Original Research

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

 

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

 

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

 

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

 

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

 

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

 

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

Take-Home Points

• With EHR implementation there are small changes in the level of billing coding.
• Although these changes may be statistically significant they are relatively minor.
• In the general internal medicine department, level 4 coding increased by 1.2% while level 3 coding decreased by 0.5%.
• In the orthopedics department, level 4 coding increased by 3.3% while level 3 coding decreased by 3.1%.
• Reports in the lay media regarding dramatic up-coding after EHR implementation may be misleading.

The Health Information Technology for Economic and Clinical Health (HITECH) Act, which was signed into law in 2009, mandated that hospitals that care for Medicare patients either begin using electronic health records (EHRs) or pay a nontrivial penalty.¹ By now, the majority of orthopedic surgeons have implemented EHRs in their practices.² Despite ongoing debate in the orthopedic literature,³ EHRs are expected to improve coordination of care, reduce duplicate testing, and reduce costs over the long term as healthcare insurance coverage is extended to millions more Americans.

In early coverage, however, media reported that EHR implementation at some hospitals was correlated with substantial increases in Medicare payments.⁴ Journalists suggested the billion dollars more paid by Medicare to hospitals in 2010 than in 2005 were partly attributable to up-coding facilitated by EHRs.⁵ The secretary of the Department of Health and Human Services (DHHS) and the attorney general of the Department of Justice also weighed in on this controversy by expressing their concerns in a letter to the presidents of 5 hospital associations.⁶ The inspector general of DHHS also published a report critical of Medicare officials’ oversight of EHRs.⁷Responding to the critical reception of EHR implementations, investigators studied the validity of the early reports and anecdotes. Some initial reports cited the emergency department (ED) as an area at high risk for using the convenience of EHRs to up-code visits.⁵ The DHHS Office of the Inspector General noted that, between 2001 and 2010, the proportion of claims for lower reimbursement categories of American Medical Association Current Procedural Terminology (CPT) codes decreased while the proportion for higher-paid billing codes increased for all visit types.⁸ Addressing these concerns, the American Hospital Association⁹ issued a brief that noted that any observed coding increases were more likely attributable to more ED use by Medicare patients and increased average illness severity. In a thoughtful perspective, Pitts¹⁰ conceded that, though utilization and illness severity may explain part of the trend, the trend may also be related to technological innovations and changes in culture and practice style in the ED.

Because these studies and reports variously suggested that EHR implementation affects patient volume and up-coding, and because none of the reports specifically addressed orthopedics, we conducted a study to determine whether any significant up-coding or change in patient volumes occurred around the time of EHR implementation in ambulatory practices at our academic medical center. In a recent national study, Adler-Milstein and Jha¹¹ compared billing data of hospitals that adopted EHRs and hospitals that did not. Although both groups showed increased billing trends, the increases were not significantly different between the EHR adopters and nonadopters. To more effectively control for the confounding differences between groups of EHR adopters and nonadopters, we studied individual departments during EHR implementation at our institution.

Methods

In 2011, our academic medical center began the transition to EHRs (Epic). We examined our center’s trends in patient volumes and billing coding around the time of the transition in the outpatient practice of the general internal medicine (GIM) department (EHR transition, October 2011) and the outpatient practice of the orthopedics department (EHR transition, March 2012). These departments were chosen because they are representative of a GIM practice and a subspecialty practice, and because a recent study found that GIM practitioners and orthopedic surgeons were among those specialists who used EHRs the most.¹²

After this study was approved by our Human Investigations Committee, we began using CPT codes to identify all outpatient visits (new, consultation, and return) on a monthly basis. We compared the volume of patient visits and the billing coding level in the GIM and orthopedics departments before and after EHR implementation. Pearson χ² test was used when appropriate, and statistical analyses were performed with SPSS for Windows Version 16.0.

Results

 

In the GIM department, mean monthly volume of patient visits in the 12 months before EHR implementation was similar to that in the 12 months afterward (613 vs 587; P = .439). Even when normalized for changes in provider availability (maternity leave), the decrease in volume of patient visits after EHR implementation in the GIM department was not significant (6.9%; P = .107). Likewise, in the orthopedics department, mean monthly volume of patient visits in the 17 months before EHR implementation was similar to that in the 7 months afterward (2157 vs 2317; P = .156). In fact, patient volumes remained constant during the EHR transition (Figure 1).

EHR implementation brought small changes in billing coding levels. In the GIM department, the largest change was a 1.2% increase in level 4 billing coding—an increase accompanied by a 0.5% decrease in level 3 coding.

In the orthopedics department, the largest change was a 3.3% increase in level 4 coding—accompanied by a 3.1% decrease in level 3 coding (Figure 2). In both departments, these small changes across all levels represent minor but statistically significant shifts in billing coding levels (Pearson χ², P < .001) (Table).

Discussion

It is remarkable that the volumes of patient visits in the GIM and orthopedics departments at our academic center were not affected by EHR implementation.

Some EHR vendors have recommended decreasing patient scheduling by 10%, for 1 month after the transition, to adjust for providers’ learning curves; managers of an academic pediatric primary care center reported maintaining the 10% scheduling reduction for 3 months because of the prevalence of inconsistent EHR users in continuity clinics and transient users such as medical students and interns.¹³

Rather than reduce scheduling during the EHR transition, surgeons in our practice either added or lengthened clinic sessions, and the level of ancillary staffing was adjusted accordingly. As staffing costs at any given time are multifactorial and vary widely, estimating the cost of these staffing changes during the EHR transition is difficult. We should note that extending ancillary staff hours during the transition very likely increased costs, and it is unclear whether they were higher or lower than the costs that would have been incurred had we reduced scheduling or tried some combination of these strategies.

Although billing coding levels changed with EHR implementation, the changes were small. In the GIM department, level 4 CPT coded visits as percentages of all visits increased to 59.5% from 58.3%, and level 5 visits increased to 6.2% from 6.0%; in the orthopedics department, level 4 visits increased to 40.2% from 37.1%, and level 5 visits increased to 5.5% from 3.8% (Table). The 1.2% and 0.2% absolute increases in level 4 and level 5 visits in the GIM department represent 2.1% and 3.3% relative increases in level 4 and level 5 visits, and the 3.3% and 1.7% absolute increases in the orthopedics department represent 8.4% and 44.7% relative increases in level 4 and level 5 visits after EHR implementation.

Although the absolute increases in level 4 and level 5 visits were relatively minor, popular media have raised the alarm about 43% and 82% relative increases in level 5 visits after EHR implementation in some hospitals’ EDs.⁴ Although our orthopedics department showed a 44.7% relative increase in level 5 visits after EHR implementation, this represented an increase of only 1.7% of patient visits overall. Our findings therefore indicate that lay media reports could be misleading. Nevertheless, the small changes we found were statistically significant.

One explanation for these small changes is that EHRs facilitate better documentation of services provided. Therefore, what seem to be billing coding changes could be more accurate reports of high-level care that is the same as before. In addition, because of meaningful use mandates that coincided with the requirement to implement EHRs, additional data elements are now being consistently collected and reviewed (these may not necessarily have been collected and reviewed before). In some patient encounters, these additional data elements may have contributed to higher levels of service, and this effect could be especially apparent in EDs.

Some have suggested a potential for large-scale up-coding during EHR transitions. Others have contended that coding level increases are a consequence of a time-intensive data entry process, collection and review of additional data, and more accurate reporting of services already being provided. We are not convinced that large coding changes are attributable solely to EHR implementation, as the changes at our center have been relatively small.

Nevertheless, minor coding level changes could translate to large changes in healthcare costs when scaled nationally. Although causes may be innocuous, any increases in national healthcare costs are concerning in our time of limited budgets and scrutinized healthcare utilization.

This study had its limitations. First, including billing data from only 2 departments at a single center may limit the generalizability of findings. However, we specifically selected a GIM department and a specialty (orthopedics) department in an attempt to capture a representative sample of practices. Another limitation is that we investigated billing codes over only 2 years, around the implementation of EHRs in these departments, and therefore may have captured only short-term changes. However, as patient volumes and billing are subject to many factors, including staffing changes (eg, new partners, new hires, retirements, other departures), we attempted to limit the effect of confounding variables by limiting the period of analysis.

Overall, changes in patient volume and coded level of service during EHR implementation at our institution were relatively small. Although the trend toward higher billing coding levels was statistically significant, these 0.2% and 1.7% increases in level 5 coding hardly deserve the negative attention from lay media. These small increases are unlikely caused by intentional up-coding, and more likely reflect better documentation of an already high level of care. We hope these findings allay the concern that up-coding increased dramatically with EHR implementation.

Am J Orthop. 2017;46(3):E172-E176. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

 

Take-Home Points

• With EHR implementation there are small changes in the level of billing coding.
• Although these changes may be statistically significant they are relatively minor.
• In the general internal medicine department, level 4 coding increased by 1.2% while level 3 coding decreased by 0.5%.
• In the orthopedics department, level 4 coding increased by 3.3% while level 3 coding decreased by 3.1%.
• Reports in the lay media regarding dramatic up-coding after EHR implementation may be misleading.

The Health Information Technology for Economic and Clinical Health (HITECH) Act, which was signed into law in 2009, mandated that hospitals that care for Medicare patients either begin using electronic health records (EHRs) or pay a nontrivial penalty.¹ By now, the majority of orthopedic surgeons have implemented EHRs in their practices.² Despite ongoing debate in the orthopedic literature,³ EHRs are expected to improve coordination of care, reduce duplicate testing, and reduce costs over the long term as healthcare insurance coverage is extended to millions more Americans.

In early coverage, however, media reported that EHR implementation at some hospitals was correlated with substantial increases in Medicare payments.⁴ Journalists suggested the billion dollars more paid by Medicare to hospitals in 2010 than in 2005 were partly attributable to up-coding facilitated by EHRs.⁵ The secretary of the Department of Health and Human Services (DHHS) and the attorney general of the Department of Justice also weighed in on this controversy by expressing their concerns in a letter to the presidents of 5 hospital associations.⁶ The inspector general of DHHS also published a report critical of Medicare officials’ oversight of EHRs.⁷Responding to the critical reception of EHR implementations, investigators studied the validity of the early reports and anecdotes. Some initial reports cited the emergency department (ED) as an area at high risk for using the convenience of EHRs to up-code visits.⁵ The DHHS Office of the Inspector General noted that, between 2001 and 2010, the proportion of claims for lower reimbursement categories of American Medical Association Current Procedural Terminology (CPT) codes decreased while the proportion for higher-paid billing codes increased for all visit types.⁸ Addressing these concerns, the American Hospital Association⁹ issued a brief that noted that any observed coding increases were more likely attributable to more ED use by Medicare patients and increased average illness severity. In a thoughtful perspective, Pitts¹⁰ conceded that, though utilization and illness severity may explain part of the trend, the trend may also be related to technological innovations and changes in culture and practice style in the ED.

Because these studies and reports variously suggested that EHR implementation affects patient volume and up-coding, and because none of the reports specifically addressed orthopedics, we conducted a study to determine whether any significant up-coding or change in patient volumes occurred around the time of EHR implementation in ambulatory practices at our academic medical center. In a recent national study, Adler-Milstein and Jha¹¹ compared billing data of hospitals that adopted EHRs and hospitals that did not. Although both groups showed increased billing trends, the increases were not significantly different between the EHR adopters and nonadopters. To more effectively control for the confounding differences between groups of EHR adopters and nonadopters, we studied individual departments during EHR implementation at our institution.

Methods

In 2011, our academic medical center began the transition to EHRs (Epic). We examined our center’s trends in patient volumes and billing coding around the time of the transition in the outpatient practice of the general internal medicine (GIM) department (EHR transition, October 2011) and the outpatient practice of the orthopedics department (EHR transition, March 2012). These departments were chosen because they are representative of a GIM practice and a subspecialty practice, and because a recent study found that GIM practitioners and orthopedic surgeons were among those specialists who used EHRs the most.¹²

After this study was approved by our Human Investigations Committee, we began using CPT codes to identify all outpatient visits (new, consultation, and return) on a monthly basis. We compared the volume of patient visits and the billing coding level in the GIM and orthopedics departments before and after EHR implementation. Pearson χ² test was used when appropriate, and statistical analyses were performed with SPSS for Windows Version 16.0.

Results

 

In the GIM department, mean monthly volume of patient visits in the 12 months before EHR implementation was similar to that in the 12 months afterward (613 vs 587; P = .439). Even when normalized for changes in provider availability (maternity leave), the decrease in volume of patient visits after EHR implementation in the GIM department was not significant (6.9%; P = .107). Likewise, in the orthopedics department, mean monthly volume of patient visits in the 17 months before EHR implementation was similar to that in the 7 months afterward (2157 vs 2317; P = .156). In fact, patient volumes remained constant during the EHR transition (Figure 1).

EHR implementation brought small changes in billing coding levels. In the GIM department, the largest change was a 1.2% increase in level 4 billing coding—an increase accompanied by a 0.5% decrease in level 3 coding.

In the orthopedics department, the largest change was a 3.3% increase in level 4 coding—accompanied by a 3.1% decrease in level 3 coding (Figure 2). In both departments, these small changes across all levels represent minor but statistically significant shifts in billing coding levels (Pearson χ², P < .001) (Table).

Discussion

It is remarkable that the volumes of patient visits in the GIM and orthopedics departments at our academic center were not affected by EHR implementation.

Some EHR vendors have recommended decreasing patient scheduling by 10%, for 1 month after the transition, to adjust for providers’ learning curves; managers of an academic pediatric primary care center reported maintaining the 10% scheduling reduction for 3 months because of the prevalence of inconsistent EHR users in continuity clinics and transient users such as medical students and interns.¹³

Rather than reduce scheduling during the EHR transition, surgeons in our practice either added or lengthened clinic sessions, and the level of ancillary staffing was adjusted accordingly. As staffing costs at any given time are multifactorial and vary widely, estimating the cost of these staffing changes during the EHR transition is difficult. We should note that extending ancillary staff hours during the transition very likely increased costs, and it is unclear whether they were higher or lower than the costs that would have been incurred had we reduced scheduling or tried some combination of these strategies.

Although billing coding levels changed with EHR implementation, the changes were small. In the GIM department, level 4 CPT coded visits as percentages of all visits increased to 59.5% from 58.3%, and level 5 visits increased to 6.2% from 6.0%; in the orthopedics department, level 4 visits increased to 40.2% from 37.1%, and level 5 visits increased to 5.5% from 3.8% (Table). The 1.2% and 0.2% absolute increases in level 4 and level 5 visits in the GIM department represent 2.1% and 3.3% relative increases in level 4 and level 5 visits, and the 3.3% and 1.7% absolute increases in the orthopedics department represent 8.4% and 44.7% relative increases in level 4 and level 5 visits after EHR implementation.

Although the absolute increases in level 4 and level 5 visits were relatively minor, popular media have raised the alarm about 43% and 82% relative increases in level 5 visits after EHR implementation in some hospitals’ EDs.⁴ Although our orthopedics department showed a 44.7% relative increase in level 5 visits after EHR implementation, this represented an increase of only 1.7% of patient visits overall. Our findings therefore indicate that lay media reports could be misleading. Nevertheless, the small changes we found were statistically significant.

One explanation for these small changes is that EHRs facilitate better documentation of services provided. Therefore, what seem to be billing coding changes could be more accurate reports of high-level care that is the same as before. In addition, because of meaningful use mandates that coincided with the requirement to implement EHRs, additional data elements are now being consistently collected and reviewed (these may not necessarily have been collected and reviewed before). In some patient encounters, these additional data elements may have contributed to higher levels of service, and this effect could be especially apparent in EDs.

Some have suggested a potential for large-scale up-coding during EHR transitions. Others have contended that coding level increases are a consequence of a time-intensive data entry process, collection and review of additional data, and more accurate reporting of services already being provided. We are not convinced that large coding changes are attributable solely to EHR implementation, as the changes at our center have been relatively small.

Nevertheless, minor coding level changes could translate to large changes in healthcare costs when scaled nationally. Although causes may be innocuous, any increases in national healthcare costs are concerning in our time of limited budgets and scrutinized healthcare utilization.

This study had its limitations. First, including billing data from only 2 departments at a single center may limit the generalizability of findings. However, we specifically selected a GIM department and a specialty (orthopedics) department in an attempt to capture a representative sample of practices. Another limitation is that we investigated billing codes over only 2 years, around the implementation of EHRs in these departments, and therefore may have captured only short-term changes. However, as patient volumes and billing are subject to many factors, including staffing changes (eg, new partners, new hires, retirements, other departures), we attempted to limit the effect of confounding variables by limiting the period of analysis.

Overall, changes in patient volume and coded level of service during EHR implementation at our institution were relatively small. Although the trend toward higher billing coding levels was statistically significant, these 0.2% and 1.7% increases in level 5 coding hardly deserve the negative attention from lay media. These small increases are unlikely caused by intentional up-coding, and more likely reflect better documentation of an already high level of care. We hope these findings allay the concern that up-coding increased dramatically with EHR implementation.

Am J Orthop. 2017;46(3):E172-E176. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

 

Take-Home Points

• With EHR implementation there are small changes in the level of billing coding.
• Although these changes may be statistically significant they are relatively minor.
• In the general internal medicine department, level 4 coding increased by 1.2% while level 3 coding decreased by 0.5%.
• In the orthopedics department, level 4 coding increased by 3.3% while level 3 coding decreased by 3.1%.
• Reports in the lay media regarding dramatic up-coding after EHR implementation may be misleading.

The Health Information Technology for Economic and Clinical Health (HITECH) Act, which was signed into law in 2009, mandated that hospitals that care for Medicare patients either begin using electronic health records (EHRs) or pay a nontrivial penalty.¹ By now, the majority of orthopedic surgeons have implemented EHRs in their practices.² Despite ongoing debate in the orthopedic literature,³ EHRs are expected to improve coordination of care, reduce duplicate testing, and reduce costs over the long term as healthcare insurance coverage is extended to millions more Americans.

In early coverage, however, media reported that EHR implementation at some hospitals was correlated with substantial increases in Medicare payments.⁴ Journalists suggested the billion dollars more paid by Medicare to hospitals in 2010 than in 2005 were partly attributable to up-coding facilitated by EHRs.⁵ The secretary of the Department of Health and Human Services (DHHS) and the attorney general of the Department of Justice also weighed in on this controversy by expressing their concerns in a letter to the presidents of 5 hospital associations.⁶ The inspector general of DHHS also published a report critical of Medicare officials’ oversight of EHRs.⁷Responding to the critical reception of EHR implementations, investigators studied the validity of the early reports and anecdotes. Some initial reports cited the emergency department (ED) as an area at high risk for using the convenience of EHRs to up-code visits.⁵ The DHHS Office of the Inspector General noted that, between 2001 and 2010, the proportion of claims for lower reimbursement categories of American Medical Association Current Procedural Terminology (CPT) codes decreased while the proportion for higher-paid billing codes increased for all visit types.⁸ Addressing these concerns, the American Hospital Association⁹ issued a brief that noted that any observed coding increases were more likely attributable to more ED use by Medicare patients and increased average illness severity. In a thoughtful perspective, Pitts¹⁰ conceded that, though utilization and illness severity may explain part of the trend, the trend may also be related to technological innovations and changes in culture and practice style in the ED.

Because these studies and reports variously suggested that EHR implementation affects patient volume and up-coding, and because none of the reports specifically addressed orthopedics, we conducted a study to determine whether any significant up-coding or change in patient volumes occurred around the time of EHR implementation in ambulatory practices at our academic medical center. In a recent national study, Adler-Milstein and Jha¹¹ compared billing data of hospitals that adopted EHRs and hospitals that did not. Although both groups showed increased billing trends, the increases were not significantly different between the EHR adopters and nonadopters. To more effectively control for the confounding differences between groups of EHR adopters and nonadopters, we studied individual departments during EHR implementation at our institution.

Methods

In 2011, our academic medical center began the transition to EHRs (Epic). We examined our center’s trends in patient volumes and billing coding around the time of the transition in the outpatient practice of the general internal medicine (GIM) department (EHR transition, October 2011) and the outpatient practice of the orthopedics department (EHR transition, March 2012). These departments were chosen because they are representative of a GIM practice and a subspecialty practice, and because a recent study found that GIM practitioners and orthopedic surgeons were among those specialists who used EHRs the most.¹²

After this study was approved by our Human Investigations Committee, we began using CPT codes to identify all outpatient visits (new, consultation, and return) on a monthly basis. We compared the volume of patient visits and the billing coding level in the GIM and orthopedics departments before and after EHR implementation. Pearson χ² test was used when appropriate, and statistical analyses were performed with SPSS for Windows Version 16.0.

