). Of these, 174 (18.0%), 33 (10.8%), and 14 (2.4%) patients, respectively, had true bacteremia. The presence of poor food consumption had a sensitivity of 93.7% (95% CI, 89.4%-97.9%), specificity of 34.6% (95% CI, 33.0%-36.2%), and a positive LR of 1.43 (95% CI, 1.37-1.50) for predicting true bacteremia. Conversely, the absence of poor food consumption (ie, normal food consumption) had a negative LR of 0.18 (95% CI, 0.17-0.19).

Chills

The no, mild, moderate, and shaking chills groups consisted of 1,514 (82.0%), 148 (8.0%), 53 (2.9%), and 132 (7.1%) patients, respectively (Table 1). Of these, 136 (9.0%), 25 (16.9%), 8 (15.1%), and 52 (39.4%) patients, respectively, had true bacteremia. The presence of shaking chills had a sensitivity of 23.5% (95% CI, 22.5%-24.6%), a specificity of 95.1% (95% CI, 90.7%-99.4%), and a positive LR of 4.78 (95% CI, 4.56–5.00) for predicting true bacteremia. Conversely, the absence of shaking chills had a negative LR of 0.80 (95% CI, 0.77-0.84).

Prediction Model for True Bacteremia

The components identified as significantly related to true bacteremia by multiple logistic regression analysis are indicated in Table 2. The significant predictors of true bacteremia were shaking chills (odds ratio [OR], 5.6; 95% CI, 3.6-8.6; P < .01), SBP <90 mmHg (OR, 3.1; 95% CI, 1.6-5.7; P < 01), CRP levels >10.0 mg/dL (OR, 2.2; 95% CI, 1.6-3.1; P < .01), BT <36°C or >38°C (OR, 1.8; 95% CI, 1.3-2.6; P < .01), WBC <4 × 10³/μL or >12 × 10³/μL (OR, 1.6; 95% CI, 1.2-2.3; P = .003), HR >90 bpm (OR, 1.5; 95% CI, 1.1-2.1; P = .021), and female (OR, 1.4; 95% CI, 1.0-1.9; P = .036). An RPA to create an ideal prediction model for patients with true bacteremia or nonbacteremia is shown in Figure 2. The original group consisted of 1,847 patients, including 221 patients with true bacteremia. The pretest probability of true bacteremia was 2.4% (14/577) for those with normal food consumption (Group 1) and 2.4% (13/552) for those with both normal food consumption and the absence of shaking chills (Group 2). Conversely, the pretest probability of true bacteremia was 16.3% (207/1270) for those with poor food consumption and 47.7% (51/107) for those with both poor food consumption and shaking chills. The patients with true bacteremia with normal food consumption and without shaking chills consisted of 4 cases of CRBSI and UTI, 2 cases of osteomyelitis, 1 case of IE, 1 case of chorioamnionitis, and 1 case for which the focus was unknown (Supplemental Table 4).

DISCUSSION

In this observational study, we evaluated if a simple algorithm using food consumption and shaking chills was useful for assessing whether a patient had true bacteremia. A 2-item screening checklist (nursing assessment of food consumption and shaking chills) had excellent statistical properties as a brief screening instrument for true bacteremia.

We have prospectively validated that food consumption, as assessed by nurses, is a reliable predictor of true bacteremia.⁸ A previous single-center retrospective study showed similar findings, but these could not be generalized across all institutions because of the limited nature of the study. In this multicenter study, we used 2 statistical methods to reduce selection bias. First, we performed a kappa analysis across the hospitals to evaluate the interrater reliability of the evaluation of food consumption. Second, we used an RPA (Figure 2), also known as a decision tree model. RPA is a step-by-step process by which a decision tree is constructed by either splitting or not splitting each node on the tree into 2 daughter nodes.¹⁰ By using this method, we successfully generated an ideal approach to predict true bacteremia using food consumption and shaking chills. After adjusting for food consumption and shaking chills, groups 1 to 2 had sequentially decreasing diagnoses of true bacteremia, varying from 221 patients to only 13 patients.

Appetite is influenced by many factors that are integrated by the brain, most importantly within the hypothalamus. Signals that impinge on the hypothalamic center include neural afferents, hormones, cytokines, and metabolites.¹¹ These factors elicit “sickness behavior,” which includes a decrease in food-motivated behavior.¹² Furthermore, exposure to pathogenic bacteria increases serotonin, which has been shown to decrease metabolism in amphid neurons by transcriptional and post-transcriptional mechanisms.¹³ Therefore, nonbacteremic patients retain their appetites. Shaking chills are a well-known predictor of true bacteremia.^4,5 Several cytokines, including tumor necrosis factor-alpha and interleukins 6 and 10, may be related to shaking chills.¹⁴ Coburn et al. reviewed that shaking chills appear to be useful for identifying true bacteremia (positive LR, 4.7; 95% CI, 3.0-7.2),^5,6 similar to our study. In our study, the pretest probability of true bacteremia was the same whether shaking chills was included or not (ie, 2.4% for normal food consumption and 2.4% for normal food consumption plus absence of shaking chills). This would seem to imply that the assessment of shaking chills does not appear to add anything over food assessment alone when trying to rule out bacteremia. Rather, shaking chills seem more important for ruling in bacteremia rather than ruling it out. Moreover, the recent retrospective study revealed that age >60 years (OR = 2.75, 95% CI, 1.23-6.48, P = .015), female sex (OR = 2.21, 95% CI, 1.07- 4.67, P = .038), heart rate >90 bpm (OR = 5.18, 95% CI, 2.25-12.48, P < .001) and neutrophil percentage >80% (OR = 3.61, 95% CI, 1.71- 8.00, P = .001) were independent risk factors for true bacteremia.¹⁵ Conversely, the lack of the SIRS criteria was reported as the best negative indicator of true bacteremia with a negative LR of 0.09 (95% CI, 0.03-0.3).^6,7 However, the evaluation of SIRS criteria requires the acquisition of laboratory data. To our knowledge, no previous prospective studies have evaluated food consumption in terms of a risk prediction for true bacteremia. This extremely simple model can enable a physician to make a rapid bedside estimation of the risk of true bacteremia.

The strengths of this study include its relatively large sample size, multicenter design, uniformity of data collection across sites, and completeness of data collection from study participants. All of these factors allowed for a robust analysis.

However, there are several limitations of this study. First, the physicians or nurses asked the patients about the presence of shaking chills when they obtained the BCs. It may be difficult for patients, especially elderly patients, to provide this information promptly and accurately. Some patients did not call the nurse when they had shaking chills, and the chills were not witnessed by a healthcare provider. However, we used a more specific definition for shaking chills: a feeling of being extremely cold with rigors and generalized bodily shaking, even under a thick blanket. Second, this algorithm is not applicable to patients with immunosuppressed states because none of the hospitals involved in this study perform bone marrow or organ transplantation. Third, although we included patients with dementia in our cohort, we did not specifically evaluate performance of the algorithm in patients with this medical condition. It is possible that the algorithm would not perform well in this subset of patients owing to their unreliable appetite and food intake. Fourth, some medications may affect appetite, leading to reduced food consumption. Although we have not considered the details of medications in this study, we found that the pretest probability of true bacteremia was low for those patients with normal food consumption regardless of whether the medication affected their appetites or not. However, the question of whether medications truly affect patients’ appetites concurrently with bacteremia would need to be specifically addressed in a future study.

 

CONCLUSION

In conclusion, we have established a simple algorithm to identify patients with suspected true bacteremia who require the acquisition of blood cultures. This extremely simple model can enable physicians to make a rapid bedside estimation of the risk of true bacteremia.

Acknowledgment

The authors thank Drs. H. Honda and S. Saint, and Ms. A. Okada for their helpful discussions with regard to this study; Ms. M. Takigawa for the collection of data; and Ms. T. Oguri for providing infectious disease consultation on the pathogenicity of the identified organisms.

Disclosure

This work was supported by JSPS KAKENHI Grant Number 15K19294 (to TK) and 20590840 (to KI) from the Japan Society for the Promotion of Science. The authors report no potential conflicts of interest relevant to this article.

Implantable cardioverter-defibrillators (ICDs) offer lifesaving therapies to many patients and have been implanted in hundreds of thousands of patients.¹ The population of patients with ICDs is growing rapidly, and the national ICD Registry reports over 12,000 devices are implanted monthly.² This population includes patients with congenital heart disease, ischemic cardiomyopathy, and idiopathic arrhythmias. If these patients experience ventricular tachycardia or fibrillation, an ICD attempts to restore sinus rhythm and prevent death. While a shock from an ICD may be lifesaving, it can be a traumatic and startling experience for the patient and perhaps distressful for families to witness.^3,4

Although ICDs are intended to save lives, they do not slow the progress of the patient’s underlying cardiac and noncardiac comorbidities. All these patients will eventually die, whether from their cardiac disease or another condition. The literature includes many anecdotes about patients shocked multiple times by their defibrillator while actively dying.⁴ These situations could be prevented with preemptive ICD deactivation. (ICDs can function not only as cardioverters and defibrillators, as implied by their name, but also as pacemakers. “Deactivation” as used in this paper refers only to disabling the tachycardia therapies. No distinction was made between defibrillation with a shock and anti-tachycardia pacing.) Therefore, research on ICD deactivation has emphasized patients who are acutely terminally ill, while less emphasis has been placed on patients who are not actively dying.^4–8

Patients may, for a variety of reasons, request a do-not-resuscitate/do-not-intubate (DNR/DNI) order as their code status. However, it is not necessarily clear what a DNR/DNI order implies for ICD management. One survey of attending physicians found that 19% of respondents felt a DNR/DNI order was equivalent to requesting ICD deactivation.⁹ On the other hand, patients are split on whether they would want their device deactivated while in hospice or even at the very end of life.⁶ Heart Rhythm Society (HRS) guidelines favor a nuanced approach to ICD deactivation in DNR/DNI patients that emphasizes the individual patient’s comorbidities and goals.¹⁰ A patient’s individual circumstances might justify a choice to be DNR/DNI without deactivating the ICD. Decision-making in these circumstances requires a careful conversation between the patient and clinician. It is important to identify barriers that might prevent optimal shared decision-making.

Clinicians have been surveyed on ICD management at the end of life, but these studies have generally focused on attending physicians.^5,9,11 However, physician trainees (ie, residents and fellows) as well as advanced practice providers (ie, physician assistants and nurse practitioners) are responsible for much of the clinical care provided to hospitalized patients. In particular, they are often the clinicians to discuss code status with patients. Different specialties (eg, cardiology, general medicine, and geriatrics) manage different sets of patients, which might affect clinicians’ opinions on ICD management. We therefore designed a survey to assess clinician attitudes and beliefs regarding ICD deactivation, including in non-terminally ill patients, and to evaluate for differences according to training level and specialty.

 

METHODS

Case-based and Likert-scale questions were considered for this survey, with the latter being chosen for ease of completion by respondents. An online survey tool (SurveyMonkey; San Mateo, CA) was used for data collection; no identifying data were collected. E-mail invitations to participate were sent to a combination of mailing lists and individual addresses for residents, fellows, advanced practice providers, and attending physicians in general internal medicine, cardiology, electrophysiology, and geriatrics. The survey remained open for 2.5 weeks. It was conducted 5 months into the academic year, thus trainees were well-established in their current roles. Two $25 gift cards were offered to respondents who entered their e-mail into a drawing; responses were not tied to e-mail addresses. Approval for the study was obtained from the University of Michigan Institutional Review Board.

The survey posed 12 questions assessing general attitudes about ICDs as well as individual beliefs and behaviors relating to ICD deactivation. Answers were on a Likert scale of 1 to 5 with 1 representing “strongly disagree” and 5 representing “strongly agree.” A score of 3 indicated “unsure or neutral.” The first 3 questions appeared together on the first page and were prefaced with “Please respond to the following statements about ICD shocks.” The next 9 were likewise grouped on the next page and were prefaced with “Please respond to the following statements about ICD deactivation.” All 12 questions are shown in Figures 1 and 2. Respondents could easily return to previous questions and change their answers. The survey ended with a third page showing 3 multiple choice demographic questions. The demographic questions were about clinical role (first-, second-, third-, or fourth-year resident, fellow, advanced practice provider, and attending), specialty, and number of ICD deactivations the respondent had been directly involved in (0, 1 to 5, 5 to 10, and more than 10). Specialty options were internal medicine resident, inpatient general medicine, outpatient general medicine, cardiology, electrophysiology, and geriatrics.

Likert scale answers of “agree” or “strongly agree” were grouped together as an affirmative response, while all other answers were grouped together as a nonaffirmative response. For analysis, residents were grouped together and their responses compared with attending physicians as a group. Additional analysis was done comparing attending physicians stratified by clinical specialty. Given the small number of responses from attending electrophysiologists, they were grouped with attending cardiologists for analysis. Due to the limited number of fellows and advanced practice providers who responded, further evaluation of these groups was not performed. Finally, the number of ICD deactivations respondents had been involved in was stratified by training level. All comparisons were performed using the two-tailed Pearson’s chi-squared test.

RESULTS

A total of 170 responses were collected from 508 individuals on the e-mail lists. Two responses were from registered nurses who were not part of the intended study sample and 7 responses were incomplete, having only answered the first 3 questions. These 9 responses were excluded from further analysis, yielding an overall response rate of 32%. The demographics of the remaining 161 respondents are shown in Table 1. Figure 1 shows overall responses to each question.

When comparing residents to attending physicians, there were no statistically significant between-differences except on questions 5 and 6. Specifically, residents were less comfortable than attending physicians discussing ICD deactivation and did so with less regularity (P < .001 and P = .018, respectively; Figure 2). Comfort levels improved markedly with experience: 29.2% of interns expressed comfort asking about ICD deactivation as compared with 60.7% of third- and-fourth year residents and 78.8% of attending physicians. Furthermore, comfort level seemed to parallel the regularity with which respondents asked about ICD deactivation: 4.2% of interns routinely asked about ICD deactivation as compared with 21.4% of third- and fourth-year residents and 34.8% of attending physicians.

The only statistically significant difference when comparing attending physicians by specialty was on question 6 of the survey with the groups being unequal in their reliability at asking about ICD deactivation during code status discussions (P < .001; Figure 2). Of cardiologists and electrophysiologists, 73.3% said they routinely ask about ICD deactivation, as well as 83.3% of geriatricians. By contrast, only 19.2% of outpatient general internists and 10.5% of inpatient general internists (ie, hospitalists) said they routinely ask about ICD deactivation.

There were no differences between groups when asked whether ICD deactivation was part of a DNR/DNI order (question 8), or if an ICD should be deactivated in DNR/DNI patients (questions 9 and 10). As shown in Figure 1, 21.1% of respondents felt that a DNR/DNI order is equivalent to requesting ICD deactivation, 60.2% felt that terminally ill DNR/DNI patients should have their device deactivated, and 28% felt that non-terminally ill DNR/DNI patients should have their device deactivated.

Groups were unequal with respect to the number of ICD deactivations in which they had been directly involved (Figure 3; P < .001). Over half of interns had not been involved in any ICD deactivations as compared with only 10.7% of third- or fourth-year residents. Of the 20 geriatricians, cardiologists, and electrophysiologists, 45% had been involved in at least 5 ICD deactivations. Of note, although 77.8% of fellows reported being involved in more than 10 ICD deactivations, these 9 respondents were all in cardiology or electrophysiology.

 

DISCUSSION

Overall, our major findings were (1) residents, who provide much of the clinical care in a teaching hospital, are remarkably uncomfortable discussing ICD deactivation, (2) general internists and residents ask about ICD deactivation infrequently compared to geriatricians and cardiologists, and (3) about one fifth of our respondents believe ICD deactivation is automatically part of a DNR/DNI order.

Although the majority of respondents did not routinely address ICD deactivation in conjunction with code status, there was significant variability among subgroups. For example, 83.3% of geriatricians routinely discussed ICD deactivation as part of code status compared with only 4% of first-year residents and 10.5% of inpatient general internists. This finding is interesting because 90.7% of all respondents believed that discussions of code status should address preferences on ICD deactivation. This apparent discrepancy could be explained by the relatively small number of patients admitted to the hospital who have both an ICD and a request to be DNR/DNI. Residents and inpatient general internists see a very broad spectrum of patients; ICD deactivation is frequently irrelevant in the cases these physicians manage. The subset of patients seen in consultation by cardiologists and geriatricians, by contrast, is expected to include a larger proportion of patients with ICDs. Therefore, discussing ICD deactivation will be more relevant to their daily practice. Fear of alienating patients was not a reason for our findings, as our respondents did not express concern that recommending ICD deactivation would harm the patient-clinician relationship.

There are several possible reasons that residents, particularly interns, are uncomfortable discussing ICD deactivation. A lack of exposure to ICD deactivation is probably a key contributor. Over half of interns had never been involved in any ICD deactivations. Residents and hospitalists may also feel as if they are overstepping their boundaries to discuss deactivating ICD therapies. Their feelings may not be misplaced, as one survey of ICD patients found that over 75% thought responsibility for discussing ICD deactivation, at least at the end of life, rests with cardiologists or electrophysiologists.⁶

The HRS guidelines call for individualized decisions regarding ICD deactivation, even if a patient is DNR/DNI. However, our respondents frequently felt a standardized approach was indicated, with 21% believing that a DNR/DNI order included ICD deactivation. Additionally, 28% agreed that even non-terminally ill DNR/DNI patients should have their device deactivated. This is relevant because it is the role of clinicians to engage in shared decision-making with their patients. If the clinician holds the fixed belief that a DNR/DNI order, regardless of the precise clinical scenario, should include ICD deactivation, they may pressure a patient to have their device deactivated even if it could still benefit them.

In 2009, Kelley et al published results of a survey on ICD deactivation at the end of life.⁹ They contacted 4,876 attending physicians in cardiology, electrophysiology, geriatrics, and general medicine, receiving 558 responses. The survey included Likert-scale questions assessing attitudes and knowledge about ICD functionality. Demographic information was also collected, including how many patients in their practice had ICDs and how often they had previously discussed ICD deactivation.

There are some interesting comparisons between Kelley et al’s findings and ours, although we included trainees and the precise wording of questions was different. The specific questions used by Kelley et al to ask whether ICD shocks were painful or distressing and to ask if ICD deactivation is part of a DNR order were: “The shock from an ICD is very painful for most patients.” “The shock of an ICD at the end of life is distressing to a patient and their loved ones.” “A DNR order is equivalent to deactivation of an ICD.”

Only 47% of general internists in the Kelley et al survey thought that ICD shocks were painful, compared with 83% of electrophysiologists. In addition, 65% of general internists and 85% of electrophysiologists viewed shocks at the end of life to be distressing to patients and families. By contrast, our respondents were nearly unanimous in believing shocks to be painful and distressing. This discrepancy may be due to the growing prevalence of ICDs over the past several years as well as the growing body of literature on unnecessary shocks at the end of life. In line with our study, 19% of their respondents believed a DNR order was equivalent to ICD deactivation.⁹

Taken together, our findings indicate that additional education for clinicians of all levels could be helpful. Didactic lessons cannot replace experience, and it is important for residents to be exposed to discussions of ICD deactivation. However, lessons about ICD therapies and practical examples of how to broach the topic of deactivation could be beneficial, especially for interns whose responsibility includes discussions of code status. Within the context of an internal medicine residency, the fundamentals of ICD functionality could be covered during rotations on cardiology or palliative care services. Additionally, the recommendations of the HRS for device management can be covered in didactic sessions. Similar opportunities could be built into continuing medical education for practicing physicians and the training of advanced practice providers.

There are limitations to this survey, most notably the fact that it was restricted to a single academic medical center, the patient population and practices of which may not be generalizable to medical practice at large. Selection bias is also a distinct possibility given the 32% overall response rate; those who responded may feel more strongly about the survey topic. Our study subgroups may have interpreted questions differently because of their particular area of clinical practice. The small sample size also precluded an effective analysis of fellows and advanced practice practitioners due to lack of power. A major strength of this survey was the inclusion of a large number of residents upon whom the majority of inpatient contact rests. Future work could include expanding the survey to multiple medical centers, which would enhance generalizability and improve the ability to recruit sufficient fellows and advanced practice providers.

 

CONCLUSION

In summary, we conducted a single-center survey of residents, fellows, advanced practice providers, and attending physicians on their attitudes and beliefs about ICD deactivation in DNR/DNI patients. Residents are particularly uncomfortable discussing ICD deactivation with patients, which is an important finding because of their crucial role in providing patient care. Additionally, residents and hospitalists do not broach the topic of deactivation regularly, especially when compared to geriatricians and cardiologists. Despite HRS guidelines to the contrary, a fifth of our respondents believed that DNR/DNI orders include ICD deactivation. Overall, ICD deactivation in DNR/DNI patients is a topic that needs further attention in clinical education so that patients receive care that respects their individual wishes.

Disclosure

Nothing to report.

 

Implantable cardioverter-defibrillators (ICDs) offer lifesaving therapies to many patients and have been implanted in hundreds of thousands of patients.¹ The population of patients with ICDs is growing rapidly, and the national ICD Registry reports over 12,000 devices are implanted monthly.² This population includes patients with congenital heart disease, ischemic cardiomyopathy, and idiopathic arrhythmias. If these patients experience ventricular tachycardia or fibrillation, an ICD attempts to restore sinus rhythm and prevent death. While a shock from an ICD may be lifesaving, it can be a traumatic and startling experience for the patient and perhaps distressful for families to witness.^3,4

Although ICDs are intended to save lives, they do not slow the progress of the patient’s underlying cardiac and noncardiac comorbidities. All these patients will eventually die, whether from their cardiac disease or another condition. The literature includes many anecdotes about patients shocked multiple times by their defibrillator while actively dying.⁴ These situations could be prevented with preemptive ICD deactivation. (ICDs can function not only as cardioverters and defibrillators, as implied by their name, but also as pacemakers. “Deactivation” as used in this paper refers only to disabling the tachycardia therapies. No distinction was made between defibrillation with a shock and anti-tachycardia pacing.) Therefore, research on ICD deactivation has emphasized patients who are acutely terminally ill, while less emphasis has been placed on patients who are not actively dying.^4–8

Patients may, for a variety of reasons, request a do-not-resuscitate/do-not-intubate (DNR/DNI) order as their code status. However, it is not necessarily clear what a DNR/DNI order implies for ICD management. One survey of attending physicians found that 19% of respondents felt a DNR/DNI order was equivalent to requesting ICD deactivation.⁹ On the other hand, patients are split on whether they would want their device deactivated while in hospice or even at the very end of life.⁶ Heart Rhythm Society (HRS) guidelines favor a nuanced approach to ICD deactivation in DNR/DNI patients that emphasizes the individual patient’s comorbidities and goals.¹⁰ A patient’s individual circumstances might justify a choice to be DNR/DNI without deactivating the ICD. Decision-making in these circumstances requires a careful conversation between the patient and clinician. It is important to identify barriers that might prevent optimal shared decision-making.

Clinicians have been surveyed on ICD management at the end of life, but these studies have generally focused on attending physicians.^5,9,11 However, physician trainees (ie, residents and fellows) as well as advanced practice providers (ie, physician assistants and nurse practitioners) are responsible for much of the clinical care provided to hospitalized patients. In particular, they are often the clinicians to discuss code status with patients. Different specialties (eg, cardiology, general medicine, and geriatrics) manage different sets of patients, which might affect clinicians’ opinions on ICD management. We therefore designed a survey to assess clinician attitudes and beliefs regarding ICD deactivation, including in non-terminally ill patients, and to evaluate for differences according to training level and specialty.

 

METHODS

Case-based and Likert-scale questions were considered for this survey, with the latter being chosen for ease of completion by respondents. An online survey tool (SurveyMonkey; San Mateo, CA) was used for data collection; no identifying data were collected. E-mail invitations to participate were sent to a combination of mailing lists and individual addresses for residents, fellows, advanced practice providers, and attending physicians in general internal medicine, cardiology, electrophysiology, and geriatrics. The survey remained open for 2.5 weeks. It was conducted 5 months into the academic year, thus trainees were well-established in their current roles. Two $25 gift cards were offered to respondents who entered their e-mail into a drawing; responses were not tied to e-mail addresses. Approval for the study was obtained from the University of Michigan Institutional Review Board.

The survey posed 12 questions assessing general attitudes about ICDs as well as individual beliefs and behaviors relating to ICD deactivation. Answers were on a Likert scale of 1 to 5 with 1 representing “strongly disagree” and 5 representing “strongly agree.” A score of 3 indicated “unsure or neutral.” The first 3 questions appeared together on the first page and were prefaced with “Please respond to the following statements about ICD shocks.” The next 9 were likewise grouped on the next page and were prefaced with “Please respond to the following statements about ICD deactivation.” All 12 questions are shown in Figures 1 and 2. Respondents could easily return to previous questions and change their answers. The survey ended with a third page showing 3 multiple choice demographic questions. The demographic questions were about clinical role (first-, second-, third-, or fourth-year resident, fellow, advanced practice provider, and attending), specialty, and number of ICD deactivations the respondent had been directly involved in (0, 1 to 5, 5 to 10, and more than 10). Specialty options were internal medicine resident, inpatient general medicine, outpatient general medicine, cardiology, electrophysiology, and geriatrics.

Likert scale answers of “agree” or “strongly agree” were grouped together as an affirmative response, while all other answers were grouped together as a nonaffirmative response. For analysis, residents were grouped together and their responses compared with attending physicians as a group. Additional analysis was done comparing attending physicians stratified by clinical specialty. Given the small number of responses from attending electrophysiologists, they were grouped with attending cardiologists for analysis. Due to the limited number of fellows and advanced practice providers who responded, further evaluation of these groups was not performed. Finally, the number of ICD deactivations respondents had been involved in was stratified by training level. All comparisons were performed using the two-tailed Pearson’s chi-squared test.

RESULTS

A total of 170 responses were collected from 508 individuals on the e-mail lists. Two responses were from registered nurses who were not part of the intended study sample and 7 responses were incomplete, having only answered the first 3 questions. These 9 responses were excluded from further analysis, yielding an overall response rate of 32%. The demographics of the remaining 161 respondents are shown in Table 1. Figure 1 shows overall responses to each question.

When comparing residents to attending physicians, there were no statistically significant between-differences except on questions 5 and 6. Specifically, residents were less comfortable than attending physicians discussing ICD deactivation and did so with less regularity (P < .001 and P = .018, respectively; Figure 2). Comfort levels improved markedly with experience: 29.2% of interns expressed comfort asking about ICD deactivation as compared with 60.7% of third- and-fourth year residents and 78.8% of attending physicians. Furthermore, comfort level seemed to parallel the regularity with which respondents asked about ICD deactivation: 4.2% of interns routinely asked about ICD deactivation as compared with 21.4% of third- and fourth-year residents and 34.8% of attending physicians.

The only statistically significant difference when comparing attending physicians by specialty was on question 6 of the survey with the groups being unequal in their reliability at asking about ICD deactivation during code status discussions (P < .001; Figure 2). Of cardiologists and electrophysiologists, 73.3% said they routinely ask about ICD deactivation, as well as 83.3% of geriatricians. By contrast, only 19.2% of outpatient general internists and 10.5% of inpatient general internists (ie, hospitalists) said they routinely ask about ICD deactivation.

There were no differences between groups when asked whether ICD deactivation was part of a DNR/DNI order (question 8), or if an ICD should be deactivated in DNR/DNI patients (questions 9 and 10). As shown in Figure 1, 21.1% of respondents felt that a DNR/DNI order is equivalent to requesting ICD deactivation, 60.2% felt that terminally ill DNR/DNI patients should have their device deactivated, and 28% felt that non-terminally ill DNR/DNI patients should have their device deactivated.

Groups were unequal with respect to the number of ICD deactivations in which they had been directly involved (Figure 3; P < .001). Over half of interns had not been involved in any ICD deactivations as compared with only 10.7% of third- or fourth-year residents. Of the 20 geriatricians, cardiologists, and electrophysiologists, 45% had been involved in at least 5 ICD deactivations. Of note, although 77.8% of fellows reported being involved in more than 10 ICD deactivations, these 9 respondents were all in cardiology or electrophysiology.

 

DISCUSSION

Overall, our major findings were (1) residents, who provide much of the clinical care in a teaching hospital, are remarkably uncomfortable discussing ICD deactivation, (2) general internists and residents ask about ICD deactivation infrequently compared to geriatricians and cardiologists, and (3) about one fifth of our respondents believe ICD deactivation is automatically part of a DNR/DNI order.

Although the majority of respondents did not routinely address ICD deactivation in conjunction with code status, there was significant variability among subgroups. For example, 83.3% of geriatricians routinely discussed ICD deactivation as part of code status compared with only 4% of first-year residents and 10.5% of inpatient general internists. This finding is interesting because 90.7% of all respondents believed that discussions of code status should address preferences on ICD deactivation. This apparent discrepancy could be explained by the relatively small number of patients admitted to the hospital who have both an ICD and a request to be DNR/DNI. Residents and inpatient general internists see a very broad spectrum of patients; ICD deactivation is frequently irrelevant in the cases these physicians manage. The subset of patients seen in consultation by cardiologists and geriatricians, by contrast, is expected to include a larger proportion of patients with ICDs. Therefore, discussing ICD deactivation will be more relevant to their daily practice. Fear of alienating patients was not a reason for our findings, as our respondents did not express concern that recommending ICD deactivation would harm the patient-clinician relationship.