Results

 

In the GIM department, mean monthly volume of patient visits in the 12 months before EHR implementation was similar to that in the 12 months afterward (613 vs 587; P = .439). Even when normalized for changes in provider availability (maternity leave), the decrease in volume of patient visits after EHR implementation in the GIM department was not significant (6.9%; P = .107). Likewise, in the orthopedics department, mean monthly volume of patient visits in the 17 months before EHR implementation was similar to that in the 7 months afterward (2157 vs 2317; P = .156). In fact, patient volumes remained constant during the EHR transition (Figure 1).

EHR implementation brought small changes in billing coding levels. In the GIM department, the largest change was a 1.2% increase in level 4 billing coding—an increase accompanied by a 0.5% decrease in level 3 coding.

In the orthopedics department, the largest change was a 3.3% increase in level 4 coding—accompanied by a 3.1% decrease in level 3 coding (Figure 2). In both departments, these small changes across all levels represent minor but statistically significant shifts in billing coding levels (Pearson χ², P < .001) (Table).

Discussion

It is remarkable that the volumes of patient visits in the GIM and orthopedics departments at our academic center were not affected by EHR implementation.

Some EHR vendors have recommended decreasing patient scheduling by 10%, for 1 month after the transition, to adjust for providers’ learning curves; managers of an academic pediatric primary care center reported maintaining the 10% scheduling reduction for 3 months because of the prevalence of inconsistent EHR users in continuity clinics and transient users such as medical students and interns.¹³

Rather than reduce scheduling during the EHR transition, surgeons in our practice either added or lengthened clinic sessions, and the level of ancillary staffing was adjusted accordingly. As staffing costs at any given time are multifactorial and vary widely, estimating the cost of these staffing changes during the EHR transition is difficult. We should note that extending ancillary staff hours during the transition very likely increased costs, and it is unclear whether they were higher or lower than the costs that would have been incurred had we reduced scheduling or tried some combination of these strategies.

Although billing coding levels changed with EHR implementation, the changes were small. In the GIM department, level 4 CPT coded visits as percentages of all visits increased to 59.5% from 58.3%, and level 5 visits increased to 6.2% from 6.0%; in the orthopedics department, level 4 visits increased to 40.2% from 37.1%, and level 5 visits increased to 5.5% from 3.8% (Table). The 1.2% and 0.2% absolute increases in level 4 and level 5 visits in the GIM department represent 2.1% and 3.3% relative increases in level 4 and level 5 visits, and the 3.3% and 1.7% absolute increases in the orthopedics department represent 8.4% and 44.7% relative increases in level 4 and level 5 visits after EHR implementation.

Although the absolute increases in level 4 and level 5 visits were relatively minor, popular media have raised the alarm about 43% and 82% relative increases in level 5 visits after EHR implementation in some hospitals’ EDs.⁴ Although our orthopedics department showed a 44.7% relative increase in level 5 visits after EHR implementation, this represented an increase of only 1.7% of patient visits overall. Our findings therefore indicate that lay media reports could be misleading. Nevertheless, the small changes we found were statistically significant.

One explanation for these small changes is that EHRs facilitate better documentation of services provided. Therefore, what seem to be billing coding changes could be more accurate reports of high-level care that is the same as before. In addition, because of meaningful use mandates that coincided with the requirement to implement EHRs, additional data elements are now being consistently collected and reviewed (these may not necessarily have been collected and reviewed before). In some patient encounters, these additional data elements may have contributed to higher levels of service, and this effect could be especially apparent in EDs.

Some have suggested a potential for large-scale up-coding during EHR transitions. Others have contended that coding level increases are a consequence of a time-intensive data entry process, collection and review of additional data, and more accurate reporting of services already being provided. We are not convinced that large coding changes are attributable solely to EHR implementation, as the changes at our center have been relatively small.

Nevertheless, minor coding level changes could translate to large changes in healthcare costs when scaled nationally. Although causes may be innocuous, any increases in national healthcare costs are concerning in our time of limited budgets and scrutinized healthcare utilization.

This study had its limitations. First, including billing data from only 2 departments at a single center may limit the generalizability of findings. However, we specifically selected a GIM department and a specialty (orthopedics) department in an attempt to capture a representative sample of practices. Another limitation is that we investigated billing codes over only 2 years, around the implementation of EHRs in these departments, and therefore may have captured only short-term changes. However, as patient volumes and billing are subject to many factors, including staffing changes (eg, new partners, new hires, retirements, other departures), we attempted to limit the effect of confounding variables by limiting the period of analysis.

Overall, changes in patient volume and coded level of service during EHR implementation at our institution were relatively small. Although the trend toward higher billing coding levels was statistically significant, these 0.2% and 1.7% increases in level 5 coding hardly deserve the negative attention from lay media. These small increases are unlikely caused by intentional up-coding, and more likely reflect better documentation of an already high level of care. We hope these findings allay the concern that up-coding increased dramatically with EHR implementation.

Am J Orthop. 2017;46(3):E172-E176. Copyright Frontline Medical Communications Inc. 2017. All rights reserved.

 

The number of patients who undergo total knee arthroplasty (TKA) and total hip arthroplasty (THA) procedures has significantly increased over the past 2 to 3 decades. As life expectancy in the U.S. increases and medical advances allow patients with preexisting conditions to successfully undergo joint replacements, the demand for these procedures is expected to grow.¹ In 2010, the CDC estimated that 719,000 patients underwent TKA procedures and 332,000 patients underwent THA procedures in the U.S.² Kurtz and colleagues have projected that by 2030 the annual number of TKA procedures will increase to 3.5 million and the number of THA procedures will increase to 572,000.¹

Although becoming more prevalent, these procedures are still associated with considerable intra- and postoperative blood loss that may lead to complications and blood transfusions.³ Previous studies have shown that perioperative anemia and red blood cell transfusions are associated with negative outcomes, including increased health care resource utilization; length of hospitalization; pulmonary, septic, wound, or thromboembolic complications; and mortality.^4,5

In order to prevent excessive blood loss during TKA and THA procedures, antifibrinolytic agents, such as tranexamic acid, have been used. Tranexamic acid is a synthetic form of lysine that binds to the lysine-binding sites on plasminogen and slows the conversion of plasminogen to plasmin. This interaction inhibits fibrinolysis and theoretically decreases bleeding.⁶ Because tranexamic acid is an antifibrinolytic, its mechanism of action has raised concerns that it could increase the risk of clotting complications, such as venous thromboembolism and myocardial infarction.⁷

Several published meta-analyses and systematic reviews have shown that tranexamic acid reduces blood loss in patients undergoing orthopedic surgery. Despite positive results, many study authors have acknowledged limitations in their analyses, such as heterogeneity of study results, small trial size, and varied dosing strategies.^3,7-12 There is no FDA-approved tranexamic acid dosing strategy for orthopedic procedures; therefore, its use in TKA and THA procedures is off label. This lack of guidance results in the medication being used at varied doses, timing of doses, and routes of administration with no clear dosing strategy showing the best outcomes.

Tranexamic acid was first used in TKA and THA surgical procedures at the Sioux Falls VA Health Care System (SFVAHCS) in South Dakota in October 2012. The dose used during these procedures was 1 g IV at first surgical incision and 1 g IV at incision closure. The objective of this study was to determine whether this tranexamic acid dosing strategy utilized at SFVAHCS safely improved outcomes related to blood loss.

Methods

A single-center retrospective chart review was performed on all patients who underwent TKA and THA procedures by 4 orthopedic surgeons between January 2010 and August 2015 at SFVAHCS. This study received approval by the local institutional review board and research and development committee in September 2015.

Patients were included in the study if they were aged ≥ 18 years and underwent primary unilateral TKA or THA procedures during the study time frame. Patients were excluded if they underwent bilateral or revision TKA or THA procedures, did not have recorded blood loss measurements during and/or after the procedure, or did not receive tranexamic acid between October 2012 and August 2015 at the standard dosing strategy utilized at SFVAHCS.

Patients who underwent surgery between January 2010 and October 2012 and did not receive tranexamic acid were included in the control groups. The treatment groups contained patients who underwent surgery between October 2012 and August 2015 and received tranexamic acid at the standard SFVAHCS dosing strategy. Patients in the control and treatment groups were divided and compared with patients who underwent the same type of surgery.

The primary endpoint of this study was total blood loss, which included intraoperative and postoperative blood loss. Intraoperative blood loss was measured by a suctioning device that the surgical physician’s assistant used to keep the surgical site clear of bodily fluids. The suctioning device collected blood as well as irrigation fluids used throughout the surgical procedure. The volume of irrigation fluids used during the procedure was subtracted from the total volume collected by the suctioning device to estimate the total blood volume lost during surgery. Sponges and other surgical materials that may have collected blood were not included in the intraoperative blood loss calculation. Postoperative blood loss was collected by a drain that was placed in the surgical site prior to incision closure. The drain collected postoperative blood loss until it was removed 1 day after surgery.

The secondary endpoints for the study were changes in hemoglobin (Hgb) and hematocrit (Hct) from before surgery to after surgery. These changes were calculated by subtracting the lowest measured postoperative Hgb or Hct level within 21 days postsurgery from the closest measured Hgb or Hct level obtained presurgery.

Follow-up appointments were routinely conducted 2 weeks after surgery, so the 21-day time frame would include any laboratory results drawn at these appointments. Other secondary endpoints included the number of patients receiving at least 1 blood transfusion during hospitalization and the number of patients experiencing clotting complications within 30 days of surgery. Postoperative and progress notes were reviewed by a single study investigator in order to record blood transfusions and clotting complications.

All patients who underwent TKA or THA procedures were instructed to stop taking antiplatelet agents 7 days prior to surgery and warfarin 5 days prior to surgery. If patients were determined to be at high risk for thromboembolic complications following warfarin discontinuation, therapeutic doses of low-molecular weight heparin or unfractionated heparin were used as a bridging therapy pre- and postsurgery. Enoxaparin 30 mg twice daily was started the day after surgery in all patients not previously on warfarin therapy prior to surgery to prevent clotting complications. If a patient was on warfarin therapy prior to the procedure but not considered to be at high risk of thromboembolic complications by the surgeon, warfarin was restarted after surgery, and enoxaparin 30 mg twice daily was used until therapeutic international normalized ratio values were obtained. If the patient had ongoing bleeding after the procedure or was determined to be at high risk for bleeding complications, the provider may have delayed anticoagulant use.

Some patients who underwent TKA or THA procedures during the study time frame received periarticular pain injections during surgery. These pain injections included a combination of ropivacaine 200 mg, ketorolac 30 mg, epinephrine 0.5 mg, and clonidine 0.08 mg and were compounded in a sterile mixture with normal saline. Several injections of this mixture were administered into the surgical site to reduce postoperative pain. These periarticular pain injections were first implemented into TKA and THA procedures in August 2012 and were used in patients at the surgeon’s discretion.

Baseline characteristics were analyzed using a chi-square test for categoric variables and an unpaired t test for continuous variables to determine whether any differences were present. Total blood loss, change in Hgb, and change in Hct were analyzed using an unpaired t test. Patients receiving at least 1 blood transfusion during hospitalization and patients experiencing a clotting complication were analyzed using a chi-square test. P values < .05 were considered to indicate statistical significance. Descriptive statistics were calculated using Microsoft Excel (Redmond, WA), and GraphPad Prism (La Jolla, CA) was used for all statistical analyses.

 

Results

Initially, a total of 443 TKA patients and 111 THA patients were reviewed. Of these patients, 418 TKA patients and 100 THA patients met the inclusion criteria. Due to the retrospective design of this study, not all of the baseline characteristics were equal between groups (Table 1). Most notably, the number of patients who received the periarticular pain injection and the distribution of surgeons performing the procedures were different between groups in both the TKA and THA procedures.

Baseline Hgb levels were not found to be different between groups in either type of procedure; however, the baseline Hct levels of patients undergoing TKA who received tranexamic acid were found to be statistically higher when compared with those who did not receive tranexamic acid. Other baseline characteristics with statistically higher values included average weight and BMI in patients who received tranexamic acid and underwent THA and serum creatinine in patients who did not receive tranexamic acid and underwent TKA.

In the primary analysis (Tables 2 and 3), the mean estimated total blood loss in TKA patients was lower in patients who received tranexamic acid than it was in the control group (339.4 mL vs 457.4 mL, P < .001). Patients who underwent THA receiving tranexamic acid similarly had significantly less total blood loss than that of the control group (419.7 mL vs 585.7 mL, P < .001). Consistent with previous studies, patients undergoing TKA procedures in the treatment group when compared to the control group, respectively, were likely to have more blood loss postoperatively (275.9 mL vs 399.7 mL) than intraoperatively (63.5 mL vs 57.7 mL) regardless of tranexamic acid administration.⁶ On the other hand, patients who had undergone THA were more likely to experience more intraoperative blood loss (281.6 mL in treatment group vs 328.9 mL in control group) than postoperative blood loss (138.1 mL in treatment group vs 256.8 mL in control group) regardless of tranexamic acid administration.

In the secondary analysis, the change between preoperative and postoperative Hgb and Hct had results consistent with the total blood loss results. Patients receiving tranexamic acid in TKA procedures had a lower decrease in Hgb compared with the control group (3.3 mg/dL vs 4.0 mg/dL, P < .001). Similarly, patients undergoing TKA who received tranexamic acid had a smaller decrease in Hct than that of the control group (9.4% vs 11.1%, P < .001). Consistent with the TKA procedure results, patients undergoing THA who received tranexamic acid had a smaller decrease in Hgb (3.6 mg/dL vs 4.7 mg/dL, P < .001) and Hct (10.5% vs 13.0%, P = .0012) than that of the control group.

Patients who did not receive tranexamic acid were more likely to require at least 1 blood transfusion than were patients who received tranexamic acid in TKA (14 patients vs 0 patients, P = .0005) and THA procedures (8 patients vs 2 patients, P = .019). Despite the theoretically increased likelihood of clotting complications with tranexamic acid, no significant differences were observed between the treatment and control groups in either TKA (0 vs 4, P = .065) or THA (1 vs 0, P = .363) procedures.

Discussion

Patients undergoing total joint arthroplasty are at risk for significant blood loss with a potential need for postoperative blood transfusions.^6,13 The use of tranexamic acid during these procedures offers a possible solution to decrease blood loss and minimize blood transfusions. This study retrospectively evaluated whether tranexamic acid safely decreased blood loss and the need for blood transfusions at SFVAHCS with the dosing strategy of 1 g IV at first incision and 1 g IV at incision closure.

Patients who received tranexamic acid in TKA and THA procedures had significantly less blood loss than did patients who did not receive tranexamic acid. Patients receiving tranexamic acid also had a significantly lower change from preoperative to postoperative Hgb and Hct levels than did patients who did not receive tranexamic acid in TKA and THA procedures. In addition to decreasing blood loss, the tranexamic acid groups had significantly fewer patients who required blood transfusions than that of the control groups.

This reduction in blood transfusions should be considered clinically significant. Blood transfusions require substantial health care resource utilization and may cause complications in recipients.³ There was no clear association between tranexamic acid and clotting complications in this study, which may suggest that this risk is theoretical. However, larger prospective studies would be needed to further examine this potential risk.

Even though baseline Hct levels were found to be significantly different between the tranexamic acid group and the control group for patients who underwent TKA, medical record documentation indicated that the determination whether postoperative blood transfusions were needed was based primarily on Hgb levels. Baseline Hgb levels were found not to be significantly different between the tranexamic acid and control groups for either TKA or THA procedures. This suggests that at baseline the tranexamic acid and control groups had the same risk of reaching the Hgb threshold where blood transfusions would be required.

There was a significant difference in the proportion of patients receiving the periarticular pain injection of ropivacaine, ketorolac, epinephrine, and clonidine between the groups who received tranexamic acid and those who did not. Originally, this baseline characteristic was theorized to be a major confounder in the primary and secondary analyses because epinephrine is a vasoconstrictor and ketorolac is a reversible antiplatelet agent. However, Vendittoli and colleagues showed that patients receiving these periarticular pain injections during surgery actually had greater total blood loss than did patients who did not receive the injections, although this comparison did not reach statistical significance.¹⁴ The Vendittoli and colleagues results suggest that the pain injections did not confound the primary and secondary analyses by aiding in the process of reducing blood loss in these procedures.

 

Limitations

There are several limitations for extrapolating the results from this study to the general population. Due to the retrospective study design, there was no way to actively control potentially confounding variables during the TKA and THA procedures. Surgeons and surgery teams likely had slightly different techniques and protocols during and after surgery. Several baseline characteristics were not equal between patients who received tranexamic acid and those who did not. Therefore, it is unknown whether these baseline characteristics affected the results of this study. Postoperative anticoagulant use was not recorded and may have differed between study groups, depending on the patient’s risk of thromboembolic complications; however, the drains that collected blood loss were removed prior to the first dose of enoxaparin, which was administered the day after surgery.

Another limitation is that the method of measuring blood loss during and after the procedure was imprecise. Blood not suctioned through the suctioning device during surgery or not collected in the drain after surgery was not measured and may have increased the total blood loss. Hemoglobin and Hct levels also are sensitive to intravascular volume changes. If a patient required more IV fluids during or after a procedure, the fluids may have lowered the Hgb and/or Hct levels by dilution.

Conclusion

This study suggests that using tranexamic acid at a dose of 1 g IV at first incision and 1 g IV at incision closure safely and effectively reduced blood loss and the need for transfusions in patients undergoing TKA and THA procedures at SFVAHCS. Further prospective studies are needed to compare different tranexamic dosing strategies to minimize blood loss during these procedures. ˜

Acknowledgments
This study is the result of work supported with resources and the use of facilities at the Sioux Falls VA Health Care System in South Dakota.

The number of patients who undergo total knee arthroplasty (TKA) and total hip arthroplasty (THA) procedures has significantly increased over the past 2 to 3 decades. As life expectancy in the U.S. increases and medical advances allow patients with preexisting conditions to successfully undergo joint replacements, the demand for these procedures is expected to grow.¹ In 2010, the CDC estimated that 719,000 patients underwent TKA procedures and 332,000 patients underwent THA procedures in the U.S.² Kurtz and colleagues have projected that by 2030 the annual number of TKA procedures will increase to 3.5 million and the number of THA procedures will increase to 572,000.¹

Although becoming more prevalent, these procedures are still associated with considerable intra- and postoperative blood loss that may lead to complications and blood transfusions.³ Previous studies have shown that perioperative anemia and red blood cell transfusions are associated with negative outcomes, including increased health care resource utilization; length of hospitalization; pulmonary, septic, wound, or thromboembolic complications; and mortality.^4,5

In order to prevent excessive blood loss during TKA and THA procedures, antifibrinolytic agents, such as tranexamic acid, have been used. Tranexamic acid is a synthetic form of lysine that binds to the lysine-binding sites on plasminogen and slows the conversion of plasminogen to plasmin. This interaction inhibits fibrinolysis and theoretically decreases bleeding.⁶ Because tranexamic acid is an antifibrinolytic, its mechanism of action has raised concerns that it could increase the risk of clotting complications, such as venous thromboembolism and myocardial infarction.⁷

Several published meta-analyses and systematic reviews have shown that tranexamic acid reduces blood loss in patients undergoing orthopedic surgery. Despite positive results, many study authors have acknowledged limitations in their analyses, such as heterogeneity of study results, small trial size, and varied dosing strategies.^3,7-12 There is no FDA-approved tranexamic acid dosing strategy for orthopedic procedures; therefore, its use in TKA and THA procedures is off label. This lack of guidance results in the medication being used at varied doses, timing of doses, and routes of administration with no clear dosing strategy showing the best outcomes.

Tranexamic acid was first used in TKA and THA surgical procedures at the Sioux Falls VA Health Care System (SFVAHCS) in South Dakota in October 2012. The dose used during these procedures was 1 g IV at first surgical incision and 1 g IV at incision closure. The objective of this study was to determine whether this tranexamic acid dosing strategy utilized at SFVAHCS safely improved outcomes related to blood loss.

Methods

A single-center retrospective chart review was performed on all patients who underwent TKA and THA procedures by 4 orthopedic surgeons between January 2010 and August 2015 at SFVAHCS. This study received approval by the local institutional review board and research and development committee in September 2015.

Patients were included in the study if they were aged ≥ 18 years and underwent primary unilateral TKA or THA procedures during the study time frame. Patients were excluded if they underwent bilateral or revision TKA or THA procedures, did not have recorded blood loss measurements during and/or after the procedure, or did not receive tranexamic acid between October 2012 and August 2015 at the standard dosing strategy utilized at SFVAHCS.

Patients who underwent surgery between January 2010 and October 2012 and did not receive tranexamic acid were included in the control groups. The treatment groups contained patients who underwent surgery between October 2012 and August 2015 and received tranexamic acid at the standard SFVAHCS dosing strategy. Patients in the control and treatment groups were divided and compared with patients who underwent the same type of surgery.

The primary endpoint of this study was total blood loss, which included intraoperative and postoperative blood loss. Intraoperative blood loss was measured by a suctioning device that the surgical physician’s assistant used to keep the surgical site clear of bodily fluids. The suctioning device collected blood as well as irrigation fluids used throughout the surgical procedure. The volume of irrigation fluids used during the procedure was subtracted from the total volume collected by the suctioning device to estimate the total blood volume lost during surgery. Sponges and other surgical materials that may have collected blood were not included in the intraoperative blood loss calculation. Postoperative blood loss was collected by a drain that was placed in the surgical site prior to incision closure. The drain collected postoperative blood loss until it was removed 1 day after surgery.

The secondary endpoints for the study were changes in hemoglobin (Hgb) and hematocrit (Hct) from before surgery to after surgery. These changes were calculated by subtracting the lowest measured postoperative Hgb or Hct level within 21 days postsurgery from the closest measured Hgb or Hct level obtained presurgery.

Follow-up appointments were routinely conducted 2 weeks after surgery, so the 21-day time frame would include any laboratory results drawn at these appointments. Other secondary endpoints included the number of patients receiving at least 1 blood transfusion during hospitalization and the number of patients experiencing clotting complications within 30 days of surgery. Postoperative and progress notes were reviewed by a single study investigator in order to record blood transfusions and clotting complications.

All patients who underwent TKA or THA procedures were instructed to stop taking antiplatelet agents 7 days prior to surgery and warfarin 5 days prior to surgery. If patients were determined to be at high risk for thromboembolic complications following warfarin discontinuation, therapeutic doses of low-molecular weight heparin or unfractionated heparin were used as a bridging therapy pre- and postsurgery. Enoxaparin 30 mg twice daily was started the day after surgery in all patients not previously on warfarin therapy prior to surgery to prevent clotting complications. If a patient was on warfarin therapy prior to the procedure but not considered to be at high risk of thromboembolic complications by the surgeon, warfarin was restarted after surgery, and enoxaparin 30 mg twice daily was used until therapeutic international normalized ratio values were obtained. If the patient had ongoing bleeding after the procedure or was determined to be at high risk for bleeding complications, the provider may have delayed anticoagulant use.

Some patients who underwent TKA or THA procedures during the study time frame received periarticular pain injections during surgery. These pain injections included a combination of ropivacaine 200 mg, ketorolac 30 mg, epinephrine 0.5 mg, and clonidine 0.08 mg and were compounded in a sterile mixture with normal saline. Several injections of this mixture were administered into the surgical site to reduce postoperative pain. These periarticular pain injections were first implemented into TKA and THA procedures in August 2012 and were used in patients at the surgeon’s discretion.

Baseline characteristics were analyzed using a chi-square test for categoric variables and an unpaired t test for continuous variables to determine whether any differences were present. Total blood loss, change in Hgb, and change in Hct were analyzed using an unpaired t test. Patients receiving at least 1 blood transfusion during hospitalization and patients experiencing a clotting complication were analyzed using a chi-square test. P values < .05 were considered to indicate statistical significance. Descriptive statistics were calculated using Microsoft Excel (Redmond, WA), and GraphPad Prism (La Jolla, CA) was used for all statistical analyses.

 

Results

Initially, a total of 443 TKA patients and 111 THA patients were reviewed. Of these patients, 418 TKA patients and 100 THA patients met the inclusion criteria. Due to the retrospective design of this study, not all of the baseline characteristics were equal between groups (Table 1). Most notably, the number of patients who received the periarticular pain injection and the distribution of surgeons performing the procedures were different between groups in both the TKA and THA procedures.

Baseline Hgb levels were not found to be different between groups in either type of procedure; however, the baseline Hct levels of patients undergoing TKA who received tranexamic acid were found to be statistically higher when compared with those who did not receive tranexamic acid. Other baseline characteristics with statistically higher values included average weight and BMI in patients who received tranexamic acid and underwent THA and serum creatinine in patients who did not receive tranexamic acid and underwent TKA.

In the primary analysis (Tables 2 and 3), the mean estimated total blood loss in TKA patients was lower in patients who received tranexamic acid than it was in the control group (339.4 mL vs 457.4 mL, P < .001). Patients who underwent THA receiving tranexamic acid similarly had significantly less total blood loss than that of the control group (419.7 mL vs 585.7 mL, P < .001). Consistent with previous studies, patients undergoing TKA procedures in the treatment group when compared to the control group, respectively, were likely to have more blood loss postoperatively (275.9 mL vs 399.7 mL) than intraoperatively (63.5 mL vs 57.7 mL) regardless of tranexamic acid administration.⁶ On the other hand, patients who had undergone THA were more likely to experience more intraoperative blood loss (281.6 mL in treatment group vs 328.9 mL in control group) than postoperative blood loss (138.1 mL in treatment group vs 256.8 mL in control group) regardless of tranexamic acid administration.

In the secondary analysis, the change between preoperative and postoperative Hgb and Hct had results consistent with the total blood loss results. Patients receiving tranexamic acid in TKA procedures had a lower decrease in Hgb compared with the control group (3.3 mg/dL vs 4.0 mg/dL, P < .001). Similarly, patients undergoing TKA who received tranexamic acid had a smaller decrease in Hct than that of the control group (9.4% vs 11.1%, P < .001). Consistent with the TKA procedure results, patients undergoing THA who received tranexamic acid had a smaller decrease in Hgb (3.6 mg/dL vs 4.7 mg/dL, P < .001) and Hct (10.5% vs 13.0%, P = .0012) than that of the control group.

Patients who did not receive tranexamic acid were more likely to require at least 1 blood transfusion than were patients who received tranexamic acid in TKA (14 patients vs 0 patients, P = .0005) and THA procedures (8 patients vs 2 patients, P = .019). Despite the theoretically increased likelihood of clotting complications with tranexamic acid, no significant differences were observed between the treatment and control groups in either TKA (0 vs 4, P = .065) or THA (1 vs 0, P = .363) procedures.

Discussion

Patients undergoing total joint arthroplasty are at risk for significant blood loss with a potential need for postoperative blood transfusions.^6,13 The use of tranexamic acid during these procedures offers a possible solution to decrease blood loss and minimize blood transfusions. This study retrospectively evaluated whether tranexamic acid safely decreased blood loss and the need for blood transfusions at SFVAHCS with the dosing strategy of 1 g IV at first incision and 1 g IV at incision closure.

Patients who received tranexamic acid in TKA and THA procedures had significantly less blood loss than did patients who did not receive tranexamic acid. Patients receiving tranexamic acid also had a significantly lower change from preoperative to postoperative Hgb and Hct levels than did patients who did not receive tranexamic acid in TKA and THA procedures. In addition to decreasing blood loss, the tranexamic acid groups had significantly fewer patients who required blood transfusions than that of the control groups.

This reduction in blood transfusions should be considered clinically significant. Blood transfusions require substantial health care resource utilization and may cause complications in recipients.³ There was no clear association between tranexamic acid and clotting complications in this study, which may suggest that this risk is theoretical. However, larger prospective studies would be needed to further examine this potential risk.

Even though baseline Hct levels were found to be significantly different between the tranexamic acid group and the control group for patients who underwent TKA, medical record documentation indicated that the determination whether postoperative blood transfusions were needed was based primarily on Hgb levels. Baseline Hgb levels were found not to be significantly different between the tranexamic acid and control groups for either TKA or THA procedures. This suggests that at baseline the tranexamic acid and control groups had the same risk of reaching the Hgb threshold where blood transfusions would be required.

There was a significant difference in the proportion of patients receiving the periarticular pain injection of ropivacaine, ketorolac, epinephrine, and clonidine between the groups who received tranexamic acid and those who did not. Originally, this baseline characteristic was theorized to be a major confounder in the primary and secondary analyses because epinephrine is a vasoconstrictor and ketorolac is a reversible antiplatelet agent. However, Vendittoli and colleagues showed that patients receiving these periarticular pain injections during surgery actually had greater total blood loss than did patients who did not receive the injections, although this comparison did not reach statistical significance.¹⁴ The Vendittoli and colleagues results suggest that the pain injections did not confound the primary and secondary analyses by aiding in the process of reducing blood loss in these procedures.

 

Limitations

There are several limitations for extrapolating the results from this study to the general population. Due to the retrospective study design, there was no way to actively control potentially confounding variables during the TKA and THA procedures. Surgeons and surgery teams likely had slightly different techniques and protocols during and after surgery. Several baseline characteristics were not equal between patients who received tranexamic acid and those who did not. Therefore, it is unknown whether these baseline characteristics affected the results of this study. Postoperative anticoagulant use was not recorded and may have differed between study groups, depending on the patient’s risk of thromboembolic complications; however, the drains that collected blood loss were removed prior to the first dose of enoxaparin, which was administered the day after surgery.

Another limitation is that the method of measuring blood loss during and after the procedure was imprecise. Blood not suctioned through the suctioning device during surgery or not collected in the drain after surgery was not measured and may have increased the total blood loss. Hemoglobin and Hct levels also are sensitive to intravascular volume changes. If a patient required more IV fluids during or after a procedure, the fluids may have lowered the Hgb and/or Hct levels by dilution.

Conclusion

This study suggests that using tranexamic acid at a dose of 1 g IV at first incision and 1 g IV at incision closure safely and effectively reduced blood loss and the need for transfusions in patients undergoing TKA and THA procedures at SFVAHCS. Further prospective studies are needed to compare different tranexamic dosing strategies to minimize blood loss during these procedures. ˜

Acknowledgments
This study is the result of work supported with resources and the use of facilities at the Sioux Falls VA Health Care System in South Dakota.

The number of patients who undergo total knee arthroplasty (TKA) and total hip arthroplasty (THA) procedures has significantly increased over the past 2 to 3 decades. As life expectancy in the U.S. increases and medical advances allow patients with preexisting conditions to successfully undergo joint replacements, the demand for these procedures is expected to grow.¹ In 2010, the CDC estimated that 719,000 patients underwent TKA procedures and 332,000 patients underwent THA procedures in the U.S.² Kurtz and colleagues have projected that by 2030 the annual number of TKA procedures will increase to 3.5 million and the number of THA procedures will increase to 572,000.¹

Although becoming more prevalent, these procedures are still associated with considerable intra- and postoperative blood loss that may lead to complications and blood transfusions.³ Previous studies have shown that perioperative anemia and red blood cell transfusions are associated with negative outcomes, including increased health care resource utilization; length of hospitalization; pulmonary, septic, wound, or thromboembolic complications; and mortality.^4,5

In order to prevent excessive blood loss during TKA and THA procedures, antifibrinolytic agents, such as tranexamic acid, have been used. Tranexamic acid is a synthetic form of lysine that binds to the lysine-binding sites on plasminogen and slows the conversion of plasminogen to plasmin. This interaction inhibits fibrinolysis and theoretically decreases bleeding.⁶ Because tranexamic acid is an antifibrinolytic, its mechanism of action has raised concerns that it could increase the risk of clotting complications, such as venous thromboembolism and myocardial infarction.⁷

Several published meta-analyses and systematic reviews have shown that tranexamic acid reduces blood loss in patients undergoing orthopedic surgery. Despite positive results, many study authors have acknowledged limitations in their analyses, such as heterogeneity of study results, small trial size, and varied dosing strategies.^3,7-12 There is no FDA-approved tranexamic acid dosing strategy for orthopedic procedures; therefore, its use in TKA and THA procedures is off label. This lack of guidance results in the medication being used at varied doses, timing of doses, and routes of administration with no clear dosing strategy showing the best outcomes.

Tranexamic acid was first used in TKA and THA surgical procedures at the Sioux Falls VA Health Care System (SFVAHCS) in South Dakota in October 2012. The dose used during these procedures was 1 g IV at first surgical incision and 1 g IV at incision closure. The objective of this study was to determine whether this tranexamic acid dosing strategy utilized at SFVAHCS safely improved outcomes related to blood loss.

Methods

A single-center retrospective chart review was performed on all patients who underwent TKA and THA procedures by 4 orthopedic surgeons between January 2010 and August 2015 at SFVAHCS. This study received approval by the local institutional review board and research and development committee in September 2015.

Patients were included in the study if they were aged ≥ 18 years and underwent primary unilateral TKA or THA procedures during the study time frame. Patients were excluded if they underwent bilateral or revision TKA or THA procedures, did not have recorded blood loss measurements during and/or after the procedure, or did not receive tranexamic acid between October 2012 and August 2015 at the standard dosing strategy utilized at SFVAHCS.

Patients who underwent surgery between January 2010 and October 2012 and did not receive tranexamic acid were included in the control groups. The treatment groups contained patients who underwent surgery between October 2012 and August 2015 and received tranexamic acid at the standard SFVAHCS dosing strategy. Patients in the control and treatment groups were divided and compared with patients who underwent the same type of surgery.

The primary endpoint of this study was total blood loss, which included intraoperative and postoperative blood loss. Intraoperative blood loss was measured by a suctioning device that the surgical physician’s assistant used to keep the surgical site clear of bodily fluids. The suctioning device collected blood as well as irrigation fluids used throughout the surgical procedure. The volume of irrigation fluids used during the procedure was subtracted from the total volume collected by the suctioning device to estimate the total blood volume lost during surgery. Sponges and other surgical materials that may have collected blood were not included in the intraoperative blood loss calculation. Postoperative blood loss was collected by a drain that was placed in the surgical site prior to incision closure. The drain collected postoperative blood loss until it was removed 1 day after surgery.

The secondary endpoints for the study were changes in hemoglobin (Hgb) and hematocrit (Hct) from before surgery to after surgery. These changes were calculated by subtracting the lowest measured postoperative Hgb or Hct level within 21 days postsurgery from the closest measured Hgb or Hct level obtained presurgery.

Follow-up appointments were routinely conducted 2 weeks after surgery, so the 21-day time frame would include any laboratory results drawn at these appointments. Other secondary endpoints included the number of patients receiving at least 1 blood transfusion during hospitalization and the number of patients experiencing clotting complications within 30 days of surgery. Postoperative and progress notes were reviewed by a single study investigator in order to record blood transfusions and clotting complications.

All patients who underwent TKA or THA procedures were instructed to stop taking antiplatelet agents 7 days prior to surgery and warfarin 5 days prior to surgery. If patients were determined to be at high risk for thromboembolic complications following warfarin discontinuation, therapeutic doses of low-molecular weight heparin or unfractionated heparin were used as a bridging therapy pre- and postsurgery. Enoxaparin 30 mg twice daily was started the day after surgery in all patients not previously on warfarin therapy prior to surgery to prevent clotting complications. If a patient was on warfarin therapy prior to the procedure but not considered to be at high risk of thromboembolic complications by the surgeon, warfarin was restarted after surgery, and enoxaparin 30 mg twice daily was used until therapeutic international normalized ratio values were obtained. If the patient had ongoing bleeding after the procedure or was determined to be at high risk for bleeding complications, the provider may have delayed anticoagulant use.

Some patients who underwent TKA or THA procedures during the study time frame received periarticular pain injections during surgery. These pain injections included a combination of ropivacaine 200 mg, ketorolac 30 mg, epinephrine 0.5 mg, and clonidine 0.08 mg and were compounded in a sterile mixture with normal saline. Several injections of this mixture were administered into the surgical site to reduce postoperative pain. These periarticular pain injections were first implemented into TKA and THA procedures in August 2012 and were used in patients at the surgeon’s discretion.

Baseline characteristics were analyzed using a chi-square test for categoric variables and an unpaired t test for continuous variables to determine whether any differences were present. Total blood loss, change in Hgb, and change in Hct were analyzed using an unpaired t test. Patients receiving at least 1 blood transfusion during hospitalization and patients experiencing a clotting complication were analyzed using a chi-square test. P values < .05 were considered to indicate statistical significance. Descriptive statistics were calculated using Microsoft Excel (Redmond, WA), and GraphPad Prism (La Jolla, CA) was used for all statistical analyses.

 

Results

Initially, a total of 443 TKA patients and 111 THA patients were reviewed. Of these patients, 418 TKA patients and 100 THA patients met the inclusion criteria. Due to the retrospective design of this study, not all of the baseline characteristics were equal between groups (Table 1). Most notably, the number of patients who received the periarticular pain injection and the distribution of surgeons performing the procedures were different between groups in both the TKA and THA procedures.

Baseline Hgb levels were not found to be different between groups in either type of procedure; however, the baseline Hct levels of patients undergoing TKA who received tranexamic acid were found to be statistically higher when compared with those who did not receive tranexamic acid. Other baseline characteristics with statistically higher values included average weight and BMI in patients who received tranexamic acid and underwent THA and serum creatinine in patients who did not receive tranexamic acid and underwent TKA.

In the primary analysis (Tables 2 and 3), the mean estimated total blood loss in TKA patients was lower in patients who received tranexamic acid than it was in the control group (339.4 mL vs 457.4 mL, P < .001). Patients who underwent THA receiving tranexamic acid similarly had significantly less total blood loss than that of the control group (419.7 mL vs 585.7 mL, P < .001). Consistent with previous studies, patients undergoing TKA procedures in the treatment group when compared to the control group, respectively, were likely to have more blood loss postoperatively (275.9 mL vs 399.7 mL) than intraoperatively (63.5 mL vs 57.7 mL) regardless of tranexamic acid administration.⁶ On the other hand, patients who had undergone THA were more likely to experience more intraoperative blood loss (281.6 mL in treatment group vs 328.9 mL in control group) than postoperative blood loss (138.1 mL in treatment group vs 256.8 mL in control group) regardless of tranexamic acid administration.

In the secondary analysis, the change between preoperative and postoperative Hgb and Hct had results consistent with the total blood loss results. Patients receiving tranexamic acid in TKA procedures had a lower decrease in Hgb compared with the control group (3.3 mg/dL vs 4.0 mg/dL, P < .001). Similarly, patients undergoing TKA who received tranexamic acid had a smaller decrease in Hct than that of the control group (9.4% vs 11.1%, P < .001). Consistent with the TKA procedure results, patients undergoing THA who received tranexamic acid had a smaller decrease in Hgb (3.6 mg/dL vs 4.7 mg/dL, P < .001) and Hct (10.5% vs 13.0%, P = .0012) than that of the control group.

Patients who did not receive tranexamic acid were more likely to require at least 1 blood transfusion than were patients who received tranexamic acid in TKA (14 patients vs 0 patients, P = .0005) and THA procedures (8 patients vs 2 patients, P = .019). Despite the theoretically increased likelihood of clotting complications with tranexamic acid, no significant differences were observed between the treatment and control groups in either TKA (0 vs 4, P = .065) or THA (1 vs 0, P = .363) procedures.

Discussion

Patients undergoing total joint arthroplasty are at risk for significant blood loss with a potential need for postoperative blood transfusions.^6,13 The use of tranexamic acid during these procedures offers a possible solution to decrease blood loss and minimize blood transfusions. This study retrospectively evaluated whether tranexamic acid safely decreased blood loss and the need for blood transfusions at SFVAHCS with the dosing strategy of 1 g IV at first incision and 1 g IV at incision closure.

Patients who received tranexamic acid in TKA and THA procedures had significantly less blood loss than did patients who did not receive tranexamic acid. Patients receiving tranexamic acid also had a significantly lower change from preoperative to postoperative Hgb and Hct levels than did patients who did not receive tranexamic acid in TKA and THA procedures. In addition to decreasing blood loss, the tranexamic acid groups had significantly fewer patients who required blood transfusions than that of the control groups.

This reduction in blood transfusions should be considered clinically significant. Blood transfusions require substantial health care resource utilization and may cause complications in recipients.³ There was no clear association between tranexamic acid and clotting complications in this study, which may suggest that this risk is theoretical. However, larger prospective studies would be needed to further examine this potential risk.

Even though baseline Hct levels were found to be significantly different between the tranexamic acid group and the control group for patients who underwent TKA, medical record documentation indicated that the determination whether postoperative blood transfusions were needed was based primarily on Hgb levels. Baseline Hgb levels were found not to be significantly different between the tranexamic acid and control groups for either TKA or THA procedures. This suggests that at baseline the tranexamic acid and control groups had the same risk of reaching the Hgb threshold where blood transfusions would be required.

There was a significant difference in the proportion of patients receiving the periarticular pain injection of ropivacaine, ketorolac, epinephrine, and clonidine between the groups who received tranexamic acid and those who did not. Originally, this baseline characteristic was theorized to be a major confounder in the primary and secondary analyses because epinephrine is a vasoconstrictor and ketorolac is a reversible antiplatelet agent. However, Vendittoli and colleagues showed that patients receiving these periarticular pain injections during surgery actually had greater total blood loss than did patients who did not receive the injections, although this comparison did not reach statistical significance.¹⁴ The Vendittoli and colleagues results suggest that the pain injections did not confound the primary and secondary analyses by aiding in the process of reducing blood loss in these procedures.