There are several possible reasons that residents, particularly interns, are uncomfortable discussing ICD deactivation. A lack of exposure to ICD deactivation is probably a key contributor. Over half of interns had never been involved in any ICD deactivations. Residents and hospitalists may also feel as if they are overstepping their boundaries to discuss deactivating ICD therapies. Their feelings may not be misplaced, as one survey of ICD patients found that over 75% thought responsibility for discussing ICD deactivation, at least at the end of life, rests with cardiologists or electrophysiologists.⁶

The HRS guidelines call for individualized decisions regarding ICD deactivation, even if a patient is DNR/DNI. However, our respondents frequently felt a standardized approach was indicated, with 21% believing that a DNR/DNI order included ICD deactivation. Additionally, 28% agreed that even non-terminally ill DNR/DNI patients should have their device deactivated. This is relevant because it is the role of clinicians to engage in shared decision-making with their patients. If the clinician holds the fixed belief that a DNR/DNI order, regardless of the precise clinical scenario, should include ICD deactivation, they may pressure a patient to have their device deactivated even if it could still benefit them.

In 2009, Kelley et al published results of a survey on ICD deactivation at the end of life.⁹ They contacted 4,876 attending physicians in cardiology, electrophysiology, geriatrics, and general medicine, receiving 558 responses. The survey included Likert-scale questions assessing attitudes and knowledge about ICD functionality. Demographic information was also collected, including how many patients in their practice had ICDs and how often they had previously discussed ICD deactivation.

There are some interesting comparisons between Kelley et al’s findings and ours, although we included trainees and the precise wording of questions was different. The specific questions used by Kelley et al to ask whether ICD shocks were painful or distressing and to ask if ICD deactivation is part of a DNR order were: “The shock from an ICD is very painful for most patients.” “The shock of an ICD at the end of life is distressing to a patient and their loved ones.” “A DNR order is equivalent to deactivation of an ICD.”

Only 47% of general internists in the Kelley et al survey thought that ICD shocks were painful, compared with 83% of electrophysiologists. In addition, 65% of general internists and 85% of electrophysiologists viewed shocks at the end of life to be distressing to patients and families. By contrast, our respondents were nearly unanimous in believing shocks to be painful and distressing. This discrepancy may be due to the growing prevalence of ICDs over the past several years as well as the growing body of literature on unnecessary shocks at the end of life. In line with our study, 19% of their respondents believed a DNR order was equivalent to ICD deactivation.⁹

Taken together, our findings indicate that additional education for clinicians of all levels could be helpful. Didactic lessons cannot replace experience, and it is important for residents to be exposed to discussions of ICD deactivation. However, lessons about ICD therapies and practical examples of how to broach the topic of deactivation could be beneficial, especially for interns whose responsibility includes discussions of code status. Within the context of an internal medicine residency, the fundamentals of ICD functionality could be covered during rotations on cardiology or palliative care services. Additionally, the recommendations of the HRS for device management can be covered in didactic sessions. Similar opportunities could be built into continuing medical education for practicing physicians and the training of advanced practice providers.

There are limitations to this survey, most notably the fact that it was restricted to a single academic medical center, the patient population and practices of which may not be generalizable to medical practice at large. Selection bias is also a distinct possibility given the 32% overall response rate; those who responded may feel more strongly about the survey topic. Our study subgroups may have interpreted questions differently because of their particular area of clinical practice. The small sample size also precluded an effective analysis of fellows and advanced practice practitioners due to lack of power. A major strength of this survey was the inclusion of a large number of residents upon whom the majority of inpatient contact rests. Future work could include expanding the survey to multiple medical centers, which would enhance generalizability and improve the ability to recruit sufficient fellows and advanced practice providers.

 

CONCLUSION

In summary, we conducted a single-center survey of residents, fellows, advanced practice providers, and attending physicians on their attitudes and beliefs about ICD deactivation in DNR/DNI patients. Residents are particularly uncomfortable discussing ICD deactivation with patients, which is an important finding because of their crucial role in providing patient care. Additionally, residents and hospitalists do not broach the topic of deactivation regularly, especially when compared to geriatricians and cardiologists. Despite HRS guidelines to the contrary, a fifth of our respondents believed that DNR/DNI orders include ICD deactivation. Overall, ICD deactivation in DNR/DNI patients is a topic that needs further attention in clinical education so that patients receive care that respects their individual wishes.

Disclosure

Nothing to report.

 

Implantable cardioverter-defibrillators (ICDs) offer lifesaving therapies to many patients and have been implanted in hundreds of thousands of patients.¹ The population of patients with ICDs is growing rapidly, and the national ICD Registry reports over 12,000 devices are implanted monthly.² This population includes patients with congenital heart disease, ischemic cardiomyopathy, and idiopathic arrhythmias. If these patients experience ventricular tachycardia or fibrillation, an ICD attempts to restore sinus rhythm and prevent death. While a shock from an ICD may be lifesaving, it can be a traumatic and startling experience for the patient and perhaps distressful for families to witness.^3,4

Although ICDs are intended to save lives, they do not slow the progress of the patient’s underlying cardiac and noncardiac comorbidities. All these patients will eventually die, whether from their cardiac disease or another condition. The literature includes many anecdotes about patients shocked multiple times by their defibrillator while actively dying.⁴ These situations could be prevented with preemptive ICD deactivation. (ICDs can function not only as cardioverters and defibrillators, as implied by their name, but also as pacemakers. “Deactivation” as used in this paper refers only to disabling the tachycardia therapies. No distinction was made between defibrillation with a shock and anti-tachycardia pacing.) Therefore, research on ICD deactivation has emphasized patients who are acutely terminally ill, while less emphasis has been placed on patients who are not actively dying.^4–8

Patients may, for a variety of reasons, request a do-not-resuscitate/do-not-intubate (DNR/DNI) order as their code status. However, it is not necessarily clear what a DNR/DNI order implies for ICD management. One survey of attending physicians found that 19% of respondents felt a DNR/DNI order was equivalent to requesting ICD deactivation.⁹ On the other hand, patients are split on whether they would want their device deactivated while in hospice or even at the very end of life.⁶ Heart Rhythm Society (HRS) guidelines favor a nuanced approach to ICD deactivation in DNR/DNI patients that emphasizes the individual patient’s comorbidities and goals.¹⁰ A patient’s individual circumstances might justify a choice to be DNR/DNI without deactivating the ICD. Decision-making in these circumstances requires a careful conversation between the patient and clinician. It is important to identify barriers that might prevent optimal shared decision-making.

Clinicians have been surveyed on ICD management at the end of life, but these studies have generally focused on attending physicians.^5,9,11 However, physician trainees (ie, residents and fellows) as well as advanced practice providers (ie, physician assistants and nurse practitioners) are responsible for much of the clinical care provided to hospitalized patients. In particular, they are often the clinicians to discuss code status with patients. Different specialties (eg, cardiology, general medicine, and geriatrics) manage different sets of patients, which might affect clinicians’ opinions on ICD management. We therefore designed a survey to assess clinician attitudes and beliefs regarding ICD deactivation, including in non-terminally ill patients, and to evaluate for differences according to training level and specialty.

 

METHODS

Case-based and Likert-scale questions were considered for this survey, with the latter being chosen for ease of completion by respondents. An online survey tool (SurveyMonkey; San Mateo, CA) was used for data collection; no identifying data were collected. E-mail invitations to participate were sent to a combination of mailing lists and individual addresses for residents, fellows, advanced practice providers, and attending physicians in general internal medicine, cardiology, electrophysiology, and geriatrics. The survey remained open for 2.5 weeks. It was conducted 5 months into the academic year, thus trainees were well-established in their current roles. Two $25 gift cards were offered to respondents who entered their e-mail into a drawing; responses were not tied to e-mail addresses. Approval for the study was obtained from the University of Michigan Institutional Review Board.

The survey posed 12 questions assessing general attitudes about ICDs as well as individual beliefs and behaviors relating to ICD deactivation. Answers were on a Likert scale of 1 to 5 with 1 representing “strongly disagree” and 5 representing “strongly agree.” A score of 3 indicated “unsure or neutral.” The first 3 questions appeared together on the first page and were prefaced with “Please respond to the following statements about ICD shocks.” The next 9 were likewise grouped on the next page and were prefaced with “Please respond to the following statements about ICD deactivation.” All 12 questions are shown in Figures 1 and 2. Respondents could easily return to previous questions and change their answers. The survey ended with a third page showing 3 multiple choice demographic questions. The demographic questions were about clinical role (first-, second-, third-, or fourth-year resident, fellow, advanced practice provider, and attending), specialty, and number of ICD deactivations the respondent had been directly involved in (0, 1 to 5, 5 to 10, and more than 10). Specialty options were internal medicine resident, inpatient general medicine, outpatient general medicine, cardiology, electrophysiology, and geriatrics.

Likert scale answers of “agree” or “strongly agree” were grouped together as an affirmative response, while all other answers were grouped together as a nonaffirmative response. For analysis, residents were grouped together and their responses compared with attending physicians as a group. Additional analysis was done comparing attending physicians stratified by clinical specialty. Given the small number of responses from attending electrophysiologists, they were grouped with attending cardiologists for analysis. Due to the limited number of fellows and advanced practice providers who responded, further evaluation of these groups was not performed. Finally, the number of ICD deactivations respondents had been involved in was stratified by training level. All comparisons were performed using the two-tailed Pearson’s chi-squared test.

RESULTS

A total of 170 responses were collected from 508 individuals on the e-mail lists. Two responses were from registered nurses who were not part of the intended study sample and 7 responses were incomplete, having only answered the first 3 questions. These 9 responses were excluded from further analysis, yielding an overall response rate of 32%. The demographics of the remaining 161 respondents are shown in Table 1. Figure 1 shows overall responses to each question.

When comparing residents to attending physicians, there were no statistically significant between-differences except on questions 5 and 6. Specifically, residents were less comfortable than attending physicians discussing ICD deactivation and did so with less regularity (P < .001 and P = .018, respectively; Figure 2). Comfort levels improved markedly with experience: 29.2% of interns expressed comfort asking about ICD deactivation as compared with 60.7% of third- and-fourth year residents and 78.8% of attending physicians. Furthermore, comfort level seemed to parallel the regularity with which respondents asked about ICD deactivation: 4.2% of interns routinely asked about ICD deactivation as compared with 21.4% of third- and fourth-year residents and 34.8% of attending physicians.

The only statistically significant difference when comparing attending physicians by specialty was on question 6 of the survey with the groups being unequal in their reliability at asking about ICD deactivation during code status discussions (P < .001; Figure 2). Of cardiologists and electrophysiologists, 73.3% said they routinely ask about ICD deactivation, as well as 83.3% of geriatricians. By contrast, only 19.2% of outpatient general internists and 10.5% of inpatient general internists (ie, hospitalists) said they routinely ask about ICD deactivation.

There were no differences between groups when asked whether ICD deactivation was part of a DNR/DNI order (question 8), or if an ICD should be deactivated in DNR/DNI patients (questions 9 and 10). As shown in Figure 1, 21.1% of respondents felt that a DNR/DNI order is equivalent to requesting ICD deactivation, 60.2% felt that terminally ill DNR/DNI patients should have their device deactivated, and 28% felt that non-terminally ill DNR/DNI patients should have their device deactivated.

Groups were unequal with respect to the number of ICD deactivations in which they had been directly involved (Figure 3; P < .001). Over half of interns had not been involved in any ICD deactivations as compared with only 10.7% of third- or fourth-year residents. Of the 20 geriatricians, cardiologists, and electrophysiologists, 45% had been involved in at least 5 ICD deactivations. Of note, although 77.8% of fellows reported being involved in more than 10 ICD deactivations, these 9 respondents were all in cardiology or electrophysiology.

 

DISCUSSION

Overall, our major findings were (1) residents, who provide much of the clinical care in a teaching hospital, are remarkably uncomfortable discussing ICD deactivation, (2) general internists and residents ask about ICD deactivation infrequently compared to geriatricians and cardiologists, and (3) about one fifth of our respondents believe ICD deactivation is automatically part of a DNR/DNI order.

Although the majority of respondents did not routinely address ICD deactivation in conjunction with code status, there was significant variability among subgroups. For example, 83.3% of geriatricians routinely discussed ICD deactivation as part of code status compared with only 4% of first-year residents and 10.5% of inpatient general internists. This finding is interesting because 90.7% of all respondents believed that discussions of code status should address preferences on ICD deactivation. This apparent discrepancy could be explained by the relatively small number of patients admitted to the hospital who have both an ICD and a request to be DNR/DNI. Residents and inpatient general internists see a very broad spectrum of patients; ICD deactivation is frequently irrelevant in the cases these physicians manage. The subset of patients seen in consultation by cardiologists and geriatricians, by contrast, is expected to include a larger proportion of patients with ICDs. Therefore, discussing ICD deactivation will be more relevant to their daily practice. Fear of alienating patients was not a reason for our findings, as our respondents did not express concern that recommending ICD deactivation would harm the patient-clinician relationship.

There are several possible reasons that residents, particularly interns, are uncomfortable discussing ICD deactivation. A lack of exposure to ICD deactivation is probably a key contributor. Over half of interns had never been involved in any ICD deactivations. Residents and hospitalists may also feel as if they are overstepping their boundaries to discuss deactivating ICD therapies. Their feelings may not be misplaced, as one survey of ICD patients found that over 75% thought responsibility for discussing ICD deactivation, at least at the end of life, rests with cardiologists or electrophysiologists.⁶

The HRS guidelines call for individualized decisions regarding ICD deactivation, even if a patient is DNR/DNI. However, our respondents frequently felt a standardized approach was indicated, with 21% believing that a DNR/DNI order included ICD deactivation. Additionally, 28% agreed that even non-terminally ill DNR/DNI patients should have their device deactivated. This is relevant because it is the role of clinicians to engage in shared decision-making with their patients. If the clinician holds the fixed belief that a DNR/DNI order, regardless of the precise clinical scenario, should include ICD deactivation, they may pressure a patient to have their device deactivated even if it could still benefit them.

In 2009, Kelley et al published results of a survey on ICD deactivation at the end of life.⁹ They contacted 4,876 attending physicians in cardiology, electrophysiology, geriatrics, and general medicine, receiving 558 responses. The survey included Likert-scale questions assessing attitudes and knowledge about ICD functionality. Demographic information was also collected, including how many patients in their practice had ICDs and how often they had previously discussed ICD deactivation.

There are some interesting comparisons between Kelley et al’s findings and ours, although we included trainees and the precise wording of questions was different. The specific questions used by Kelley et al to ask whether ICD shocks were painful or distressing and to ask if ICD deactivation is part of a DNR order were: “The shock from an ICD is very painful for most patients.” “The shock of an ICD at the end of life is distressing to a patient and their loved ones.” “A DNR order is equivalent to deactivation of an ICD.”

Only 47% of general internists in the Kelley et al survey thought that ICD shocks were painful, compared with 83% of electrophysiologists. In addition, 65% of general internists and 85% of electrophysiologists viewed shocks at the end of life to be distressing to patients and families. By contrast, our respondents were nearly unanimous in believing shocks to be painful and distressing. This discrepancy may be due to the growing prevalence of ICDs over the past several years as well as the growing body of literature on unnecessary shocks at the end of life. In line with our study, 19% of their respondents believed a DNR order was equivalent to ICD deactivation.⁹

Taken together, our findings indicate that additional education for clinicians of all levels could be helpful. Didactic lessons cannot replace experience, and it is important for residents to be exposed to discussions of ICD deactivation. However, lessons about ICD therapies and practical examples of how to broach the topic of deactivation could be beneficial, especially for interns whose responsibility includes discussions of code status. Within the context of an internal medicine residency, the fundamentals of ICD functionality could be covered during rotations on cardiology or palliative care services. Additionally, the recommendations of the HRS for device management can be covered in didactic sessions. Similar opportunities could be built into continuing medical education for practicing physicians and the training of advanced practice providers.

There are limitations to this survey, most notably the fact that it was restricted to a single academic medical center, the patient population and practices of which may not be generalizable to medical practice at large. Selection bias is also a distinct possibility given the 32% overall response rate; those who responded may feel more strongly about the survey topic. Our study subgroups may have interpreted questions differently because of their particular area of clinical practice. The small sample size also precluded an effective analysis of fellows and advanced practice practitioners due to lack of power. A major strength of this survey was the inclusion of a large number of residents upon whom the majority of inpatient contact rests. Future work could include expanding the survey to multiple medical centers, which would enhance generalizability and improve the ability to recruit sufficient fellows and advanced practice providers.

 

CONCLUSION

In summary, we conducted a single-center survey of residents, fellows, advanced practice providers, and attending physicians on their attitudes and beliefs about ICD deactivation in DNR/DNI patients. Residents are particularly uncomfortable discussing ICD deactivation with patients, which is an important finding because of their crucial role in providing patient care. Additionally, residents and hospitalists do not broach the topic of deactivation regularly, especially when compared to geriatricians and cardiologists. Despite HRS guidelines to the contrary, a fifth of our respondents believed that DNR/DNI orders include ICD deactivation. Overall, ICD deactivation in DNR/DNI patients is a topic that needs further attention in clinical education so that patients receive care that respects their individual wishes.

Disclosure

Nothing to report.

 

In recent years, the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely™ campaign has advanced the dialogue on cost-consciousness by identifying potential examples of overuse in clinical practice.¹ Eliminating low-value care can decrease costs, improve quality, and potentially decrease patient harm.² In fact, there is growing consensus among health leaders and educators on the need for a physician workforce that is conscious of high-value care.^3,4 The Institute of Medicine has issued a call-to-action for graduate medical education (GME) to emphasize value-based care,⁵ and the Accreditation Council for Graduate Medical Education has outlined expectations that residents receive formal and experiential training on overuse as a part of its Clinical Learning Environment Review.⁶

However, recent reports highlight a lack of emphasis on value-based care in medical education.⁷ For example, few residency program directors believe that residents are prepared to incorporate value and cost into their medical decisions.⁸ In 2012, only 15% of medicine residencies reported having formal curricula addressing value, although many were developing one.⁸ Of the curricula reported, most were didactic in nature and did not include an assessment component.⁸

Experiential learning through simulation is one promising method to teach clinicians-in-training to practice value-based care. Simulation-based training promotes situational awareness (defined as being cognizant of one’s working environment), a concept that is crucial for recognizing both low-value and unsafe care.^9,10 Simulated training exercises are often included in GME orientation “boot-camps,” which have typically addressed safety.¹¹ The incorporation of value into existing GME boot-camp exercises could provide a promising model for the addition of value-based training to GME.

At the University of Chicago, we had successfully implemented the “Room of Horrors,” a simulation for entering interns to promote the detection of patient safety hazards.¹¹ Here, we describe a modification to this simulation to embed low-value hazards in addition to traditional patient safety hazards. The aim of this study is to assess the entering interns’ recognition of low-value care and their ability to recognize unsafe care in a simulation designed to promote situational awareness.

METHODS

Setting and Participants

The simulation was conducted during GME orientation at a large, urban academic medical institution. One hundred and twenty-five entering postgraduate year one (PGY1) interns participated in the simulation, which was a required component of a multiday orientation “boot-camp” experience. All eligible interns participated in the simulation, representing 13 specialty programs and 60 medical schools. Interns entering into pathology were excluded because of infrequent patient contact. Participating interns were divided into 7 specialty groups for analysis in order to preserve the anonymity of interns in smaller residency programs (surgical subspecialties combined with general surgery, medicine-pediatrics grouped with internal medicine). The University of Chicago Institutional Review Board deemed this study exempt from review.

 

Program Description

A simulation of an inpatient hospital room, known as the “Room of Horrors,” was constructed in collaboration with the University of Chicago Simulation Center and adapted from a previous version of the exercise.¹¹ The simulation consisted of a mock door chart highlighting the patient had been admitted for diarrhea (Clostridium difficile positive) following a recent hospitalization for pneumonia. A clinical scenario was constructed by using a patient mannequin and an accompanying door chart that listed information on the patient’s hospital course, allergies, and medications. In addition to the 8 patient safety hazards utilized in the prior version, our team selected 4 low-value hazards to be included in the simulation.

The 8 safety hazards have been detailed in a prior study and were previously selected from Medicare’s Hospital-Acquired Conditions (HAC) Reduction Program and Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators.^11-13 Each of the hazards was represented either physically in the simulation room and/or was indicated on the patient’s chart. For example, the latex allergy hazard was represented by latex gloves at the bedside despite an allergy indicated on the patient’s chart and wristband. A complete list of the 8 safety hazards and their representations in the simulation is shown in Table 1.

The Choosing Wisely™ lists were reviewed to identify low-value hazards for addition to the simulation.¹⁴ Our team selected 3 low-value hazards from the Society of Hospital Medicine (SHM) list,¹⁵ including (1) arbitrary blood transfusion despite the patient’s stable hemoglobin level of 8.0 g/dL and absence of cardiac symptoms,¹⁶ (2) addition of a proton pump inhibitor (PPI) for stress ulcer prophylaxis in a patient without high risk for gastrointestinal (GI) complications who was not on a PPI prior to admission, and (3) placement of a urinary catheter without medical indication. We had originally selected continuous telemetry monitoring as a fourth hazard from the SHM list, but were unable to operationalize, as it was difficult to simulate continuous telemetry on a mannequin. Because many inpatients are older than 65 years, we reviewed the American Geriatrics Society list¹⁷ and selected our fourth low-value hazard: (4) unnecessary use of physical restraints to manage behavioral symptoms in a hospitalized patient with delirium. Several of these hazards were also quality and safety priorities at our institution, including the overuse of urinary catheters, physical restraints, and blood transfusions. All 4 low-value hazards were referenced in the patient’s door chart, and 3 were also physically represented in the room via presence of a hanging unit of blood, Foley catheter, and upper-arm restraints (Table 1). See Appendix for a photograph of the simulation setup.

Each intern was allowed 10 minutes inside the simulation room. During this time, they were instructed to read the 1-page door chart, inspect the simulation room, and write down as many potential low-value and safety hazards as they could identify on a free-response form (see Appendix). Upon exiting the room, they were allotted 5 additional minutes to complete their free-response answers and provide written feedback on the simulation. The simulation was conducted in 3 simulated hospital rooms over the course of 2 days, and the correct answers were provided via e-mail after all interns had completed the exercise.

To assess prior training and safety knowledge, interns were asked to complete a 3-question preassessment on a ScanTron^TM (Tustin, CA) form. The preassessment asked interns whether they had received training on hospital safety during medical school (yes, no, or unsure), if they were satisfied with the hospital safety training they received during medical school (strongly disagree to strongly agree on a Likert scale), and if they were confident in their ability to identify potential hazards in a hospital setting (strongly disagree to strongly agree). Interns were also given the opportunity to provide feedback on the simulation experience on the ScanTron^TM (Tustin, CA) form.

One month after participating in the simulation, interns were asked to complete an online follow-up survey on MedHub^TM (Ann Arbor, MI), which included 2 Likert-scale questions (strongly disagree to strongly agree) assessing the simulation’s impact on their experience mitigating hospital hazards during the first month of internship.

Data Analysis

Interns’ free-response answers were manually coded, and descriptive statistics were used to summarize the mean percent correct for each hazard. A paired t test was used to compare intern identification of low-value vs safety hazards. T tests were used to compare hazard identification for interns entering highly procedural-intensive specialties (ie, surgical specialties, emergency medicine, anesthesia, obstetrics/gynecology) and those entering less procedural-intensive specialties (ie, internal medicine, pediatrics, psychiatry), as well as among those graduating from “Top 30” medical schools (based on US News & World Report Medical School Rankings¹⁸) and our own institution. One-way analysis of variance (ANOVA) calculations were used to test for differences in hazard identification based on interns’ prior hospital safety training, with interns who rated their satisfaction with prior training or confidence in identifying hazards as a “4” or a “5” considered “satisfied” and “confident,” respectively. Responses to the MedHub^TM (Ann Arbor, MI) survey were dichotomized with “strongly agree” and “agree” considered positive responses. Statistical significance was defined at P < .05. All data analysis was conducted using Stata 14^TM software (College Station, TX).

 

RESULTS

Intern Characteristics

One hundred twenty-five entering PGY1 interns participated in the simulation, representing 60 medical schools and 7 different specialty groups (Table 2). Thirty-five percent (44/125) were graduates from “Top 30” medical schools, and 8.8% (11/125) graduated from our own institution. Seventy-four percent (89/121) had received prior hospital safety training during medical school, and 62.9% (56/89) were satisfied with their training. A majority of interns (64.2%, 79/123) felt confident in their ability to identify potential hazards in a hospital setting, although confidence was much higher among those with prior safety training (71.9%, 64/89) compared to those without prior training or who were unsure about their training (40.6%, 13/32; P = .09, t test).

Identification of Hazards

The mean percentage of hazards correctly identified by interns during the simulation was 50.4% (standard deviation [SD] 11.8%), with a normal distribution (Figure 1). Interns identified a significantly lower percentage of low-value hazards than safety hazards in the simulation (mean 19.2% [SD 18.6%] vs 66.0% [SD 16.0%], respectively; P < .001, paired t test). Interns also identified significantly more room-based errors than chart-based errors (mean 58.6% [SD 13.4%] vs 9.6% [SD 19.8%], respectively; P < .001, paired t test). The 3 most commonly identified hazards were unavailability of hand hygiene (120/125, 96.0%), presence of latex gloves despite the patient’s allergy (111/125, 88.8%), and fall risk due to the lowered bed rail (107/125, 85.6%). More than half of interns identified the incorrect name on the patient’s wristband and IV bag (91/125, 72.8%), a lack of isolation precautions (90/125, 72.0%), administration of penicillin despite the patient’s allergy (67/125, 53.6%), and unnecessary restraints (64/125, 51.2%). Less than half of interns identified the wrong medication being administered (50/125, 40.0%), unnecessary Foley catheter (25/125, 20.0%), and absence of venous thromboembolism (VTE) prophylaxis (24/125, 19.2%). Few interns identified the unnecessary blood transfusion (7/125, 5.6%), and no one identified the unnecessary stress ulcer prophylaxis (0/125, 0.0%; Figure 2).

Predictors of Hazard Identification

Interns who self-reported as confident in their ability to identify hazards were not any more likely to correctly identify hazards than those who were not confident (50.9% overall hazard identification vs 49.6%, respectively; P = .56, t test). Interns entering into less procedural-intensive specialties identified significantly more safety hazards than those entering highly procedural-intensive specialties (mean 69.1% [SD 16.9%] vs 61.8% [SD 13.7%], respectively; P = .01, t test). However, there was no statistically significant difference in their identification of low-value hazards (mean 19.8% [SD 18.3%] for less procedural-intensive vs 18.4% [SD 19.1%] for highly procedural-intensive; P = .68, t test). There was no statistically significant difference in hazard identification among graduates of “Top 30” medical schools or graduates of our own institution. Prior hospital safety training had no significant impact on interns’ ability to identify safety or low-value hazards. Overall, interns who were satisfied with their prior training identified a mean of 51.8% of hazards present (SD 11.8%), interns who were not satisfied with their prior training identified 51.5% (SD 12.7%), interns with no prior training identified 48.7% (SD 11.7%), and interns who were unsure about their prior training identified 47.4% (SD 11.5%) [F(3,117) = .79; P = .51, ANOVA]. There was also no significant association between prior training and the identification of any one of the 12 specific hazards (chi-square tests, all P values > .1).

Intern Feedback and Follow-Up Survey

Debriefing revealed that most interns passively assumed the patient’s chart was correct and did not think they should question the patient’s current care regimen. For example, many interns commented that they did not think to consider the patient’s blood transfusion as unnecessary, even though they were aware of the recommended hemoglobin cutoffs for stable patients.

Interns also provided formal feedback on the simulation through open-ended comments on their ScanTron^TM (Tustin, CA) form. For example, one intern wrote that they would “inherently approach every patient room ‘looking’ for safety issues, probably directly because of this exercise.” Another commented that the simulation was “more difficult than I expected, but very necessary to facilitate discussion and learning.” One intern wrote that “I wish I had done this earlier in my career.”

Ninety-six percent of participating interns (120/125) completed an online follow-up survey 1 month after beginning internship. In the survey, 68.9% (82/119) of interns indicated they were more aware of how to identify potential hazards facing hospitalized patients as a result of the simulation. Furthermore, 52.1% (62/119) of interns had taken action during internship to reduce a potential hazard that was present in the simulation.

DISCUSSION

While many GME orientations include simulation and safety training, this study is the first of its kind to incorporate low-value care from Choosing Wisely™ recommendations into simulated training. It is concerning that interns identified significantly fewer low-value hazards than safety hazards in the simulation. In some cases, no interns identified the low-value hazard. For example, while almost all interns identified the hand hygiene hazard, not one could identify the unnecessary stress ulcer prophylaxis. Furthermore, interns who self-reported as confident in their ability to identify hazards did not perform any better in the simulation. Interns entering less procedural-intensive specialties identified more safety hazards overall.