 

Limitations

There are several limitations for extrapolating the results from this study to the general population. Due to the retrospective study design, there was no way to actively control potentially confounding variables during the TKA and THA procedures. Surgeons and surgery teams likely had slightly different techniques and protocols during and after surgery. Several baseline characteristics were not equal between patients who received tranexamic acid and those who did not. Therefore, it is unknown whether these baseline characteristics affected the results of this study. Postoperative anticoagulant use was not recorded and may have differed between study groups, depending on the patient’s risk of thromboembolic complications; however, the drains that collected blood loss were removed prior to the first dose of enoxaparin, which was administered the day after surgery.

Another limitation is that the method of measuring blood loss during and after the procedure was imprecise. Blood not suctioned through the suctioning device during surgery or not collected in the drain after surgery was not measured and may have increased the total blood loss. Hemoglobin and Hct levels also are sensitive to intravascular volume changes. If a patient required more IV fluids during or after a procedure, the fluids may have lowered the Hgb and/or Hct levels by dilution.

Conclusion

This study suggests that using tranexamic acid at a dose of 1 g IV at first incision and 1 g IV at incision closure safely and effectively reduced blood loss and the need for transfusions in patients undergoing TKA and THA procedures at SFVAHCS. Further prospective studies are needed to compare different tranexamic dosing strategies to minimize blood loss during these procedures. ˜

Acknowledgments
This study is the result of work supported with resources and the use of facilities at the Sioux Falls VA Health Care System in South Dakota.

Lentigo maligna (LM) and LM melanoma (LMM) represent diagnostic and therapeutic challenges due to their heterogeneous nature and location on cosmetically sensitive areas. Newer ancillary technologies such as reflectance confocal microscopy (RCM) have helped improve diagnosis and management of these challenging lesions.^1,2

Reflectance confocal microscopy is a noninvasive laser system that provides real-time imaging of the epidermis and dermis with cellular resolution and improves diagnostic accuracy of melanocytic lesions.^2,3 Normal melanocytes appear as round bright structures on RCM that are similar in size to surrounding keratinocytes located in the basal layer and regularly distributed around the dermal papillae (junctional nevi) or form regular dense nests in the dermis (intradermal nevi).^4,5 In LM/LMM, there may be widespread infiltration of atypical melanocytes invading hair follicles; large, round, pagetoid melanocytes (larger than surrounding keratinocytes); sheets of large atypical cells at the dermoepidermal junction (DEJ); loss of contour in the dermal papillae; and atypical melanocytes invading the dermal papillae.² Indeed, RCM has good correlation with the degree of histologic atypia and is useful to distinguish between benign nevi, atypical nevi, and melanoma.⁶ By combining lateral mosaics with vertical stacks, RCM allows 3-dimensional approximation of tumor margins and monitoring of nonsurgical therapies.^7,8 The advent of handheld RCM (HRCM) has allowed assessment of large lesions as well as those presenting in difficult locations.⁹ Furthermore, the generation of videomosaics overcomes the limited field of view of traditional RCM and allows for accurate assessment of large lesions.¹⁰

Traditional and handheld RCM have been used to diagnose and map primary LM.^1,2,11 Guitera et al² developed an algorithm using traditional RCM to distinguish benign facial macules and LM. In their training set, they found that when their score resulted in 2 or more points, the sensitivity and specificity to diagnose LM was 85% and 76%, respectively, with an odds ratio of 18.6 for LM. They later applied the algorithm in a test set of 44 benign facial macules and 29 LM and obtained an odds ratio of 60.7 for LM, with sensitivity and specificity rates of 93% and 82%, respectively.² This algorithm also was tested by Menge et al11 using the HRCM. They found 100% sensitivity and 71% specificity for LM when evaluating 63 equivocal facial lesions. Although these results suggest that RCM can accurately distinguish LM from benign lesions in the primary setting, few reports have studied the impact of HRCM in the recurrent setting and its impact in monitoring treatment of LM.^12,13

Herein, we present 5 cases in which HRCM was used to manage complex facial LM/LMM, highlighting its versatility and potential for use in the clinical setting (eTable).

 

Case Series

Following institutional review board approval, cases of facial LM/LMM presenting for assessment and treatment from January 2014 to December 2015 were retrospectively reviewed. Initially, the clinical margins of the lesions were determined using Wood lamp and/or dermoscopy. Using HRCM, vertical stacks were taken at the 12-, 3-, 6-, and 9-o'clock positions, and videos were captured along the peripheral margins at the DEJ. To create videomosaics, HRCM video frames were extracted and later stitched using a computer algorithm written in a fourth-generation programming language based on prior studies.^10,14 An example HRCM video that was captured and turned into a videomosaic accompanies this article online (http://bit.ly/2oDYS6k). Additional stacks were taken in suspicious areas. We considered an area positive for LM under HRCM when the LM score developed by Guitera et al² was 2 or more. The algorithm scoring includes 2 major criteria--nonedged papillae and round large pagetoid cells--which score 2 points, and 4 minor criteria, including 3 positive criteria--atypical cells at the DEJ, follicular invasion, nucleated cells in the papillae--which each score 1 point, and 1 negative criterion--broadened honeycomb pattern--which scores -1 point.²

RELATED VIDEO: RCM Videomosaic of Melanoma In Situ

Patient 1

An 82-year-old woman was referred to us for management of an LMM on the left side of the forehead (Figure 1A). Handheld RCM from the biopsy site showed large atypical cells in the epidermis, DEJ, and papillary dermis. Superiorly, HRCM showed large dendritic processes but did not reveal LM features in 3 additional clinically worrisome areas. Biopsies showed LMM at the prior biopsy site, LM superiorly, and actinic keratosis in the remaining 3 areas, supporting the HRCM findings. Due to upstaging, the patient was referred for head and neck surgery. To aid in resection, HRCM was performed intraoperatively in a multidisciplinary approach (Figure 1B). Due to the large size of the lesion, surgical margins were taken right outside the HRCM border. Pathology showed LMM extending focally into the margins that were reexcised, achieving clearance.

Patient 2

An 88-year-old woman presented with a slightly pigmented, 2.5×2.3-cm LMM on the left cheek. Because of her age and comorbidities (eg, osteoporosis, deep vein thrombosis in both lower legs requiring anticoagulation therapy, presence of an inferior vena cava filter, bilateral lymphedema of the legs, irritable bowel syndrome, hyperparathyroidism), she was treated with imiquimod cream 5% achieving partial response. The lesion was subsequently excised showing LMM extending to the margins. Not wanting to undergo further surgery, she opted for radiation therapy. Handheld RCM was performed to guide the radiation field, showing pagetoid cells within 1 cm of the scar and clear margins beyond 2 cm. She underwent radiation therapy followed by treatment with imiquimod. On 6-month follow-up, no clinical lesion was apparent, but HRCM showed atypical cells. Biopsies revealed an atypical intraepidermal melanocytic proliferation, but due to patient's comorbidities, close observation was decided.

Patient 3

A 78-year-old man presented with an LMM on the right preauricular area. Handheld RCM demonstrated pleomorphic pagetoid cells along and beyond the clinical margins. Wide excision with sentinel lymph node biopsy was planned, and to aid surgery a confocal map was created (Figure 2). Margins were clear at 1 cm, except inferiorly where they extended to 1.5 cm. Using this preoperative HRCM map, all intraoperative sections were clear. Final pathology confirmed clear margins throughout.

Patient 4

A 62-year-old man presented with hyperpigmentation and bleeding on the left cheek where an LMM was previously removed 8 times over 18 years. Handheld RCM showed pleomorphic cells along the graft border and interestingly within the graft. Ten biopsies were taken, 8 at sites with confocal features that were worrisome for LM (Figures 3A and 3B) and 2 at clinically suspicious sites. The former revealed melanomas (2 that were invasive to 0.3 mm), and the latter revealed solar lentigines. The patient underwent staged excision guided by HRCM (Figure 3C), achieving clear histologic margins except for a focus in the helix. This area was RCM positive but was intentionally not resected due to reconstructive difficulties; imiquimod was indicated in this area.

Patient 5

An 85-year-old woman with 6 prior melanomas over 15 years presented with ill-defined light brown patches on the left cheek at the site where an LM was previously excised 15 years prior. Biopsies showed LM, and due to the patient's age, health, and personal preference to avoid extensive surgery, treatment with imiquimod cream 5% was decided. Over a period of 6 to 12 months, she developed multiple erythematous macules with 2 faintly pigmented areas. Handheld RCM demonstrated atypical cells within the papillae in previously biopsied sites that were rebiopsied, revealing LMM (Breslow depth, 0.2 mm). Staged excision achieved clear margins, but after 8 months HRCM showed LM features. Histology confirmed the diagnosis and imiquimod was reapplied.

 

Comment

Diagnosis and choice of treatment modality for cases of facial LM is a challenge, and there are a number of factors that may create even more of a clinical dilemma. Surgical excision is the treatment of choice for LM/LMM, and better results are achieved when using histologically controlled surgical procedures such as Mohs micrographic surgery, staged excision, or the "spaghetti technique."^15-17 However, advanced patient age, multiple comorbidities (eg, coronary artery disease, deep vein thrombosis, other conditions requiring anticoagulation therapy), large lesion size in functionally or aesthetically sensitive areas, and indiscriminate borders on photodamaged skin may make surgical excision complicated or not feasible. Additionally, prior treatments to the affected area may further obscure clinical borders, complicating the diagnosis of recurrence/persistence when observed with the naked eye, dermoscopy, or Wood lamp. Because RCM can detect small amounts of melanin and has cellular resolution, it has been suggested as a great diagnostic tool to be combined with dermoscopy when evaluating lightly pigmented/amelanotic facial lesions arising on sun-damaged skin.^18,19 In this case series, we highlighted these difficulties and showed how HRCM can be useful in a variety of scenarios, both pretreatment and posttreatment in complex LM/LMM cases.

Pretreatment Evaluation

Blind mapping biopsies of LM are prone to sample bias and depend greatly on biopsy technique; however, HRCM can guide mapping biopsies by detecting features of LM in vivo with high sensitivity.¹¹ Due to the cosmetically sensitive nature of the lesions, many physicians are discouraged to do multiple mapping biopsies, making it difficult to assess the breadth of the lesion and occult invasion. Multiple studies have shown that occult invasion was not apparent until complete lesion excision was done.^15,20,21 Agarwal-Antal et al²⁰ reported 92 cases of LM, of which 16% (15/92) had unsuspected invasion on final excisional pathology. A long-standing disadvantage of treating LM with nonsurgical modalities has been the inability to detect occult invasion or multifocal invasion within the lesion. As described in patients 1, 4, and 5 in the current case series, utilizing real-time video imaging of the DEJ at the margins and within the lesion has allowed for the detection of deep atypical melanocytes suspicious for perifollicular infiltration and invasion. Knowing the depth of invasion before treatment is essential for not only counseling the patient about disease risk but also for choosing an appropriate treatment modality. Therefore, prospective studies evaluating the performance of RCM to identify invasion are crucial to improve sampling error and avoid unnecessary biopsies.

Surgical Treatment

Although surgery is the first-line treatment option for facial LM, it is not without associated morbidity, and LM is known to have histological subclinical extension, which makes margin assessment difficult. Wide surgical margins on the face are not always possible and become further complicated when trying to maintain adequate functional and cosmetic outcomes. Additionally, the margin for surgical clearance may not be straightforward for facial lesions. Hazan et al¹⁵ showed the mean total surgical margins required for excision of LM and LMM was 7.1 and 10.3 mm, respectively; of the 91 tumors initially diagnosed as LM on biopsy, 16% (15/91) had unsuspected invasion. Guitera et al² reported that the presence of atypical cells within the dermal papillae might be a sign of invasion, which occasionally is not detected histologically due to sampling bias. Handheld RCM offers the advantage of a rapid real-time assessment in areas that may not have been amenable to previous iterations of the device, and it also provides a larger field of view that would be time consuming if performed using conventional RCM. Compared to prior RCM devices that were not handheld, the use of the HRCM does not need to attach a ring to the skin and is less bulky, permitting its use at the bedside of the patient or even intraoperatively.13 In our experience, HRCM has helped to better characterize subclinical spread of LM during the initial consultation and better counsel patients about the extent of the lesion. Handheld RCM also has been used to guide the spaghetti technique in patients with LM/LMM with good correlation between HRCM and histology.²² In our case series, HRCM was used in complex LM/LMM to delineate surgical margins, though in some cases the histologic margins were too close or affected, suggesting HRCM underestimation. Lentigo maligna margin assessment with RCM uses an algorithm that evaluates confocal features in the center of the lesion.^1,2 Therefore, further studies using HRCM should evaluate minor confocal features in the margins as potential markers of positivity to accurately delineate surgical margins.

Nonsurgical Treatment Options

For patients unable or unwilling to pursue surgical treatment, therapies such as imiquimod or radiation have been suggested.^23,24 However, the lack of histological confirmation and possibility for invasive spread has limited these modalities. Lentigo malignas treated with radiation have a 5% recurrence rate, with a median follow-up time of 3 years.²³ Recurrence often can be difficult to detect clinically, as it may manifest as an amelanotic lesion, or postradiation changes can hinder detection. Handheld RCM allows for a cellular-level observation of the irradiated field and can identify radiation-induced changes in LM lesions, including superficial necrosis, apoptotic cells, dilated vessels, and increased inflammatory cells.²⁵ Handheld RCM has previously been used to assess LM treated with radiation and, as in patient 2, can help define the radiation field and detect treatment failure or recurrence.^12,25

Similarly, as described in patient 5, HRCM was utilized to monitor treatment with imiquimod. Many reports use imiquimod for treatment of LM, but application and response vary greatly. Reflectance confocal microscopy has been shown to be useful in monitoring LM treated with imiquimod,⁸ which is important because clinical findings such as inflammation and erythema do not correlate well with response to therapy. Thus, RCM is an appealing noninvasive modality to monitor response to treatment and assess the need for longer treatment duration. Moreover, similar to postradiation changes, treatment with imiquimod may cause an alteration of the clinically apparent pigment. Therefore, it is difficult to assess treatment success by clinical inspection alone. The use of RCM before, during, and after treatment provides a longitudinal assessment of the lesion and has augmented dermatologists' ability to determine treatment success or failure; however, prospective studies evaluating the usefulness of HRCM in the recurrent setting are needed to validate these results.

Limitations

Limitations of this technology include the time needed to image large areas; technology cost; and associated learning curve, which may take from 6 months to 1 year based on our experience. Others have reported the training required for accurate RCM interpretation to be less than that of dermoscopy.²⁶ It has been shown that key RCM diagnostic criteria for lesions including melanoma and basal cell carcinoma are reproducibly recognized among RCM users and that diagnostic accuracy increases with experience.²⁷ These limitations can be overcome with advances in videomosaicing that may streamline the imaging as well as an eventual decrease in cost with greater user adoption and the development of training platforms that enable a faster learning of RCM.²⁸

Conclusion

The use of HRCM can help in the diagnosis and management of facial LMs. Handheld RCM provides longitudinal assessment of LM/LMM that may help determine treatment success or failure and has proven to be useful in detecting the presence of recurrence/persistence in cases that were clinically poorly evident. Moreover, HRCM is a notable ancillary tool, as it can be performed at the bedside of the patient or even intraoperatively and provides a faster approach than conventional RCM in cases where large areas need to be mapped.

In summary, HRCM may eventually be a useful screening tool to guide scouting biopsies to diagnose de novo LM; guide surgical and nonsurgical therapies; and evaluate the presence of recurrence/persistence, especially in large, complex, amelanotic or poorly pigmented lesions. A more standardized use of HRCM in mapping surgical and nonsurgical approaches needs to be evaluated in further studies to provide a fast and reliable complement to histology in such complex cases; therefore, larger studies need to be performed to validate this technique in such complex cases.

Lentigo maligna (LM) and LM melanoma (LMM) represent diagnostic and therapeutic challenges due to their heterogeneous nature and location on cosmetically sensitive areas. Newer ancillary technologies such as reflectance confocal microscopy (RCM) have helped improve diagnosis and management of these challenging lesions.^1,2

Reflectance confocal microscopy is a noninvasive laser system that provides real-time imaging of the epidermis and dermis with cellular resolution and improves diagnostic accuracy of melanocytic lesions.^2,3 Normal melanocytes appear as round bright structures on RCM that are similar in size to surrounding keratinocytes located in the basal layer and regularly distributed around the dermal papillae (junctional nevi) or form regular dense nests in the dermis (intradermal nevi).^4,5 In LM/LMM, there may be widespread infiltration of atypical melanocytes invading hair follicles; large, round, pagetoid melanocytes (larger than surrounding keratinocytes); sheets of large atypical cells at the dermoepidermal junction (DEJ); loss of contour in the dermal papillae; and atypical melanocytes invading the dermal papillae.² Indeed, RCM has good correlation with the degree of histologic atypia and is useful to distinguish between benign nevi, atypical nevi, and melanoma.⁶ By combining lateral mosaics with vertical stacks, RCM allows 3-dimensional approximation of tumor margins and monitoring of nonsurgical therapies.^7,8 The advent of handheld RCM (HRCM) has allowed assessment of large lesions as well as those presenting in difficult locations.⁹ Furthermore, the generation of videomosaics overcomes the limited field of view of traditional RCM and allows for accurate assessment of large lesions.¹⁰

Traditional and handheld RCM have been used to diagnose and map primary LM.^1,2,11 Guitera et al² developed an algorithm using traditional RCM to distinguish benign facial macules and LM. In their training set, they found that when their score resulted in 2 or more points, the sensitivity and specificity to diagnose LM was 85% and 76%, respectively, with an odds ratio of 18.6 for LM. They later applied the algorithm in a test set of 44 benign facial macules and 29 LM and obtained an odds ratio of 60.7 for LM, with sensitivity and specificity rates of 93% and 82%, respectively.² This algorithm also was tested by Menge et al11 using the HRCM. They found 100% sensitivity and 71% specificity for LM when evaluating 63 equivocal facial lesions. Although these results suggest that RCM can accurately distinguish LM from benign lesions in the primary setting, few reports have studied the impact of HRCM in the recurrent setting and its impact in monitoring treatment of LM.^12,13

Herein, we present 5 cases in which HRCM was used to manage complex facial LM/LMM, highlighting its versatility and potential for use in the clinical setting (eTable).

 

Case Series

Following institutional review board approval, cases of facial LM/LMM presenting for assessment and treatment from January 2014 to December 2015 were retrospectively reviewed. Initially, the clinical margins of the lesions were determined using Wood lamp and/or dermoscopy. Using HRCM, vertical stacks were taken at the 12-, 3-, 6-, and 9-o'clock positions, and videos were captured along the peripheral margins at the DEJ. To create videomosaics, HRCM video frames were extracted and later stitched using a computer algorithm written in a fourth-generation programming language based on prior studies.^10,14 An example HRCM video that was captured and turned into a videomosaic accompanies this article online (http://bit.ly/2oDYS6k). Additional stacks were taken in suspicious areas. We considered an area positive for LM under HRCM when the LM score developed by Guitera et al² was 2 or more. The algorithm scoring includes 2 major criteria--nonedged papillae and round large pagetoid cells--which score 2 points, and 4 minor criteria, including 3 positive criteria--atypical cells at the DEJ, follicular invasion, nucleated cells in the papillae--which each score 1 point, and 1 negative criterion--broadened honeycomb pattern--which scores -1 point.²

RELATED VIDEO: RCM Videomosaic of Melanoma In Situ

Patient 1

An 82-year-old woman was referred to us for management of an LMM on the left side of the forehead (Figure 1A). Handheld RCM from the biopsy site showed large atypical cells in the epidermis, DEJ, and papillary dermis. Superiorly, HRCM showed large dendritic processes but did not reveal LM features in 3 additional clinically worrisome areas. Biopsies showed LMM at the prior biopsy site, LM superiorly, and actinic keratosis in the remaining 3 areas, supporting the HRCM findings. Due to upstaging, the patient was referred for head and neck surgery. To aid in resection, HRCM was performed intraoperatively in a multidisciplinary approach (Figure 1B). Due to the large size of the lesion, surgical margins were taken right outside the HRCM border. Pathology showed LMM extending focally into the margins that were reexcised, achieving clearance.

Patient 2

An 88-year-old woman presented with a slightly pigmented, 2.5×2.3-cm LMM on the left cheek. Because of her age and comorbidities (eg, osteoporosis, deep vein thrombosis in both lower legs requiring anticoagulation therapy, presence of an inferior vena cava filter, bilateral lymphedema of the legs, irritable bowel syndrome, hyperparathyroidism), she was treated with imiquimod cream 5% achieving partial response. The lesion was subsequently excised showing LMM extending to the margins. Not wanting to undergo further surgery, she opted for radiation therapy. Handheld RCM was performed to guide the radiation field, showing pagetoid cells within 1 cm of the scar and clear margins beyond 2 cm. She underwent radiation therapy followed by treatment with imiquimod. On 6-month follow-up, no clinical lesion was apparent, but HRCM showed atypical cells. Biopsies revealed an atypical intraepidermal melanocytic proliferation, but due to patient's comorbidities, close observation was decided.

Patient 3

A 78-year-old man presented with an LMM on the right preauricular area. Handheld RCM demonstrated pleomorphic pagetoid cells along and beyond the clinical margins. Wide excision with sentinel lymph node biopsy was planned, and to aid surgery a confocal map was created (Figure 2). Margins were clear at 1 cm, except inferiorly where they extended to 1.5 cm. Using this preoperative HRCM map, all intraoperative sections were clear. Final pathology confirmed clear margins throughout.

Patient 4

A 62-year-old man presented with hyperpigmentation and bleeding on the left cheek where an LMM was previously removed 8 times over 18 years. Handheld RCM showed pleomorphic cells along the graft border and interestingly within the graft. Ten biopsies were taken, 8 at sites with confocal features that were worrisome for LM (Figures 3A and 3B) and 2 at clinically suspicious sites. The former revealed melanomas (2 that were invasive to 0.3 mm), and the latter revealed solar lentigines. The patient underwent staged excision guided by HRCM (Figure 3C), achieving clear histologic margins except for a focus in the helix. This area was RCM positive but was intentionally not resected due to reconstructive difficulties; imiquimod was indicated in this area.

Patient 5

An 85-year-old woman with 6 prior melanomas over 15 years presented with ill-defined light brown patches on the left cheek at the site where an LM was previously excised 15 years prior. Biopsies showed LM, and due to the patient's age, health, and personal preference to avoid extensive surgery, treatment with imiquimod cream 5% was decided. Over a period of 6 to 12 months, she developed multiple erythematous macules with 2 faintly pigmented areas. Handheld RCM demonstrated atypical cells within the papillae in previously biopsied sites that were rebiopsied, revealing LMM (Breslow depth, 0.2 mm). Staged excision achieved clear margins, but after 8 months HRCM showed LM features. Histology confirmed the diagnosis and imiquimod was reapplied.

 

Comment

Diagnosis and choice of treatment modality for cases of facial LM is a challenge, and there are a number of factors that may create even more of a clinical dilemma. Surgical excision is the treatment of choice for LM/LMM, and better results are achieved when using histologically controlled surgical procedures such as Mohs micrographic surgery, staged excision, or the "spaghetti technique."^15-17 However, advanced patient age, multiple comorbidities (eg, coronary artery disease, deep vein thrombosis, other conditions requiring anticoagulation therapy), large lesion size in functionally or aesthetically sensitive areas, and indiscriminate borders on photodamaged skin may make surgical excision complicated or not feasible. Additionally, prior treatments to the affected area may further obscure clinical borders, complicating the diagnosis of recurrence/persistence when observed with the naked eye, dermoscopy, or Wood lamp. Because RCM can detect small amounts of melanin and has cellular resolution, it has been suggested as a great diagnostic tool to be combined with dermoscopy when evaluating lightly pigmented/amelanotic facial lesions arising on sun-damaged skin.^18,19 In this case series, we highlighted these difficulties and showed how HRCM can be useful in a variety of scenarios, both pretreatment and posttreatment in complex LM/LMM cases.

Pretreatment Evaluation

Blind mapping biopsies of LM are prone to sample bias and depend greatly on biopsy technique; however, HRCM can guide mapping biopsies by detecting features of LM in vivo with high sensitivity.¹¹ Due to the cosmetically sensitive nature of the lesions, many physicians are discouraged to do multiple mapping biopsies, making it difficult to assess the breadth of the lesion and occult invasion. Multiple studies have shown that occult invasion was not apparent until complete lesion excision was done.^15,20,21 Agarwal-Antal et al²⁰ reported 92 cases of LM, of which 16% (15/92) had unsuspected invasion on final excisional pathology. A long-standing disadvantage of treating LM with nonsurgical modalities has been the inability to detect occult invasion or multifocal invasion within the lesion. As described in patients 1, 4, and 5 in the current case series, utilizing real-time video imaging of the DEJ at the margins and within the lesion has allowed for the detection of deep atypical melanocytes suspicious for perifollicular infiltration and invasion. Knowing the depth of invasion before treatment is essential for not only counseling the patient about disease risk but also for choosing an appropriate treatment modality. Therefore, prospective studies evaluating the performance of RCM to identify invasion are crucial to improve sampling error and avoid unnecessary biopsies.

Surgical Treatment

Although surgery is the first-line treatment option for facial LM, it is not without associated morbidity, and LM is known to have histological subclinical extension, which makes margin assessment difficult. Wide surgical margins on the face are not always possible and become further complicated when trying to maintain adequate functional and cosmetic outcomes. Additionally, the margin for surgical clearance may not be straightforward for facial lesions. Hazan et al¹⁵ showed the mean total surgical margins required for excision of LM and LMM was 7.1 and 10.3 mm, respectively; of the 91 tumors initially diagnosed as LM on biopsy, 16% (15/91) had unsuspected invasion. Guitera et al² reported that the presence of atypical cells within the dermal papillae might be a sign of invasion, which occasionally is not detected histologically due to sampling bias. Handheld RCM offers the advantage of a rapid real-time assessment in areas that may not have been amenable to previous iterations of the device, and it also provides a larger field of view that would be time consuming if performed using conventional RCM. Compared to prior RCM devices that were not handheld, the use of the HRCM does not need to attach a ring to the skin and is less bulky, permitting its use at the bedside of the patient or even intraoperatively.13 In our experience, HRCM has helped to better characterize subclinical spread of LM during the initial consultation and better counsel patients about the extent of the lesion. Handheld RCM also has been used to guide the spaghetti technique in patients with LM/LMM with good correlation between HRCM and histology.²² In our case series, HRCM was used in complex LM/LMM to delineate surgical margins, though in some cases the histologic margins were too close or affected, suggesting HRCM underestimation. Lentigo maligna margin assessment with RCM uses an algorithm that evaluates confocal features in the center of the lesion.^1,2 Therefore, further studies using HRCM should evaluate minor confocal features in the margins as potential markers of positivity to accurately delineate surgical margins.

Nonsurgical Treatment Options

For patients unable or unwilling to pursue surgical treatment, therapies such as imiquimod or radiation have been suggested.^23,24 However, the lack of histological confirmation and possibility for invasive spread has limited these modalities. Lentigo malignas treated with radiation have a 5% recurrence rate, with a median follow-up time of 3 years.²³ Recurrence often can be difficult to detect clinically, as it may manifest as an amelanotic lesion, or postradiation changes can hinder detection. Handheld RCM allows for a cellular-level observation of the irradiated field and can identify radiation-induced changes in LM lesions, including superficial necrosis, apoptotic cells, dilated vessels, and increased inflammatory cells.²⁵ Handheld RCM has previously been used to assess LM treated with radiation and, as in patient 2, can help define the radiation field and detect treatment failure or recurrence.^12,25

Similarly, as described in patient 5, HRCM was utilized to monitor treatment with imiquimod. Many reports use imiquimod for treatment of LM, but application and response vary greatly. Reflectance confocal microscopy has been shown to be useful in monitoring LM treated with imiquimod,⁸ which is important because clinical findings such as inflammation and erythema do not correlate well with response to therapy. Thus, RCM is an appealing noninvasive modality to monitor response to treatment and assess the need for longer treatment duration. Moreover, similar to postradiation changes, treatment with imiquimod may cause an alteration of the clinically apparent pigment. Therefore, it is difficult to assess treatment success by clinical inspection alone. The use of RCM before, during, and after treatment provides a longitudinal assessment of the lesion and has augmented dermatologists' ability to determine treatment success or failure; however, prospective studies evaluating the usefulness of HRCM in the recurrent setting are needed to validate these results.

Limitations

Limitations of this technology include the time needed to image large areas; technology cost; and associated learning curve, which may take from 6 months to 1 year based on our experience. Others have reported the training required for accurate RCM interpretation to be less than that of dermoscopy.²⁶ It has been shown that key RCM diagnostic criteria for lesions including melanoma and basal cell carcinoma are reproducibly recognized among RCM users and that diagnostic accuracy increases with experience.²⁷ These limitations can be overcome with advances in videomosaicing that may streamline the imaging as well as an eventual decrease in cost with greater user adoption and the development of training platforms that enable a faster learning of RCM.²⁸

Conclusion

The use of HRCM can help in the diagnosis and management of facial LMs. Handheld RCM provides longitudinal assessment of LM/LMM that may help determine treatment success or failure and has proven to be useful in detecting the presence of recurrence/persistence in cases that were clinically poorly evident. Moreover, HRCM is a notable ancillary tool, as it can be performed at the bedside of the patient or even intraoperatively and provides a faster approach than conventional RCM in cases where large areas need to be mapped.

In summary, HRCM may eventually be a useful screening tool to guide scouting biopsies to diagnose de novo LM; guide surgical and nonsurgical therapies; and evaluate the presence of recurrence/persistence, especially in large, complex, amelanotic or poorly pigmented lesions. A more standardized use of HRCM in mapping surgical and nonsurgical approaches needs to be evaluated in further studies to provide a fast and reliable complement to histology in such complex cases; therefore, larger studies need to be performed to validate this technique in such complex cases.

Lentigo maligna (LM) and LM melanoma (LMM) represent diagnostic and therapeutic challenges due to their heterogeneous nature and location on cosmetically sensitive areas. Newer ancillary technologies such as reflectance confocal microscopy (RCM) have helped improve diagnosis and management of these challenging lesions.^1,2

Reflectance confocal microscopy is a noninvasive laser system that provides real-time imaging of the epidermis and dermis with cellular resolution and improves diagnostic accuracy of melanocytic lesions.^2,3 Normal melanocytes appear as round bright structures on RCM that are similar in size to surrounding keratinocytes located in the basal layer and regularly distributed around the dermal papillae (junctional nevi) or form regular dense nests in the dermis (intradermal nevi).^4,5 In LM/LMM, there may be widespread infiltration of atypical melanocytes invading hair follicles; large, round, pagetoid melanocytes (larger than surrounding keratinocytes); sheets of large atypical cells at the dermoepidermal junction (DEJ); loss of contour in the dermal papillae; and atypical melanocytes invading the dermal papillae.² Indeed, RCM has good correlation with the degree of histologic atypia and is useful to distinguish between benign nevi, atypical nevi, and melanoma.⁶ By combining lateral mosaics with vertical stacks, RCM allows 3-dimensional approximation of tumor margins and monitoring of nonsurgical therapies.^7,8 The advent of handheld RCM (HRCM) has allowed assessment of large lesions as well as those presenting in difficult locations.⁹ Furthermore, the generation of videomosaics overcomes the limited field of view of traditional RCM and allows for accurate assessment of large lesions.¹⁰

Traditional and handheld RCM have been used to diagnose and map primary LM.^1,2,11 Guitera et al² developed an algorithm using traditional RCM to distinguish benign facial macules and LM. In their training set, they found that when their score resulted in 2 or more points, the sensitivity and specificity to diagnose LM was 85% and 76%, respectively, with an odds ratio of 18.6 for LM. They later applied the algorithm in a test set of 44 benign facial macules and 29 LM and obtained an odds ratio of 60.7 for LM, with sensitivity and specificity rates of 93% and 82%, respectively.² This algorithm also was tested by Menge et al11 using the HRCM. They found 100% sensitivity and 71% specificity for LM when evaluating 63 equivocal facial lesions. Although these results suggest that RCM can accurately distinguish LM from benign lesions in the primary setting, few reports have studied the impact of HRCM in the recurrent setting and its impact in monitoring treatment of LM.^12,13

Herein, we present 5 cases in which HRCM was used to manage complex facial LM/LMM, highlighting its versatility and potential for use in the clinical setting (eTable).

 

Case Series

Following institutional review board approval, cases of facial LM/LMM presenting for assessment and treatment from January 2014 to December 2015 were retrospectively reviewed. Initially, the clinical margins of the lesions were determined using Wood lamp and/or dermoscopy. Using HRCM, vertical stacks were taken at the 12-, 3-, 6-, and 9-o'clock positions, and videos were captured along the peripheral margins at the DEJ. To create videomosaics, HRCM video frames were extracted and later stitched using a computer algorithm written in a fourth-generation programming language based on prior studies.^10,14 An example HRCM video that was captured and turned into a videomosaic accompanies this article online (http://bit.ly/2oDYS6k). Additional stacks were taken in suspicious areas. We considered an area positive for LM under HRCM when the LM score developed by Guitera et al² was 2 or more. The algorithm scoring includes 2 major criteria--nonedged papillae and round large pagetoid cells--which score 2 points, and 4 minor criteria, including 3 positive criteria--atypical cells at the DEJ, follicular invasion, nucleated cells in the papillae--which each score 1 point, and 1 negative criterion--broadened honeycomb pattern--which scores -1 point.²

RELATED VIDEO: RCM Videomosaic of Melanoma In Situ

Patient 1

An 82-year-old woman was referred to us for management of an LMM on the left side of the forehead (Figure 1A). Handheld RCM from the biopsy site showed large atypical cells in the epidermis, DEJ, and papillary dermis. Superiorly, HRCM showed large dendritic processes but did not reveal LM features in 3 additional clinically worrisome areas. Biopsies showed LMM at the prior biopsy site, LM superiorly, and actinic keratosis in the remaining 3 areas, supporting the HRCM findings. Due to upstaging, the patient was referred for head and neck surgery. To aid in resection, HRCM was performed intraoperatively in a multidisciplinary approach (Figure 1B). Due to the large size of the lesion, surgical margins were taken right outside the HRCM border. Pathology showed LMM extending focally into the margins that were reexcised, achieving clearance.

Patient 2

An 88-year-old woman presented with a slightly pigmented, 2.5×2.3-cm LMM on the left cheek. Because of her age and comorbidities (eg, osteoporosis, deep vein thrombosis in both lower legs requiring anticoagulation therapy, presence of an inferior vena cava filter, bilateral lymphedema of the legs, irritable bowel syndrome, hyperparathyroidism), she was treated with imiquimod cream 5% achieving partial response. The lesion was subsequently excised showing LMM extending to the margins. Not wanting to undergo further surgery, she opted for radiation therapy. Handheld RCM was performed to guide the radiation field, showing pagetoid cells within 1 cm of the scar and clear margins beyond 2 cm. She underwent radiation therapy followed by treatment with imiquimod. On 6-month follow-up, no clinical lesion was apparent, but HRCM showed atypical cells. Biopsies revealed an atypical intraepidermal melanocytic proliferation, but due to patient's comorbidities, close observation was decided.

Patient 3

A 78-year-old man presented with an LMM on the right preauricular area. Handheld RCM demonstrated pleomorphic pagetoid cells along and beyond the clinical margins. Wide excision with sentinel lymph node biopsy was planned, and to aid surgery a confocal map was created (Figure 2). Margins were clear at 1 cm, except inferiorly where they extended to 1.5 cm. Using this preoperative HRCM map, all intraoperative sections were clear. Final pathology confirmed clear margins throughout.

Patient 4

A 62-year-old man presented with hyperpigmentation and bleeding on the left cheek where an LMM was previously removed 8 times over 18 years. Handheld RCM showed pleomorphic cells along the graft border and interestingly within the graft. Ten biopsies were taken, 8 at sites with confocal features that were worrisome for LM (Figures 3A and 3B) and 2 at clinically suspicious sites. The former revealed melanomas (2 that were invasive to 0.3 mm), and the latter revealed solar lentigines. The patient underwent staged excision guided by HRCM (Figure 3C), achieving clear histologic margins except for a focus in the helix. This area was RCM positive but was intentionally not resected due to reconstructive difficulties; imiquimod was indicated in this area.

Patient 5

An 85-year-old woman with 6 prior melanomas over 15 years presented with ill-defined light brown patches on the left cheek at the site where an LM was previously excised 15 years prior. Biopsies showed LM, and due to the patient's age, health, and personal preference to avoid extensive surgery, treatment with imiquimod cream 5% was decided. Over a period of 6 to 12 months, she developed multiple erythematous macules with 2 faintly pigmented areas. Handheld RCM demonstrated atypical cells within the papillae in previously biopsied sites that were rebiopsied, revealing LMM (Breslow depth, 0.2 mm). Staged excision achieved clear margins, but after 8 months HRCM showed LM features. Histology confirmed the diagnosis and imiquimod was reapplied.

 

Comment

Diagnosis and choice of treatment modality for cases of facial LM is a challenge, and there are a number of factors that may create even more of a clinical dilemma. Surgical excision is the treatment of choice for LM/LMM, and better results are achieved when using histologically controlled surgical procedures such as Mohs micrographic surgery, staged excision, or the "spaghetti technique."^15-17 However, advanced patient age, multiple comorbidities (eg, coronary artery disease, deep vein thrombosis, other conditions requiring anticoagulation therapy), large lesion size in functionally or aesthetically sensitive areas, and indiscriminate borders on photodamaged skin may make surgical excision complicated or not feasible. Additionally, prior treatments to the affected area may further obscure clinical borders, complicating the diagnosis of recurrence/persistence when observed with the naked eye, dermoscopy, or Wood lamp. Because RCM can detect small amounts of melanin and has cellular resolution, it has been suggested as a great diagnostic tool to be combined with dermoscopy when evaluating lightly pigmented/amelanotic facial lesions arising on sun-damaged skin.^18,19 In this case series, we highlighted these difficulties and showed how HRCM can be useful in a variety of scenarios, both pretreatment and posttreatment in complex LM/LMM cases.

Pretreatment Evaluation

Blind mapping biopsies of LM are prone to sample bias and depend greatly on biopsy technique; however, HRCM can guide mapping biopsies by detecting features of LM in vivo with high sensitivity.¹¹ Due to the cosmetically sensitive nature of the lesions, many physicians are discouraged to do multiple mapping biopsies, making it difficult to assess the breadth of the lesion and occult invasion. Multiple studies have shown that occult invasion was not apparent until complete lesion excision was done.^15,20,21 Agarwal-Antal et al²⁰ reported 92 cases of LM, of which 16% (15/92) had unsuspected invasion on final excisional pathology. A long-standing disadvantage of treating LM with nonsurgical modalities has been the inability to detect occult invasion or multifocal invasion within the lesion. As described in patients 1, 4, and 5 in the current case series, utilizing real-time video imaging of the DEJ at the margins and within the lesion has allowed for the detection of deep atypical melanocytes suspicious for perifollicular infiltration and invasion. Knowing the depth of invasion before treatment is essential for not only counseling the patient about disease risk but also for choosing an appropriate treatment modality. Therefore, prospective studies evaluating the performance of RCM to identify invasion are crucial to improve sampling error and avoid unnecessary biopsies.

Surgical Treatment

Although surgery is the first-line treatment option for facial LM, it is not without associated morbidity, and LM is known to have histological subclinical extension, which makes margin assessment difficult. Wide surgical margins on the face are not always possible and become further complicated when trying to maintain adequate functional and cosmetic outcomes. Additionally, the margin for surgical clearance may not be straightforward for facial lesions. Hazan et al¹⁵ showed the mean total surgical margins required for excision of LM and LMM was 7.1 and 10.3 mm, respectively; of the 91 tumors initially diagnosed as LM on biopsy, 16% (15/91) had unsuspected invasion. Guitera et al² reported that the presence of atypical cells within the dermal papillae might be a sign of invasion, which occasionally is not detected histologically due to sampling bias. Handheld RCM offers the advantage of a rapid real-time assessment in areas that may not have been amenable to previous iterations of the device, and it also provides a larger field of view that would be time consuming if performed using conventional RCM. Compared to prior RCM devices that were not handheld, the use of the HRCM does not need to attach a ring to the skin and is less bulky, permitting its use at the bedside of the patient or even intraoperatively.13 In our experience, HRCM has helped to better characterize subclinical spread of LM during the initial consultation and better counsel patients about the extent of the lesion. Handheld RCM also has been used to guide the spaghetti technique in patients with LM/LMM with good correlation between HRCM and histology.²² In our case series, HRCM was used in complex LM/LMM to delineate surgical margins, though in some cases the histologic margins were too close or affected, suggesting HRCM underestimation. Lentigo maligna margin assessment with RCM uses an algorithm that evaluates confocal features in the center of the lesion.^1,2 Therefore, further studies using HRCM should evaluate minor confocal features in the margins as potential markers of positivity to accurately delineate surgical margins.