 

The simulation was well received by interns. Many commented that the experience was engaging, challenging, and effective in cultivating situational awareness towards low-value care. Our follow-up survey demonstrated the majority of interns reported taking action during their first month of internship to reduce a hazard included in the simulation. Most interns also reported a greater awareness of how to identify hospital hazards as a result of the simulation. These findings suggest that a brief simulation-based experience has the potential to create a lasting retention of situational awareness and behavior change.

It is worth exploring why interns identified significantly fewer low-value hazards than safety hazards in the simulation. One hypothesis is that interns were less attuned to low-value hazards, which may reflect a lacking emphasis on value-based care in undergraduate medical education (UME). It is especially concerning that so few interns identified the catheter-associated urinary tract infection (CAUTI) risk, as interns are primarily responsible for recognizing and removing an unnecessary catheter. Although the risks of low-value care should be apparent to most trainees, the process of recognizing and deliberately stopping or avoiding low-value care can be challenging for young clinicians.¹⁹ To promote value-based thinking among entering residents, UME programs should teach students to question the utility of the interventions their patients are receiving. One promising framework for doing so is the Subjective, Objective, Assessment, Plan- (SOAP)-V, in which a V for “Value” is added to the traditional SOAP note.²⁰ SOAP-V notes serve as a cognitive forcing function that requires students to pause and assess the value and cost-consciousness of their patients’ care.²⁰

The results from the “Room of Horrors” simulation can also guide health leaders and educators in identifying institutional areas of focus towards providing high-value and safe care. For example, at the University of Chicago we launched an initiative to improve the inappropriate use of urinary catheters after learning that few of our incoming interns recognized this during the simulation. Institutions could use this model to raise awareness of initiatives and redirect resources from areas that trainees perform well in (eg, hand hygiene) to areas that need improvement (eg, recognition of low-value care). Given the simulation’s low cost and minimal material requirements, it could be easily integrated into existing training programs with the support of an institution’s simulation center.

This study’s limitations include its conduction at single-institution, although the participants represented graduates of 60 different institutions. Furthermore, while the 12 hazards included in the simulation represent patient safety and value initiatives from a wide array of medical societies, they were not intended to be comprehensive and were not tailored to specific specialties. The simulation included only 4 low-value hazards, and future iterations of this exercise should aim to include an equal number of safety and low-value hazards. Furthermore, the evaluation of interns’ prior hospital safety training relied on self-reporting, and the specific context and content of each interns’ training was not examined. Finally, at this point we are unable to provide objective longitudinal data assessing the simulation’s impact on clinical practice and patient outcomes. Subsequent work will assess the sustained impact of the simulation by correlating with institutional data on measurable occurrences of low-value care.

In conclusion, interns identified significantly fewer low-value hazards than safety hazards in an inpatient simulation designed to promote situational awareness. Our results suggest that interns are on the lookout for errors of omission (eg, absence of hand hygiene, absence of isolation precautions) but are often blinded to errors of commission, such that when patients are started on therapies there is an assumption that the therapies are correct and necessary (eg, blood transfusions, stress ulcer prophylaxis). These findings suggest poor awareness of low-value care among incoming interns and highlight the need for additional training in both UME and GME to place a greater emphasis on preventing low-value care.

Disclosure

Dr. Arora is a member of the American Board of Medicine Board of Directors and has received grant funding from ABIM Foundation via Costs of Care for the Teaching Value Choosing Wisely™ Challenge. Dr. Farnan, Dr. Arora, and Ms. Hirsch receive grant funds from Accreditation Council of Graduate Medical Education as part of the Pursuing Excellence Initiative. Dr. Arora and Dr. Farnan also receive grant funds from the American Medical Association Accelerating Change in Medical Education initiative. Kathleen Wiest and Lukas Matern were funded through matching funds of the Pritzker Summer Research Program for NIA T35AG029795.

In recent years, the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely™ campaign has advanced the dialogue on cost-consciousness by identifying potential examples of overuse in clinical practice.¹ Eliminating low-value care can decrease costs, improve quality, and potentially decrease patient harm.² In fact, there is growing consensus among health leaders and educators on the need for a physician workforce that is conscious of high-value care.^3,4 The Institute of Medicine has issued a call-to-action for graduate medical education (GME) to emphasize value-based care,⁵ and the Accreditation Council for Graduate Medical Education has outlined expectations that residents receive formal and experiential training on overuse as a part of its Clinical Learning Environment Review.⁶

However, recent reports highlight a lack of emphasis on value-based care in medical education.⁷ For example, few residency program directors believe that residents are prepared to incorporate value and cost into their medical decisions.⁸ In 2012, only 15% of medicine residencies reported having formal curricula addressing value, although many were developing one.⁸ Of the curricula reported, most were didactic in nature and did not include an assessment component.⁸

Experiential learning through simulation is one promising method to teach clinicians-in-training to practice value-based care. Simulation-based training promotes situational awareness (defined as being cognizant of one’s working environment), a concept that is crucial for recognizing both low-value and unsafe care.^9,10 Simulated training exercises are often included in GME orientation “boot-camps,” which have typically addressed safety.¹¹ The incorporation of value into existing GME boot-camp exercises could provide a promising model for the addition of value-based training to GME.

At the University of Chicago, we had successfully implemented the “Room of Horrors,” a simulation for entering interns to promote the detection of patient safety hazards.¹¹ Here, we describe a modification to this simulation to embed low-value hazards in addition to traditional patient safety hazards. The aim of this study is to assess the entering interns’ recognition of low-value care and their ability to recognize unsafe care in a simulation designed to promote situational awareness.

METHODS

Setting and Participants

The simulation was conducted during GME orientation at a large, urban academic medical institution. One hundred and twenty-five entering postgraduate year one (PGY1) interns participated in the simulation, which was a required component of a multiday orientation “boot-camp” experience. All eligible interns participated in the simulation, representing 13 specialty programs and 60 medical schools. Interns entering into pathology were excluded because of infrequent patient contact. Participating interns were divided into 7 specialty groups for analysis in order to preserve the anonymity of interns in smaller residency programs (surgical subspecialties combined with general surgery, medicine-pediatrics grouped with internal medicine). The University of Chicago Institutional Review Board deemed this study exempt from review.

 

Program Description

A simulation of an inpatient hospital room, known as the “Room of Horrors,” was constructed in collaboration with the University of Chicago Simulation Center and adapted from a previous version of the exercise.¹¹ The simulation consisted of a mock door chart highlighting the patient had been admitted for diarrhea (Clostridium difficile positive) following a recent hospitalization for pneumonia. A clinical scenario was constructed by using a patient mannequin and an accompanying door chart that listed information on the patient’s hospital course, allergies, and medications. In addition to the 8 patient safety hazards utilized in the prior version, our team selected 4 low-value hazards to be included in the simulation.

The 8 safety hazards have been detailed in a prior study and were previously selected from Medicare’s Hospital-Acquired Conditions (HAC) Reduction Program and Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators.^11-13 Each of the hazards was represented either physically in the simulation room and/or was indicated on the patient’s chart. For example, the latex allergy hazard was represented by latex gloves at the bedside despite an allergy indicated on the patient’s chart and wristband. A complete list of the 8 safety hazards and their representations in the simulation is shown in Table 1.

The Choosing Wisely™ lists were reviewed to identify low-value hazards for addition to the simulation.¹⁴ Our team selected 3 low-value hazards from the Society of Hospital Medicine (SHM) list,¹⁵ including (1) arbitrary blood transfusion despite the patient’s stable hemoglobin level of 8.0 g/dL and absence of cardiac symptoms,¹⁶ (2) addition of a proton pump inhibitor (PPI) for stress ulcer prophylaxis in a patient without high risk for gastrointestinal (GI) complications who was not on a PPI prior to admission, and (3) placement of a urinary catheter without medical indication. We had originally selected continuous telemetry monitoring as a fourth hazard from the SHM list, but were unable to operationalize, as it was difficult to simulate continuous telemetry on a mannequin. Because many inpatients are older than 65 years, we reviewed the American Geriatrics Society list¹⁷ and selected our fourth low-value hazard: (4) unnecessary use of physical restraints to manage behavioral symptoms in a hospitalized patient with delirium. Several of these hazards were also quality and safety priorities at our institution, including the overuse of urinary catheters, physical restraints, and blood transfusions. All 4 low-value hazards were referenced in the patient’s door chart, and 3 were also physically represented in the room via presence of a hanging unit of blood, Foley catheter, and upper-arm restraints (Table 1). See Appendix for a photograph of the simulation setup.

Each intern was allowed 10 minutes inside the simulation room. During this time, they were instructed to read the 1-page door chart, inspect the simulation room, and write down as many potential low-value and safety hazards as they could identify on a free-response form (see Appendix). Upon exiting the room, they were allotted 5 additional minutes to complete their free-response answers and provide written feedback on the simulation. The simulation was conducted in 3 simulated hospital rooms over the course of 2 days, and the correct answers were provided via e-mail after all interns had completed the exercise.

To assess prior training and safety knowledge, interns were asked to complete a 3-question preassessment on a ScanTron^TM (Tustin, CA) form. The preassessment asked interns whether they had received training on hospital safety during medical school (yes, no, or unsure), if they were satisfied with the hospital safety training they received during medical school (strongly disagree to strongly agree on a Likert scale), and if they were confident in their ability to identify potential hazards in a hospital setting (strongly disagree to strongly agree). Interns were also given the opportunity to provide feedback on the simulation experience on the ScanTron^TM (Tustin, CA) form.

One month after participating in the simulation, interns were asked to complete an online follow-up survey on MedHub^TM (Ann Arbor, MI), which included 2 Likert-scale questions (strongly disagree to strongly agree) assessing the simulation’s impact on their experience mitigating hospital hazards during the first month of internship.

Data Analysis

Interns’ free-response answers were manually coded, and descriptive statistics were used to summarize the mean percent correct for each hazard. A paired t test was used to compare intern identification of low-value vs safety hazards. T tests were used to compare hazard identification for interns entering highly procedural-intensive specialties (ie, surgical specialties, emergency medicine, anesthesia, obstetrics/gynecology) and those entering less procedural-intensive specialties (ie, internal medicine, pediatrics, psychiatry), as well as among those graduating from “Top 30” medical schools (based on US News & World Report Medical School Rankings¹⁸) and our own institution. One-way analysis of variance (ANOVA) calculations were used to test for differences in hazard identification based on interns’ prior hospital safety training, with interns who rated their satisfaction with prior training or confidence in identifying hazards as a “4” or a “5” considered “satisfied” and “confident,” respectively. Responses to the MedHub^TM (Ann Arbor, MI) survey were dichotomized with “strongly agree” and “agree” considered positive responses. Statistical significance was defined at P < .05. All data analysis was conducted using Stata 14^TM software (College Station, TX).

 

RESULTS

Intern Characteristics

One hundred twenty-five entering PGY1 interns participated in the simulation, representing 60 medical schools and 7 different specialty groups (Table 2). Thirty-five percent (44/125) were graduates from “Top 30” medical schools, and 8.8% (11/125) graduated from our own institution. Seventy-four percent (89/121) had received prior hospital safety training during medical school, and 62.9% (56/89) were satisfied with their training. A majority of interns (64.2%, 79/123) felt confident in their ability to identify potential hazards in a hospital setting, although confidence was much higher among those with prior safety training (71.9%, 64/89) compared to those without prior training or who were unsure about their training (40.6%, 13/32; P = .09, t test).

Identification of Hazards

The mean percentage of hazards correctly identified by interns during the simulation was 50.4% (standard deviation [SD] 11.8%), with a normal distribution (Figure 1). Interns identified a significantly lower percentage of low-value hazards than safety hazards in the simulation (mean 19.2% [SD 18.6%] vs 66.0% [SD 16.0%], respectively; P < .001, paired t test). Interns also identified significantly more room-based errors than chart-based errors (mean 58.6% [SD 13.4%] vs 9.6% [SD 19.8%], respectively; P < .001, paired t test). The 3 most commonly identified hazards were unavailability of hand hygiene (120/125, 96.0%), presence of latex gloves despite the patient’s allergy (111/125, 88.8%), and fall risk due to the lowered bed rail (107/125, 85.6%). More than half of interns identified the incorrect name on the patient’s wristband and IV bag (91/125, 72.8%), a lack of isolation precautions (90/125, 72.0%), administration of penicillin despite the patient’s allergy (67/125, 53.6%), and unnecessary restraints (64/125, 51.2%). Less than half of interns identified the wrong medication being administered (50/125, 40.0%), unnecessary Foley catheter (25/125, 20.0%), and absence of venous thromboembolism (VTE) prophylaxis (24/125, 19.2%). Few interns identified the unnecessary blood transfusion (7/125, 5.6%), and no one identified the unnecessary stress ulcer prophylaxis (0/125, 0.0%; Figure 2).

Predictors of Hazard Identification

Interns who self-reported as confident in their ability to identify hazards were not any more likely to correctly identify hazards than those who were not confident (50.9% overall hazard identification vs 49.6%, respectively; P = .56, t test). Interns entering into less procedural-intensive specialties identified significantly more safety hazards than those entering highly procedural-intensive specialties (mean 69.1% [SD 16.9%] vs 61.8% [SD 13.7%], respectively; P = .01, t test). However, there was no statistically significant difference in their identification of low-value hazards (mean 19.8% [SD 18.3%] for less procedural-intensive vs 18.4% [SD 19.1%] for highly procedural-intensive; P = .68, t test). There was no statistically significant difference in hazard identification among graduates of “Top 30” medical schools or graduates of our own institution. Prior hospital safety training had no significant impact on interns’ ability to identify safety or low-value hazards. Overall, interns who were satisfied with their prior training identified a mean of 51.8% of hazards present (SD 11.8%), interns who were not satisfied with their prior training identified 51.5% (SD 12.7%), interns with no prior training identified 48.7% (SD 11.7%), and interns who were unsure about their prior training identified 47.4% (SD 11.5%) [F(3,117) = .79; P = .51, ANOVA]. There was also no significant association between prior training and the identification of any one of the 12 specific hazards (chi-square tests, all P values > .1).

Intern Feedback and Follow-Up Survey

Debriefing revealed that most interns passively assumed the patient’s chart was correct and did not think they should question the patient’s current care regimen. For example, many interns commented that they did not think to consider the patient’s blood transfusion as unnecessary, even though they were aware of the recommended hemoglobin cutoffs for stable patients.

Interns also provided formal feedback on the simulation through open-ended comments on their ScanTron^TM (Tustin, CA) form. For example, one intern wrote that they would “inherently approach every patient room ‘looking’ for safety issues, probably directly because of this exercise.” Another commented that the simulation was “more difficult than I expected, but very necessary to facilitate discussion and learning.” One intern wrote that “I wish I had done this earlier in my career.”

Ninety-six percent of participating interns (120/125) completed an online follow-up survey 1 month after beginning internship. In the survey, 68.9% (82/119) of interns indicated they were more aware of how to identify potential hazards facing hospitalized patients as a result of the simulation. Furthermore, 52.1% (62/119) of interns had taken action during internship to reduce a potential hazard that was present in the simulation.

DISCUSSION

While many GME orientations include simulation and safety training, this study is the first of its kind to incorporate low-value care from Choosing Wisely™ recommendations into simulated training. It is concerning that interns identified significantly fewer low-value hazards than safety hazards in the simulation. In some cases, no interns identified the low-value hazard. For example, while almost all interns identified the hand hygiene hazard, not one could identify the unnecessary stress ulcer prophylaxis. Furthermore, interns who self-reported as confident in their ability to identify hazards did not perform any better in the simulation. Interns entering less procedural-intensive specialties identified more safety hazards overall.

 

The simulation was well received by interns. Many commented that the experience was engaging, challenging, and effective in cultivating situational awareness towards low-value care. Our follow-up survey demonstrated the majority of interns reported taking action during their first month of internship to reduce a hazard included in the simulation. Most interns also reported a greater awareness of how to identify hospital hazards as a result of the simulation. These findings suggest that a brief simulation-based experience has the potential to create a lasting retention of situational awareness and behavior change.

It is worth exploring why interns identified significantly fewer low-value hazards than safety hazards in the simulation. One hypothesis is that interns were less attuned to low-value hazards, which may reflect a lacking emphasis on value-based care in undergraduate medical education (UME). It is especially concerning that so few interns identified the catheter-associated urinary tract infection (CAUTI) risk, as interns are primarily responsible for recognizing and removing an unnecessary catheter. Although the risks of low-value care should be apparent to most trainees, the process of recognizing and deliberately stopping or avoiding low-value care can be challenging for young clinicians.¹⁹ To promote value-based thinking among entering residents, UME programs should teach students to question the utility of the interventions their patients are receiving. One promising framework for doing so is the Subjective, Objective, Assessment, Plan- (SOAP)-V, in which a V for “Value” is added to the traditional SOAP note.²⁰ SOAP-V notes serve as a cognitive forcing function that requires students to pause and assess the value and cost-consciousness of their patients’ care.²⁰

The results from the “Room of Horrors” simulation can also guide health leaders and educators in identifying institutional areas of focus towards providing high-value and safe care. For example, at the University of Chicago we launched an initiative to improve the inappropriate use of urinary catheters after learning that few of our incoming interns recognized this during the simulation. Institutions could use this model to raise awareness of initiatives and redirect resources from areas that trainees perform well in (eg, hand hygiene) to areas that need improvement (eg, recognition of low-value care). Given the simulation’s low cost and minimal material requirements, it could be easily integrated into existing training programs with the support of an institution’s simulation center.

This study’s limitations include its conduction at single-institution, although the participants represented graduates of 60 different institutions. Furthermore, while the 12 hazards included in the simulation represent patient safety and value initiatives from a wide array of medical societies, they were not intended to be comprehensive and were not tailored to specific specialties. The simulation included only 4 low-value hazards, and future iterations of this exercise should aim to include an equal number of safety and low-value hazards. Furthermore, the evaluation of interns’ prior hospital safety training relied on self-reporting, and the specific context and content of each interns’ training was not examined. Finally, at this point we are unable to provide objective longitudinal data assessing the simulation’s impact on clinical practice and patient outcomes. Subsequent work will assess the sustained impact of the simulation by correlating with institutional data on measurable occurrences of low-value care.

In conclusion, interns identified significantly fewer low-value hazards than safety hazards in an inpatient simulation designed to promote situational awareness. Our results suggest that interns are on the lookout for errors of omission (eg, absence of hand hygiene, absence of isolation precautions) but are often blinded to errors of commission, such that when patients are started on therapies there is an assumption that the therapies are correct and necessary (eg, blood transfusions, stress ulcer prophylaxis). These findings suggest poor awareness of low-value care among incoming interns and highlight the need for additional training in both UME and GME to place a greater emphasis on preventing low-value care.

Disclosure

Dr. Arora is a member of the American Board of Medicine Board of Directors and has received grant funding from ABIM Foundation via Costs of Care for the Teaching Value Choosing Wisely™ Challenge. Dr. Farnan, Dr. Arora, and Ms. Hirsch receive grant funds from Accreditation Council of Graduate Medical Education as part of the Pursuing Excellence Initiative. Dr. Arora and Dr. Farnan also receive grant funds from the American Medical Association Accelerating Change in Medical Education initiative. Kathleen Wiest and Lukas Matern were funded through matching funds of the Pritzker Summer Research Program for NIA T35AG029795.

In recent years, the American Board of Internal Medicine (ABIM) Foundation’s Choosing Wisely™ campaign has advanced the dialogue on cost-consciousness by identifying potential examples of overuse in clinical practice.¹ Eliminating low-value care can decrease costs, improve quality, and potentially decrease patient harm.² In fact, there is growing consensus among health leaders and educators on the need for a physician workforce that is conscious of high-value care.^3,4 The Institute of Medicine has issued a call-to-action for graduate medical education (GME) to emphasize value-based care,⁵ and the Accreditation Council for Graduate Medical Education has outlined expectations that residents receive formal and experiential training on overuse as a part of its Clinical Learning Environment Review.⁶

However, recent reports highlight a lack of emphasis on value-based care in medical education.⁷ For example, few residency program directors believe that residents are prepared to incorporate value and cost into their medical decisions.⁸ In 2012, only 15% of medicine residencies reported having formal curricula addressing value, although many were developing one.⁸ Of the curricula reported, most were didactic in nature and did not include an assessment component.⁸

Experiential learning through simulation is one promising method to teach clinicians-in-training to practice value-based care. Simulation-based training promotes situational awareness (defined as being cognizant of one’s working environment), a concept that is crucial for recognizing both low-value and unsafe care.^9,10 Simulated training exercises are often included in GME orientation “boot-camps,” which have typically addressed safety.¹¹ The incorporation of value into existing GME boot-camp exercises could provide a promising model for the addition of value-based training to GME.

At the University of Chicago, we had successfully implemented the “Room of Horrors,” a simulation for entering interns to promote the detection of patient safety hazards.¹¹ Here, we describe a modification to this simulation to embed low-value hazards in addition to traditional patient safety hazards. The aim of this study is to assess the entering interns’ recognition of low-value care and their ability to recognize unsafe care in a simulation designed to promote situational awareness.

METHODS

Setting and Participants

The simulation was conducted during GME orientation at a large, urban academic medical institution. One hundred and twenty-five entering postgraduate year one (PGY1) interns participated in the simulation, which was a required component of a multiday orientation “boot-camp” experience. All eligible interns participated in the simulation, representing 13 specialty programs and 60 medical schools. Interns entering into pathology were excluded because of infrequent patient contact. Participating interns were divided into 7 specialty groups for analysis in order to preserve the anonymity of interns in smaller residency programs (surgical subspecialties combined with general surgery, medicine-pediatrics grouped with internal medicine). The University of Chicago Institutional Review Board deemed this study exempt from review.

 

Program Description

A simulation of an inpatient hospital room, known as the “Room of Horrors,” was constructed in collaboration with the University of Chicago Simulation Center and adapted from a previous version of the exercise.¹¹ The simulation consisted of a mock door chart highlighting the patient had been admitted for diarrhea (Clostridium difficile positive) following a recent hospitalization for pneumonia. A clinical scenario was constructed by using a patient mannequin and an accompanying door chart that listed information on the patient’s hospital course, allergies, and medications. In addition to the 8 patient safety hazards utilized in the prior version, our team selected 4 low-value hazards to be included in the simulation.

The 8 safety hazards have been detailed in a prior study and were previously selected from Medicare’s Hospital-Acquired Conditions (HAC) Reduction Program and Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators.^11-13 Each of the hazards was represented either physically in the simulation room and/or was indicated on the patient’s chart. For example, the latex allergy hazard was represented by latex gloves at the bedside despite an allergy indicated on the patient’s chart and wristband. A complete list of the 8 safety hazards and their representations in the simulation is shown in Table 1.

The Choosing Wisely™ lists were reviewed to identify low-value hazards for addition to the simulation.¹⁴ Our team selected 3 low-value hazards from the Society of Hospital Medicine (SHM) list,¹⁵ including (1) arbitrary blood transfusion despite the patient’s stable hemoglobin level of 8.0 g/dL and absence of cardiac symptoms,¹⁶ (2) addition of a proton pump inhibitor (PPI) for stress ulcer prophylaxis in a patient without high risk for gastrointestinal (GI) complications who was not on a PPI prior to admission, and (3) placement of a urinary catheter without medical indication. We had originally selected continuous telemetry monitoring as a fourth hazard from the SHM list, but were unable to operationalize, as it was difficult to simulate continuous telemetry on a mannequin. Because many inpatients are older than 65 years, we reviewed the American Geriatrics Society list¹⁷ and selected our fourth low-value hazard: (4) unnecessary use of physical restraints to manage behavioral symptoms in a hospitalized patient with delirium. Several of these hazards were also quality and safety priorities at our institution, including the overuse of urinary catheters, physical restraints, and blood transfusions. All 4 low-value hazards were referenced in the patient’s door chart, and 3 were also physically represented in the room via presence of a hanging unit of blood, Foley catheter, and upper-arm restraints (Table 1). See Appendix for a photograph of the simulation setup.

Each intern was allowed 10 minutes inside the simulation room. During this time, they were instructed to read the 1-page door chart, inspect the simulation room, and write down as many potential low-value and safety hazards as they could identify on a free-response form (see Appendix). Upon exiting the room, they were allotted 5 additional minutes to complete their free-response answers and provide written feedback on the simulation. The simulation was conducted in 3 simulated hospital rooms over the course of 2 days, and the correct answers were provided via e-mail after all interns had completed the exercise.

To assess prior training and safety knowledge, interns were asked to complete a 3-question preassessment on a ScanTron^TM (Tustin, CA) form. The preassessment asked interns whether they had received training on hospital safety during medical school (yes, no, or unsure), if they were satisfied with the hospital safety training they received during medical school (strongly disagree to strongly agree on a Likert scale), and if they were confident in their ability to identify potential hazards in a hospital setting (strongly disagree to strongly agree). Interns were also given the opportunity to provide feedback on the simulation experience on the ScanTron^TM (Tustin, CA) form.

One month after participating in the simulation, interns were asked to complete an online follow-up survey on MedHub^TM (Ann Arbor, MI), which included 2 Likert-scale questions (strongly disagree to strongly agree) assessing the simulation’s impact on their experience mitigating hospital hazards during the first month of internship.

Data Analysis

Interns’ free-response answers were manually coded, and descriptive statistics were used to summarize the mean percent correct for each hazard. A paired t test was used to compare intern identification of low-value vs safety hazards. T tests were used to compare hazard identification for interns entering highly procedural-intensive specialties (ie, surgical specialties, emergency medicine, anesthesia, obstetrics/gynecology) and those entering less procedural-intensive specialties (ie, internal medicine, pediatrics, psychiatry), as well as among those graduating from “Top 30” medical schools (based on US News & World Report Medical School Rankings¹⁸) and our own institution. One-way analysis of variance (ANOVA) calculations were used to test for differences in hazard identification based on interns’ prior hospital safety training, with interns who rated their satisfaction with prior training or confidence in identifying hazards as a “4” or a “5” considered “satisfied” and “confident,” respectively. Responses to the MedHub^TM (Ann Arbor, MI) survey were dichotomized with “strongly agree” and “agree” considered positive responses. Statistical significance was defined at P < .05. All data analysis was conducted using Stata 14^TM software (College Station, TX).

 

RESULTS

Intern Characteristics

One hundred twenty-five entering PGY1 interns participated in the simulation, representing 60 medical schools and 7 different specialty groups (Table 2). Thirty-five percent (44/125) were graduates from “Top 30” medical schools, and 8.8% (11/125) graduated from our own institution. Seventy-four percent (89/121) had received prior hospital safety training during medical school, and 62.9% (56/89) were satisfied with their training. A majority of interns (64.2%, 79/123) felt confident in their ability to identify potential hazards in a hospital setting, although confidence was much higher among those with prior safety training (71.9%, 64/89) compared to those without prior training or who were unsure about their training (40.6%, 13/32; P = .09, t test).

Identification of Hazards

The mean percentage of hazards correctly identified by interns during the simulation was 50.4% (standard deviation [SD] 11.8%), with a normal distribution (Figure 1). Interns identified a significantly lower percentage of low-value hazards than safety hazards in the simulation (mean 19.2% [SD 18.6%] vs 66.0% [SD 16.0%], respectively; P < .001, paired t test). Interns also identified significantly more room-based errors than chart-based errors (mean 58.6% [SD 13.4%] vs 9.6% [SD 19.8%], respectively; P < .001, paired t test). The 3 most commonly identified hazards were unavailability of hand hygiene (120/125, 96.0%), presence of latex gloves despite the patient’s allergy (111/125, 88.8%), and fall risk due to the lowered bed rail (107/125, 85.6%). More than half of interns identified the incorrect name on the patient’s wristband and IV bag (91/125, 72.8%), a lack of isolation precautions (90/125, 72.0%), administration of penicillin despite the patient’s allergy (67/125, 53.6%), and unnecessary restraints (64/125, 51.2%). Less than half of interns identified the wrong medication being administered (50/125, 40.0%), unnecessary Foley catheter (25/125, 20.0%), and absence of venous thromboembolism (VTE) prophylaxis (24/125, 19.2%). Few interns identified the unnecessary blood transfusion (7/125, 5.6%), and no one identified the unnecessary stress ulcer prophylaxis (0/125, 0.0%; Figure 2).

Predictors of Hazard Identification

Interns who self-reported as confident in their ability to identify hazards were not any more likely to correctly identify hazards than those who were not confident (50.9% overall hazard identification vs 49.6%, respectively; P = .56, t test). Interns entering into less procedural-intensive specialties identified significantly more safety hazards than those entering highly procedural-intensive specialties (mean 69.1% [SD 16.9%] vs 61.8% [SD 13.7%], respectively; P = .01, t test). However, there was no statistically significant difference in their identification of low-value hazards (mean 19.8% [SD 18.3%] for less procedural-intensive vs 18.4% [SD 19.1%] for highly procedural-intensive; P = .68, t test). There was no statistically significant difference in hazard identification among graduates of “Top 30” medical schools or graduates of our own institution. Prior hospital safety training had no significant impact on interns’ ability to identify safety or low-value hazards. Overall, interns who were satisfied with their prior training identified a mean of 51.8% of hazards present (SD 11.8%), interns who were not satisfied with their prior training identified 51.5% (SD 12.7%), interns with no prior training identified 48.7% (SD 11.7%), and interns who were unsure about their prior training identified 47.4% (SD 11.5%) [F(3,117) = .79; P = .51, ANOVA]. There was also no significant association between prior training and the identification of any one of the 12 specific hazards (chi-square tests, all P values > .1).

Intern Feedback and Follow-Up Survey

Debriefing revealed that most interns passively assumed the patient’s chart was correct and did not think they should question the patient’s current care regimen. For example, many interns commented that they did not think to consider the patient’s blood transfusion as unnecessary, even though they were aware of the recommended hemoglobin cutoffs for stable patients.

Interns also provided formal feedback on the simulation through open-ended comments on their ScanTron^TM (Tustin, CA) form. For example, one intern wrote that they would “inherently approach every patient room ‘looking’ for safety issues, probably directly because of this exercise.” Another commented that the simulation was “more difficult than I expected, but very necessary to facilitate discussion and learning.” One intern wrote that “I wish I had done this earlier in my career.”

Ninety-six percent of participating interns (120/125) completed an online follow-up survey 1 month after beginning internship. In the survey, 68.9% (82/119) of interns indicated they were more aware of how to identify potential hazards facing hospitalized patients as a result of the simulation. Furthermore, 52.1% (62/119) of interns had taken action during internship to reduce a potential hazard that was present in the simulation.

DISCUSSION

While many GME orientations include simulation and safety training, this study is the first of its kind to incorporate low-value care from Choosing Wisely™ recommendations into simulated training. It is concerning that interns identified significantly fewer low-value hazards than safety hazards in the simulation. In some cases, no interns identified the low-value hazard. For example, while almost all interns identified the hand hygiene hazard, not one could identify the unnecessary stress ulcer prophylaxis. Furthermore, interns who self-reported as confident in their ability to identify hazards did not perform any better in the simulation. Interns entering less procedural-intensive specialties identified more safety hazards overall.

 

The simulation was well received by interns. Many commented that the experience was engaging, challenging, and effective in cultivating situational awareness towards low-value care. Our follow-up survey demonstrated the majority of interns reported taking action during their first month of internship to reduce a hazard included in the simulation. Most interns also reported a greater awareness of how to identify hospital hazards as a result of the simulation. These findings suggest that a brief simulation-based experience has the potential to create a lasting retention of situational awareness and behavior change.

It is worth exploring why interns identified significantly fewer low-value hazards than safety hazards in the simulation. One hypothesis is that interns were less attuned to low-value hazards, which may reflect a lacking emphasis on value-based care in undergraduate medical education (UME). It is especially concerning that so few interns identified the catheter-associated urinary tract infection (CAUTI) risk, as interns are primarily responsible for recognizing and removing an unnecessary catheter. Although the risks of low-value care should be apparent to most trainees, the process of recognizing and deliberately stopping or avoiding low-value care can be challenging for young clinicians.¹⁹ To promote value-based thinking among entering residents, UME programs should teach students to question the utility of the interventions their patients are receiving. One promising framework for doing so is the Subjective, Objective, Assessment, Plan- (SOAP)-V, in which a V for “Value” is added to the traditional SOAP note.²⁰ SOAP-V notes serve as a cognitive forcing function that requires students to pause and assess the value and cost-consciousness of their patients’ care.²⁰

The results from the “Room of Horrors” simulation can also guide health leaders and educators in identifying institutional areas of focus towards providing high-value and safe care. For example, at the University of Chicago we launched an initiative to improve the inappropriate use of urinary catheters after learning that few of our incoming interns recognized this during the simulation. Institutions could use this model to raise awareness of initiatives and redirect resources from areas that trainees perform well in (eg, hand hygiene) to areas that need improvement (eg, recognition of low-value care). Given the simulation’s low cost and minimal material requirements, it could be easily integrated into existing training programs with the support of an institution’s simulation center.

This study’s limitations include its conduction at single-institution, although the participants represented graduates of 60 different institutions. Furthermore, while the 12 hazards included in the simulation represent patient safety and value initiatives from a wide array of medical societies, they were not intended to be comprehensive and were not tailored to specific specialties. The simulation included only 4 low-value hazards, and future iterations of this exercise should aim to include an equal number of safety and low-value hazards. Furthermore, the evaluation of interns’ prior hospital safety training relied on self-reporting, and the specific context and content of each interns’ training was not examined. Finally, at this point we are unable to provide objective longitudinal data assessing the simulation’s impact on clinical practice and patient outcomes. Subsequent work will assess the sustained impact of the simulation by correlating with institutional data on measurable occurrences of low-value care.

In conclusion, interns identified significantly fewer low-value hazards than safety hazards in an inpatient simulation designed to promote situational awareness. Our results suggest that interns are on the lookout for errors of omission (eg, absence of hand hygiene, absence of isolation precautions) but are often blinded to errors of commission, such that when patients are started on therapies there is an assumption that the therapies are correct and necessary (eg, blood transfusions, stress ulcer prophylaxis). These findings suggest poor awareness of low-value care among incoming interns and highlight the need for additional training in both UME and GME to place a greater emphasis on preventing low-value care.

Disclosure

Dr. Arora is a member of the American Board of Medicine Board of Directors and has received grant funding from ABIM Foundation via Costs of Care for the Teaching Value Choosing Wisely™ Challenge. Dr. Farnan, Dr. Arora, and Ms. Hirsch receive grant funds from Accreditation Council of Graduate Medical Education as part of the Pursuing Excellence Initiative. Dr. Arora and Dr. Farnan also receive grant funds from the American Medical Association Accelerating Change in Medical Education initiative. Kathleen Wiest and Lukas Matern were funded through matching funds of the Pritzker Summer Research Program for NIA T35AG029795.

Chronic urticaria (CU) is clinically defined as the daily or almost daily presence of wheals on the skin for at least 6 weeks.¹ Chronic urticaria severely affects patients’ quality of life and can cause emotional disability and distress.² In clinical practice, CU is one of the most common and challenging conditions for general practitioners, dermatologists, and allergists. It can be provoked by a wide variety of different causes or may be the clinical presentation of certain systemic diseases^3,4; thus, CU often requires a detailed and time-consuming diagnostic procedure that includes screening for allergies, autoimmune diseases, parasites, malignancies, infections, and metabolic disorders.^5,6 In many patients (up to 50% in some case series), the cause or pathogenic mechanism cannot be identified, and the disease is then classified as chronic idiopathic urticaria (CIU).⁷

It has previously been shown that contact sensitization could have some relation with CIU,⁸ which was further explored in this study. This study sought to evaluate if contact allergy may play a role in disease development in CIU patients in Saudi Arabia and if patch testing should be routinely performed for CIU patients to determine if any allergens can be avoided.

Methods

This prospective study was conducted at the King Khalid University Hospital Allergy Clinic (Riyadh, Saudi Arabia) in patients aged 18 to 60 years who had CU for more than 6 weeks. It was a clinic-based study conducted over a period of 2 years (March 2010 to February 2012). The study protocol was approved by the local ethics committee at King Khalid University Hospital. Valid written consent was obtained from each patient.

Patients were excluded if they had CU caused by physical factors (eg, hot or cold temperature, water, physical contact) or drug reactions that were possible causative factors or if they had taken oral prednisolone or other oral immunosuppressive drugs (eg, azathioprine, cyclosporine) in the last month. However, patients taking antihistamines were not excluded because it was impossible for the patients to discontinue their urticaria treatment. Other exclusion criteria included CU associated with any systemic disease, thyroid disease, diabetes mellitus, autoimmune disorder, or atopic dermatitis. Pregnant and lactating women were not included in this study.

All new adult CU patients (ie, disease duration >6 weeks) were worked up using the routine diagnostic tests that are typically performed for any new CU patient, including complete blood cell count with differential, erythrocyte sedimentation rate, liver function tests, urine analysis, and hepatitis B and C screenings. Further diagnostic tests also were carried out when appropriate according to the patient’s history and physical examination, including levels of urea, electrolytes, thyrotropin, thyroid antibodies (antithyroglobulin and antimicrosomal), and antinuclear antibodies, as well as a Helicobacter pylori test.

All of the patients enrolled in the study were evaluated by skin prick testing to establish the link between CU and its cause. Patch testing was performed in patients who were negative on skin prick testing.

Skin Prick Testing
All patients were advised to temporarily discontinue the use of antihistamines and corticosteroids 5 to 6 days prior to testing. To assess the presence of allergen-specific IgE antibodies, skin prick testing is preferred because it is more sensitive and specific, is simple to use, is inexpensive, and is not associated with any complications.⁹

Patch Testing
Patch tests were carried out using a ready-to-use epicutaneous patch test system for the diagnosis of allergic contact dermatitis (ACD).¹⁰ A European standard series was used with the addition of 4 allergens of local relevance: black seed oil, local perfume mix, henna, and myrrh (a topical herbal medicine used to promote healing). Patients with a negative skin prick test who had a positive patch test were enrolled in an allergen-avoidance program to avoid the offending allergen for 8 weeks.

Assessment of Improvement
Assessment of urticaria severity using the Chronic Urticaria Severity Score (CUSS), a simple semiquantitative assessment of disease activity, was calculated as the sum of the number of wheals and the degree of itch severity graded from 0 (none) to 3 (severe), according to the guidelines established by the Dermatology Section of the European Academy of Allergology and Clinical Immunology, the Global Allergy and Asthma European Network, the European Dermatology Forum, and the World Allergy Organization.¹¹ The avoidance group of patients was assessed at baseline and after 1 month to evaluate changes in their CUSS after allergen avoidance for 8 weeks.

Statistical Analysis
All of the statistical analyses were carried out using SPSS software version 16. Results were presented as the median with the range or the mean (SD). Descriptive statistics were used to describe the demographic data. The comparability of demographic and baseline characteristics among CIU patients was assessed using the Student t test, and P<.05 was considered statistically significant.

 

Results

During the study period, a total of 120 CU patients were seen at the clinic. Ninety-three patients with CU met our selection criteria (77.5%) and were enrolled in the study. The mean age (SD) of the patients was 34.7 (12.4) years. Women comprised 68.8% (64/93) of the study population (Table 1).

The duration of urticaria ranged from 0.6 to 20 years, with a median duration of 4 years. Approximately half of the patients (50/93) experienced severe symptoms of urticaria, but only 26.9% (25/93) had graded their urticaria as very severe.

Negative results from the skin prick test were reported in 62.4% (58/93) of patients and were subsequently patch tested. These patients also had no other etiologic factors (eg, infection; thyroid, autoimmune, or metabolic disease). Patients who had positive skin prick test results (35/93 [37.6%]) were not considered to be cases of CIU, according to diagnostic recommendations.¹² Of the 58 CIU patients who were patch tested, 31 (53.4%) had positive results and 27 (46.5%) had negative results to both skin prick and patch tests (Figure).

Univariate analysis revealed significant associations between age, gender, and duration of urticaria and patch test positivity (χ² test, P<.05). Twenty of 31 (64.5%) patch test–positive patients were aged 30 to 45 years. Positive patch test results were observed in 31 of 43 female patients (72.1%; P<.001). Of the patch test–positive patients, disease duration was greater than 5 years in 16 of 31 patients (51.6%).

Of the 31 patients with positive patch tests, there were 20 positive reactions to nickel, 6 to formaldehyde, 4 to phenylenediamine, 3 to cobalt, and 3 to a fragrance mix (Table 2). Some patients showed patch test reactivity to more than 1 allergen concomitantly. Overall, these 31 patients had positive reactions to 16 allergens. None of the patients showed actual signs of contact dermatitis (Table 2).

Of the 31 patch test–positive patients, 10 were enrolled but only 8 (25.8%) agreed to take appropriate avoidance measures for the sensitizing substances; 5 (62.5%) showed excellent improvement in their baseline symptoms at a 1-month follow-up visit.

Comment

Chronic idiopathic urticaria is the diagnosis given when urticarial vasculitis, physical urticaria, and all other possible etiologic factors have been excluded in patients with CU. Our study was designed to assess patch test reactivity in patients with CU without any identifiable systemic etiologic factor after detailed laboratory testing and negative skin prick tests.

Chronic idiopathic urticaria can be an extremely disabling and difficult-to-treat condition. Because the cause is unknown, the management of CIU often is frustrating. The efficacy of performing patch tests in CIU has not yet been proven, as there are conflicting results regarding the role of contact sensitization in CIU. Prior studies in this field have shown that contact allergy can play a role in the etiopathogenesis of CU; these findings have stimulated new approaches for investigation of CIU.^8,12 There were no details of how a common allergen such as nickel was avoided, which caused remission in the majority of patch test–positive patients.

Patch testing is commonly performed to diagnose ACD, and if contact allergens are found via patch testing, patients can often be cured of their dermatitis by avoiding these agents. However, patch testing is not routinely performed in the evaluation of patients with CIU. It is a relatively inexpensive and safe procedure to determine a causal link between sensitization to a specific agent and ACD. In patch test clinics, agents often are tested in standard and screening series. Sensitization that is not suspected from the patient’s history and/or clinical examination can be detected in this manner. Requirements for the inclusion of a chemical in a standard series have been formulated by Bruze et al.¹³ In addition, ready-to-use materials relevant to the specific leisure activities and working conditions also can be selected for patch testing.

A study conducted in Saudi Arabia showed that the European standard series is suitable for patch testing patients in this community¹⁴; however, 3 allergens of local relevance were added in our study: black seed oil, local perfume mix, and henna. Moreover, in our study we added a local allergen known as myrrh, which is a topical herbal medicine used to promote healing that has been reported to cause ACD in some cases.¹⁵ We sought to determine if contact allergens can be identified with patch testing in patients with CU and if avoiding these contact allergens would resolve the CU.

Urticaria was once considered an IgE-mediated hypersensitivity reaction, but recent studies have demonstrated the existence of different subgroupsof urticaria, some with an autoimmune mechanism.^1-4,11 In CU, skin prick tests are recommended for etiologic workup, while patch testing generally is not recommended.¹⁶

It has been observed in clinical practice that a substantial number of patients with CU are positive to patch tests, even without a clear clinical history or signs of contact dermatitis.¹⁷ In 2007, Guerra et al¹⁷ reported that of 121 patients with CU, 50 (41.3%) tested positive for contact allergens. In all of the patch test–positive patients, avoidance measures led to complete remission within 10 days to 1 month. Therefore, this result suggested that testing for contact sensitization could be helpful in the management of CU. Patients with nickel sensitivity were subsequently allowed to ingest small amounts of nickel-containing foods after 8 weeks of a completely nickel-free diet, and remission persisted.¹⁷

Contact dermatitis affects approximately 20% of the general population¹⁸; however, there has been little investigation (limited to nickel) into the relationship between contact allergens and CU,^19,20 and the underlying mechanisms of the disease are unknown. It has been hypothesized that small amounts of the substances are absorbed through the skin or the digestive tract into the bloodstream over the long-term and are delivered to antigen-presenting cells in the skin, which provide the necessary signals for mast cell activation. Nonetheless, the reasons for a selectively cutaneous localization of the reaction remain largely unclear.

Management of CU is debated among physicians, and several diagnostic flowcharts have been proposed.^1,2 In general, patch tests for contact dermatitis are not recommended as a fundamental part of the diagnostic procedure, but Guerra et al¹⁷ suggested that contact allergy often plays a role in CU.

There have been inadequate reports of CU found to be caused by common contact sensitizers.^21-24 Interestingly, no signs of contact allergy were demonstrated in CU patients before urticarial attack.

Our findings supported our patient selection criteria and also confirmed that contact sensitization may be one of the many possible mechanisms involved in the etiology of CU. Urticaria may have a delayed-type hypersensitivity reaction element, and patients with CU without an obvious causal factor can have positive patch test results.

The role of contact sensitization in CU has not yet been established, as another study showed no relationship between avoidance of contact allergens and the course of CIU.²⁵ In that study, patients with severe CIU who previously had been patch tested were retrospectively studied. Three groups were studied: CIU patients with positive patch tests; CIU patients with negative patch tests; and a control group, which included patients with CIU who had not been patch tested. The groups were followed monthly to assess changes in CUSS after allergen avoidance. Forty-three patients with severe CIU were patch tested. Nickel sulfate testing was positive in 4 cases (9.3%); potassium dichromate testing was positive in 2 cases (4.7%); and cobalt, balsam of Peru, paraphenylenediamine, fragrance mix, and epoxy resin testing were positive in 1 case (2.3%) each. The mean (SD) baseline CUSS score (5.4 [0.5]) significantly improved after 1 month of allergen avoidance (3.2 [1.1]; P<.001); however, similar improvement in CUSS was observed in 34 patients with CIU with negative patch test results (5.3 [0.5] to 3.2 [1.3]; P<.001) and in 49 patients with CIU in the control group after 1 month (5.2 [0.4] to 3.4 [1.3]; P<.001).²⁵

The main findings of our study were that 53.4% of patients with CIU had positive patch test results and that avoidance of the sensitizing substance was effective in 5 of 8 patients who completed an avoidance program. Almost all of the patients showed notable remission of symptoms after limiting their exposure to the offending allergens. This study clearly showed that a cause or pathogenesis for CIU could be identified, thus showing that CIU occurs less frequently than is usually assumed.

Our study had limitations. The first is our lack of a controlled challenge test, which is important to confirm an allergen as a cause of CIU.²⁶ Nonetheless, avoidance of the revealed contact allergen was associated with comparable improvement of CIU severity after 1 month in 5 of 8 patients, though such measures were not tested in all 31 of 58 CIU patients who had positive patch test results.

 

Conclusion

We propose that patch tests should be performed while investigating CU because they give effective diagnostic and therapeutic results in a substantial number of patients. Urticaria, or at least a subgroup of the disease, may have a delayed-type reaction element, which may explain the disease etiology for many CIU patients. Patients with CU without a detectable underlying etiologic factor can have positive patch test results.

Chronic urticaria (CU) is clinically defined as the daily or almost daily presence of wheals on the skin for at least 6 weeks.¹ Chronic urticaria severely affects patients’ quality of life and can cause emotional disability and distress.² In clinical practice, CU is one of the most common and challenging conditions for general practitioners, dermatologists, and allergists. It can be provoked by a wide variety of different causes or may be the clinical presentation of certain systemic diseases^3,4; thus, CU often requires a detailed and time-consuming diagnostic procedure that includes screening for allergies, autoimmune diseases, parasites, malignancies, infections, and metabolic disorders.^5,6 In many patients (up to 50% in some case series), the cause or pathogenic mechanism cannot be identified, and the disease is then classified as chronic idiopathic urticaria (CIU).⁷

It has previously been shown that contact sensitization could have some relation with CIU,⁸ which was further explored in this study. This study sought to evaluate if contact allergy may play a role in disease development in CIU patients in Saudi Arabia and if patch testing should be routinely performed for CIU patients to determine if any allergens can be avoided.

Methods

This prospective study was conducted at the King Khalid University Hospital Allergy Clinic (Riyadh, Saudi Arabia) in patients aged 18 to 60 years who had CU for more than 6 weeks. It was a clinic-based study conducted over a period of 2 years (March 2010 to February 2012). The study protocol was approved by the local ethics committee at King Khalid University Hospital. Valid written consent was obtained from each patient.

Patients were excluded if they had CU caused by physical factors (eg, hot or cold temperature, water, physical contact) or drug reactions that were possible causative factors or if they had taken oral prednisolone or other oral immunosuppressive drugs (eg, azathioprine, cyclosporine) in the last month. However, patients taking antihistamines were not excluded because it was impossible for the patients to discontinue their urticaria treatment. Other exclusion criteria included CU associated with any systemic disease, thyroid disease, diabetes mellitus, autoimmune disorder, or atopic dermatitis. Pregnant and lactating women were not included in this study.

All new adult CU patients (ie, disease duration >6 weeks) were worked up using the routine diagnostic tests that are typically performed for any new CU patient, including complete blood cell count with differential, erythrocyte sedimentation rate, liver function tests, urine analysis, and hepatitis B and C screenings. Further diagnostic tests also were carried out when appropriate according to the patient’s history and physical examination, including levels of urea, electrolytes, thyrotropin, thyroid antibodies (antithyroglobulin and antimicrosomal), and antinuclear antibodies, as well as a Helicobacter pylori test.

All of the patients enrolled in the study were evaluated by skin prick testing to establish the link between CU and its cause. Patch testing was performed in patients who were negative on skin prick testing.

Skin Prick Testing
All patients were advised to temporarily discontinue the use of antihistamines and corticosteroids 5 to 6 days prior to testing. To assess the presence of allergen-specific IgE antibodies, skin prick testing is preferred because it is more sensitive and specific, is simple to use, is inexpensive, and is not associated with any complications.⁹

Patch Testing
Patch tests were carried out using a ready-to-use epicutaneous patch test system for the diagnosis of allergic contact dermatitis (ACD).¹⁰ A European standard series was used with the addition of 4 allergens of local relevance: black seed oil, local perfume mix, henna, and myrrh (a topical herbal medicine used to promote healing). Patients with a negative skin prick test who had a positive patch test were enrolled in an allergen-avoidance program to avoid the offending allergen for 8 weeks.

Assessment of Improvement
Assessment of urticaria severity using the Chronic Urticaria Severity Score (CUSS), a simple semiquantitative assessment of disease activity, was calculated as the sum of the number of wheals and the degree of itch severity graded from 0 (none) to 3 (severe), according to the guidelines established by the Dermatology Section of the European Academy of Allergology and Clinical Immunology, the Global Allergy and Asthma European Network, the European Dermatology Forum, and the World Allergy Organization.¹¹ The avoidance group of patients was assessed at baseline and after 1 month to evaluate changes in their CUSS after allergen avoidance for 8 weeks.

Statistical Analysis
All of the statistical analyses were carried out using SPSS software version 16. Results were presented as the median with the range or the mean (SD). Descriptive statistics were used to describe the demographic data. The comparability of demographic and baseline characteristics among CIU patients was assessed using the Student t test, and P<.05 was considered statistically significant.

 

Results

During the study period, a total of 120 CU patients were seen at the clinic. Ninety-three patients with CU met our selection criteria (77.5%) and were enrolled in the study. The mean age (SD) of the patients was 34.7 (12.4) years. Women comprised 68.8% (64/93) of the study population (Table 1).

The duration of urticaria ranged from 0.6 to 20 years, with a median duration of 4 years. Approximately half of the patients (50/93) experienced severe symptoms of urticaria, but only 26.9% (25/93) had graded their urticaria as very severe.

Negative results from the skin prick test were reported in 62.4% (58/93) of patients and were subsequently patch tested. These patients also had no other etiologic factors (eg, infection; thyroid, autoimmune, or metabolic disease). Patients who had positive skin prick test results (35/93 [37.6%]) were not considered to be cases of CIU, according to diagnostic recommendations.¹² Of the 58 CIU patients who were patch tested, 31 (53.4%) had positive results and 27 (46.5%) had negative results to both skin prick and patch tests (Figure).

Univariate analysis revealed significant associations between age, gender, and duration of urticaria and patch test positivity (χ² test, P<.05). Twenty of 31 (64.5%) patch test–positive patients were aged 30 to 45 years. Positive patch test results were observed in 31 of 43 female patients (72.1%; P<.001). Of the patch test–positive patients, disease duration was greater than 5 years in 16 of 31 patients (51.6%).

Of the 31 patients with positive patch tests, there were 20 positive reactions to nickel, 6 to formaldehyde, 4 to phenylenediamine, 3 to cobalt, and 3 to a fragrance mix (Table 2). Some patients showed patch test reactivity to more than 1 allergen concomitantly. Overall, these 31 patients had positive reactions to 16 allergens. None of the patients showed actual signs of contact dermatitis (Table 2).

Of the 31 patch test–positive patients, 10 were enrolled but only 8 (25.8%) agreed to take appropriate avoidance measures for the sensitizing substances; 5 (62.5%) showed excellent improvement in their baseline symptoms at a 1-month follow-up visit.

Comment

Chronic idiopathic urticaria is the diagnosis given when urticarial vasculitis, physical urticaria, and all other possible etiologic factors have been excluded in patients with CU. Our study was designed to assess patch test reactivity in patients with CU without any identifiable systemic etiologic factor after detailed laboratory testing and negative skin prick tests.

Chronic idiopathic urticaria can be an extremely disabling and difficult-to-treat condition. Because the cause is unknown, the management of CIU often is frustrating. The efficacy of performing patch tests in CIU has not yet been proven, as there are conflicting results regarding the role of contact sensitization in CIU. Prior studies in this field have shown that contact allergy can play a role in the etiopathogenesis of CU; these findings have stimulated new approaches for investigation of CIU.^8,12 There were no details of how a common allergen such as nickel was avoided, which caused remission in the majority of patch test–positive patients.

Patch testing is commonly performed to diagnose ACD, and if contact allergens are found via patch testing, patients can often be cured of their dermatitis by avoiding these agents. However, patch testing is not routinely performed in the evaluation of patients with CIU. It is a relatively inexpensive and safe procedure to determine a causal link between sensitization to a specific agent and ACD. In patch test clinics, agents often are tested in standard and screening series. Sensitization that is not suspected from the patient’s history and/or clinical examination can be detected in this manner. Requirements for the inclusion of a chemical in a standard series have been formulated by Bruze et al.¹³ In addition, ready-to-use materials relevant to the specific leisure activities and working conditions also can be selected for patch testing.

A study conducted in Saudi Arabia showed that the European standard series is suitable for patch testing patients in this community¹⁴; however, 3 allergens of local relevance were added in our study: black seed oil, local perfume mix, and henna. Moreover, in our study we added a local allergen known as myrrh, which is a topical herbal medicine used to promote healing that has been reported to cause ACD in some cases.¹⁵ We sought to determine if contact allergens can be identified with patch testing in patients with CU and if avoiding these contact allergens would resolve the CU.

Urticaria was once considered an IgE-mediated hypersensitivity reaction, but recent studies have demonstrated the existence of different subgroupsof urticaria, some with an autoimmune mechanism.^1-4,11 In CU, skin prick tests are recommended for etiologic workup, while patch testing generally is not recommended.¹⁶

It has been observed in clinical practice that a substantial number of patients with CU are positive to patch tests, even without a clear clinical history or signs of contact dermatitis.¹⁷ In 2007, Guerra et al¹⁷ reported that of 121 patients with CU, 50 (41.3%) tested positive for contact allergens. In all of the patch test–positive patients, avoidance measures led to complete remission within 10 days to 1 month. Therefore, this result suggested that testing for contact sensitization could be helpful in the management of CU. Patients with nickel sensitivity were subsequently allowed to ingest small amounts of nickel-containing foods after 8 weeks of a completely nickel-free diet, and remission persisted.¹⁷

Contact dermatitis affects approximately 20% of the general population¹⁸; however, there has been little investigation (limited to nickel) into the relationship between contact allergens and CU,^19,20 and the underlying mechanisms of the disease are unknown. It has been hypothesized that small amounts of the substances are absorbed through the skin or the digestive tract into the bloodstream over the long-term and are delivered to antigen-presenting cells in the skin, which provide the necessary signals for mast cell activation. Nonetheless, the reasons for a selectively cutaneous localization of the reaction remain largely unclear.

Management of CU is debated among physicians, and several diagnostic flowcharts have been proposed.^1,2 In general, patch tests for contact dermatitis are not recommended as a fundamental part of the diagnostic procedure, but Guerra et al¹⁷ suggested that contact allergy often plays a role in CU.

There have been inadequate reports of CU found to be caused by common contact sensitizers.^21-24 Interestingly, no signs of contact allergy were demonstrated in CU patients before urticarial attack.

Our findings supported our patient selection criteria and also confirmed that contact sensitization may be one of the many possible mechanisms involved in the etiology of CU. Urticaria may have a delayed-type hypersensitivity reaction element, and patients with CU without an obvious causal factor can have positive patch test results.

The role of contact sensitization in CU has not yet been established, as another study showed no relationship between avoidance of contact allergens and the course of CIU.²⁵ In that study, patients with severe CIU who previously had been patch tested were retrospectively studied. Three groups were studied: CIU patients with positive patch tests; CIU patients with negative patch tests; and a control group, which included patients with CIU who had not been patch tested. The groups were followed monthly to assess changes in CUSS after allergen avoidance. Forty-three patients with severe CIU were patch tested. Nickel sulfate testing was positive in 4 cases (9.3%); potassium dichromate testing was positive in 2 cases (4.7%); and cobalt, balsam of Peru, paraphenylenediamine, fragrance mix, and epoxy resin testing were positive in 1 case (2.3%) each. The mean (SD) baseline CUSS score (5.4 [0.5]) significantly improved after 1 month of allergen avoidance (3.2 [1.1]; P<.001); however, similar improvement in CUSS was observed in 34 patients with CIU with negative patch test results (5.3 [0.5] to 3.2 [1.3]; P<.001) and in 49 patients with CIU in the control group after 1 month (5.2 [0.4] to 3.4 [1.3]; P<.001).²⁵

The main findings of our study were that 53.4% of patients with CIU had positive patch test results and that avoidance of the sensitizing substance was effective in 5 of 8 patients who completed an avoidance program. Almost all of the patients showed notable remission of symptoms after limiting their exposure to the offending allergens. This study clearly showed that a cause or pathogenesis for CIU could be identified, thus showing that CIU occurs less frequently than is usually assumed.