Nonsurgical Treatment Options

For patients unable or unwilling to pursue surgical treatment, therapies such as imiquimod or radiation have been suggested.^23,24 However, the lack of histological confirmation and possibility for invasive spread has limited these modalities. Lentigo malignas treated with radiation have a 5% recurrence rate, with a median follow-up time of 3 years.²³ Recurrence often can be difficult to detect clinically, as it may manifest as an amelanotic lesion, or postradiation changes can hinder detection. Handheld RCM allows for a cellular-level observation of the irradiated field and can identify radiation-induced changes in LM lesions, including superficial necrosis, apoptotic cells, dilated vessels, and increased inflammatory cells.²⁵ Handheld RCM has previously been used to assess LM treated with radiation and, as in patient 2, can help define the radiation field and detect treatment failure or recurrence.^12,25

Similarly, as described in patient 5, HRCM was utilized to monitor treatment with imiquimod. Many reports use imiquimod for treatment of LM, but application and response vary greatly. Reflectance confocal microscopy has been shown to be useful in monitoring LM treated with imiquimod,⁸ which is important because clinical findings such as inflammation and erythema do not correlate well with response to therapy. Thus, RCM is an appealing noninvasive modality to monitor response to treatment and assess the need for longer treatment duration. Moreover, similar to postradiation changes, treatment with imiquimod may cause an alteration of the clinically apparent pigment. Therefore, it is difficult to assess treatment success by clinical inspection alone. The use of RCM before, during, and after treatment provides a longitudinal assessment of the lesion and has augmented dermatologists' ability to determine treatment success or failure; however, prospective studies evaluating the usefulness of HRCM in the recurrent setting are needed to validate these results.

Limitations

Limitations of this technology include the time needed to image large areas; technology cost; and associated learning curve, which may take from 6 months to 1 year based on our experience. Others have reported the training required for accurate RCM interpretation to be less than that of dermoscopy.²⁶ It has been shown that key RCM diagnostic criteria for lesions including melanoma and basal cell carcinoma are reproducibly recognized among RCM users and that diagnostic accuracy increases with experience.²⁷ These limitations can be overcome with advances in videomosaicing that may streamline the imaging as well as an eventual decrease in cost with greater user adoption and the development of training platforms that enable a faster learning of RCM.²⁸

Conclusion

The use of HRCM can help in the diagnosis and management of facial LMs. Handheld RCM provides longitudinal assessment of LM/LMM that may help determine treatment success or failure and has proven to be useful in detecting the presence of recurrence/persistence in cases that were clinically poorly evident. Moreover, HRCM is a notable ancillary tool, as it can be performed at the bedside of the patient or even intraoperatively and provides a faster approach than conventional RCM in cases where large areas need to be mapped.

In summary, HRCM may eventually be a useful screening tool to guide scouting biopsies to diagnose de novo LM; guide surgical and nonsurgical therapies; and evaluate the presence of recurrence/persistence, especially in large, complex, amelanotic or poorly pigmented lesions. A more standardized use of HRCM in mapping surgical and nonsurgical approaches needs to be evaluated in further studies to provide a fast and reliable complement to histology in such complex cases; therefore, larger studies need to be performed to validate this technique in such complex cases.

Sun exposure and sunburns sustained during childhood are linked to an increased risk for development of skin cancers in adulthood. In infants, the skin is particularly vulnerable and is considered to be at increased risk for UV radiation damage,¹ even as early as the first 6 months of life.² Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ To effectively teach parents proper sun-safe practices, it is essential to understand their existing perceptions and behaviors. This study sought to examine differences in infant sun-safety practices during the first 6 months of life among black, Hispanic, and non-Hispanic white (NHW) parents in Miami, Florida.

Methods

Parents presenting to the University of Miami general pediatrics clinic from February 2015 through April 2015 with a child younger than 5 years were administered a 15-item questionnaire that included items on demographics, sun-safety strategies, sunburns and tanning, beliefs and limitations regarding sunscreen, and primary information source regarding sun safety (eg, physician, Internet, media, instincts). Parents were approached by the investigators consecutively for participation in scheduled blocks, with the exception of those who were otherwise engaged in appointment-related tasks (eg, paperwork). The study was approved by the University of Miami Miller School of Medicine institutional review board. The primary objective of this study was to determine the sun protection behaviors that black and Hispanic parents in Miami, Florida, employ in infants younger than 6 months. Secondary objectives included determining if this patient population is at risk for infant sunburns and tanning, beliefs among parents regarding sunscreen's efficacy in the prevention of skin cancers, and limitations of sunscreen use.

All data were analyzed using SAS software version 9.3. Wilcoxon signed rank test, Kruskal-Wallis test, Fisher exact test, and proportional-odds cumulative logit model were used to compare nonparametric data. Parents reporting on the full first 6 months of life (ie, the child was older than 6 months at the time of study completion) were included for analysis of sun-safety strategies. All survey respondents were included for analysis of secondary objectives. Responses from parents of infants of mixed racial and ethnic backgrounds were excluded from applicable subgroup analyses.

Results

Ninety-eight parents were approached for participation in the study; 97 consented to participate and 95 completed the survey. Seventy parents had children who were at least 6 months of age and were included for analysis of the primary objectives (ie, sun-protection strategies in the first 6 months of life). The cohort included 49 Hispanic parents, 26 black parents, and 9 NHW parents; 5 parents indicated their child was of mixed racial and ethnic background. Six respondents indicated another minority group (eg, Native American, Pacific Islander). Eighty-three respondents were mothers, 72 were educated beyond high school, and 14 were Spanish-speaking only. Four reported a known family history of skin cancer.

There were notable differences in application of sunscreen, belief in the efficacy of sunscreen, and primary source of information between parents (Tables 1 and 2). Hispanic parents reported applying sunscreen more consistently than black parents (odds ratio, 4.656; 95% confidence interval, 1.154-18.782; P<.01). Hispanic parents also were more likely than black parents to believe sunscreen is effective in the prevention of skin cancers (odds ratio, 7.499; 95% confidence interval, 1.535-36.620; P<.01). Hispanic parents were more likely to report receiving information regarding sun-safety practices for infants from their pediatrician, whereas NHW parents were more likely to follow their instincts regarding how and if infants should be exposed to the sun (P<.05). No significant differences were found in the reported primary source of information in black versus Hispanic parents or in black versus NHW parents. Three percent (3/95) of respondents reported a sunburn in the infant's first 6 months of life, and 12% (11/95) reported tanning of infants' skin from sun exposure. Tanning was associated with inconsistent shade (P<.01), inconsistent clothing coverage (P<.01), and consistently allowing infants to "develop tolerance to the sun's rays by slowly increasing sun exposure each day" (P<.05).

 

Comment

The survey results indicated suboptimal sun-protection practices among parents of black and Hispanic infants in Miami. Although the majority of respondents (83% [58/70]) reported keeping their infants in the shade, less than half of parents consistently covered their infants adequately with clothing and hats (40% [28/70] and 43% [30/70], respectively). More alarmingly, one-third of parents reported intentionally increasing their infant's level of sun exposure to develop his/her tolerance to the sun. A minority of parents reported sunburns (3%) and tanning (12%) within the first 6 months of life. Twenty-nine percent of parents (20/70) reported consistently applying sunscreen to their infants who were younger than 6 months despite limited safety data available for this age group.

Although our study included a limited sample size and represents a narrow geographic distribution, these results suggest that shortcomings in current practices in sun protection for black and Hispanic infants younger than 6 months may be a widespread problem. Black and Hispanic patients have a lower incidence of skin cancer, but the diagnosis often is delayed and the mortality is higher when skin cancer does occur.⁴ The common perception among laypeople as well as many health care providers that black and Hispanic individuals are not at risk for skin cancer may limit sun-safety counseling as well as the overall knowledge base of this patient demographic. As demonstrated by the results of this study, there is a need for counseling on sun-safe behaviors from a young age among this population.

Conclusion

This study highlights potential shortcomings in current sun-protection practices for black and Hispanic infants younger than 6 months. Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ Additional studies are needed to further define sun-safety behaviors in black and Hispanic children across the United States. Further, additional studies should focus on developing interventions that positively influence sun-safety behaviors in this patient population. 

Sun exposure and sunburns sustained during childhood are linked to an increased risk for development of skin cancers in adulthood. In infants, the skin is particularly vulnerable and is considered to be at increased risk for UV radiation damage,¹ even as early as the first 6 months of life.² Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ To effectively teach parents proper sun-safe practices, it is essential to understand their existing perceptions and behaviors. This study sought to examine differences in infant sun-safety practices during the first 6 months of life among black, Hispanic, and non-Hispanic white (NHW) parents in Miami, Florida.

Methods

Parents presenting to the University of Miami general pediatrics clinic from February 2015 through April 2015 with a child younger than 5 years were administered a 15-item questionnaire that included items on demographics, sun-safety strategies, sunburns and tanning, beliefs and limitations regarding sunscreen, and primary information source regarding sun safety (eg, physician, Internet, media, instincts). Parents were approached by the investigators consecutively for participation in scheduled blocks, with the exception of those who were otherwise engaged in appointment-related tasks (eg, paperwork). The study was approved by the University of Miami Miller School of Medicine institutional review board. The primary objective of this study was to determine the sun protection behaviors that black and Hispanic parents in Miami, Florida, employ in infants younger than 6 months. Secondary objectives included determining if this patient population is at risk for infant sunburns and tanning, beliefs among parents regarding sunscreen's efficacy in the prevention of skin cancers, and limitations of sunscreen use.

All data were analyzed using SAS software version 9.3. Wilcoxon signed rank test, Kruskal-Wallis test, Fisher exact test, and proportional-odds cumulative logit model were used to compare nonparametric data. Parents reporting on the full first 6 months of life (ie, the child was older than 6 months at the time of study completion) were included for analysis of sun-safety strategies. All survey respondents were included for analysis of secondary objectives. Responses from parents of infants of mixed racial and ethnic backgrounds were excluded from applicable subgroup analyses.

Results

Ninety-eight parents were approached for participation in the study; 97 consented to participate and 95 completed the survey. Seventy parents had children who were at least 6 months of age and were included for analysis of the primary objectives (ie, sun-protection strategies in the first 6 months of life). The cohort included 49 Hispanic parents, 26 black parents, and 9 NHW parents; 5 parents indicated their child was of mixed racial and ethnic background. Six respondents indicated another minority group (eg, Native American, Pacific Islander). Eighty-three respondents were mothers, 72 were educated beyond high school, and 14 were Spanish-speaking only. Four reported a known family history of skin cancer.

There were notable differences in application of sunscreen, belief in the efficacy of sunscreen, and primary source of information between parents (Tables 1 and 2). Hispanic parents reported applying sunscreen more consistently than black parents (odds ratio, 4.656; 95% confidence interval, 1.154-18.782; P<.01). Hispanic parents also were more likely than black parents to believe sunscreen is effective in the prevention of skin cancers (odds ratio, 7.499; 95% confidence interval, 1.535-36.620; P<.01). Hispanic parents were more likely to report receiving information regarding sun-safety practices for infants from their pediatrician, whereas NHW parents were more likely to follow their instincts regarding how and if infants should be exposed to the sun (P<.05). No significant differences were found in the reported primary source of information in black versus Hispanic parents or in black versus NHW parents. Three percent (3/95) of respondents reported a sunburn in the infant's first 6 months of life, and 12% (11/95) reported tanning of infants' skin from sun exposure. Tanning was associated with inconsistent shade (P<.01), inconsistent clothing coverage (P<.01), and consistently allowing infants to "develop tolerance to the sun's rays by slowly increasing sun exposure each day" (P<.05).

 

Comment

The survey results indicated suboptimal sun-protection practices among parents of black and Hispanic infants in Miami. Although the majority of respondents (83% [58/70]) reported keeping their infants in the shade, less than half of parents consistently covered their infants adequately with clothing and hats (40% [28/70] and 43% [30/70], respectively). More alarmingly, one-third of parents reported intentionally increasing their infant's level of sun exposure to develop his/her tolerance to the sun. A minority of parents reported sunburns (3%) and tanning (12%) within the first 6 months of life. Twenty-nine percent of parents (20/70) reported consistently applying sunscreen to their infants who were younger than 6 months despite limited safety data available for this age group.

Although our study included a limited sample size and represents a narrow geographic distribution, these results suggest that shortcomings in current practices in sun protection for black and Hispanic infants younger than 6 months may be a widespread problem. Black and Hispanic patients have a lower incidence of skin cancer, but the diagnosis often is delayed and the mortality is higher when skin cancer does occur.⁴ The common perception among laypeople as well as many health care providers that black and Hispanic individuals are not at risk for skin cancer may limit sun-safety counseling as well as the overall knowledge base of this patient demographic. As demonstrated by the results of this study, there is a need for counseling on sun-safe behaviors from a young age among this population.

Conclusion

This study highlights potential shortcomings in current sun-protection practices for black and Hispanic infants younger than 6 months. Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ Additional studies are needed to further define sun-safety behaviors in black and Hispanic children across the United States. Further, additional studies should focus on developing interventions that positively influence sun-safety behaviors in this patient population. 

Sun exposure and sunburns sustained during childhood are linked to an increased risk for development of skin cancers in adulthood. In infants, the skin is particularly vulnerable and is considered to be at increased risk for UV radiation damage,¹ even as early as the first 6 months of life.² Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ To effectively teach parents proper sun-safe practices, it is essential to understand their existing perceptions and behaviors. This study sought to examine differences in infant sun-safety practices during the first 6 months of life among black, Hispanic, and non-Hispanic white (NHW) parents in Miami, Florida.

Methods

Parents presenting to the University of Miami general pediatrics clinic from February 2015 through April 2015 with a child younger than 5 years were administered a 15-item questionnaire that included items on demographics, sun-safety strategies, sunburns and tanning, beliefs and limitations regarding sunscreen, and primary information source regarding sun safety (eg, physician, Internet, media, instincts). Parents were approached by the investigators consecutively for participation in scheduled blocks, with the exception of those who were otherwise engaged in appointment-related tasks (eg, paperwork). The study was approved by the University of Miami Miller School of Medicine institutional review board. The primary objective of this study was to determine the sun protection behaviors that black and Hispanic parents in Miami, Florida, employ in infants younger than 6 months. Secondary objectives included determining if this patient population is at risk for infant sunburns and tanning, beliefs among parents regarding sunscreen's efficacy in the prevention of skin cancers, and limitations of sunscreen use.

All data were analyzed using SAS software version 9.3. Wilcoxon signed rank test, Kruskal-Wallis test, Fisher exact test, and proportional-odds cumulative logit model were used to compare nonparametric data. Parents reporting on the full first 6 months of life (ie, the child was older than 6 months at the time of study completion) were included for analysis of sun-safety strategies. All survey respondents were included for analysis of secondary objectives. Responses from parents of infants of mixed racial and ethnic backgrounds were excluded from applicable subgroup analyses.

Results

Ninety-eight parents were approached for participation in the study; 97 consented to participate and 95 completed the survey. Seventy parents had children who were at least 6 months of age and were included for analysis of the primary objectives (ie, sun-protection strategies in the first 6 months of life). The cohort included 49 Hispanic parents, 26 black parents, and 9 NHW parents; 5 parents indicated their child was of mixed racial and ethnic background. Six respondents indicated another minority group (eg, Native American, Pacific Islander). Eighty-three respondents were mothers, 72 were educated beyond high school, and 14 were Spanish-speaking only. Four reported a known family history of skin cancer.

There were notable differences in application of sunscreen, belief in the efficacy of sunscreen, and primary source of information between parents (Tables 1 and 2). Hispanic parents reported applying sunscreen more consistently than black parents (odds ratio, 4.656; 95% confidence interval, 1.154-18.782; P<.01). Hispanic parents also were more likely than black parents to believe sunscreen is effective in the prevention of skin cancers (odds ratio, 7.499; 95% confidence interval, 1.535-36.620; P<.01). Hispanic parents were more likely to report receiving information regarding sun-safety practices for infants from their pediatrician, whereas NHW parents were more likely to follow their instincts regarding how and if infants should be exposed to the sun (P<.05). No significant differences were found in the reported primary source of information in black versus Hispanic parents or in black versus NHW parents. Three percent (3/95) of respondents reported a sunburn in the infant's first 6 months of life, and 12% (11/95) reported tanning of infants' skin from sun exposure. Tanning was associated with inconsistent shade (P<.01), inconsistent clothing coverage (P<.01), and consistently allowing infants to "develop tolerance to the sun's rays by slowly increasing sun exposure each day" (P<.05).

 

Comment

The survey results indicated suboptimal sun-protection practices among parents of black and Hispanic infants in Miami. Although the majority of respondents (83% [58/70]) reported keeping their infants in the shade, less than half of parents consistently covered their infants adequately with clothing and hats (40% [28/70] and 43% [30/70], respectively). More alarmingly, one-third of parents reported intentionally increasing their infant's level of sun exposure to develop his/her tolerance to the sun. A minority of parents reported sunburns (3%) and tanning (12%) within the first 6 months of life. Twenty-nine percent of parents (20/70) reported consistently applying sunscreen to their infants who were younger than 6 months despite limited safety data available for this age group.

Although our study included a limited sample size and represents a narrow geographic distribution, these results suggest that shortcomings in current practices in sun protection for black and Hispanic infants younger than 6 months may be a widespread problem. Black and Hispanic patients have a lower incidence of skin cancer, but the diagnosis often is delayed and the mortality is higher when skin cancer does occur.⁴ The common perception among laypeople as well as many health care providers that black and Hispanic individuals are not at risk for skin cancer may limit sun-safety counseling as well as the overall knowledge base of this patient demographic. As demonstrated by the results of this study, there is a need for counseling on sun-safe behaviors from a young age among this population.

Conclusion

This study highlights potential shortcomings in current sun-protection practices for black and Hispanic infants younger than 6 months. Sun-safe behaviors instituted from a young age may help reduce the risk for future skin cancers.³ Additional studies are needed to further define sun-safety behaviors in black and Hispanic children across the United States. Further, additional studies should focus on developing interventions that positively influence sun-safety behaviors in this patient population. 

ABSTRACT

Objective To reduce unnecessary orthopedic referrals by developing a protocol for managing physiologic bow legs in the primary care environment through the use of a noninvasive technique that simultaneously tracks normal varus progression and screens for potential pathologic bowing requiring an orthopedic referral.

Methods Retrospective study of 155 patients with physiologic genu varum and 10 with infantile Blount’s disease. We used fingerbreadth measurements to document progression or resolution of bow legs. Final diagnoses were made by one orthopedic surgeon using clinical and radiographic evidence. We divided genu varum patients into 3 groups: patients presenting with bow legs before 18 months of age (MOA), patients presenting between 18 and 23 MOA, and patients presenting at 24 MOA or older for analyses relevant to the development of the follow-up protocol.

Results Physiologic genu varum patients walked earlier than average infants (10 months vs 12-15 months; P<.001). Physiologic genu varum patients presenting before 18 MOA demonstrated initial signs of correction between 18 and 24 MOA and resolution by 30 MOA. Physiologic genu varum patients presenting between 18 and 23 MOA demonstrated initial signs of correction between 24 MOA and 30 MOA and resolution by 36 MOA.

Conclusion Primary care physicians can manage most children presenting with bow legs. Management focuses on following the progression or resolution of varus with regular follow-up. For patients presenting with bow legs, we recommend a follow-up protocol using mainly well-child checkups and a simple clinical assessment to monitor varus progression and screen for pathologic bowing.

Bow legs in young children can be a concern for parents.^1,2 By far, the most common reason for bow legs is physiologic genu varum,^3-5 a nonprogressive stage of normal development in young children that generally resolves spontaneously without treatment.^1,6-11 Normally developing children undergo a varus phase between birth and 18 to 24 months of age (MOA), at which time there is usually a transition in alignment from varus to straight to valgus (knock knees), which will correct to straight or mild valgus throughout adolescence.^{1,6,7,9,10,12-17}

The most common form of pathologic bow legs is Blount’s disease, also known as tibia vara, which must be differentiated from physiologic genu varum.^{8-10,15,18-24} The progressive varus deformity of Blount’s disease usually requires orthopedic intervention.^1,10,23-26 Early diagnosis may spare patients complex interventions, improve prognosis, and limit complications that include gait abnormalities,^4,8,10,27 knee joint instability,^4,24,27 osteoarthritis,^9,20,27 meniscal tears,²⁷ and degenerative joint disease.^19,20,27

Although variables such as walking age, race, weight, and gender have been suggested as risk factors for Blount’s disease, they have not been useful in differentiating between Blount’s pathology and physiologic genu varum.^{1,4,5,7,10,20,28} In the primary care setting, distinguishing physiologic from pathologic forms of bow legs is possible with a thorough history and physical exam and with radiographs, as warranted.^1,2,15 More than 40% of genu varum/genu valgum cases referred for orthopedic consultation turn out to be the physiologic form,² suggesting a need for guidelines in the primary care setting to help direct referral and follow-up. The purpose of this study was to provide recommendations to family physicians for evaluating and managing children with bow legs.