Our study had limitations. The first is our lack of a controlled challenge test, which is important to confirm an allergen as a cause of CIU.²⁶ Nonetheless, avoidance of the revealed contact allergen was associated with comparable improvement of CIU severity after 1 month in 5 of 8 patients, though such measures were not tested in all 31 of 58 CIU patients who had positive patch test results.

 

Conclusion

We propose that patch tests should be performed while investigating CU because they give effective diagnostic and therapeutic results in a substantial number of patients. Urticaria, or at least a subgroup of the disease, may have a delayed-type reaction element, which may explain the disease etiology for many CIU patients. Patients with CU without a detectable underlying etiologic factor can have positive patch test results.

Chronic urticaria (CU) is clinically defined as the daily or almost daily presence of wheals on the skin for at least 6 weeks.¹ Chronic urticaria severely affects patients’ quality of life and can cause emotional disability and distress.² In clinical practice, CU is one of the most common and challenging conditions for general practitioners, dermatologists, and allergists. It can be provoked by a wide variety of different causes or may be the clinical presentation of certain systemic diseases^3,4; thus, CU often requires a detailed and time-consuming diagnostic procedure that includes screening for allergies, autoimmune diseases, parasites, malignancies, infections, and metabolic disorders.^5,6 In many patients (up to 50% in some case series), the cause or pathogenic mechanism cannot be identified, and the disease is then classified as chronic idiopathic urticaria (CIU).⁷

It has previously been shown that contact sensitization could have some relation with CIU,⁸ which was further explored in this study. This study sought to evaluate if contact allergy may play a role in disease development in CIU patients in Saudi Arabia and if patch testing should be routinely performed for CIU patients to determine if any allergens can be avoided.

Methods

This prospective study was conducted at the King Khalid University Hospital Allergy Clinic (Riyadh, Saudi Arabia) in patients aged 18 to 60 years who had CU for more than 6 weeks. It was a clinic-based study conducted over a period of 2 years (March 2010 to February 2012). The study protocol was approved by the local ethics committee at King Khalid University Hospital. Valid written consent was obtained from each patient.

Patients were excluded if they had CU caused by physical factors (eg, hot or cold temperature, water, physical contact) or drug reactions that were possible causative factors or if they had taken oral prednisolone or other oral immunosuppressive drugs (eg, azathioprine, cyclosporine) in the last month. However, patients taking antihistamines were not excluded because it was impossible for the patients to discontinue their urticaria treatment. Other exclusion criteria included CU associated with any systemic disease, thyroid disease, diabetes mellitus, autoimmune disorder, or atopic dermatitis. Pregnant and lactating women were not included in this study.

All new adult CU patients (ie, disease duration >6 weeks) were worked up using the routine diagnostic tests that are typically performed for any new CU patient, including complete blood cell count with differential, erythrocyte sedimentation rate, liver function tests, urine analysis, and hepatitis B and C screenings. Further diagnostic tests also were carried out when appropriate according to the patient’s history and physical examination, including levels of urea, electrolytes, thyrotropin, thyroid antibodies (antithyroglobulin and antimicrosomal), and antinuclear antibodies, as well as a Helicobacter pylori test.

All of the patients enrolled in the study were evaluated by skin prick testing to establish the link between CU and its cause. Patch testing was performed in patients who were negative on skin prick testing.

Skin Prick Testing
All patients were advised to temporarily discontinue the use of antihistamines and corticosteroids 5 to 6 days prior to testing. To assess the presence of allergen-specific IgE antibodies, skin prick testing is preferred because it is more sensitive and specific, is simple to use, is inexpensive, and is not associated with any complications.⁹

Patch Testing
Patch tests were carried out using a ready-to-use epicutaneous patch test system for the diagnosis of allergic contact dermatitis (ACD).¹⁰ A European standard series was used with the addition of 4 allergens of local relevance: black seed oil, local perfume mix, henna, and myrrh (a topical herbal medicine used to promote healing). Patients with a negative skin prick test who had a positive patch test were enrolled in an allergen-avoidance program to avoid the offending allergen for 8 weeks.

Assessment of Improvement
Assessment of urticaria severity using the Chronic Urticaria Severity Score (CUSS), a simple semiquantitative assessment of disease activity, was calculated as the sum of the number of wheals and the degree of itch severity graded from 0 (none) to 3 (severe), according to the guidelines established by the Dermatology Section of the European Academy of Allergology and Clinical Immunology, the Global Allergy and Asthma European Network, the European Dermatology Forum, and the World Allergy Organization.¹¹ The avoidance group of patients was assessed at baseline and after 1 month to evaluate changes in their CUSS after allergen avoidance for 8 weeks.

Statistical Analysis
All of the statistical analyses were carried out using SPSS software version 16. Results were presented as the median with the range or the mean (SD). Descriptive statistics were used to describe the demographic data. The comparability of demographic and baseline characteristics among CIU patients was assessed using the Student t test, and P<.05 was considered statistically significant.

 

Results

During the study period, a total of 120 CU patients were seen at the clinic. Ninety-three patients with CU met our selection criteria (77.5%) and were enrolled in the study. The mean age (SD) of the patients was 34.7 (12.4) years. Women comprised 68.8% (64/93) of the study population (Table 1).

The duration of urticaria ranged from 0.6 to 20 years, with a median duration of 4 years. Approximately half of the patients (50/93) experienced severe symptoms of urticaria, but only 26.9% (25/93) had graded their urticaria as very severe.

Negative results from the skin prick test were reported in 62.4% (58/93) of patients and were subsequently patch tested. These patients also had no other etiologic factors (eg, infection; thyroid, autoimmune, or metabolic disease). Patients who had positive skin prick test results (35/93 [37.6%]) were not considered to be cases of CIU, according to diagnostic recommendations.¹² Of the 58 CIU patients who were patch tested, 31 (53.4%) had positive results and 27 (46.5%) had negative results to both skin prick and patch tests (Figure).

Univariate analysis revealed significant associations between age, gender, and duration of urticaria and patch test positivity (χ² test, P<.05). Twenty of 31 (64.5%) patch test–positive patients were aged 30 to 45 years. Positive patch test results were observed in 31 of 43 female patients (72.1%; P<.001). Of the patch test–positive patients, disease duration was greater than 5 years in 16 of 31 patients (51.6%).

Of the 31 patients with positive patch tests, there were 20 positive reactions to nickel, 6 to formaldehyde, 4 to phenylenediamine, 3 to cobalt, and 3 to a fragrance mix (Table 2). Some patients showed patch test reactivity to more than 1 allergen concomitantly. Overall, these 31 patients had positive reactions to 16 allergens. None of the patients showed actual signs of contact dermatitis (Table 2).

Of the 31 patch test–positive patients, 10 were enrolled but only 8 (25.8%) agreed to take appropriate avoidance measures for the sensitizing substances; 5 (62.5%) showed excellent improvement in their baseline symptoms at a 1-month follow-up visit.

Comment

Chronic idiopathic urticaria is the diagnosis given when urticarial vasculitis, physical urticaria, and all other possible etiologic factors have been excluded in patients with CU. Our study was designed to assess patch test reactivity in patients with CU without any identifiable systemic etiologic factor after detailed laboratory testing and negative skin prick tests.

Chronic idiopathic urticaria can be an extremely disabling and difficult-to-treat condition. Because the cause is unknown, the management of CIU often is frustrating. The efficacy of performing patch tests in CIU has not yet been proven, as there are conflicting results regarding the role of contact sensitization in CIU. Prior studies in this field have shown that contact allergy can play a role in the etiopathogenesis of CU; these findings have stimulated new approaches for investigation of CIU.^8,12 There were no details of how a common allergen such as nickel was avoided, which caused remission in the majority of patch test–positive patients.

Patch testing is commonly performed to diagnose ACD, and if contact allergens are found via patch testing, patients can often be cured of their dermatitis by avoiding these agents. However, patch testing is not routinely performed in the evaluation of patients with CIU. It is a relatively inexpensive and safe procedure to determine a causal link between sensitization to a specific agent and ACD. In patch test clinics, agents often are tested in standard and screening series. Sensitization that is not suspected from the patient’s history and/or clinical examination can be detected in this manner. Requirements for the inclusion of a chemical in a standard series have been formulated by Bruze et al.¹³ In addition, ready-to-use materials relevant to the specific leisure activities and working conditions also can be selected for patch testing.

A study conducted in Saudi Arabia showed that the European standard series is suitable for patch testing patients in this community¹⁴; however, 3 allergens of local relevance were added in our study: black seed oil, local perfume mix, and henna. Moreover, in our study we added a local allergen known as myrrh, which is a topical herbal medicine used to promote healing that has been reported to cause ACD in some cases.¹⁵ We sought to determine if contact allergens can be identified with patch testing in patients with CU and if avoiding these contact allergens would resolve the CU.

Urticaria was once considered an IgE-mediated hypersensitivity reaction, but recent studies have demonstrated the existence of different subgroupsof urticaria, some with an autoimmune mechanism.^1-4,11 In CU, skin prick tests are recommended for etiologic workup, while patch testing generally is not recommended.¹⁶

It has been observed in clinical practice that a substantial number of patients with CU are positive to patch tests, even without a clear clinical history or signs of contact dermatitis.¹⁷ In 2007, Guerra et al¹⁷ reported that of 121 patients with CU, 50 (41.3%) tested positive for contact allergens. In all of the patch test–positive patients, avoidance measures led to complete remission within 10 days to 1 month. Therefore, this result suggested that testing for contact sensitization could be helpful in the management of CU. Patients with nickel sensitivity were subsequently allowed to ingest small amounts of nickel-containing foods after 8 weeks of a completely nickel-free diet, and remission persisted.¹⁷

Contact dermatitis affects approximately 20% of the general population¹⁸; however, there has been little investigation (limited to nickel) into the relationship between contact allergens and CU,^19,20 and the underlying mechanisms of the disease are unknown. It has been hypothesized that small amounts of the substances are absorbed through the skin or the digestive tract into the bloodstream over the long-term and are delivered to antigen-presenting cells in the skin, which provide the necessary signals for mast cell activation. Nonetheless, the reasons for a selectively cutaneous localization of the reaction remain largely unclear.

Management of CU is debated among physicians, and several diagnostic flowcharts have been proposed.^1,2 In general, patch tests for contact dermatitis are not recommended as a fundamental part of the diagnostic procedure, but Guerra et al¹⁷ suggested that contact allergy often plays a role in CU.

There have been inadequate reports of CU found to be caused by common contact sensitizers.^21-24 Interestingly, no signs of contact allergy were demonstrated in CU patients before urticarial attack.

Our findings supported our patient selection criteria and also confirmed that contact sensitization may be one of the many possible mechanisms involved in the etiology of CU. Urticaria may have a delayed-type hypersensitivity reaction element, and patients with CU without an obvious causal factor can have positive patch test results.

The role of contact sensitization in CU has not yet been established, as another study showed no relationship between avoidance of contact allergens and the course of CIU.²⁵ In that study, patients with severe CIU who previously had been patch tested were retrospectively studied. Three groups were studied: CIU patients with positive patch tests; CIU patients with negative patch tests; and a control group, which included patients with CIU who had not been patch tested. The groups were followed monthly to assess changes in CUSS after allergen avoidance. Forty-three patients with severe CIU were patch tested. Nickel sulfate testing was positive in 4 cases (9.3%); potassium dichromate testing was positive in 2 cases (4.7%); and cobalt, balsam of Peru, paraphenylenediamine, fragrance mix, and epoxy resin testing were positive in 1 case (2.3%) each. The mean (SD) baseline CUSS score (5.4 [0.5]) significantly improved after 1 month of allergen avoidance (3.2 [1.1]; P<.001); however, similar improvement in CUSS was observed in 34 patients with CIU with negative patch test results (5.3 [0.5] to 3.2 [1.3]; P<.001) and in 49 patients with CIU in the control group after 1 month (5.2 [0.4] to 3.4 [1.3]; P<.001).²⁵

The main findings of our study were that 53.4% of patients with CIU had positive patch test results and that avoidance of the sensitizing substance was effective in 5 of 8 patients who completed an avoidance program. Almost all of the patients showed notable remission of symptoms after limiting their exposure to the offending allergens. This study clearly showed that a cause or pathogenesis for CIU could be identified, thus showing that CIU occurs less frequently than is usually assumed.

Our study had limitations. The first is our lack of a controlled challenge test, which is important to confirm an allergen as a cause of CIU.²⁶ Nonetheless, avoidance of the revealed contact allergen was associated with comparable improvement of CIU severity after 1 month in 5 of 8 patients, though such measures were not tested in all 31 of 58 CIU patients who had positive patch test results.

 

Conclusion

We propose that patch tests should be performed while investigating CU because they give effective diagnostic and therapeutic results in a substantial number of patients. Urticaria, or at least a subgroup of the disease, may have a delayed-type reaction element, which may explain the disease etiology for many CIU patients. Patients with CU without a detectable underlying etiologic factor can have positive patch test results.

Take-Home Points

• Torn, displaced, and entrapped UCL is a Stener lesion.
• Hyperabduction injury with pain and joint laxity on examination.
• MRI and ultrasound are useful in evaluating UCL tears.
• Ultrasound offers dynamic evaluation.
• Must be treated appropriately to avoid pain, instability, and osteoarthritis.

In the literature, hyperabduction injuries to the thumb metacarpophalangeal (MCP) joint have been referred to interchangeably as gamekeeper’s thumb and skier’s thumb. Historically, though, gamekeeper’s thumb was initially described in hunters with chronic injury to the ulnar collateral ligament (UCL),¹ and skier’s thumb typically has been described as an acute hyperabduction injury of the UCL.^2-5 The proximal portion of a torn UCL may retract with further abduction and displace dorsally, becoming entrapped by the adductor pollicis aponeurosis insertion, known as a Stener lesion.⁶

The first MCP joint is stabilized by static and dynamic structures that contribute in varying degrees in flexion and extension of the joint. The static stabilizers include the proper and accessory radial and UCLs, the palmar plate, and the dorsal capsule. The UCL originates at the dorsal ulnar aspect of the first metacarpal head at the metacarpal tubercle about 5 mm proximal to the articular surface. The UCL courses distally in the palmar direction to insert volar and proximal to the medial tubercle of the proximal phalanx about 3 mm distal to the articular surface.⁷ In flexion, the proper collateral ligament is taut and is the primary static stabilizer. In extension, the accessory collateral ligament, which inserts on the palmar plate, is taut and is the primary static stabilizer.^8-11

The dynamic stabilizers include the extrinsic muscles (flexor pollicis longus, extensor pollicis longus and brevis) and the intrinsic muscles (abductor pollicis brevis, adductor pollicis, flexor pollicis brevis) inserting on the thumb at the distal phalanx and proximal phalanx and at the base of the first metacarpal.^8-10

The adductor pollicis originates from the volar third metacarpal, capitate, and hamate and has a dual insertion on the thumb.¹² There is a direct insertion onto the palmar proximal phalanx at the medial tubercle, distal and dorsal to the phalangeal insertion of the UCL.

There is also a broad aponeurosis that inserts onto the extensor hood expansion, dorsal to the insertion of the UCL (Figures 1A-1C and 2A, 2B).^7,8,13

We report the case of an acute hyperabduction injury of the thumb MCP joint with radiographic, ultrasound, and magnetic resonance imaging (MRI) findings consistent with a Stener lesion and subsequently confirmed with intraoperative photographs. The patient provided written informed consent for print and electronic publication of this case report.

Clinical Findings

A 33-year-old healthy man had persistent left hand pain and grip weakness after performing a handstand. He presented to the orthopedic hand clinic 20 days after injury, having failed nonoperative management (use of nonsteroidal anti-inflammatory drugs and soft thumb spica splint). Physical examination revealed soft-tissue swelling and focal tenderness to palpation at the ulnar aspect of the thumb MCP joint. Despite bilateral first MCP joint laxity on varus and valgus stress without identification of a firm endpoint, pain was elicited only on valgus stress of the left first MCP joint. Given the laxity and the left thumb soft-tissue swelling with pain, plain radiographs, ultrasound, and MRI were used to evaluate for severity of presumed left thumb UCL injury.

Imaging Findings

Plain radiographs showed normal bony anatomy without fracture, normal joint space, and mild soft-tissue swelling at the left thumb MCP level (Figures 3A, 3B).

Ultrasound confirmed a complete tear of the UCL, which was flipped in a proximal direction and projected dorsally in relation to the direct insertion of the adductor tendon (Figure 2B). MRI showed focal disruption of the UCL at the level of the left thumb MCP joint with associated MCP joint effusion (Figures 4A-4F).

Low T1 signal intensity over the adductor aponeurosis at the level of the metacarpal head corresponded with the torn and proximally retracted UCL. There was associated bone marrow edema at the radial and volar aspects of the thumb metacarpal head and low-grade strain of the abductor pollicis brevis. The thumb flexor and extensor tendons appeared normal. Although possibly secondary to patient positioning, mild volar subluxation of the proximal phalanx in relation to the metacarpal head was queried.

 

Surgical Findings

Given laxity with pain at the UCL on stress testing, MRI and ultrasound findings, and continued pain and instability of the thumb with pinching and grasping during activities of daily living, the patient and orthopedic hand surgeon proceeded with surgical intervention. Preoperative examination under anesthesia confirmed significant laxity on valgus stress without a palpable endpoint (Figures 5A, 5B).

During surgery, retraction of the extensor hood revealed the completely torn and displaced UCL, entrapped dorsally and proximally to the adductor aponeurosis, characteristic of a Stener lesion. After the primary repair of the UCL, the extensor hood was seen partially retracted in a normal location superficial to the normal deep position of the repaired UCL (Figures 6A, 6B).

Discussion

Hyperabduction injuries to the thumb may rupture the UCL of the MCP joint of the thumb or cause a bony avulsion of the base of the proximal phalanx. Injury to the UCL, most often at its distal portion,^4,14,15 may result in a sprain or full-thickness tear of the ligament.

Subsequently, the ligament may remain in situ, or the proximal segment may retract proximal to the adductor aponeurosis with continued abduction of the thumb. On release of the abduction force, the proximal UCL segment is displaced dorsally and proximally by the inferior aspect of the adductor aponeurosis. The UCL becomes entrapped by the adductor aponeurosis and cannot reduce spontaneously.¹⁵ This displacement was initially described by Stener⁶ in 1962 and is referred to as a Stener lesion (Figures 1A-1C).

It is vital for the radiologist to identify a Stener lesion because a nondisplaced tear of the UCL is often treated nonsurgically, but UCL tears displaced more than 3 mm and Stener lesions usually must be operated on to avoid chronic instability, pain, and osteoarthritis.^2-5,8,12-23 Sensitivity and specificity of MRI in evaluating UCL injuries are reported to be almost 100%, with resolution of 1 mm using current surface coils.²³ There are various UCL injury patterns, including partial tears, displaced and nondisplaced complete tears, and even complex injuries, such as an incomplete tear with the torn portion retracted as a Stener lesion.²² MRI is needed to establish the extent of injury, as 90% of complete tears that are displaced at least 3 mm, and all tears with retraction proximal and superficial to the aponeurosis (true Stener lesions), failed immobilization and required surgical treatment.²³Although they vary in the literature, mean sensitivity and specificity of ultrasound in detecting UCL tears in level I studies have been reported as 76% and 81%, respectively.²⁴ When Melville and colleagues²¹ applied their ultrasound criteria—including absence of normal UCL fibers traversing the first MCP joint as well as heterogeneous masslike tissue at least partially proximal to the apex of the metacarpal lateral tubercle—they were able to distinguish displaced full-thickness tears from nondisplaced full-thickness tears with 100% accuracy. Hergan and colleagues²⁵ found that the diagnostic accuracy of MRI was superior to that of ultrasound; while MRI accuracy was perfect, 12% of patients were incorrectly diagnosed with ultrasound, with false-positive or false-negative tendon-edge displacement. In our experience, ultrasound is uniquely useful in its ability to characterize the real-time dynamic interaction of the UCL with the adductor aponeurosis. It has been observed that passive flexion of the first interphalangeal joint moves the adductor aponeurosis in isolation, allowing differentiation from the subjacent UCL.²¹ Had a partial tear been in the differential diagnosis of our patient’s Stener lesion, such a maneuver under ultrasound visualization would have solved the dilemma. In addition, ultrasound allows for comparison with the contralateral ligament at the time of examination should a diagnostic dilemma arise.

As many have reported both bony avulsion of the base of the proximal phalanx and concomitant injury to the UCL, identification of a bony avulsion does not exclude a ligamentous injury and the possibility of a Stener lesion (Figure 7).^16,19

In one study, 14% of patients with injury to the UCL sustained a concomitant bony avulsion of the UCL insertion.²³ However, presence of the avulsion fragment did not alter management, and only those fragments involving more than 20% of the articular surface were considered true fractures and treated as such.

Conclusion

A Stener lesion—retraction of a completely torn UCL becoming entrapped dorsally and proximally to the adductor insertion—can cause pain, instability, and ultimately osteoarthritis if not treated appropriately. The orthopedic surgeon should have a high index of suspicion for a Stener lesion in the appropriate clinical scenario and consider all imaging modalities for diagnosis. Likewise, it is of utmost importance for the radiologist to identify imaging findings of a Stener lesion, as physical examination alone may be limited in its ability to characterize injury severity. Both MRI and ultrasound are useful in evaluating UCL tears, and ultrasound provides the additional benefit of dynamic visualization and comparison with the contralateral side.

Am J Orthop. 2017;46(3):E195-E199. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• Torn, displaced, and entrapped UCL is a Stener lesion.
• Hyperabduction injury with pain and joint laxity on examination.
• MRI and ultrasound are useful in evaluating UCL tears.
• Ultrasound offers dynamic evaluation.
• Must be treated appropriately to avoid pain, instability, and osteoarthritis.

In the literature, hyperabduction injuries to the thumb metacarpophalangeal (MCP) joint have been referred to interchangeably as gamekeeper’s thumb and skier’s thumb. Historically, though, gamekeeper’s thumb was initially described in hunters with chronic injury to the ulnar collateral ligament (UCL),¹ and skier’s thumb typically has been described as an acute hyperabduction injury of the UCL.^2-5 The proximal portion of a torn UCL may retract with further abduction and displace dorsally, becoming entrapped by the adductor pollicis aponeurosis insertion, known as a Stener lesion.⁶

The first MCP joint is stabilized by static and dynamic structures that contribute in varying degrees in flexion and extension of the joint. The static stabilizers include the proper and accessory radial and UCLs, the palmar plate, and the dorsal capsule. The UCL originates at the dorsal ulnar aspect of the first metacarpal head at the metacarpal tubercle about 5 mm proximal to the articular surface. The UCL courses distally in the palmar direction to insert volar and proximal to the medial tubercle of the proximal phalanx about 3 mm distal to the articular surface.⁷ In flexion, the proper collateral ligament is taut and is the primary static stabilizer. In extension, the accessory collateral ligament, which inserts on the palmar plate, is taut and is the primary static stabilizer.^8-11

The dynamic stabilizers include the extrinsic muscles (flexor pollicis longus, extensor pollicis longus and brevis) and the intrinsic muscles (abductor pollicis brevis, adductor pollicis, flexor pollicis brevis) inserting on the thumb at the distal phalanx and proximal phalanx and at the base of the first metacarpal.^8-10

The adductor pollicis originates from the volar third metacarpal, capitate, and hamate and has a dual insertion on the thumb.¹² There is a direct insertion onto the palmar proximal phalanx at the medial tubercle, distal and dorsal to the phalangeal insertion of the UCL.

There is also a broad aponeurosis that inserts onto the extensor hood expansion, dorsal to the insertion of the UCL (Figures 1A-1C and 2A, 2B).^7,8,13

We report the case of an acute hyperabduction injury of the thumb MCP joint with radiographic, ultrasound, and magnetic resonance imaging (MRI) findings consistent with a Stener lesion and subsequently confirmed with intraoperative photographs. The patient provided written informed consent for print and electronic publication of this case report.

Clinical Findings

A 33-year-old healthy man had persistent left hand pain and grip weakness after performing a handstand. He presented to the orthopedic hand clinic 20 days after injury, having failed nonoperative management (use of nonsteroidal anti-inflammatory drugs and soft thumb spica splint). Physical examination revealed soft-tissue swelling and focal tenderness to palpation at the ulnar aspect of the thumb MCP joint. Despite bilateral first MCP joint laxity on varus and valgus stress without identification of a firm endpoint, pain was elicited only on valgus stress of the left first MCP joint. Given the laxity and the left thumb soft-tissue swelling with pain, plain radiographs, ultrasound, and MRI were used to evaluate for severity of presumed left thumb UCL injury.

Imaging Findings

Plain radiographs showed normal bony anatomy without fracture, normal joint space, and mild soft-tissue swelling at the left thumb MCP level (Figures 3A, 3B).

Ultrasound confirmed a complete tear of the UCL, which was flipped in a proximal direction and projected dorsally in relation to the direct insertion of the adductor tendon (Figure 2B). MRI showed focal disruption of the UCL at the level of the left thumb MCP joint with associated MCP joint effusion (Figures 4A-4F).

Low T1 signal intensity over the adductor aponeurosis at the level of the metacarpal head corresponded with the torn and proximally retracted UCL. There was associated bone marrow edema at the radial and volar aspects of the thumb metacarpal head and low-grade strain of the abductor pollicis brevis. The thumb flexor and extensor tendons appeared normal. Although possibly secondary to patient positioning, mild volar subluxation of the proximal phalanx in relation to the metacarpal head was queried.

 

Surgical Findings

Given laxity with pain at the UCL on stress testing, MRI and ultrasound findings, and continued pain and instability of the thumb with pinching and grasping during activities of daily living, the patient and orthopedic hand surgeon proceeded with surgical intervention. Preoperative examination under anesthesia confirmed significant laxity on valgus stress without a palpable endpoint (Figures 5A, 5B).

During surgery, retraction of the extensor hood revealed the completely torn and displaced UCL, entrapped dorsally and proximally to the adductor aponeurosis, characteristic of a Stener lesion. After the primary repair of the UCL, the extensor hood was seen partially retracted in a normal location superficial to the normal deep position of the repaired UCL (Figures 6A, 6B).

Discussion

Hyperabduction injuries to the thumb may rupture the UCL of the MCP joint of the thumb or cause a bony avulsion of the base of the proximal phalanx. Injury to the UCL, most often at its distal portion,^4,14,15 may result in a sprain or full-thickness tear of the ligament.

Subsequently, the ligament may remain in situ, or the proximal segment may retract proximal to the adductor aponeurosis with continued abduction of the thumb. On release of the abduction force, the proximal UCL segment is displaced dorsally and proximally by the inferior aspect of the adductor aponeurosis. The UCL becomes entrapped by the adductor aponeurosis and cannot reduce spontaneously.¹⁵ This displacement was initially described by Stener⁶ in 1962 and is referred to as a Stener lesion (Figures 1A-1C).

It is vital for the radiologist to identify a Stener lesion because a nondisplaced tear of the UCL is often treated nonsurgically, but UCL tears displaced more than 3 mm and Stener lesions usually must be operated on to avoid chronic instability, pain, and osteoarthritis.^2-5,8,12-23 Sensitivity and specificity of MRI in evaluating UCL injuries are reported to be almost 100%, with resolution of 1 mm using current surface coils.²³ There are various UCL injury patterns, including partial tears, displaced and nondisplaced complete tears, and even complex injuries, such as an incomplete tear with the torn portion retracted as a Stener lesion.²² MRI is needed to establish the extent of injury, as 90% of complete tears that are displaced at least 3 mm, and all tears with retraction proximal and superficial to the aponeurosis (true Stener lesions), failed immobilization and required surgical treatment.²³Although they vary in the literature, mean sensitivity and specificity of ultrasound in detecting UCL tears in level I studies have been reported as 76% and 81%, respectively.²⁴ When Melville and colleagues²¹ applied their ultrasound criteria—including absence of normal UCL fibers traversing the first MCP joint as well as heterogeneous masslike tissue at least partially proximal to the apex of the metacarpal lateral tubercle—they were able to distinguish displaced full-thickness tears from nondisplaced full-thickness tears with 100% accuracy. Hergan and colleagues²⁵ found that the diagnostic accuracy of MRI was superior to that of ultrasound; while MRI accuracy was perfect, 12% of patients were incorrectly diagnosed with ultrasound, with false-positive or false-negative tendon-edge displacement. In our experience, ultrasound is uniquely useful in its ability to characterize the real-time dynamic interaction of the UCL with the adductor aponeurosis. It has been observed that passive flexion of the first interphalangeal joint moves the adductor aponeurosis in isolation, allowing differentiation from the subjacent UCL.²¹ Had a partial tear been in the differential diagnosis of our patient’s Stener lesion, such a maneuver under ultrasound visualization would have solved the dilemma. In addition, ultrasound allows for comparison with the contralateral ligament at the time of examination should a diagnostic dilemma arise.