Materials and methods

This study, approved by the Internal Review Board of Akron Children’s Hospital, is a retrospective review of children seen by a single pediatric orthopedic surgeon (DSW) from 1970 to 2012. Four-hundred twenty-four children were received for evaluation of bow legs. Excluded from our final analysis were 220 subjects seen only once for this specific referral and 39 subjects diagnosed with a condition other than genu varum or Blount’s disease (ie, rickets, skeletal dysplasia, sequelae of trauma, or infection). Ten subjects with Blount’s disease and 155 subjects with physiologic genu varum were included in the final data analysis.

More than 40% of genu varum cases referred for orthopedic consultation turn out to be the physiologic form.In addition to noting the age at which a patient walked independently, at each visit we documented age and the fingerbreadth (varus) distance between the medial femoral condyles with the child’s ankles held together. Parents reported age of independent walking for just 3 children with Blount’s disease and for 134 children with physiologic genu varum. Study variables for the genu varum data analysis were age of walking, age at presentation, age at varus correction, age at varus resolution, time between presentation and varus correction, and time between presentation and varus resolution. Varus correction is defined as any decrease in varus angulation since presentation. Varus resolution is defined as varus correction to less than or equal to half of the varus angulation at presentation. For inclusion in the age-at-resolution analysis, a child must have been evaluated at regular follow-up visits (all rechecks within 8 months).

To measure varus distance, we used the fingerbreadth method described by Weiner in a study of 600 cases (FIGURE).⁶ This simple technique, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development. The patient should be supine on the examination table with legs extended. With one hand, the examiner holds the child’s ankles together, ensuring the medial malleoli are in contact. With the other hand, the examiner measures the fingerbreadth distance between the medial femoral condyles. Alternatively, a ruler may be used to measure the distance. This latter method may be especially useful in practices where the patient is likely to see more than one provider for well child care.

We divided the genu varum subject group into 3 subgroups by age at presentation: 103 subjects were younger than 18 months; 47 were 18 to 23 months; and 5 were 24 months or older. We used the data analysis toolkit in Microsoft Excel 2013 to perform a statistical analysis of study variables. We assumed the genu varum population is a normally distributed population. We used a 95% confidence level (α=0.05) for all calculations of confidence intervals (CIs), student t-tests, and tolerance intervals. Based on the data analysis results, we developed a series of follow-up and referral guidelines for practitioners.

 

Results

The mean walking age for those diagnosed with physiologic genu varum was 10 months (95% CI, 9.8-10.4), which is significantly younger than the 12 months of age (at the earliest) typical of toddlers in general (P<.001). There was no significant difference between the walking age of male and female children diagnosed with genu varum (P=.37).

Of the children presenting with the primary complaint of bow legs, 6% subsequently developed Blount’s disease. These patients presented at a mean age of 20.9 months and were diagnosed at a mean age of 23.9 months. Following the Blount’s disease diagnosis, we initiated therapy in all cases (3 surgical, 7 bracing).

Physiologic genu varum patients presented at a mean age of 16.4 months, with only 3.23% presenting at older than 23 months. On average, physiologic genu varum patients presenting before 24 months of age showed measurable varus correction 5 months after presentation and achieved varus resolution 7.3 months after presentation (TABLE 1). Assuming the patient population is normally distributed, we can be 95% confident that 95% of physiologic genu varum patients presenting before 18 months of age will show measurable varus correction by 24 months and will resolve without intervention by 30 months (TABLE 2). Patients presenting between 18 and 23 months of age should show measurable varus correction by 30 months and resolution by 36 months (TABLE 3).

Discussion

Primary care physicians have the ability to differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination, if necessary^1,2,13 (TABLE 4^{1,7,8,10,12,14,18-20,22,24,27}). Several approaches to differentiating Blount’s disease and physiologic genu varum have been described in the literature.^{1,4,7,8,10,14,22,23}

The average age at which children begin to walk independently is between 13 and 15 months.^5,18,29-31 Recently, it has been suggested that the range be expanded to include 12 months of age.³⁰ The association between early walking (at 10-11 months)^12,20,22 and Blount’s disease is generally accepted in the orthopedic literature.^{1,4,7,10,19-22} However, some authors have suggested early walking also contributes to genu varum.^{1,5,8,10,18,28} The mean age of independent walking for children with physiologic genu varum suggested in the literature (10 months) was confirmed in our study and found to be significantly younger than the average for toddlers generally.^1,22 Early walking is clearly associated with both physiologic genu varum and Blount’s disease, but no direct causation has been identified in either case. An alternative means of differentiating these entities is needed.

Primary care physicians can differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination (if needed).Radiographic examination of the knee is essential to the diagnosis of Blount’s disease as well as other, less common causes of pathologic bow legs (skeletal dysplasia, rickets, traumatic growth plate insults, infections, neoplasms).^1,8,14,19 The common radiologic classification of staging for Blount’s disease is the Langenskiöld staging system, which involves identification of characteristic radiographic changes at the tibial physis.^{5,8,14,15,18,22,24}

The fingerbreadth method, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development.Sequential measurement of genu varum is most useful in differentiating between physiologic and pathologic processes. Physiologic genu varum, an exaggeration of the normal developmental pattern, characteristically resolves and evolves into physiologic genu valgum by 3 years of age.^1,6-11 The pathophysiology of Blount’s disease is believed to be related to biomechanical overloading of the posteromedial proximal tibia during gait with the knee in a varus orientation. Excess loading on the proximal medial physis contributes to varus progression.^{4,10,14,20,25,27} Patients with Blount’s disease progress with varus and concomitant internal tibial torsion associated with growth plate irregularities and eventually exhibit premature closure.^{1,10,14,18,20,23,24,26} In the months prior to Blount’s disease diagnosis, increasing varus has been reported.^4,7,10,19 Varus progression that differs from the expected pattern indicates possible pathologic bow legs and should prompt radiologic evaluation and, often, an orthopedic referral.^{3,4,7-9,12,13,21}

 

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount’s disease patients. We recommend considering orthopedic referral for any patient presenting with bow legs at 24 months of age or older. Additionally, consider orthopedic referral for any patient whose varus has not begun to correct within 8 months or has not resolved within 14 months of presentation, as more than 95% of patients with physiologic genu varum are expected to meet these milestones (TABLE 1). And do not hesitate to refer patients at any stage of follow-up if you suspect pathology or if parents are anxious.

If no sign of pathology is immediately identified, we recommend the following course of action:

• Record a reference fingerbreadth or ruler measurement at the initial presentation.
• Re-examine the knee varus at the next regular well-child visit (TABLE 5).

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount's disease patients.Re-examining the patient prior to the next well-child visit is unnecessary, as some degree of bowing is typical until age 18 to 24 months.^{1,6,7,9,12,13,17} Recommend orthopedic referral for any patient with varus that has progressed since initial presentation. Without signs of pathology, repeat varus assessment at the next well-child visit. This schedule minimizes the need for additional physician appointments by integrating follow-up into the typical well-child visits at 18, 24, 30, and 36 months of age.³² The 6-month follow-up interval was a feature of our study and is recommended in the related literature.¹²

• Consider orthopedic referral for patients whose varus has not corrected by the second follow-up appointment, as more than 95% of patients should have measurable varus correction at this visit. Most patients will have exhibited varus resolution by this time and will not require additional follow-up. For patients with observable correction who do not yet meet the criteria for resolution, we recommend a third, final follow-up appointment in another 6 months.
• Refer any patient whose varus has not resolved by the third follow-up appointment, as more than 95% of genu varum cases should have resolved by this time. This finding is echoed in the literature; any varus beyond 36 months of age is considered abnormal and suggestive of pathology.^5,7,8,13,14 If evidence of Blount’s or skeletal dysplasia is identified, orthopedic management will likely consist of bracing (orthotics) or surgical management.

CORRESPONDENCE
Dennis S. Weiner, MD, Department of Orthopedic Surgery, Akron Children’s Hospital, 300 Locust Street, Suite 250, Akron, OH, 44302; [email protected].

ACKNOWLEDGEMENTS
The authors thank Meadow Newton, BS, assistant research coordinator, Akron Children’s Hospital, for her editing and technical assistance and Richard Steiner, PhD, The University of Akron, for his statistical review.

ABSTRACT

Objective To reduce unnecessary orthopedic referrals by developing a protocol for managing physiologic bow legs in the primary care environment through the use of a noninvasive technique that simultaneously tracks normal varus progression and screens for potential pathologic bowing requiring an orthopedic referral.

Methods Retrospective study of 155 patients with physiologic genu varum and 10 with infantile Blount’s disease. We used fingerbreadth measurements to document progression or resolution of bow legs. Final diagnoses were made by one orthopedic surgeon using clinical and radiographic evidence. We divided genu varum patients into 3 groups: patients presenting with bow legs before 18 months of age (MOA), patients presenting between 18 and 23 MOA, and patients presenting at 24 MOA or older for analyses relevant to the development of the follow-up protocol.

Results Physiologic genu varum patients walked earlier than average infants (10 months vs 12-15 months; P<.001). Physiologic genu varum patients presenting before 18 MOA demonstrated initial signs of correction between 18 and 24 MOA and resolution by 30 MOA. Physiologic genu varum patients presenting between 18 and 23 MOA demonstrated initial signs of correction between 24 MOA and 30 MOA and resolution by 36 MOA.

Conclusion Primary care physicians can manage most children presenting with bow legs. Management focuses on following the progression or resolution of varus with regular follow-up. For patients presenting with bow legs, we recommend a follow-up protocol using mainly well-child checkups and a simple clinical assessment to monitor varus progression and screen for pathologic bowing.

Bow legs in young children can be a concern for parents.^1,2 By far, the most common reason for bow legs is physiologic genu varum,^3-5 a nonprogressive stage of normal development in young children that generally resolves spontaneously without treatment.^1,6-11 Normally developing children undergo a varus phase between birth and 18 to 24 months of age (MOA), at which time there is usually a transition in alignment from varus to straight to valgus (knock knees), which will correct to straight or mild valgus throughout adolescence.^{1,6,7,9,10,12-17}

The most common form of pathologic bow legs is Blount’s disease, also known as tibia vara, which must be differentiated from physiologic genu varum.^{8-10,15,18-24} The progressive varus deformity of Blount’s disease usually requires orthopedic intervention.^1,10,23-26 Early diagnosis may spare patients complex interventions, improve prognosis, and limit complications that include gait abnormalities,^4,8,10,27 knee joint instability,^4,24,27 osteoarthritis,^9,20,27 meniscal tears,²⁷ and degenerative joint disease.^19,20,27

Although variables such as walking age, race, weight, and gender have been suggested as risk factors for Blount’s disease, they have not been useful in differentiating between Blount’s pathology and physiologic genu varum.^{1,4,5,7,10,20,28} In the primary care setting, distinguishing physiologic from pathologic forms of bow legs is possible with a thorough history and physical exam and with radiographs, as warranted.^1,2,15 More than 40% of genu varum/genu valgum cases referred for orthopedic consultation turn out to be the physiologic form,² suggesting a need for guidelines in the primary care setting to help direct referral and follow-up. The purpose of this study was to provide recommendations to family physicians for evaluating and managing children with bow legs.

Materials and methods

This study, approved by the Internal Review Board of Akron Children’s Hospital, is a retrospective review of children seen by a single pediatric orthopedic surgeon (DSW) from 1970 to 2012. Four-hundred twenty-four children were received for evaluation of bow legs. Excluded from our final analysis were 220 subjects seen only once for this specific referral and 39 subjects diagnosed with a condition other than genu varum or Blount’s disease (ie, rickets, skeletal dysplasia, sequelae of trauma, or infection). Ten subjects with Blount’s disease and 155 subjects with physiologic genu varum were included in the final data analysis.

More than 40% of genu varum cases referred for orthopedic consultation turn out to be the physiologic form.In addition to noting the age at which a patient walked independently, at each visit we documented age and the fingerbreadth (varus) distance between the medial femoral condyles with the child’s ankles held together. Parents reported age of independent walking for just 3 children with Blount’s disease and for 134 children with physiologic genu varum. Study variables for the genu varum data analysis were age of walking, age at presentation, age at varus correction, age at varus resolution, time between presentation and varus correction, and time between presentation and varus resolution. Varus correction is defined as any decrease in varus angulation since presentation. Varus resolution is defined as varus correction to less than or equal to half of the varus angulation at presentation. For inclusion in the age-at-resolution analysis, a child must have been evaluated at regular follow-up visits (all rechecks within 8 months).

To measure varus distance, we used the fingerbreadth method described by Weiner in a study of 600 cases (FIGURE).⁶ This simple technique, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development. The patient should be supine on the examination table with legs extended. With one hand, the examiner holds the child’s ankles together, ensuring the medial malleoli are in contact. With the other hand, the examiner measures the fingerbreadth distance between the medial femoral condyles. Alternatively, a ruler may be used to measure the distance. This latter method may be especially useful in practices where the patient is likely to see more than one provider for well child care.

We divided the genu varum subject group into 3 subgroups by age at presentation: 103 subjects were younger than 18 months; 47 were 18 to 23 months; and 5 were 24 months or older. We used the data analysis toolkit in Microsoft Excel 2013 to perform a statistical analysis of study variables. We assumed the genu varum population is a normally distributed population. We used a 95% confidence level (α=0.05) for all calculations of confidence intervals (CIs), student t-tests, and tolerance intervals. Based on the data analysis results, we developed a series of follow-up and referral guidelines for practitioners.

 

Results

The mean walking age for those diagnosed with physiologic genu varum was 10 months (95% CI, 9.8-10.4), which is significantly younger than the 12 months of age (at the earliest) typical of toddlers in general (P<.001). There was no significant difference between the walking age of male and female children diagnosed with genu varum (P=.37).

Of the children presenting with the primary complaint of bow legs, 6% subsequently developed Blount’s disease. These patients presented at a mean age of 20.9 months and were diagnosed at a mean age of 23.9 months. Following the Blount’s disease diagnosis, we initiated therapy in all cases (3 surgical, 7 bracing).

Physiologic genu varum patients presented at a mean age of 16.4 months, with only 3.23% presenting at older than 23 months. On average, physiologic genu varum patients presenting before 24 months of age showed measurable varus correction 5 months after presentation and achieved varus resolution 7.3 months after presentation (TABLE 1). Assuming the patient population is normally distributed, we can be 95% confident that 95% of physiologic genu varum patients presenting before 18 months of age will show measurable varus correction by 24 months and will resolve without intervention by 30 months (TABLE 2). Patients presenting between 18 and 23 months of age should show measurable varus correction by 30 months and resolution by 36 months (TABLE 3).

Discussion

Primary care physicians have the ability to differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination, if necessary^1,2,13 (TABLE 4^{1,7,8,10,12,14,18-20,22,24,27}). Several approaches to differentiating Blount’s disease and physiologic genu varum have been described in the literature.^{1,4,7,8,10,14,22,23}

The average age at which children begin to walk independently is between 13 and 15 months.^5,18,29-31 Recently, it has been suggested that the range be expanded to include 12 months of age.³⁰ The association between early walking (at 10-11 months)^12,20,22 and Blount’s disease is generally accepted in the orthopedic literature.^{1,4,7,10,19-22} However, some authors have suggested early walking also contributes to genu varum.^{1,5,8,10,18,28} The mean age of independent walking for children with physiologic genu varum suggested in the literature (10 months) was confirmed in our study and found to be significantly younger than the average for toddlers generally.^1,22 Early walking is clearly associated with both physiologic genu varum and Blount’s disease, but no direct causation has been identified in either case. An alternative means of differentiating these entities is needed.

Primary care physicians can differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination (if needed).Radiographic examination of the knee is essential to the diagnosis of Blount’s disease as well as other, less common causes of pathologic bow legs (skeletal dysplasia, rickets, traumatic growth plate insults, infections, neoplasms).^1,8,14,19 The common radiologic classification of staging for Blount’s disease is the Langenskiöld staging system, which involves identification of characteristic radiographic changes at the tibial physis.^{5,8,14,15,18,22,24}

The fingerbreadth method, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development.Sequential measurement of genu varum is most useful in differentiating between physiologic and pathologic processes. Physiologic genu varum, an exaggeration of the normal developmental pattern, characteristically resolves and evolves into physiologic genu valgum by 3 years of age.^1,6-11 The pathophysiology of Blount’s disease is believed to be related to biomechanical overloading of the posteromedial proximal tibia during gait with the knee in a varus orientation. Excess loading on the proximal medial physis contributes to varus progression.^{4,10,14,20,25,27} Patients with Blount’s disease progress with varus and concomitant internal tibial torsion associated with growth plate irregularities and eventually exhibit premature closure.^{1,10,14,18,20,23,24,26} In the months prior to Blount’s disease diagnosis, increasing varus has been reported.^4,7,10,19 Varus progression that differs from the expected pattern indicates possible pathologic bow legs and should prompt radiologic evaluation and, often, an orthopedic referral.^{3,4,7-9,12,13,21}

 

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount’s disease patients. We recommend considering orthopedic referral for any patient presenting with bow legs at 24 months of age or older. Additionally, consider orthopedic referral for any patient whose varus has not begun to correct within 8 months or has not resolved within 14 months of presentation, as more than 95% of patients with physiologic genu varum are expected to meet these milestones (TABLE 1). And do not hesitate to refer patients at any stage of follow-up if you suspect pathology or if parents are anxious.

If no sign of pathology is immediately identified, we recommend the following course of action:

• Record a reference fingerbreadth or ruler measurement at the initial presentation.
• Re-examine the knee varus at the next regular well-child visit (TABLE 5).

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount's disease patients.Re-examining the patient prior to the next well-child visit is unnecessary, as some degree of bowing is typical until age 18 to 24 months.^{1,6,7,9,12,13,17} Recommend orthopedic referral for any patient with varus that has progressed since initial presentation. Without signs of pathology, repeat varus assessment at the next well-child visit. This schedule minimizes the need for additional physician appointments by integrating follow-up into the typical well-child visits at 18, 24, 30, and 36 months of age.³² The 6-month follow-up interval was a feature of our study and is recommended in the related literature.¹²

• Consider orthopedic referral for patients whose varus has not corrected by the second follow-up appointment, as more than 95% of patients should have measurable varus correction at this visit. Most patients will have exhibited varus resolution by this time and will not require additional follow-up. For patients with observable correction who do not yet meet the criteria for resolution, we recommend a third, final follow-up appointment in another 6 months.
• Refer any patient whose varus has not resolved by the third follow-up appointment, as more than 95% of genu varum cases should have resolved by this time. This finding is echoed in the literature; any varus beyond 36 months of age is considered abnormal and suggestive of pathology.^5,7,8,13,14 If evidence of Blount’s or skeletal dysplasia is identified, orthopedic management will likely consist of bracing (orthotics) or surgical management.

CORRESPONDENCE
Dennis S. Weiner, MD, Department of Orthopedic Surgery, Akron Children’s Hospital, 300 Locust Street, Suite 250, Akron, OH, 44302; [email protected].

ACKNOWLEDGEMENTS
The authors thank Meadow Newton, BS, assistant research coordinator, Akron Children’s Hospital, for her editing and technical assistance and Richard Steiner, PhD, The University of Akron, for his statistical review.

ABSTRACT

Objective To reduce unnecessary orthopedic referrals by developing a protocol for managing physiologic bow legs in the primary care environment through the use of a noninvasive technique that simultaneously tracks normal varus progression and screens for potential pathologic bowing requiring an orthopedic referral.

Methods Retrospective study of 155 patients with physiologic genu varum and 10 with infantile Blount’s disease. We used fingerbreadth measurements to document progression or resolution of bow legs. Final diagnoses were made by one orthopedic surgeon using clinical and radiographic evidence. We divided genu varum patients into 3 groups: patients presenting with bow legs before 18 months of age (MOA), patients presenting between 18 and 23 MOA, and patients presenting at 24 MOA or older for analyses relevant to the development of the follow-up protocol.

Results Physiologic genu varum patients walked earlier than average infants (10 months vs 12-15 months; P<.001). Physiologic genu varum patients presenting before 18 MOA demonstrated initial signs of correction between 18 and 24 MOA and resolution by 30 MOA. Physiologic genu varum patients presenting between 18 and 23 MOA demonstrated initial signs of correction between 24 MOA and 30 MOA and resolution by 36 MOA.