As many have reported both bony avulsion of the base of the proximal phalanx and concomitant injury to the UCL, identification of a bony avulsion does not exclude a ligamentous injury and the possibility of a Stener lesion (Figure 7).^16,19

In one study, 14% of patients with injury to the UCL sustained a concomitant bony avulsion of the UCL insertion.²³ However, presence of the avulsion fragment did not alter management, and only those fragments involving more than 20% of the articular surface were considered true fractures and treated as such.

Conclusion

A Stener lesion—retraction of a completely torn UCL becoming entrapped dorsally and proximally to the adductor insertion—can cause pain, instability, and ultimately osteoarthritis if not treated appropriately. The orthopedic surgeon should have a high index of suspicion for a Stener lesion in the appropriate clinical scenario and consider all imaging modalities for diagnosis. Likewise, it is of utmost importance for the radiologist to identify imaging findings of a Stener lesion, as physical examination alone may be limited in its ability to characterize injury severity. Both MRI and ultrasound are useful in evaluating UCL tears, and ultrasound provides the additional benefit of dynamic visualization and comparison with the contralateral side.

Am J Orthop. 2017;46(3):E195-E199. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

Take-Home Points

• Torn, displaced, and entrapped UCL is a Stener lesion.
• Hyperabduction injury with pain and joint laxity on examination.
• MRI and ultrasound are useful in evaluating UCL tears.
• Ultrasound offers dynamic evaluation.
• Must be treated appropriately to avoid pain, instability, and osteoarthritis.

In the literature, hyperabduction injuries to the thumb metacarpophalangeal (MCP) joint have been referred to interchangeably as gamekeeper’s thumb and skier’s thumb. Historically, though, gamekeeper’s thumb was initially described in hunters with chronic injury to the ulnar collateral ligament (UCL),¹ and skier’s thumb typically has been described as an acute hyperabduction injury of the UCL.^2-5 The proximal portion of a torn UCL may retract with further abduction and displace dorsally, becoming entrapped by the adductor pollicis aponeurosis insertion, known as a Stener lesion.⁶

The first MCP joint is stabilized by static and dynamic structures that contribute in varying degrees in flexion and extension of the joint. The static stabilizers include the proper and accessory radial and UCLs, the palmar plate, and the dorsal capsule. The UCL originates at the dorsal ulnar aspect of the first metacarpal head at the metacarpal tubercle about 5 mm proximal to the articular surface. The UCL courses distally in the palmar direction to insert volar and proximal to the medial tubercle of the proximal phalanx about 3 mm distal to the articular surface.⁷ In flexion, the proper collateral ligament is taut and is the primary static stabilizer. In extension, the accessory collateral ligament, which inserts on the palmar plate, is taut and is the primary static stabilizer.^8-11

The dynamic stabilizers include the extrinsic muscles (flexor pollicis longus, extensor pollicis longus and brevis) and the intrinsic muscles (abductor pollicis brevis, adductor pollicis, flexor pollicis brevis) inserting on the thumb at the distal phalanx and proximal phalanx and at the base of the first metacarpal.^8-10

The adductor pollicis originates from the volar third metacarpal, capitate, and hamate and has a dual insertion on the thumb.¹² There is a direct insertion onto the palmar proximal phalanx at the medial tubercle, distal and dorsal to the phalangeal insertion of the UCL.

There is also a broad aponeurosis that inserts onto the extensor hood expansion, dorsal to the insertion of the UCL (Figures 1A-1C and 2A, 2B).^7,8,13

We report the case of an acute hyperabduction injury of the thumb MCP joint with radiographic, ultrasound, and magnetic resonance imaging (MRI) findings consistent with a Stener lesion and subsequently confirmed with intraoperative photographs. The patient provided written informed consent for print and electronic publication of this case report.

Clinical Findings

A 33-year-old healthy man had persistent left hand pain and grip weakness after performing a handstand. He presented to the orthopedic hand clinic 20 days after injury, having failed nonoperative management (use of nonsteroidal anti-inflammatory drugs and soft thumb spica splint). Physical examination revealed soft-tissue swelling and focal tenderness to palpation at the ulnar aspect of the thumb MCP joint. Despite bilateral first MCP joint laxity on varus and valgus stress without identification of a firm endpoint, pain was elicited only on valgus stress of the left first MCP joint. Given the laxity and the left thumb soft-tissue swelling with pain, plain radiographs, ultrasound, and MRI were used to evaluate for severity of presumed left thumb UCL injury.

Imaging Findings

Plain radiographs showed normal bony anatomy without fracture, normal joint space, and mild soft-tissue swelling at the left thumb MCP level (Figures 3A, 3B).

Ultrasound confirmed a complete tear of the UCL, which was flipped in a proximal direction and projected dorsally in relation to the direct insertion of the adductor tendon (Figure 2B). MRI showed focal disruption of the UCL at the level of the left thumb MCP joint with associated MCP joint effusion (Figures 4A-4F).

Low T1 signal intensity over the adductor aponeurosis at the level of the metacarpal head corresponded with the torn and proximally retracted UCL. There was associated bone marrow edema at the radial and volar aspects of the thumb metacarpal head and low-grade strain of the abductor pollicis brevis. The thumb flexor and extensor tendons appeared normal. Although possibly secondary to patient positioning, mild volar subluxation of the proximal phalanx in relation to the metacarpal head was queried.

 

Surgical Findings

Given laxity with pain at the UCL on stress testing, MRI and ultrasound findings, and continued pain and instability of the thumb with pinching and grasping during activities of daily living, the patient and orthopedic hand surgeon proceeded with surgical intervention. Preoperative examination under anesthesia confirmed significant laxity on valgus stress without a palpable endpoint (Figures 5A, 5B).

During surgery, retraction of the extensor hood revealed the completely torn and displaced UCL, entrapped dorsally and proximally to the adductor aponeurosis, characteristic of a Stener lesion. After the primary repair of the UCL, the extensor hood was seen partially retracted in a normal location superficial to the normal deep position of the repaired UCL (Figures 6A, 6B).

Discussion

Hyperabduction injuries to the thumb may rupture the UCL of the MCP joint of the thumb or cause a bony avulsion of the base of the proximal phalanx. Injury to the UCL, most often at its distal portion,^4,14,15 may result in a sprain or full-thickness tear of the ligament.

Subsequently, the ligament may remain in situ, or the proximal segment may retract proximal to the adductor aponeurosis with continued abduction of the thumb. On release of the abduction force, the proximal UCL segment is displaced dorsally and proximally by the inferior aspect of the adductor aponeurosis. The UCL becomes entrapped by the adductor aponeurosis and cannot reduce spontaneously.¹⁵ This displacement was initially described by Stener⁶ in 1962 and is referred to as a Stener lesion (Figures 1A-1C).

It is vital for the radiologist to identify a Stener lesion because a nondisplaced tear of the UCL is often treated nonsurgically, but UCL tears displaced more than 3 mm and Stener lesions usually must be operated on to avoid chronic instability, pain, and osteoarthritis.^2-5,8,12-23 Sensitivity and specificity of MRI in evaluating UCL injuries are reported to be almost 100%, with resolution of 1 mm using current surface coils.²³ There are various UCL injury patterns, including partial tears, displaced and nondisplaced complete tears, and even complex injuries, such as an incomplete tear with the torn portion retracted as a Stener lesion.²² MRI is needed to establish the extent of injury, as 90% of complete tears that are displaced at least 3 mm, and all tears with retraction proximal and superficial to the aponeurosis (true Stener lesions), failed immobilization and required surgical treatment.²³Although they vary in the literature, mean sensitivity and specificity of ultrasound in detecting UCL tears in level I studies have been reported as 76% and 81%, respectively.²⁴ When Melville and colleagues²¹ applied their ultrasound criteria—including absence of normal UCL fibers traversing the first MCP joint as well as heterogeneous masslike tissue at least partially proximal to the apex of the metacarpal lateral tubercle—they were able to distinguish displaced full-thickness tears from nondisplaced full-thickness tears with 100% accuracy. Hergan and colleagues²⁵ found that the diagnostic accuracy of MRI was superior to that of ultrasound; while MRI accuracy was perfect, 12% of patients were incorrectly diagnosed with ultrasound, with false-positive or false-negative tendon-edge displacement. In our experience, ultrasound is uniquely useful in its ability to characterize the real-time dynamic interaction of the UCL with the adductor aponeurosis. It has been observed that passive flexion of the first interphalangeal joint moves the adductor aponeurosis in isolation, allowing differentiation from the subjacent UCL.²¹ Had a partial tear been in the differential diagnosis of our patient’s Stener lesion, such a maneuver under ultrasound visualization would have solved the dilemma. In addition, ultrasound allows for comparison with the contralateral ligament at the time of examination should a diagnostic dilemma arise.

As many have reported both bony avulsion of the base of the proximal phalanx and concomitant injury to the UCL, identification of a bony avulsion does not exclude a ligamentous injury and the possibility of a Stener lesion (Figure 7).^16,19

In one study, 14% of patients with injury to the UCL sustained a concomitant bony avulsion of the UCL insertion.²³ However, presence of the avulsion fragment did not alter management, and only those fragments involving more than 20% of the articular surface were considered true fractures and treated as such.

Conclusion

A Stener lesion—retraction of a completely torn UCL becoming entrapped dorsally and proximally to the adductor insertion—can cause pain, instability, and ultimately osteoarthritis if not treated appropriately. The orthopedic surgeon should have a high index of suspicion for a Stener lesion in the appropriate clinical scenario and consider all imaging modalities for diagnosis. Likewise, it is of utmost importance for the radiologist to identify imaging findings of a Stener lesion, as physical examination alone may be limited in its ability to characterize injury severity. Both MRI and ultrasound are useful in evaluating UCL tears, and ultrasound provides the additional benefit of dynamic visualization and comparison with the contralateral side.

Am J Orthop. 2017;46(3):E195-E199. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

From the University of Florida (Dr. Tucker, Ms. Ukonu, Ms. Kang, Ms. Good), Gainesville, FL; the University of Florida–Jacksonville (Dr. Shah, Dr. Bilello), Jacksonville, FL; and Ball State University (Dr. Arthur), Muncie, IN.

 

Abstracts

• Objective: To assess primary care physicians’ practices, knowledge, and beliefs regarding their efforts to promote healthy lifestyles and weight management among their patients.
• Methods: Study participants consisted of 25 primary care physicians from a regional primary care practice-based research network that includes 37 university-affiliated patient-centered medical homes and 2 nearby unaffiliated primary care sites. Participating physicians completed an online modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire.
• Results: The majority (88%) of participating physicians strongly believed it was their responsibility to promote a healthy diet, physical activity, and healthy weight loss and weight maintenance among patients. The 3 most commonly endorsed barriers were (a) not enough time, (b) minimal patient interest in improving his/her weight, and (c) lack of adequate weight-loss referral resources. The top 3 physician-perceived practice improvements that would be helpful with these practices were (a) better tools to communicate diet, physical activity, or weight problems to patients or family; (b) better mechanisms to connect patients to weight-loss referral resources; and (c) better counseling tools to guide patients regarding lifestyle modifications. 76% of the participating physicians correctly identified the BMI cutoff ranges for adult obesity, but only 32% did so for childhood obesity.
• Conclusion: It is important to provide primary care physicians with knowledge, effective tools, and resources to promote healthy lifestyles and weight loss and weight management among their patients.

Key words: obesity; primary care physicians; weight loss; weight management.

 

More than two-thirds of adults in the United States are overweight, with approximately 35% considered obese (defined as a body mass index ≥ 30) [1]. Obesity is associated with many of the leading causes of death in the United States (ie, diabetes, heart disease, stroke, and some types of cancer) and with poor mental health outcomes and reduced quality of life [2]. Racial/ethnic minorities and individuals with low incomes are disproportionately impacted by obesity and obesity-related diseases and negative health outcomes [3–5].

The US Preventive Services Task Force (USPSTF) recommends screening for obesity and intensive behavioral counseling, which are often the responsibilities of primary care providers [6]. Despite these recommendations, research suggests that primary care providers rarely screen their patients for obesity or refer them for intensive behavioral counseling despite evidence that doing so would improve patient health outcomes [5–7]. Lack of time to address weight issues during clinical visits, lack of training in weight management counseling, and lack of availability of intensive weight loss programs to which they can refer their patients are some of the reasons cited for not counseling patients about weight management [8].

Primary care providers deliver more hours of patient care than other providers, yet these providers have been unable to deliver medical interventions capable of producing even modest weight loss [10]. Obesity treatment options delivered in primary care settings have limited success, likely due to the low intensity of these treatment options. Many studies have shown that most obesity treatments in health care settings typically consist of scheduled monthly or quarterly visits that are 10 to 15 minutes in duration [11], despite evidence that more intense treatments are needed. Specifically, a systematic review of the obesity treatment literature performed by the USPSTF revealed that high-intensity, multicomponent behavioral interventions that include face-to-face counseling on diet and physical activity and behavioral therapy more than once a month for 3 months are needed to produce significant weight loss (8–15 lb) among adult patients in primary care settings [12].

Since many of the characteristics of multicomponent behavioral interventions for treating obesity are both patient-centered and involve self-management, the patient-centered medical home (PCMH) seems to be the ideal setting to deliver these interventions [13]. Specifically, PCMHs provide patient-centered care that is wide-ranging, team-based, and coordinated across all elements of the health care system and the patient’s community [14]. These sites specifically provide primary care, which is the type of care that obesity disparity patient groups such as racial/ethnic minorities, sexual minorities, groups with low incomes, and the medically underserved are more likely to utilize [15].

Providing multicomponent behavioral interventions for obesity in PCMHs and other primary care sites will increase the likelihood of participation among the aforementioned obesity disparity groups. Despite the potential benefits of obesity treatment interventions offered in primary care settings, particularly for obesity disparity groups, the role of primary care providers in providing such treatment interventions is not clear [16]. We surveyed primary care physicians who primarily worked in PCMHs to assess their practices, knowledge, views/beliefs, perceived barriers, and perceived needed clinic practice improvements relative to promoting healthy lifestyles and weight management among their patients.

Methods

Participants

Primary care physicians were recruited from among a regional primary care practice-based research network that includes 37 PCMHs affiliated with an academic health center and 2 nearby primary care sites not affiliated with an academic health center. Fifty-two physicians at these centers received an invitation via email to participate in our online survey study. The invitation email included (a) a study endorsement note from the chair of the Community Health and Family Medicine Department affiliated with the PCMHs, (b) instructions about how to participate in the study, and (c) a link to the study. Participation inclusion criteria specified in the online informed consent form were: (a) working as a physician affiliated with the practice-based research network, (b) having access to a computer with internet connection, (c) being able to communicate in written English, and (d) providing written consent to participate in the study. Physicians were not provided compensation for participating in the study.

Survey Instrument

To assess physicians’ views and practices, we used a modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire [17]. The survey was sponsored by the National Cancer Institute in collaboration with several other NIH institutes and the CDC for evaluating current clinical practices among physicians, including the degree to which physicians evaluate their patients for obesity and offer them guidance designed to increase adherence to a health-promoting lifestyle (eg, recommendations on diet, weight, and physical activity). Additionally, the questionnaire assesses physicians’ perceived barriers to patient assessment, evaluation, and management. It also includes questions about physicians’ healthy lifestyle–related knowledge. In 2010, Smith and colleagues utilized the questionnaire with a nationally representative sample of primary care physicians (n = 1211) to investigate primary care physicians’ clinical practices in relation to overweight and obesity [18]. To our knowledge, no other physician survey has been developed to assess current engagement in recommended clinical practices, barriers to engaging in recommended practices, as well as beliefs and knowledge regarding helping patients follow a health-promoting lifestyle. The original survey also includes questions regarding the physicians’ personal health status and health behaviors.

For our study, we modified the survey by removing questions regarding the physicians’ (a) perceived general health and well-being, (b) current dietary practices, (c) current level of engagement in physical activity, and (d) current engagement in professional activities unrelated to patient care (eg, research, teaching). Our modified survey included 7 questions asking about current practices regarding screening for obesity and referral of patients to weight management interventions. Two questions asked about physicians’ perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. Physicians were asked to rate their top 3 barriers from among a list of 11 pre-identified barriers and to rate their top 3 desired practice-related improvements from among a list of 10 pre-identified improvements. Physicians were given the option to provide additional barriers or improvements that were not already pre-identified. Seven questions assessed physicians’ views/beliefs related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight. These questions utilize a rating scale where 1 = strongly agree, 2 = agree somewhat, 3 = neither agree nor disagree, 4 = disagree somewhat, and 5 = strongly disagree. Four questions assessed physicians’ healthy lifestyle–related knowledge (BMI ranges/percentiles for adults/children, diet and exercise guideline recommendations [recommended amounts of moderate physical activity and servings of fruits and vegetables for adults]) and 11 questions ask about the physician (height, weight, demographics, and practice population).

 

Survey Administration

The survey was administered anonymously through Qualtrics, a secure, online survey platform. The survey was administered online to increase anonymity, increase response rate, and diminish potential physician-perceived barriers to participating in the study. The participating physicians were provided with a link that enabled them to access the survey. The survey excluded questions that required disclosure of identifying information. Survey data from Qualtrics were exported to an SPSS file that was stored on a password protected, secured computer in the research lab of the principal investigator for this study.

Data Analysis

Frequency analyses were applied to survey responses to determine the participating physicians’ endorsed barriers to and views regarding evaluating and managing patients’ weight, healthy eating, and physical activity; physicians’ views related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight; and physicians’ healthy lifestyle–related knowledge. Nonparametric t tests were conducted to examine differences in survey responses of the participating physicians in association with their sex (male or female), race (Asian vs. white/Caucasian), and BMI (BMI < 25 and BMI ≥ 25).

Approval for the study was obtained through the institutional review board of the University of Florida Health Science Center.

Results

Participants

Twenty-five physicians out of 52 invited completed the survey (48% response rate). The vast majority of the study participants were PCMH-affiliated (92%–96%). Participating physicians ranged in age from 29 to 67 years old. Sixteen (64%) participating physicians identified as female, 7 (28%) participating physicians identified as male, and 2 (8%) participating physicians did not indicate a sex. Twenty (80%) participating physicians identified as being white, 3 (12%) participating physicians identified as being Asian/Asian American, and 2 (8%) did not indicate a race or ethnicity. Twenty-two (88%) participating physicians were employees of a large medical group affiliated with an academic medical center, 1 (4%) was employed in a physician-owned practice, and 2 (8%) did not indicate their main primary care practice location. Table 1 provides additional demographic data.

Participants endorsed a number of perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. The most common barriers reported, as shown in Table 2, include (a) not enough time (72%); (b) patients not interested in improving their weight (52%); and (c) lack of adequate referral resources for diet, physical activity, and weight management (48%). The participating physicians also endorsed certain practice-related improvements that they felt would help them improve their patients’ engagement in health-promoting behaviors (healthy eating and physical activity) and weight. These improvements, as shown in Table 3, include (a) better tools to communicate diet, physical activity, or weight problems to patients or family (48%); (b) better mechanisms to connect patients to specific referral sources (44%); and (c) better counseling tools to guide patients towards lifestyle modification (36%).

Overall, 88% of participating physicians strongly agreed that it was their responsibility to promote a healthy diet, physical activity, and weight loss and healthy weight maintenance among their patients. In contrast, 4% of the participating physicians strongly disagreed with this statement.

Approximately 88% of the participating physicians agreed that patients were more likely to adopt healthier lifestyles if their health care providers counseled them to do so (44% strongly agreed, 44% agreed somewhat). A majority of participating physicians endorsed the view that there are effective strategies and/or tools to (a) help patients eat a healthy diet (56% strongly agreed, 24% agreed somewhat), (b) engage in adequate amounts of physical activity (56% strongly agreed, 20% agreed somewhat), and (c) maintain a healthy weight or lose weight (48% strongly agreed, 

32% agreed somewhat). Only 4% of participating physicians neither agreed nor disagreed with this view and 4% somewhat disagreed with this view.

Many participating physicians expressed confidence in their ability to counsel their patients to (a) eat a healthy diet (64% strongly agreed, 28% agreed somewhat), (b) engage in adequate amounts of physical activity (68% strongly agreed, 24% agreed somewhat), and (c) maintain a healthy weight or lose weight (60% strongly agreed, 32% agreed somewhat). Most participating physicians at least somewhat agreed that they were effective at helping their patients (a) eat a healthy diet (24% strongly agreed, 52% agreed somewhat), (b) engage in adequate amounts of physical activity (20% strongly agreed, 56% agreed somewhat), and (c) maintain a healthy weight or lose weight (16% strongly agreed, 48% agreed somewhat). Some participating physicians expressed ambivalence about whether or not they were effective at helping their patients (a) eat a healthy diet (16% neither agreed nor disagreed), (b) engage in adequate amounts of physical activity (12% neither agreed nor disagreed), and (c) maintain a healthy weight or lose weight (20% neither agreed nor disagreed). A total of 8% of participating physicians did not endorse the belief that they were effective at helping their patients maintain a healthy weight or lose weight.

Most participating physicians at least somewhat agreed that they were effective in encouraging patients to engage in health-promoting activities (44% strongly agreed, and 44% agreed somewhat), whereas 4% neither agreed nor disagreed that they were effective in providing this encouragement. Interestingly, many participating physicians endorsed the view that they would be able to provide more credible and effective counseling to patients if they (the physicians themselves) ate a healthy diet (68% strongly agreed, 20% agreed somewhat) and engaged in adequate amounts of physical activity (68% strongly agreed, 20% agreed somewhat). A minority of participating physicians (4%) neither agreed or disagreed with this perspective.

In regards to participating physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults or percentile ranges for children, most participating physicians were able to accurately identify the correct BMI cutoff ranges for overweight (80%) and obese (76%) adults. However, only 32% of participating physicians were able to correctly identify BMI percentile ranges for children; however, nearly all of the participating physicians saw mainly adult patients. Lastly, 76% of participating physicians were able to correctly identify the recommended amounts of moderate physical activity for adults 18 years of age and older, and only 56% were able to correctly identify the recommended amount of servings of fruits and vegetables.

 

There were no significant race-related differences in participating physicians views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health promoting behaviors and weight management. There were no significant sex-related differences in these variables with the exception that women were more likely to respond that they did not know the BMI percentile range at which children or adolescents were considered to have a healthy weight (37.5% of women vs. 0% of men, P = 0.03). A similar percentage of men (66.7%) and women (64.7%) who chose among the 4 percentile range options (rather than endorsing “Don’t know”) chose an incorrect answer. Lastly, there were no significant self-reported BMI-related differences in participating physicians’ views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health-promoting behaviors and weight management.

Discussion

Given the high percentage of adults in the United States who are overweight or obese and the associated health risks, it is paramount that primary care physicians advise their patients to manage their weight and adopt a health-promoting lifestyle. Research studies indicate that such advice is effective [18,19]. Furthermore, it has been found that most overweight and obese patients want more assistance with weight management than they are receiving from their primary care physicians [21]. This study thus explored primary care physicians’ knowledge, beliefs, and perceived barriers that may prevent them from providing such assistance. The primary care setting is the site where obesity disparity groups (eg, racial/ethnic minorities, groups with low household incomes) are most likely to receive care [22,23].

Most of the PCMH-affiliated physicians in this study agreed that they had the responsibility to promote weight-loss/management and healthy lifestyles among their patients. Consistent with prior research [9], the majority of the physicians in this study felt they were effective in their ability to counsel patients to eat a healthy diet and engage in physical activity. To illustrate, in a prior study [9], 77% of primary care providers thought that they could provide useful dieting tips to patients, and in this study, 80% believed they were effective in helping patients eat a healthy diet. However, despite this confidence in their ability to provide advice about healthy diets and physical activity, the providers in both this and in another prior study [25] were less confident in their ability to actually help patients lose weight. Only 64% of the providers in the present study felt they could be effective in assisting patients with losing weight or maintaining a healthy weight. Although this percentage is higher than the 44% of physicians found in a prior study [25] who felt confident in their ability to treat obesity, both studies clearly point to a need to decrease barriers that physicians face in helping clients lose weight.

A key finding of this study was the consensus among the participating physicians regarding what they perceived to be the common barriers to helping patients adhere to a health-promoting lifestyle. Consistent with past research [8,9], the 3 most common barriers cited by the participating physicians were that they did not have enough time, patients were not interested in improving their weight, and adequate referrals for diet, physical activity, and weight were lacking. Additional barriers endorsed included a lack of effective tools and information to give patients, and a fear of offending patients. Another barrier identified by the participating physicians is the perception that patients had difficulty in changing behaviors necessary for maintaining a healthier lifestyle.

When asked what would facilitate conversations with patients, the top 3 responses given were better tools to communicate diet, physical activity, or weight problems to patients or family members; better mechanisms to connect patients to specific referral sources; and better counseling tools to guide patients towards engagement in healthy lifestyles. Of note is the significant overlap between the perceived barriers and the needed facilitation tools. The clearest example of this overlap is that physicians noted a lack of adequate referral sources to be a barrier and that better mechanisms to connect patients to specific referral sources would facilitate their treatment of patients. Weight management referrals for patients in rural areas and for non-Hispanic black adults and Hispanic adults, among whom obesity is most prevalent in the United States [3,25], are particularly needed. Addressing this need is consistent with national calls to reduce/eliminate obesity and other disparities that plague the U.S. health care system. One promising avenue to facilitate weight management referrals is the development, evaluation, and wide dissemination of remote weight-loss support interventions, particularly in rural, racial/ethnic minority, and low-income communities. Indeed, several recent articles demonstrate the success of such weight management programs across diverse patient populations [27–29].

Many of the physicians who participated in the study (72%) endorsed lack of time as a significant barrier to discussing weight and weight-related behaviors with their patients. Therefore, finding time-efficient strategies to involve physicians in weight management interventions may prove particularly beneficial. One such evidence-based behavioral counseling framework—the 5As framework—has been endorsed by the Centers for Medicare and Medicaid Services and the USPSTF for use with obese patients during a typical 20-minute visit [28].

The second highest-rated barrier, perceived patient lack of motivation, warrants additional discussion. Despite over half of the physicians surveyed citing this as a barrier, previous studies have shown that the majority of overweight patients believe they should lose weight and are interested in losing weight [21]. This study highlights a potential discrepancy between physicians’ perceptions of patients’ interest in weight-loss and their patients’ actual interest. It is possible that this discrepancy can be avoided by training physicians on how to be culturally sensitive when addressing weight with their patients. Moreover, such cultural sensitivity training may be of great use, given that 12% of physicians in this study were apprehensive about discussing weight with their patients due to fear of offending them. Such training typically involves teaching physicians how to talk with patients in ways that enable patients to feel comfortable, trusting, and respectful in patient-physician/provider interactions [29].

Two other findings pertaining to providers deserve mention. Specifically, 88% of physicians believed that effectively encouraging patients to adhere to a healthy lifestyle included personally engaging in health-promoting activities. However, of the physicians surveyed, 64% were overweight/obese. Given the high percentage of physicians in this study that were overweight/obese and these physicians’ belief that their personal engagement in health-promoting activities is important to encourage patient engagement in a healthy lifestyle, it seems that future efforts are needed to facilitate health-promoting behaviors among physicians—efforts that may in turn aid them in encouraging their patients to adhere to a healthy lifestyle.

 

Finally, this study assessed physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults and BMI percentile ranges for children, and recommended amounts of moderate physical activity and servings of fruits and vegetables for adults. Most physicians were able to correctly identify the adult BMI cutoff ranges for overweight and obesity and to identify the correct answers to questions about physical activity and fruits and vegetable consumption guidelines for adults. However, only 32% of physicians were able to correctly identify BMI percentile ranges for children and/or adolescents. This is understandable given that most of the physicians in this study provide care to adult patients. However, considering that in 2012 more than one-third of children and adolescents were overweight or obese [1], it is important that all physicians have knowledge of BMI percentile ranges for children and adolescents so that minimally they can convey this information to their adult patients who are parents. The USPSTF defines children and adolescent overweight as an age- and gender-specific BMI between the 85th and 94th percentiles, and children and adolescent obesity as an age- and gender-specific BMI ≥ 95th percentile [31]. Such knowledge of BMI cutoffs is needed in order for providers to comply with the USPSTF recommendation to screen all adults and children aged 6 years and older for obesity, and then offer or refer those with an obesity diagnosis to intensive multicomponent behavioral interventions [31–33].

While novel, the study also had several limitations. First, due to self-selection of participants, physicians who felt more confident in their abilities to address overweight or obesity with their patients might have been more likely to respond. Second, participating physicians may have given socially desirable responses to questions (ie, responses that present a favorable image of themselves) rather than true/accurate responses. Future studies could incorporate a social desirability scale in order to detect and control for any socially desirable responding [33]. Another limitation was the small sample size and the limited variability in geographic location of the participating physicians. Thus, the experiences of these physicians may not be generalizable to physicians in other geographic regions. Future similar studies to the present study are needed and such studies should use a larger and randomly selected sample of physicians that is racially/ethnically diverse. Finally, a limitation of this study is the 48% participation rate. Factors that may have contributed to this participation rate include lack of compensation for physicians and the likelihood that physicians may have extremely busy schedules that may discourage them from participating. However, it is important to note that the 48% participation rate of this study is better than the 25.6% participation rate in another similar study [25]. Future similar studies to the present study likely need to include strong incentives for physicians to be study participants.

Conclusion

Our study indicates that many primary care physicians may not talk with their patients about engaging in healthy eating, physical activity, and weight management because of perceived barriers that prevent them from doing so, rather than because of a lack of perceived responsibility to do so or a perception that counseling patients on these issues would be ineffective. This finding highlights the importance of providing physicians with the tools and resources needed to overcome the aforementioned barriers to fostering health-promoting lifestyles and a healthy weight among their patients and the importance of involving physicians in identifying these barriers and ways to overcome them.

 

Acknowledgement. We thank the patients and health care providers at the participating medical homes affiliated with University of Florida Health in Jacksonville, Florida, for making this research possible.

Corresponding author: Carolyn M. Tucker, PhD, University of Florida, [email protected].

Funding/support: Support for this research was provided by the Office of Research at UF–Gainesville, Florida, and by the National Institutes of Health and National Center for Research Resources CTSA grant UL1 TR000064.

Financial disclosures: None.

From the University of Florida (Dr. Tucker, Ms. Ukonu, Ms. Kang, Ms. Good), Gainesville, FL; the University of Florida–Jacksonville (Dr. Shah, Dr. Bilello), Jacksonville, FL; and Ball State University (Dr. Arthur), Muncie, IN.

 

Abstracts

• Objective: To assess primary care physicians’ practices, knowledge, and beliefs regarding their efforts to promote healthy lifestyles and weight management among their patients.
• Methods: Study participants consisted of 25 primary care physicians from a regional primary care practice-based research network that includes 37 university-affiliated patient-centered medical homes and 2 nearby unaffiliated primary care sites. Participating physicians completed an online modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire.
• Results: The majority (88%) of participating physicians strongly believed it was their responsibility to promote a healthy diet, physical activity, and healthy weight loss and weight maintenance among patients. The 3 most commonly endorsed barriers were (a) not enough time, (b) minimal patient interest in improving his/her weight, and (c) lack of adequate weight-loss referral resources. The top 3 physician-perceived practice improvements that would be helpful with these practices were (a) better tools to communicate diet, physical activity, or weight problems to patients or family; (b) better mechanisms to connect patients to weight-loss referral resources; and (c) better counseling tools to guide patients regarding lifestyle modifications. 76% of the participating physicians correctly identified the BMI cutoff ranges for adult obesity, but only 32% did so for childhood obesity.
• Conclusion: It is important to provide primary care physicians with knowledge, effective tools, and resources to promote healthy lifestyles and weight loss and weight management among their patients.

Key words: obesity; primary care physicians; weight loss; weight management.

 

More than two-thirds of adults in the United States are overweight, with approximately 35% considered obese (defined as a body mass index ≥ 30) [1]. Obesity is associated with many of the leading causes of death in the United States (ie, diabetes, heart disease, stroke, and some types of cancer) and with poor mental health outcomes and reduced quality of life [2]. Racial/ethnic minorities and individuals with low incomes are disproportionately impacted by obesity and obesity-related diseases and negative health outcomes [3–5].

The US Preventive Services Task Force (USPSTF) recommends screening for obesity and intensive behavioral counseling, which are often the responsibilities of primary care providers [6]. Despite these recommendations, research suggests that primary care providers rarely screen their patients for obesity or refer them for intensive behavioral counseling despite evidence that doing so would improve patient health outcomes [5–7]. Lack of time to address weight issues during clinical visits, lack of training in weight management counseling, and lack of availability of intensive weight loss programs to which they can refer their patients are some of the reasons cited for not counseling patients about weight management [8].

Primary care providers deliver more hours of patient care than other providers, yet these providers have been unable to deliver medical interventions capable of producing even modest weight loss [10]. Obesity treatment options delivered in primary care settings have limited success, likely due to the low intensity of these treatment options. Many studies have shown that most obesity treatments in health care settings typically consist of scheduled monthly or quarterly visits that are 10 to 15 minutes in duration [11], despite evidence that more intense treatments are needed. Specifically, a systematic review of the obesity treatment literature performed by the USPSTF revealed that high-intensity, multicomponent behavioral interventions that include face-to-face counseling on diet and physical activity and behavioral therapy more than once a month for 3 months are needed to produce significant weight loss (8–15 lb) among adult patients in primary care settings [12].

Since many of the characteristics of multicomponent behavioral interventions for treating obesity are both patient-centered and involve self-management, the patient-centered medical home (PCMH) seems to be the ideal setting to deliver these interventions [13]. Specifically, PCMHs provide patient-centered care that is wide-ranging, team-based, and coordinated across all elements of the health care system and the patient’s community [14]. These sites specifically provide primary care, which is the type of care that obesity disparity patient groups such as racial/ethnic minorities, sexual minorities, groups with low incomes, and the medically underserved are more likely to utilize [15].

Providing multicomponent behavioral interventions for obesity in PCMHs and other primary care sites will increase the likelihood of participation among the aforementioned obesity disparity groups. Despite the potential benefits of obesity treatment interventions offered in primary care settings, particularly for obesity disparity groups, the role of primary care providers in providing such treatment interventions is not clear [16]. We surveyed primary care physicians who primarily worked in PCMHs to assess their practices, knowledge, views/beliefs, perceived barriers, and perceived needed clinic practice improvements relative to promoting healthy lifestyles and weight management among their patients.

Methods

Participants

Primary care physicians were recruited from among a regional primary care practice-based research network that includes 37 PCMHs affiliated with an academic health center and 2 nearby primary care sites not affiliated with an academic health center. Fifty-two physicians at these centers received an invitation via email to participate in our online survey study. The invitation email included (a) a study endorsement note from the chair of the Community Health and Family Medicine Department affiliated with the PCMHs, (b) instructions about how to participate in the study, and (c) a link to the study. Participation inclusion criteria specified in the online informed consent form were: (a) working as a physician affiliated with the practice-based research network, (b) having access to a computer with internet connection, (c) being able to communicate in written English, and (d) providing written consent to participate in the study. Physicians were not provided compensation for participating in the study.

Survey Instrument

To assess physicians’ views and practices, we used a modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire [17]. The survey was sponsored by the National Cancer Institute in collaboration with several other NIH institutes and the CDC for evaluating current clinical practices among physicians, including the degree to which physicians evaluate their patients for obesity and offer them guidance designed to increase adherence to a health-promoting lifestyle (eg, recommendations on diet, weight, and physical activity). Additionally, the questionnaire assesses physicians’ perceived barriers to patient assessment, evaluation, and management. It also includes questions about physicians’ healthy lifestyle–related knowledge. In 2010, Smith and colleagues utilized the questionnaire with a nationally representative sample of primary care physicians (n = 1211) to investigate primary care physicians’ clinical practices in relation to overweight and obesity [18]. To our knowledge, no other physician survey has been developed to assess current engagement in recommended clinical practices, barriers to engaging in recommended practices, as well as beliefs and knowledge regarding helping patients follow a health-promoting lifestyle. The original survey also includes questions regarding the physicians’ personal health status and health behaviors.

For our study, we modified the survey by removing questions regarding the physicians’ (a) perceived general health and well-being, (b) current dietary practices, (c) current level of engagement in physical activity, and (d) current engagement in professional activities unrelated to patient care (eg, research, teaching). Our modified survey included 7 questions asking about current practices regarding screening for obesity and referral of patients to weight management interventions. Two questions asked about physicians’ perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. Physicians were asked to rate their top 3 barriers from among a list of 11 pre-identified barriers and to rate their top 3 desired practice-related improvements from among a list of 10 pre-identified improvements. Physicians were given the option to provide additional barriers or improvements that were not already pre-identified. Seven questions assessed physicians’ views/beliefs related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight. These questions utilize a rating scale where 1 = strongly agree, 2 = agree somewhat, 3 = neither agree nor disagree, 4 = disagree somewhat, and 5 = strongly disagree. Four questions assessed physicians’ healthy lifestyle–related knowledge (BMI ranges/percentiles for adults/children, diet and exercise guideline recommendations [recommended amounts of moderate physical activity and servings of fruits and vegetables for adults]) and 11 questions ask about the physician (height, weight, demographics, and practice population).

 

Survey Administration

The survey was administered anonymously through Qualtrics, a secure, online survey platform. The survey was administered online to increase anonymity, increase response rate, and diminish potential physician-perceived barriers to participating in the study. The participating physicians were provided with a link that enabled them to access the survey. The survey excluded questions that required disclosure of identifying information. Survey data from Qualtrics were exported to an SPSS file that was stored on a password protected, secured computer in the research lab of the principal investigator for this study.

Data Analysis

Frequency analyses were applied to survey responses to determine the participating physicians’ endorsed barriers to and views regarding evaluating and managing patients’ weight, healthy eating, and physical activity; physicians’ views related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight; and physicians’ healthy lifestyle–related knowledge. Nonparametric t tests were conducted to examine differences in survey responses of the participating physicians in association with their sex (male or female), race (Asian vs. white/Caucasian), and BMI (BMI < 25 and BMI ≥ 25).

Approval for the study was obtained through the institutional review board of the University of Florida Health Science Center.

Results

Participants

Twenty-five physicians out of 52 invited completed the survey (48% response rate). The vast majority of the study participants were PCMH-affiliated (92%–96%). Participating physicians ranged in age from 29 to 67 years old. Sixteen (64%) participating physicians identified as female, 7 (28%) participating physicians identified as male, and 2 (8%) participating physicians did not indicate a sex. Twenty (80%) participating physicians identified as being white, 3 (12%) participating physicians identified as being Asian/Asian American, and 2 (8%) did not indicate a race or ethnicity. Twenty-two (88%) participating physicians were employees of a large medical group affiliated with an academic medical center, 1 (4%) was employed in a physician-owned practice, and 2 (8%) did not indicate their main primary care practice location. Table 1 provides additional demographic data.

Participants endorsed a number of perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. The most common barriers reported, as shown in Table 2, include (a) not enough time (72%); (b) patients not interested in improving their weight (52%); and (c) lack of adequate referral resources for diet, physical activity, and weight management (48%). The participating physicians also endorsed certain practice-related improvements that they felt would help them improve their patients’ engagement in health-promoting behaviors (healthy eating and physical activity) and weight. These improvements, as shown in Table 3, include (a) better tools to communicate diet, physical activity, or weight problems to patients or family (48%); (b) better mechanisms to connect patients to specific referral sources (44%); and (c) better counseling tools to guide patients towards lifestyle modification (36%).

Overall, 88% of participating physicians strongly agreed that it was their responsibility to promote a healthy diet, physical activity, and weight loss and healthy weight maintenance among their patients. In contrast, 4% of the participating physicians strongly disagreed with this statement.

Approximately 88% of the participating physicians agreed that patients were more likely to adopt healthier lifestyles if their health care providers counseled them to do so (44% strongly agreed, 44% agreed somewhat). A majority of participating physicians endorsed the view that there are effective strategies and/or tools to (a) help patients eat a healthy diet (56% strongly agreed, 24% agreed somewhat), (b) engage in adequate amounts of physical activity (56% strongly agreed, 20% agreed somewhat), and (c) maintain a healthy weight or lose weight (48% strongly agreed, 

32% agreed somewhat). Only 4% of participating physicians neither agreed nor disagreed with this view and 4% somewhat disagreed with this view.

Many participating physicians expressed confidence in their ability to counsel their patients to (a) eat a healthy diet (64% strongly agreed, 28% agreed somewhat), (b) engage in adequate amounts of physical activity (68% strongly agreed, 24% agreed somewhat), and (c) maintain a healthy weight or lose weight (60% strongly agreed, 32% agreed somewhat). Most participating physicians at least somewhat agreed that they were effective at helping their patients (a) eat a healthy diet (24% strongly agreed, 52% agreed somewhat), (b) engage in adequate amounts of physical activity (20% strongly agreed, 56% agreed somewhat), and (c) maintain a healthy weight or lose weight (16% strongly agreed, 48% agreed somewhat). Some participating physicians expressed ambivalence about whether or not they were effective at helping their patients (a) eat a healthy diet (16% neither agreed nor disagreed), (b) engage in adequate amounts of physical activity (12% neither agreed nor disagreed), and (c) maintain a healthy weight or lose weight (20% neither agreed nor disagreed). A total of 8% of participating physicians did not endorse the belief that they were effective at helping their patients maintain a healthy weight or lose weight.

Most participating physicians at least somewhat agreed that they were effective in encouraging patients to engage in health-promoting activities (44% strongly agreed, and 44% agreed somewhat), whereas 4% neither agreed nor disagreed that they were effective in providing this encouragement. Interestingly, many participating physicians endorsed the view that they would be able to provide more credible and effective counseling to patients if they (the physicians themselves) ate a healthy diet (68% strongly agreed, 20% agreed somewhat) and engaged in adequate amounts of physical activity (68% strongly agreed, 20% agreed somewhat). A minority of participating physicians (4%) neither agreed or disagreed with this perspective.

In regards to participating physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults or percentile ranges for children, most participating physicians were able to accurately identify the correct BMI cutoff ranges for overweight (80%) and obese (76%) adults. However, only 32% of participating physicians were able to correctly identify BMI percentile ranges for children; however, nearly all of the participating physicians saw mainly adult patients. Lastly, 76% of participating physicians were able to correctly identify the recommended amounts of moderate physical activity for adults 18 years of age and older, and only 56% were able to correctly identify the recommended amount of servings of fruits and vegetables.

 

There were no significant race-related differences in participating physicians views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health promoting behaviors and weight management. There were no significant sex-related differences in these variables with the exception that women were more likely to respond that they did not know the BMI percentile range at which children or adolescents were considered to have a healthy weight (37.5% of women vs. 0% of men, P = 0.03). A similar percentage of men (66.7%) and women (64.7%) who chose among the 4 percentile range options (rather than endorsing “Don’t know”) chose an incorrect answer. Lastly, there were no significant self-reported BMI-related differences in participating physicians’ views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health-promoting behaviors and weight management.

Discussion

Given the high percentage of adults in the United States who are overweight or obese and the associated health risks, it is paramount that primary care physicians advise their patients to manage their weight and adopt a health-promoting lifestyle. Research studies indicate that such advice is effective [18,19]. Furthermore, it has been found that most overweight and obese patients want more assistance with weight management than they are receiving from their primary care physicians [21]. This study thus explored primary care physicians’ knowledge, beliefs, and perceived barriers that may prevent them from providing such assistance. The primary care setting is the site where obesity disparity groups (eg, racial/ethnic minorities, groups with low household incomes) are most likely to receive care [22,23].

Most of the PCMH-affiliated physicians in this study agreed that they had the responsibility to promote weight-loss/management and healthy lifestyles among their patients. Consistent with prior research [9], the majority of the physicians in this study felt they were effective in their ability to counsel patients to eat a healthy diet and engage in physical activity. To illustrate, in a prior study [9], 77% of primary care providers thought that they could provide useful dieting tips to patients, and in this study, 80% believed they were effective in helping patients eat a healthy diet. However, despite this confidence in their ability to provide advice about healthy diets and physical activity, the providers in both this and in another prior study [25] were less confident in their ability to actually help patients lose weight. Only 64% of the providers in the present study felt they could be effective in assisting patients with losing weight or maintaining a healthy weight. Although this percentage is higher than the 44% of physicians found in a prior study [25] who felt confident in their ability to treat obesity, both studies clearly point to a need to decrease barriers that physicians face in helping clients lose weight.

A key finding of this study was the consensus among the participating physicians regarding what they perceived to be the common barriers to helping patients adhere to a health-promoting lifestyle. Consistent with past research [8,9], the 3 most common barriers cited by the participating physicians were that they did not have enough time, patients were not interested in improving their weight, and adequate referrals for diet, physical activity, and weight were lacking. Additional barriers endorsed included a lack of effective tools and information to give patients, and a fear of offending patients. Another barrier identified by the participating physicians is the perception that patients had difficulty in changing behaviors necessary for maintaining a healthier lifestyle.

When asked what would facilitate conversations with patients, the top 3 responses given were better tools to communicate diet, physical activity, or weight problems to patients or family members; better mechanisms to connect patients to specific referral sources; and better counseling tools to guide patients towards engagement in healthy lifestyles. Of note is the significant overlap between the perceived barriers and the needed facilitation tools. The clearest example of this overlap is that physicians noted a lack of adequate referral sources to be a barrier and that better mechanisms to connect patients to specific referral sources would facilitate their treatment of patients. Weight management referrals for patients in rural areas and for non-Hispanic black adults and Hispanic adults, among whom obesity is most prevalent in the United States [3,25], are particularly needed. Addressing this need is consistent with national calls to reduce/eliminate obesity and other disparities that plague the U.S. health care system. One promising avenue to facilitate weight management referrals is the development, evaluation, and wide dissemination of remote weight-loss support interventions, particularly in rural, racial/ethnic minority, and low-income communities. Indeed, several recent articles demonstrate the success of such weight management programs across diverse patient populations [27–29].

Many of the physicians who participated in the study (72%) endorsed lack of time as a significant barrier to discussing weight and weight-related behaviors with their patients. Therefore, finding time-efficient strategies to involve physicians in weight management interventions may prove particularly beneficial. One such evidence-based behavioral counseling framework—the 5As framework—has been endorsed by the Centers for Medicare and Medicaid Services and the USPSTF for use with obese patients during a typical 20-minute visit [28].

The second highest-rated barrier, perceived patient lack of motivation, warrants additional discussion. Despite over half of the physicians surveyed citing this as a barrier, previous studies have shown that the majority of overweight patients believe they should lose weight and are interested in losing weight [21]. This study highlights a potential discrepancy between physicians’ perceptions of patients’ interest in weight-loss and their patients’ actual interest. It is possible that this discrepancy can be avoided by training physicians on how to be culturally sensitive when addressing weight with their patients. Moreover, such cultural sensitivity training may be of great use, given that 12% of physicians in this study were apprehensive about discussing weight with their patients due to fear of offending them. Such training typically involves teaching physicians how to talk with patients in ways that enable patients to feel comfortable, trusting, and respectful in patient-physician/provider interactions [29].

Two other findings pertaining to providers deserve mention. Specifically, 88% of physicians believed that effectively encouraging patients to adhere to a healthy lifestyle included personally engaging in health-promoting activities. However, of the physicians surveyed, 64% were overweight/obese. Given the high percentage of physicians in this study that were overweight/obese and these physicians’ belief that their personal engagement in health-promoting activities is important to encourage patient engagement in a healthy lifestyle, it seems that future efforts are needed to facilitate health-promoting behaviors among physicians—efforts that may in turn aid them in encouraging their patients to adhere to a healthy lifestyle.

 

Finally, this study assessed physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults and BMI percentile ranges for children, and recommended amounts of moderate physical activity and servings of fruits and vegetables for adults. Most physicians were able to correctly identify the adult BMI cutoff ranges for overweight and obesity and to identify the correct answers to questions about physical activity and fruits and vegetable consumption guidelines for adults. However, only 32% of physicians were able to correctly identify BMI percentile ranges for children and/or adolescents. This is understandable given that most of the physicians in this study provide care to adult patients. However, considering that in 2012 more than one-third of children and adolescents were overweight or obese [1], it is important that all physicians have knowledge of BMI percentile ranges for children and adolescents so that minimally they can convey this information to their adult patients who are parents. The USPSTF defines children and adolescent overweight as an age- and gender-specific BMI between the 85th and 94th percentiles, and children and adolescent obesity as an age- and gender-specific BMI ≥ 95th percentile [31]. Such knowledge of BMI cutoffs is needed in order for providers to comply with the USPSTF recommendation to screen all adults and children aged 6 years and older for obesity, and then offer or refer those with an obesity diagnosis to intensive multicomponent behavioral interventions [31–33].

While novel, the study also had several limitations. First, due to self-selection of participants, physicians who felt more confident in their abilities to address overweight or obesity with their patients might have been more likely to respond. Second, participating physicians may have given socially desirable responses to questions (ie, responses that present a favorable image of themselves) rather than true/accurate responses. Future studies could incorporate a social desirability scale in order to detect and control for any socially desirable responding [33]. Another limitation was the small sample size and the limited variability in geographic location of the participating physicians. Thus, the experiences of these physicians may not be generalizable to physicians in other geographic regions. Future similar studies to the present study are needed and such studies should use a larger and randomly selected sample of physicians that is racially/ethnically diverse. Finally, a limitation of this study is the 48% participation rate. Factors that may have contributed to this participation rate include lack of compensation for physicians and the likelihood that physicians may have extremely busy schedules that may discourage them from participating. However, it is important to note that the 48% participation rate of this study is better than the 25.6% participation rate in another similar study [25]. Future similar studies to the present study likely need to include strong incentives for physicians to be study participants.

Conclusion

Our study indicates that many primary care physicians may not talk with their patients about engaging in healthy eating, physical activity, and weight management because of perceived barriers that prevent them from doing so, rather than because of a lack of perceived responsibility to do so or a perception that counseling patients on these issues would be ineffective. This finding highlights the importance of providing physicians with the tools and resources needed to overcome the aforementioned barriers to fostering health-promoting lifestyles and a healthy weight among their patients and the importance of involving physicians in identifying these barriers and ways to overcome them.

 

Acknowledgement. We thank the patients and health care providers at the participating medical homes affiliated with University of Florida Health in Jacksonville, Florida, for making this research possible.

Corresponding author: Carolyn M. Tucker, PhD, University of Florida, [email protected].

Funding/support: Support for this research was provided by the Office of Research at UF–Gainesville, Florida, and by the National Institutes of Health and National Center for Research Resources CTSA grant UL1 TR000064.

Financial disclosures: None.

From the University of Florida (Dr. Tucker, Ms. Ukonu, Ms. Kang, Ms. Good), Gainesville, FL; the University of Florida–Jacksonville (Dr. Shah, Dr. Bilello), Jacksonville, FL; and Ball State University (Dr. Arthur), Muncie, IN.

 

Abstracts

• Objective: To assess primary care physicians’ practices, knowledge, and beliefs regarding their efforts to promote healthy lifestyles and weight management among their patients.
• Methods: Study participants consisted of 25 primary care physicians from a regional primary care practice-based research network that includes 37 university-affiliated patient-centered medical homes and 2 nearby unaffiliated primary care sites. Participating physicians completed an online modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire.
• Results: The majority (88%) of participating physicians strongly believed it was their responsibility to promote a healthy diet, physical activity, and healthy weight loss and weight maintenance among patients. The 3 most commonly endorsed barriers were (a) not enough time, (b) minimal patient interest in improving his/her weight, and (c) lack of adequate weight-loss referral resources. The top 3 physician-perceived practice improvements that would be helpful with these practices were (a) better tools to communicate diet, physical activity, or weight problems to patients or family; (b) better mechanisms to connect patients to weight-loss referral resources; and (c) better counseling tools to guide patients regarding lifestyle modifications. 76% of the participating physicians correctly identified the BMI cutoff ranges for adult obesity, but only 32% did so for childhood obesity.
• Conclusion: It is important to provide primary care physicians with knowledge, effective tools, and resources to promote healthy lifestyles and weight loss and weight management among their patients.

Key words: obesity; primary care physicians; weight loss; weight management.

 

More than two-thirds of adults in the United States are overweight, with approximately 35% considered obese (defined as a body mass index ≥ 30) [1]. Obesity is associated with many of the leading causes of death in the United States (ie, diabetes, heart disease, stroke, and some types of cancer) and with poor mental health outcomes and reduced quality of life [2]. Racial/ethnic minorities and individuals with low incomes are disproportionately impacted by obesity and obesity-related diseases and negative health outcomes [3–5].

The US Preventive Services Task Force (USPSTF) recommends screening for obesity and intensive behavioral counseling, which are often the responsibilities of primary care providers [6]. Despite these recommendations, research suggests that primary care providers rarely screen their patients for obesity or refer them for intensive behavioral counseling despite evidence that doing so would improve patient health outcomes [5–7]. Lack of time to address weight issues during clinical visits, lack of training in weight management counseling, and lack of availability of intensive weight loss programs to which they can refer their patients are some of the reasons cited for not counseling patients about weight management [8].

Primary care providers deliver more hours of patient care than other providers, yet these providers have been unable to deliver medical interventions capable of producing even modest weight loss [10]. Obesity treatment options delivered in primary care settings have limited success, likely due to the low intensity of these treatment options. Many studies have shown that most obesity treatments in health care settings typically consist of scheduled monthly or quarterly visits that are 10 to 15 minutes in duration [11], despite evidence that more intense treatments are needed. Specifically, a systematic review of the obesity treatment literature performed by the USPSTF revealed that high-intensity, multicomponent behavioral interventions that include face-to-face counseling on diet and physical activity and behavioral therapy more than once a month for 3 months are needed to produce significant weight loss (8–15 lb) among adult patients in primary care settings [12].

Since many of the characteristics of multicomponent behavioral interventions for treating obesity are both patient-centered and involve self-management, the patient-centered medical home (PCMH) seems to be the ideal setting to deliver these interventions [13]. Specifically, PCMHs provide patient-centered care that is wide-ranging, team-based, and coordinated across all elements of the health care system and the patient’s community [14]. These sites specifically provide primary care, which is the type of care that obesity disparity patient groups such as racial/ethnic minorities, sexual minorities, groups with low incomes, and the medically underserved are more likely to utilize [15].

Providing multicomponent behavioral interventions for obesity in PCMHs and other primary care sites will increase the likelihood of participation among the aforementioned obesity disparity groups. Despite the potential benefits of obesity treatment interventions offered in primary care settings, particularly for obesity disparity groups, the role of primary care providers in providing such treatment interventions is not clear [16]. We surveyed primary care physicians who primarily worked in PCMHs to assess their practices, knowledge, views/beliefs, perceived barriers, and perceived needed clinic practice improvements relative to promoting healthy lifestyles and weight management among their patients.

Methods

Participants

Primary care physicians were recruited from among a regional primary care practice-based research network that includes 37 PCMHs affiliated with an academic health center and 2 nearby primary care sites not affiliated with an academic health center. Fifty-two physicians at these centers received an invitation via email to participate in our online survey study. The invitation email included (a) a study endorsement note from the chair of the Community Health and Family Medicine Department affiliated with the PCMHs, (b) instructions about how to participate in the study, and (c) a link to the study. Participation inclusion criteria specified in the online informed consent form were: (a) working as a physician affiliated with the practice-based research network, (b) having access to a computer with internet connection, (c) being able to communicate in written English, and (d) providing written consent to participate in the study. Physicians were not provided compensation for participating in the study.

Survey Instrument

To assess physicians’ views and practices, we used a modified version of the Physician Survey of Practices on Diet, Physical Activity, and Weight Control–Adult Questionnaire [17]. The survey was sponsored by the National Cancer Institute in collaboration with several other NIH institutes and the CDC for evaluating current clinical practices among physicians, including the degree to which physicians evaluate their patients for obesity and offer them guidance designed to increase adherence to a health-promoting lifestyle (eg, recommendations on diet, weight, and physical activity). Additionally, the questionnaire assesses physicians’ perceived barriers to patient assessment, evaluation, and management. It also includes questions about physicians’ healthy lifestyle–related knowledge. In 2010, Smith and colleagues utilized the questionnaire with a nationally representative sample of primary care physicians (n = 1211) to investigate primary care physicians’ clinical practices in relation to overweight and obesity [18]. To our knowledge, no other physician survey has been developed to assess current engagement in recommended clinical practices, barriers to engaging in recommended practices, as well as beliefs and knowledge regarding helping patients follow a health-promoting lifestyle. The original survey also includes questions regarding the physicians’ personal health status and health behaviors.

For our study, we modified the survey by removing questions regarding the physicians’ (a) perceived general health and well-being, (b) current dietary practices, (c) current level of engagement in physical activity, and (d) current engagement in professional activities unrelated to patient care (eg, research, teaching). Our modified survey included 7 questions asking about current practices regarding screening for obesity and referral of patients to weight management interventions. Two questions asked about physicians’ perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. Physicians were asked to rate their top 3 barriers from among a list of 11 pre-identified barriers and to rate their top 3 desired practice-related improvements from among a list of 10 pre-identified improvements. Physicians were given the option to provide additional barriers or improvements that were not already pre-identified. Seven questions assessed physicians’ views/beliefs related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight. These questions utilize a rating scale where 1 = strongly agree, 2 = agree somewhat, 3 = neither agree nor disagree, 4 = disagree somewhat, and 5 = strongly disagree. Four questions assessed physicians’ healthy lifestyle–related knowledge (BMI ranges/percentiles for adults/children, diet and exercise guideline recommendations [recommended amounts of moderate physical activity and servings of fruits and vegetables for adults]) and 11 questions ask about the physician (height, weight, demographics, and practice population).

 

Survey Administration

The survey was administered anonymously through Qualtrics, a secure, online survey platform. The survey was administered online to increase anonymity, increase response rate, and diminish potential physician-perceived barriers to participating in the study. The participating physicians were provided with a link that enabled them to access the survey. The survey excluded questions that required disclosure of identifying information. Survey data from Qualtrics were exported to an SPSS file that was stored on a password protected, secured computer in the research lab of the principal investigator for this study.

Data Analysis

Frequency analyses were applied to survey responses to determine the participating physicians’ endorsed barriers to and views regarding evaluating and managing patients’ weight, healthy eating, and physical activity; physicians’ views related to helping patients achieve and maintain a health-promoting lifestyle and a healthy weight; and physicians’ healthy lifestyle–related knowledge. Nonparametric t tests were conducted to examine differences in survey responses of the participating physicians in association with their sex (male or female), race (Asian vs. white/Caucasian), and BMI (BMI < 25 and BMI ≥ 25).

Approval for the study was obtained through the institutional review board of the University of Florida Health Science Center.

Results

Participants

Twenty-five physicians out of 52 invited completed the survey (48% response rate). The vast majority of the study participants were PCMH-affiliated (92%–96%). Participating physicians ranged in age from 29 to 67 years old. Sixteen (64%) participating physicians identified as female, 7 (28%) participating physicians identified as male, and 2 (8%) participating physicians did not indicate a sex. Twenty (80%) participating physicians identified as being white, 3 (12%) participating physicians identified as being Asian/Asian American, and 2 (8%) did not indicate a race or ethnicity. Twenty-two (88%) participating physicians were employees of a large medical group affiliated with an academic medical center, 1 (4%) was employed in a physician-owned practice, and 2 (8%) did not indicate their main primary care practice location. Table 1 provides additional demographic data.

Participants endorsed a number of perceived barriers to helping patients adhere to a health-promoting lifestyle and maintain a healthy weight. The most common barriers reported, as shown in Table 2, include (a) not enough time (72%); (b) patients not interested in improving their weight (52%); and (c) lack of adequate referral resources for diet, physical activity, and weight management (48%). The participating physicians also endorsed certain practice-related improvements that they felt would help them improve their patients’ engagement in health-promoting behaviors (healthy eating and physical activity) and weight. These improvements, as shown in Table 3, include (a) better tools to communicate diet, physical activity, or weight problems to patients or family (48%); (b) better mechanisms to connect patients to specific referral sources (44%); and (c) better counseling tools to guide patients towards lifestyle modification (36%).

Overall, 88% of participating physicians strongly agreed that it was their responsibility to promote a healthy diet, physical activity, and weight loss and healthy weight maintenance among their patients. In contrast, 4% of the participating physicians strongly disagreed with this statement.

Approximately 88% of the participating physicians agreed that patients were more likely to adopt healthier lifestyles if their health care providers counseled them to do so (44% strongly agreed, 44% agreed somewhat). A majority of participating physicians endorsed the view that there are effective strategies and/or tools to (a) help patients eat a healthy diet (56% strongly agreed, 24% agreed somewhat), (b) engage in adequate amounts of physical activity (56% strongly agreed, 20% agreed somewhat), and (c) maintain a healthy weight or lose weight (48% strongly agreed, 

32% agreed somewhat). Only 4% of participating physicians neither agreed nor disagreed with this view and 4% somewhat disagreed with this view.

Many participating physicians expressed confidence in their ability to counsel their patients to (a) eat a healthy diet (64% strongly agreed, 28% agreed somewhat), (b) engage in adequate amounts of physical activity (68% strongly agreed, 24% agreed somewhat), and (c) maintain a healthy weight or lose weight (60% strongly agreed, 32% agreed somewhat). Most participating physicians at least somewhat agreed that they were effective at helping their patients (a) eat a healthy diet (24% strongly agreed, 52% agreed somewhat), (b) engage in adequate amounts of physical activity (20% strongly agreed, 56% agreed somewhat), and (c) maintain a healthy weight or lose weight (16% strongly agreed, 48% agreed somewhat). Some participating physicians expressed ambivalence about whether or not they were effective at helping their patients (a) eat a healthy diet (16% neither agreed nor disagreed), (b) engage in adequate amounts of physical activity (12% neither agreed nor disagreed), and (c) maintain a healthy weight or lose weight (20% neither agreed nor disagreed). A total of 8% of participating physicians did not endorse the belief that they were effective at helping their patients maintain a healthy weight or lose weight.

Most participating physicians at least somewhat agreed that they were effective in encouraging patients to engage in health-promoting activities (44% strongly agreed, and 44% agreed somewhat), whereas 4% neither agreed nor disagreed that they were effective in providing this encouragement. Interestingly, many participating physicians endorsed the view that they would be able to provide more credible and effective counseling to patients if they (the physicians themselves) ate a healthy diet (68% strongly agreed, 20% agreed somewhat) and engaged in adequate amounts of physical activity (68% strongly agreed, 20% agreed somewhat). A minority of participating physicians (4%) neither agreed or disagreed with this perspective.

In regards to participating physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults or percentile ranges for children, most participating physicians were able to accurately identify the correct BMI cutoff ranges for overweight (80%) and obese (76%) adults. However, only 32% of participating physicians were able to correctly identify BMI percentile ranges for children; however, nearly all of the participating physicians saw mainly adult patients. Lastly, 76% of participating physicians were able to correctly identify the recommended amounts of moderate physical activity for adults 18 years of age and older, and only 56% were able to correctly identify the recommended amount of servings of fruits and vegetables.

 

There were no significant race-related differences in participating physicians views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health promoting behaviors and weight management. There were no significant sex-related differences in these variables with the exception that women were more likely to respond that they did not know the BMI percentile range at which children or adolescents were considered to have a healthy weight (37.5% of women vs. 0% of men, P = 0.03). A similar percentage of men (66.7%) and women (64.7%) who chose among the 4 percentile range options (rather than endorsing “Don’t know”) chose an incorrect answer. Lastly, there were no significant self-reported BMI-related differences in participating physicians’ views/beliefs, healthy lifestyle–related knowledge, and perceived barriers to helping patients engage in health-promoting behaviors and weight management.

Discussion

Given the high percentage of adults in the United States who are overweight or obese and the associated health risks, it is paramount that primary care physicians advise their patients to manage their weight and adopt a health-promoting lifestyle. Research studies indicate that such advice is effective [18,19]. Furthermore, it has been found that most overweight and obese patients want more assistance with weight management than they are receiving from their primary care physicians [21]. This study thus explored primary care physicians’ knowledge, beliefs, and perceived barriers that may prevent them from providing such assistance. The primary care setting is the site where obesity disparity groups (eg, racial/ethnic minorities, groups with low household incomes) are most likely to receive care [22,23].

Most of the PCMH-affiliated physicians in this study agreed that they had the responsibility to promote weight-loss/management and healthy lifestyles among their patients. Consistent with prior research [9], the majority of the physicians in this study felt they were effective in their ability to counsel patients to eat a healthy diet and engage in physical activity. To illustrate, in a prior study [9], 77% of primary care providers thought that they could provide useful dieting tips to patients, and in this study, 80% believed they were effective in helping patients eat a healthy diet. However, despite this confidence in their ability to provide advice about healthy diets and physical activity, the providers in both this and in another prior study [25] were less confident in their ability to actually help patients lose weight. Only 64% of the providers in the present study felt they could be effective in assisting patients with losing weight or maintaining a healthy weight. Although this percentage is higher than the 44% of physicians found in a prior study [25] who felt confident in their ability to treat obesity, both studies clearly point to a need to decrease barriers that physicians face in helping clients lose weight.

A key finding of this study was the consensus among the participating physicians regarding what they perceived to be the common barriers to helping patients adhere to a health-promoting lifestyle. Consistent with past research [8,9], the 3 most common barriers cited by the participating physicians were that they did not have enough time, patients were not interested in improving their weight, and adequate referrals for diet, physical activity, and weight were lacking. Additional barriers endorsed included a lack of effective tools and information to give patients, and a fear of offending patients. Another barrier identified by the participating physicians is the perception that patients had difficulty in changing behaviors necessary for maintaining a healthier lifestyle.

When asked what would facilitate conversations with patients, the top 3 responses given were better tools to communicate diet, physical activity, or weight problems to patients or family members; better mechanisms to connect patients to specific referral sources; and better counseling tools to guide patients towards engagement in healthy lifestyles. Of note is the significant overlap between the perceived barriers and the needed facilitation tools. The clearest example of this overlap is that physicians noted a lack of adequate referral sources to be a barrier and that better mechanisms to connect patients to specific referral sources would facilitate their treatment of patients. Weight management referrals for patients in rural areas and for non-Hispanic black adults and Hispanic adults, among whom obesity is most prevalent in the United States [3,25], are particularly needed. Addressing this need is consistent with national calls to reduce/eliminate obesity and other disparities that plague the U.S. health care system. One promising avenue to facilitate weight management referrals is the development, evaluation, and wide dissemination of remote weight-loss support interventions, particularly in rural, racial/ethnic minority, and low-income communities. Indeed, several recent articles demonstrate the success of such weight management programs across diverse patient populations [27–29].

Many of the physicians who participated in the study (72%) endorsed lack of time as a significant barrier to discussing weight and weight-related behaviors with their patients. Therefore, finding time-efficient strategies to involve physicians in weight management interventions may prove particularly beneficial. One such evidence-based behavioral counseling framework—the 5As framework—has been endorsed by the Centers for Medicare and Medicaid Services and the USPSTF for use with obese patients during a typical 20-minute visit [28].

The second highest-rated barrier, perceived patient lack of motivation, warrants additional discussion. Despite over half of the physicians surveyed citing this as a barrier, previous studies have shown that the majority of overweight patients believe they should lose weight and are interested in losing weight [21]. This study highlights a potential discrepancy between physicians’ perceptions of patients’ interest in weight-loss and their patients’ actual interest. It is possible that this discrepancy can be avoided by training physicians on how to be culturally sensitive when addressing weight with their patients. Moreover, such cultural sensitivity training may be of great use, given that 12% of physicians in this study were apprehensive about discussing weight with their patients due to fear of offending them. Such training typically involves teaching physicians how to talk with patients in ways that enable patients to feel comfortable, trusting, and respectful in patient-physician/provider interactions [29].

Two other findings pertaining to providers deserve mention. Specifically, 88% of physicians believed that effectively encouraging patients to adhere to a healthy lifestyle included personally engaging in health-promoting activities. However, of the physicians surveyed, 64% were overweight/obese. Given the high percentage of physicians in this study that were overweight/obese and these physicians’ belief that their personal engagement in health-promoting activities is important to encourage patient engagement in a healthy lifestyle, it seems that future efforts are needed to facilitate health-promoting behaviors among physicians—efforts that may in turn aid them in encouraging their patients to adhere to a healthy lifestyle.

 

Finally, this study assessed physicians’ healthy lifestyle–related knowledge about current BMI ranges for adults and BMI percentile ranges for children, and recommended amounts of moderate physical activity and servings of fruits and vegetables for adults. Most physicians were able to correctly identify the adult BMI cutoff ranges for overweight and obesity and to identify the correct answers to questions about physical activity and fruits and vegetable consumption guidelines for adults. However, only 32% of physicians were able to correctly identify BMI percentile ranges for children and/or adolescents. This is understandable given that most of the physicians in this study provide care to adult patients. However, considering that in 2012 more than one-third of children and adolescents were overweight or obese [1], it is important that all physicians have knowledge of BMI percentile ranges for children and adolescents so that minimally they can convey this information to their adult patients who are parents. The USPSTF defines children and adolescent overweight as an age- and gender-specific BMI between the 85th and 94th percentiles, and children and adolescent obesity as an age- and gender-specific BMI ≥ 95th percentile [31]. Such knowledge of BMI cutoffs is needed in order for providers to comply with the USPSTF recommendation to screen all adults and children aged 6 years and older for obesity, and then offer or refer those with an obesity diagnosis to intensive multicomponent behavioral interventions [31–33].

While novel, the study also had several limitations. First, due to self-selection of participants, physicians who felt more confident in their abilities to address overweight or obesity with their patients might have been more likely to respond. Second, participating physicians may have given socially desirable responses to questions (ie, responses that present a favorable image of themselves) rather than true/accurate responses. Future studies could incorporate a social desirability scale in order to detect and control for any socially desirable responding [33]. Another limitation was the small sample size and the limited variability in geographic location of the participating physicians. Thus, the experiences of these physicians may not be generalizable to physicians in other geographic regions. Future similar studies to the present study are needed and such studies should use a larger and randomly selected sample of physicians that is racially/ethnically diverse. Finally, a limitation of this study is the 48% participation rate. Factors that may have contributed to this participation rate include lack of compensation for physicians and the likelihood that physicians may have extremely busy schedules that may discourage them from participating. However, it is important to note that the 48% participation rate of this study is better than the 25.6% participation rate in another similar study [25]. Future similar studies to the present study likely need to include strong incentives for physicians to be study participants.

Conclusion

Our study indicates that many primary care physicians may not talk with their patients about engaging in healthy eating, physical activity, and weight management because of perceived barriers that prevent them from doing so, rather than because of a lack of perceived responsibility to do so or a perception that counseling patients on these issues would be ineffective. This finding highlights the importance of providing physicians with the tools and resources needed to overcome the aforementioned barriers to fostering health-promoting lifestyles and a healthy weight among their patients and the importance of involving physicians in identifying these barriers and ways to overcome them.

 

Acknowledgement. We thank the patients and health care providers at the participating medical homes affiliated with University of Florida Health in Jacksonville, Florida, for making this research possible.

Corresponding author: Carolyn M. Tucker, PhD, University of Florida, [email protected].

Funding/support: Support for this research was provided by the Office of Research at UF–Gainesville, Florida, and by the National Institutes of Health and National Center for Research Resources CTSA grant UL1 TR000064.

Financial disclosures: None.

Take-Home Points

• The prevalence of preoperative pneumonia is 1.2% among hip fracture patients aged >65 years.
• Preoperative pneumonia is an independent risk factor for mortality and adverse events including renal failure, prolonged ventilator dependence, and prolonged altered mental status after geriatric hip fracture surgery.
• Underweight BMI (<18.5 kg/m²) was associated with higher mortality within 30 days among hip fracture patients admitted with pneumonia.
• The mortality rate normalized to that of patients without pneumonia within 2 weeks of hip fracture surgery.
• Time from admission to surgery was not associated with adverse events or mortality among hip fracture patients admitted with pneumonia.

Preoperative pneumonia remains relatively unexplored as a risk factor for adverse outcomes in geriatric hip fracture surgery. Dated studies report a 0.3% to 3.2% prevalence of “recent pneumonia” in patients presenting with hip fracture but provide neither a definition of pneumonia based on clinical criteria nor a subset analysis of outcomes in the pneumonia group.^1-3 Although active pneumonia has been identified as a preoperative optimization target in the management guidelines for geriatric hip fracture,⁴ we are unaware of any studies that have reported on differences in demographics, comorbidities, delay to surgery, or adverse outcomes between hip fracture patients with and without preoperative pneumonia.

This paucity of information on the effect of preoperative pneumonia in the hip fracture population may be related to low prevalence of preoperative pneumonia and a cadre of variable definitions, which limit identification of a cohort of patients with preoperative pneumonia large enough from which to draw meaningful results. Database studies, especially those using surgical registries rather than administrative or reimbursement data, offer particular advantages for investigation of such rare clinical entities.⁵Medical care of patients with pneumonia alone is known to be facilitated by assessments of mortality risk from clinical and laboratory data. The modified British Thoracic Society rule/CURB-65 (confusion, urea, respiratory rate, blood pressure) score is strongly predictive of mortality in hospitalized adults with pneumonia (odds ratio [OR], 4.59; 95% confidence interval [CI], 1.42-14.85; P = .011) and may guide antibiotic therapy, laboratory investigations, and the decision to intubate in a patient with pneumonia.^6-8 This score is predictive of adverse events (AEs), hospital length of stay, and use of intensive care services.^6,7,9-13 We hypothesized that preoperative clinical indicators assessed by pneumonia severity scores as well as patient demographics and baseline comorbidities may also have prognostic value for risk of AEs in a cohort of geriatric hip fracture surgery patients with preoperative pneumonia.

In this article, we first describe the prevalence of preoperative pneumonia in geriatric hip fracture surgery patients as well as demographic and operative differences between patients with and without the disease. We then ask 3 questions: Is preoperative pneumonia an independent risk factor for mortality and adverse outcomes in geriatric hip fracture surgery? Is there a postoperative interval during which the unadjusted mortality rate is higher among patients with preoperative pneumonia? In patients with preoperative pneumonia, what are the predictors of morbidity and mortality?

Methods

Yale University’s Human Investigations Committee approved this retrospective cohort study, which used the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database for the period 2005 to 2012. ACS-NSQIP is a prospective, multi-institutional outcomes program that collects data on preoperative comorbidities, intraoperative variables, and 30-day postoperative outcomes for patients undergoing surgical procedures in inpatient and outpatient settings.¹⁴

Unlike administrative databases, which are based on reimbursement data, ACS-NSQIP data are collected by trained surgical clinical reviewers for the purposes of quality improvement and clinical research, and data quality is ensured with routine auditing.¹⁵ The program has gained a high degree of respect as a powerful and valid data source in both general¹⁶ and orthopedic¹⁷ surgery literature. The database offers a particular advantage with respect to the study of preoperative pneumonia: Only patients with new or recently diagnosed pneumonia on antibiotic therapy who meet strict criteria for characteristic findings on chest radiography, clinical signs and symptoms of respiratory illness, and positive cultures are coded as having actively treated pneumonia at time of surgery.¹⁵

To identify hip fracture patients over the age of 65 years who underwent operative fixation of a hip fracture, we used Current Procedural Terminology (CPT) hip fracture codes, including 27235 (percutaneous screw fixation), 27236 or 27244 (plate-and-screw fixation), and 27245 (intramedullary device), as well as 27125 (hemiarthroplasty) and 27130 (arthroplasty) for patients with a postoperative International Classification of Disease, Ninth Revision (ICD-9) diagnosis code (820.x, 820.2x, or 820.8) consistent with acute hip fracture.^18,19 Procedure type, anesthesia type, and delay from admission to surgery were captured for all procedures.

Preoperative demographics included age, sex, transfer origin, functional status, and body mass index (BMI) category. Binary comorbidities were classified as preoperative anemia (hematocrit, <0.41 for men, <0.36 for women), confusion, dyspnea at rest, uremia (blood urea nitrogen, >6.8 mmol/L), history of cardiovascular disease (congestive heart failure, myocardial infarction, percutaneous coronary intervention, angina pectoris, medically treated hypertension, peripheral vascular disease, or resting claudication), chronic obstructive pulmonary disease, diabetes, renal disease (renal failure or dialysis), and cigarette use in preceding 12 months.^20,21 Although preoperative hypotension and respiratory rate are often considered in patients with pneumonia, these variables were not available from the ACS-NSQIP data.^6,22Pearson χ² test for categorical variables was used to compare baseline demographics and operative characteristics between patients with and without pneumonia, and Student t test was used to compare intervals from hospital admission to hip fracture surgery, surgery start to surgery stop, and surgery to discharge between patients with and without preoperative pneumonia.

Binary outcome measures were compared between patients with and without preoperative pneumonia. “Any AE” included any serious AE (SAE) or any minor AE. SAEs included death, acute renal failure, ventilator use >48 hours, unplanned intubation, septic shock, sepsis, return to operating room, coma >24 hours, cardiac arrest requiring cardiopulmonary resuscitation, myocardial infarction, thromboembolic event (deep vein thrombosis or pulmonary embolism), and stroke/cerebrovascular accident. Minor AEs included progressive renal insufficiency, urinary tract infection, organ/space infection, superficial surgical-site infection, deep surgical-site infection, and wound dehiscence. Other binary outcome measures included discharge destination and unplanned readmission within 30 days after hip fracture surgery.²³Poisson regression with robust error variance as described by Zou²⁴ was used to compare the rates of any, minor, and individual AEs, and any SAEs, between patients with and without pneumonia. Multivariate analysis accounted for the baseline variables in Table 1. AEs that occurred more than once in each group were included in the analyses.

Kaplan-Meier survival analysis was performed for postoperative mortality within 30 days. Within the preoperative pneumonia group, covariates from Table 1 were identified as predictors of any AE, SAE, or death within 30 days after hip fracture surgery by stepwise multivariate Poisson regression with robust error variance. When interval from admission to surgery was longer than 24, 48, 72, or 96 hours, it was also included as a covariate. Variables that did not show an association with AEs at the P < .20 level were not included in the final regression model. All analyses were performed with Stata/SE Version 12.0 statistical software (StataCorp).

 

Results

Of the 7128 geriatric hip fracture patients in this study, 82 (1.2%) had active pneumonia at time of surgery (Table 1). Age, BMI, preoperative uremia, history of cardiovascular disease, diabetes, renal disease, and smoking were similar between groups. In addition, there was no difference in anesthesia type or fixation procedure between the pneumonia and no-pneumonia groups. Patients with preoperative pneumonia differed significantly with respect to sex, transfer from facility, preoperative functional dependence, anemia, confusion, dyspnea at rest, and history of chronic obstructive pulmonary disease (Table 1).

Interval from admission to surgery was longer (P < .001) for geriatric hip fracture patients with preoperative pneumonia (mean, 6.8 days; 95% CI, 2.5-11.1 days) than for those without pneumonia (mean, 1.5 days; CI, 1.4-1.5 days). There was no difference (P = .124) in operative time between the pneumonia group (mean, 72.8 min; CI, 64.0-81.5 min) and the no-pneumonia group (mean, 66.1 min; CI, 61.2-67.0 min). Interval from surgery to discharge was longer (P < .001) for patients with preoperative pneumonia (mean, 10.1 days; CI, 6.9-13.4 days) than for those without pneumonia (mean, 6.3 days; CI, 6.1-6.4 days).

Adverse outcomes of geriatric hip fracture surgery are listed in Table 2. In the multivariate analysis, preoperative pneumonia was significantly associated with any AE (relative risk [RR]) = 1.44) and any SAE (RR = 1.79).

Specific AEs were also assessed. In terms of SAEs, patients with pneumonia were more likely to die (RR = 2.08), develop acute renal failure (RR = 14.61), become comatose for more than 24 hours (RR = 7.31), and require mechanical ventilation for more than 48 hours after surgery (RR = 6.48). In terms of minor AEs, there were no significant differences between patients with and without pneumonia.

Survival patterns diverged between patients with and without preoperative pneumonia (Figure). The unadjusted mortality rate was qualitatively higher in patients with preoperative pneumonia than in patients without pneumonia during the first days after hip fracture (slopes of unadjusted mortality curves in Figure). Of note, no patient under age 75 years with pneumonia at time of surgery died within the 30-day study period.

Among geriatric hip fracture patients with preoperative pneumonia, multivariate analyses revealed no significant association of any preoperative comorbidity with any AE or any SAE. Given the gravity of the death complication, however, death within 30 days after surgery was analyzed separately, and was found to be significantly associated (RR = 4.67) with being underweight (BMI, <18.5 kg/m²) (Table 3). Admission-to-surgery interval longer than 24, 48, 72, or 96 hours did not reach significance at the P < 0.2 level in the stepwise regressions and therefore was not associated with a higher or lower risk of any AE, SAE, or death.

Discussion

In the general US population, pneumonia accounts for 1.4% of deaths in people 65 years to 74 years old, 2.1% in people 75 years to 84 years, and 3.1% in people 85 years or older. In total, 3.4% of hospital inpatient deaths are attributed to pneumonia.²⁵ In hospitalized general orthopedic surgical patients as well as hip fracture patients, pneumonia is strongly associated with increased mortality.^26,27

We identified a preoperative pneumonia prevalence of 1.2%, which is comparable to the rates reported in the literature (0.3%-3.2%).^1-3 To our knowledge, our study represents the largest series of patients with preoperative pneumonia at time of hip fracture repair, and the first to independently associate preoperative pneumonia with increased incidence of AEs, including death.

This study had its limitations. First, the ACS-NSQIP morbidity and mortality data, which are limited to the first 30 postoperative days, may be skewed because AEs that occurred after that interval are not captured. Second, coding of pneumonia in ACS-NSQIP does not convey specific information about the disease and its severity—infectious organism(s) responsible; acquisition setting (healthcare or community); treatment given, including antibiotic(s) selection, steroid use, dosing, and duration; and measures of treatment efficacy—limiting interpretation of the difference in delay to surgery. We cannot say whether the longer interval in patients with pneumonia reflects medical optimization, or whether the delay itself or any interventions during that time positively or negatively affected outcomes. In addition, despite using a large national database, we obtained a relatively small sample of patients (82) who had pneumonia before surgical hip fracture repair.

Multivariate analysis controlling for baseline demographics and comorbidities revealed that multiple SAEs were independently associated with preoperative pneumonia (overall SAE, RR = 1.79). Postoperative use of ventilator support for longer than 48 hours (RR = 6.48) and coma longer than 24 hours (RR = 7.31) are expected given the severity of pulmonary compromise in the study cohort.^28,29 Acute renal failure (RR = 14.61) can occur in both hip fracture patients and community-acquired pneumonia patients and may be a multifactorial complication of the pulmonary infection, of the anesthesia, or of the surgical intervention in this cohort.^30-32Unadjusted mortality in hip fracture takes months to a year to normalize to that of age-matched controls.^32-34 In our series, the unadjusted death rate in the pneumonia cohort (Figure) was transiently elevated during the first weeks after surgery but then drew nearer the rate in the nondiseased hip fracture cohort by the end of the first month. Early death in the pneumonia group likely was multifactorial, potentially influenced by the increased burden of comorbidities in the pneumonia group at baseline, and the longer delay to surgery,^35-38 as well as by the natural history of treated pneumonia in hospital patients, who, compared with age-matched hospitalized controls, also exhibit higher mortality during only the first 2 to 4 months of hospitalization for pneumonia.³⁹ We regret that quality improvement strategies in the treatment of geriatric hip fracture surgery with pneumonia cannot be extrapolated from these results.

Similarly, the utility of BMI <18.5 kg/m² as an actionable preoperative finding cannot be assessed from these results. However, we propose that underweight geriatric hip fracture patients with pneumonia may benefit from more aggressive preoperative optimization that does not delay surgery. Higher acuity of postoperative care, including more intensive nursing care and early coordination of care with respiratory therapists and medical comanagement teams, may also be beneficial.

Anesthesia type did not differ between patients with and without preoperative pneumonia and was not associated with AEs in patients with preoperative pneumonia. Consistent with our findings, multiple studies have reported no significant differences in short-term outcomes of hip fracture repair between general and spinal anesthesia, though no other study has compared the benefits of general and spinal anesthesia for patients with preoperative pneumonia.^40-44 Although spinal anesthesia (relative to general anesthesia) has been reported to have benefits in hip and knee arthroplasty, these benefits appear not to translate to hip fracture repair.^45-50 The results of the present study suggest that general and spinal anesthesia may be equivalent in terms of risk for the geriatric hip fracture patient with preoperative pneumonia.^43,44Our attempt to evaluate the CURB-65 pneumonia severity score as a prognosticator of AEs was thwarted by the absence of required variables in the ACS-NSQIP dataset (confusion, uremia, dyspnea, and age were available; hypotension and blood pressure were not). In our analysis, we did include, individually, variables previously found to predict AEs in the medical pneumonia population (confusion, uremia, dyspnea at rest, anemia).^9-11,32 However, these clinical findings are nonspecific in hip fracture patients, who may become anemic, confused, dyspneic, or uremic from a multitude of factors related to their injury and unrelated to pneumonia, including but not limited to hemorrhage, muscle damage, renal injury, and pulmonary embolism. It is not surprising that confusion, uremia, dyspnea at rest, and anemia were not individually predictive of AEs or death within 30 days after surgery in the cohort of geriatric hip fracture patients with pneumonia.

There is no literature that argues for or against delaying hip fracture surgery in geriatric hip fracture patients with pneumonia. The surgical delay observed in this population is ostensibly related to medical optimization of the pneumonia and/or underlying comorbidities. However, we did not find a morbidity or mortality detriment or benefit in delaying surgery by 1 to 4 days in this population. Delay of surgery is a poor covariate, given extensive confounding by medical management and preoperative optimizing of comorbid conditions (reflected in our independent variable and covariates) as well as institutional and surgeon variations in policy and behavior and other unaccounted influences. Although some authors have found no difference in mortality or major AEs between hip fracture patients who had a surgical delay and those who did not,^31,51-53 other series and meta-analyses have suggested a mortality detriment in a surgical delay of more than 2 days^36,54 or 4 days⁵⁵ from admission. Given our data, we cannot recommend against immediate hip fracture repair in the subpopulation of geriatric hip fracture patients with pneumonia.

Our study findings suggest that preoperative pneumonia is a rare independent risk factor for AEs after hip fracture surgery in geriatric patients. Underweight BMI is predictive of death in geriatric hip fracture surgery patients who present with pneumonia, whereas early surgical repair appears not to be associated with adverse outcomes. Further investigation is warranted to determine if such patients benefit from specific preoperative and postoperative strategies for optimizing medical and surgical care based on these findings.

Am J Orthop. 2017;46(3):E177-E185. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.