Conclusion Primary care physicians can manage most children presenting with bow legs. Management focuses on following the progression or resolution of varus with regular follow-up. For patients presenting with bow legs, we recommend a follow-up protocol using mainly well-child checkups and a simple clinical assessment to monitor varus progression and screen for pathologic bowing.

Bow legs in young children can be a concern for parents.^1,2 By far, the most common reason for bow legs is physiologic genu varum,^3-5 a nonprogressive stage of normal development in young children that generally resolves spontaneously without treatment.^1,6-11 Normally developing children undergo a varus phase between birth and 18 to 24 months of age (MOA), at which time there is usually a transition in alignment from varus to straight to valgus (knock knees), which will correct to straight or mild valgus throughout adolescence.^{1,6,7,9,10,12-17}

The most common form of pathologic bow legs is Blount’s disease, also known as tibia vara, which must be differentiated from physiologic genu varum.^{8-10,15,18-24} The progressive varus deformity of Blount’s disease usually requires orthopedic intervention.^1,10,23-26 Early diagnosis may spare patients complex interventions, improve prognosis, and limit complications that include gait abnormalities,^4,8,10,27 knee joint instability,^4,24,27 osteoarthritis,^9,20,27 meniscal tears,²⁷ and degenerative joint disease.^19,20,27

Although variables such as walking age, race, weight, and gender have been suggested as risk factors for Blount’s disease, they have not been useful in differentiating between Blount’s pathology and physiologic genu varum.^{1,4,5,7,10,20,28} In the primary care setting, distinguishing physiologic from pathologic forms of bow legs is possible with a thorough history and physical exam and with radiographs, as warranted.^1,2,15 More than 40% of genu varum/genu valgum cases referred for orthopedic consultation turn out to be the physiologic form,² suggesting a need for guidelines in the primary care setting to help direct referral and follow-up. The purpose of this study was to provide recommendations to family physicians for evaluating and managing children with bow legs.

Materials and methods

This study, approved by the Internal Review Board of Akron Children’s Hospital, is a retrospective review of children seen by a single pediatric orthopedic surgeon (DSW) from 1970 to 2012. Four-hundred twenty-four children were received for evaluation of bow legs. Excluded from our final analysis were 220 subjects seen only once for this specific referral and 39 subjects diagnosed with a condition other than genu varum or Blount’s disease (ie, rickets, skeletal dysplasia, sequelae of trauma, or infection). Ten subjects with Blount’s disease and 155 subjects with physiologic genu varum were included in the final data analysis.

More than 40% of genu varum cases referred for orthopedic consultation turn out to be the physiologic form.In addition to noting the age at which a patient walked independently, at each visit we documented age and the fingerbreadth (varus) distance between the medial femoral condyles with the child’s ankles held together. Parents reported age of independent walking for just 3 children with Blount’s disease and for 134 children with physiologic genu varum. Study variables for the genu varum data analysis were age of walking, age at presentation, age at varus correction, age at varus resolution, time between presentation and varus correction, and time between presentation and varus resolution. Varus correction is defined as any decrease in varus angulation since presentation. Varus resolution is defined as varus correction to less than or equal to half of the varus angulation at presentation. For inclusion in the age-at-resolution analysis, a child must have been evaluated at regular follow-up visits (all rechecks within 8 months).

To measure varus distance, we used the fingerbreadth method described by Weiner in a study of 600 cases (FIGURE).⁶ This simple technique, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development. The patient should be supine on the examination table with legs extended. With one hand, the examiner holds the child’s ankles together, ensuring the medial malleoli are in contact. With the other hand, the examiner measures the fingerbreadth distance between the medial femoral condyles. Alternatively, a ruler may be used to measure the distance. This latter method may be especially useful in practices where the patient is likely to see more than one provider for well child care.

We divided the genu varum subject group into 3 subgroups by age at presentation: 103 subjects were younger than 18 months; 47 were 18 to 23 months; and 5 were 24 months or older. We used the data analysis toolkit in Microsoft Excel 2013 to perform a statistical analysis of study variables. We assumed the genu varum population is a normally distributed population. We used a 95% confidence level (α=0.05) for all calculations of confidence intervals (CIs), student t-tests, and tolerance intervals. Based on the data analysis results, we developed a series of follow-up and referral guidelines for practitioners.

 

Results

The mean walking age for those diagnosed with physiologic genu varum was 10 months (95% CI, 9.8-10.4), which is significantly younger than the 12 months of age (at the earliest) typical of toddlers in general (P<.001). There was no significant difference between the walking age of male and female children diagnosed with genu varum (P=.37).

Of the children presenting with the primary complaint of bow legs, 6% subsequently developed Blount’s disease. These patients presented at a mean age of 20.9 months and were diagnosed at a mean age of 23.9 months. Following the Blount’s disease diagnosis, we initiated therapy in all cases (3 surgical, 7 bracing).

Physiologic genu varum patients presented at a mean age of 16.4 months, with only 3.23% presenting at older than 23 months. On average, physiologic genu varum patients presenting before 24 months of age showed measurable varus correction 5 months after presentation and achieved varus resolution 7.3 months after presentation (TABLE 1). Assuming the patient population is normally distributed, we can be 95% confident that 95% of physiologic genu varum patients presenting before 18 months of age will show measurable varus correction by 24 months and will resolve without intervention by 30 months (TABLE 2). Patients presenting between 18 and 23 months of age should show measurable varus correction by 30 months and resolution by 36 months (TABLE 3).

Discussion

Primary care physicians have the ability to differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination, if necessary^1,2,13 (TABLE 4^{1,7,8,10,12,14,18-20,22,24,27}). Several approaches to differentiating Blount’s disease and physiologic genu varum have been described in the literature.^{1,4,7,8,10,14,22,23}

The average age at which children begin to walk independently is between 13 and 15 months.^5,18,29-31 Recently, it has been suggested that the range be expanded to include 12 months of age.³⁰ The association between early walking (at 10-11 months)^12,20,22 and Blount’s disease is generally accepted in the orthopedic literature.^{1,4,7,10,19-22} However, some authors have suggested early walking also contributes to genu varum.^{1,5,8,10,18,28} The mean age of independent walking for children with physiologic genu varum suggested in the literature (10 months) was confirmed in our study and found to be significantly younger than the average for toddlers generally.^1,22 Early walking is clearly associated with both physiologic genu varum and Blount’s disease, but no direct causation has been identified in either case. An alternative means of differentiating these entities is needed.

Primary care physicians can differentiate physiologic genu varum from pathologic forms of bow legs with a thorough history, physical exam, and radiographic examination (if needed).Radiographic examination of the knee is essential to the diagnosis of Blount’s disease as well as other, less common causes of pathologic bow legs (skeletal dysplasia, rickets, traumatic growth plate insults, infections, neoplasms).^1,8,14,19 The common radiologic classification of staging for Blount’s disease is the Langenskiöld staging system, which involves identification of characteristic radiographic changes at the tibial physis.^{5,8,14,15,18,22,24}

The fingerbreadth method, which requires no special equipment, accurately detected differences in varus angulation and tracked the normal pattern of lower limb angular development.Sequential measurement of genu varum is most useful in differentiating between physiologic and pathologic processes. Physiologic genu varum, an exaggeration of the normal developmental pattern, characteristically resolves and evolves into physiologic genu valgum by 3 years of age.^1,6-11 The pathophysiology of Blount’s disease is believed to be related to biomechanical overloading of the posteromedial proximal tibia during gait with the knee in a varus orientation. Excess loading on the proximal medial physis contributes to varus progression.^{4,10,14,20,25,27} Patients with Blount’s disease progress with varus and concomitant internal tibial torsion associated with growth plate irregularities and eventually exhibit premature closure.^{1,10,14,18,20,23,24,26} In the months prior to Blount’s disease diagnosis, increasing varus has been reported.^4,7,10,19 Varus progression that differs from the expected pattern indicates possible pathologic bow legs and should prompt radiologic evaluation and, often, an orthopedic referral.^{3,4,7-9,12,13,21}

 

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount’s disease patients. We recommend considering orthopedic referral for any patient presenting with bow legs at 24 months of age or older. Additionally, consider orthopedic referral for any patient whose varus has not begun to correct within 8 months or has not resolved within 14 months of presentation, as more than 95% of patients with physiologic genu varum are expected to meet these milestones (TABLE 1). And do not hesitate to refer patients at any stage of follow-up if you suspect pathology or if parents are anxious.

If no sign of pathology is immediately identified, we recommend the following course of action:

• Record a reference fingerbreadth or ruler measurement at the initial presentation.
• Re-examine the knee varus at the next regular well-child visit (TABLE 5).

In our study, only 3% of children with physiologic genu varum presented at 24 months of age or older, compared with 20% of Blount's disease patients.Re-examining the patient prior to the next well-child visit is unnecessary, as some degree of bowing is typical until age 18 to 24 months.^{1,6,7,9,12,13,17} Recommend orthopedic referral for any patient with varus that has progressed since initial presentation. Without signs of pathology, repeat varus assessment at the next well-child visit. This schedule minimizes the need for additional physician appointments by integrating follow-up into the typical well-child visits at 18, 24, 30, and 36 months of age.³² The 6-month follow-up interval was a feature of our study and is recommended in the related literature.¹²

• Consider orthopedic referral for patients whose varus has not corrected by the second follow-up appointment, as more than 95% of patients should have measurable varus correction at this visit. Most patients will have exhibited varus resolution by this time and will not require additional follow-up. For patients with observable correction who do not yet meet the criteria for resolution, we recommend a third, final follow-up appointment in another 6 months.
• Refer any patient whose varus has not resolved by the third follow-up appointment, as more than 95% of genu varum cases should have resolved by this time. This finding is echoed in the literature; any varus beyond 36 months of age is considered abnormal and suggestive of pathology.^5,7,8,13,14 If evidence of Blount’s or skeletal dysplasia is identified, orthopedic management will likely consist of bracing (orthotics) or surgical management.

CORRESPONDENCE
Dennis S. Weiner, MD, Department of Orthopedic Surgery, Akron Children’s Hospital, 300 Locust Street, Suite 250, Akron, OH, 44302; [email protected].

ACKNOWLEDGEMENTS
The authors thank Meadow Newton, BS, assistant research coordinator, Akron Children’s Hospital, for her editing and technical assistance and Richard Steiner, PhD, The University of Akron, for his statistical review.

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

 

METHODS

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

 

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

RESULTS

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

 

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

DISCUSSION

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

 

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

 

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

 

METHODS

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

 

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

RESULTS

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

 

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

DISCUSSION

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

 

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

 

The deleterious impact of inappropriate and/or excessive antimicrobial usage is well recognized. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that at least 2 million people become infected with antimicrobial-resistant bacteria with 23,000 subsequent deaths and at least $1 billion in excess medical costs per year.¹

In response, many healthcare organizations have developed antimicrobial stewardship programs (ASPs). Guidelines co-sponsored by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America, as well as recent statements from the CDC and the Transatlantic Taskforce on Antimicrobial Resistance, all recommend core ASP elements.^2-5 The guidelines provide general recommendations on ASP structure, strategies, and activities. The recommended ASP structure is a team of physicians and pharmacists that collaborates with facility governing committees and other stakeholders to optimize antimicrobial use. While personnel with expertise in infectious diseases (ID) often lead ASPs, hospitalists are also recognized as key contributors, especially in quality improvement.^6,7 Recommended strategies include prospective audit of antimicrobial use with intervention and feedback and formulary restriction with preauthorization. Recommended activities include education, creation of guidelines, clinical pathways, and order forms, and programs to promote de-escalation and conversion from parenteral (IV) to oral (PO) antimicrobial therapy. However, limited evidence exists regarding the effectiveness of these ASP core elements.^8,9 While Cochrane reviews found clear evidence that particular stewardship strategies (eg, audit and feedback, formulary restriction, guidelines implemented with or without feedback, protocols, computerized decision support) can be effective in reducing antimicrobial usage and improving clinical outcomes over the long term, little evidence exists favoring 1 strategy over another.⁸ Furthermore, most individual studies of ASPs are single-center, making their conclusions less generalizable.

In 2012, the VA National Antimicrobial Stewardship Task Force (ASTF), in conjunction with the VA Healthcare Analysis and Information Group (HAIG) administered a survey on the characteristics of ASPs at all 130 acute care VA facilities (Appendix A). We used these survey results to build an implementation model and then assess associations between facility-level variables and 4 antimicrobial utilization measures.

 

METHODS

Survey and Data

In 2011, the ASTF was chartered to develop, deploy, and monitor a strategic plan for optimizing antimicrobial therapy management. Monthly educational webinars and sample policies were offered to all facilities, including a sample business plan for stewardship and policies to encourage de-escalation from broad-spectrum antimicrobials, promote conversion from parenteral to oral antimicrobial therapy, avoid unnecessary double anaerobic coverage, and mitigate unnecessary antimicrobial usage in the context of Clostridium difficile infection.¹⁰

At the time that ASTF was chartered, the understanding of how ASP structures across VA facilities operated was limited. Hence, to capture baseline institutional characteristics and stewardship activities, ASTF and HAIG developed an inventory assessment of ASPs that was distributed online in November 2012. All 130 VA facilities providing inpatient acute care services responded.

We derived 57 facility characteristics relevant to antimicrobial utilization and conducted a series of factor analyses to simplify the complex dataset, and identify underlying latent constructs. We categorized resulting factors into domains of evidence, context, or facilitation as guided by the Promoting Action on Research Implementation in Health Services framework.¹¹ Briefly, the evidence domain describes how the facility uses codified and noncodified sources of knowledge (eg, research evidence, clinical experience). Organizational context comprises a facility’s characteristics that ensure a more conducive environment to put evidence into practice (eg, supportive leadership, organizational structure, evaluative systems). Facilitation emphasizes a facility personnel’s “state of preparedness” and receptivity to implementation.

Using factor analysis to identify facility factors as correlates of the outcomes, we first examined polychoric correlations among facility characteristics to assess multicollinearity. We performed independent component analysis to create latent constructs of variables that were defined by factor loadings (that indicated the proportion of variance accounted for by the construct) and uniqueness factors (that determined how well the variables were interpreted by the construct). Factors retained included variables that had uniqueness values of less than 0.7 and factor loadings greater than 0.3. Those associated with uniqueness values greater than 0.7 were left as single items, as were characteristics deemed a priori to be particularly important to antimicrobial stewardship. Factor scales that had only 2 items were converted into indices, while factor scores were generated for those factors that contained 3 or more items.^12-15

Data for facility-level antimicrobial utilization measures were obtained from the VA Corporate Data Warehouse from calendar year 2012. The analysis was conducted within the VA Informatics and Computing Infrastructure. All study procedures were approved by the VA Central Institutional Review Board.

Measures

Four utilization measures were defined as dependent measures: overall antimicrobial use; antimicrobial use in patients with non-infectious discharge diagnoses; missed opportunities to convert from parenteral to oral antimicrobial therapy; and missed opportunities to avoid double anaerobic coverage with metronidazole.

Overall antimicrobial use was defined as total acute care (ie, medical/surgical/intensive care) antibacterial use for each facility aggregated as per CDC National Healthcare Safety Network Antimicrobial Use Option guidelines (antimicrobial days per 1000 patient days present). A subanalysis of overall antimicrobial use was restricted to antimicrobial use among patients without an infection-related discharge diagnosis, as we surmised that this measure may capture a greater proportion of potentially unnecessary antimicrobial use. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)¹⁶ codes for infectious processes were identified by a combination of those classified previously in the literature,¹⁷ and those identified by finding the descendants of all infections named in the Systematized Nomenclature of Medicine--Clinical Terms.¹⁸ Next, all remaining codes for principal discharge diagnoses for which antimicrobials were administered were reviewed for potential indications for systemic antibacterial use. Discharges were considered noninfectious if no codes were identified when systemic antimicrobials were or could be indicated. For this measure, antimicrobial days were not counted if administered on or 1 day after the calendar day of surgery warranting antimicrobial prophylaxis.

Missed opportunities for conversion from parenteral to oral (IV to PO) formulations of highly bioavailable oral antimicrobials (ciprofloxacin, levofloxacin, moxifloxacin, azithromycin, clindamycin, linezolid, metronidazole, and fluconazole) were defined as the percentage of days of unnecessary IV therapy that were given when PO therapy could have been used among patients who were not in intensive care units at the time of antimicrobial administration who were receiving other oral medications, using previously described methodology.¹⁹ Missed opportunities for avoiding redundant anaerobic coverage with metronidazole were defined as the percentage of days in which patients receiving metronidazole also received antibiotics with activity against anaerobic bacteria, specifically beta-lactam/beta-lactamase inhibitors, carbapenems, cefotetan/cefoxitin, clindamycin, moxifloxacin, or tigecycline), using previously described methodology.²⁰ Patients for whom C. difficile testing was either ordered or positive within the prior 28 days (indicating potential clinical concern for C. difficile infection) were excluded from this endpoint.

 

Analysis

The variables derived above were entered into a multivariable model for each of the 4 antimicrobial utilization measures. The least absolute shrinkage and selection operator (LASSO) regression was used to determine significant associations between variables and individual utilization measures.²¹ LASSO was chosen because it offers advantages over traditional subset selection approaches in large multivariable analyses by assessing covariates simultaneously rather than sequentially, supporting prediction rather than estimation of effect.²²P values were not reported as they are not useful in determining statistical significance in this methodology. A tuning parameter of 0.025 was determined for the model based on a cross-validation approach. Significant variables remaining in the model were reported with the percent change in each utilization measure per unit change in the variable of interest. For binary factors, percent change was reported according to whether the variable was present or not. For ordinal variables, percent change was reported according to incremental increase in ordinal score. For continuous variables or variables represented by factor or index scores, percent change was reported per each 25% increase in the range of the score.

RESULTS

Inpatient Facility Antimicrobial Stewardship Characteristics and Antimicrobial Utilization

Frequencies of key facility characteristics that contributed to variable development are included in Table 1. Full survey results across all facilities are included in Appendix B. Factor analysis reduced the total number of variables to 32; however, we also included hospital size and VA complexity score. Thus, 34 variables were evaluated for association with antimicrobial utilization measures: 4 in the evidence domain, 23 in the context domain, and 7 in the facilitation domain (Table 2).

Median facility antimicrobial use was 619 antimicrobial days per 1000 days present (interquartile range [IQR], 554-700; overall range, 346-974). Median facility noninfectious antimicrobial use was 236 per 1000 days present (IQR, 200-286). Missed opportunities for conversion from IV to PO antimicrobial therapy were common, with a median facility value of 40.4% (391/969) of potentially eligible days of therapy (IQR, 32.2-47.8%). Missed opportunities to avoid double anaerobic coverage were less common (median 15.3% (186/1214) of potentially eligible days of therapy (IQR, 11.8%-20.2%; Figure).

Overall Antimicrobial Use

Four variables were associated with decreased overall antimicrobial use, although with small magnitude of change: presence of postgraduate physician/pharmacy training programs (0.03% decrease per quarter increase in factor score; on the order of 0.2 antimicrobial days per 1000 patient days present), presence of pharmacists and/or ID attendings on general medicine ward teams (0.02% decrease per quarter increase in index score), frequency of systematic de-escalation review (0.01% decrease per ordinal increase in score), and degree of involvement of ID physicians and/or fellows in antimicrobial approvals (0.007% decrease per quarter increase in index score). No variables were associated with increased overall antimicrobial use.

Antimicrobial Use among Discharges without Infectious Diagnoses

Six variables were associated with decreased antimicrobial use in patients without infectious discharge diagnoses, while 4 variables were associated with increased use. Variables associated with the greatest magnitude of decreased use included facility educational programs for prudent antimicrobial use (1.8% on the order of 4 antimicrobial days per 1000 patient days present), frequency of systematic de-escalation review (1.5% per incremental increase in score), and whether a facility’s lead antimicrobial stewardship pharmacist had ID training (1.3%). Also significantly associated with decreased use was a factor summarizing the presence of 4 condition-specific stewardship processes (de-escalation policies, policies for addressing antimicrobial use in the context of C. difficile infection, blood culture review, and automatic ID consults for certain conditions) (0.6% per quarter increase in factor score range), the extent to which postgraduate physician/pharmacy training programs were present (0.6% per quarter increase in factor score range), and the number of electronic antimicrobial-specific order sets present (0.4% per order set). The variables associated with increased use of antimicrobials included the presence of antimicrobial stop orders (4.6%), the degree to which non-ID physicians were involved in antimicrobial approvals (0.7% per increase in ordinal score), the level engagement with ASTF online resources (0.6% per quarter increase in factor score range), and hospital size (0.6% per 50-bed increase).

Missed Opportunities for Parenteral to Oral Antimicrobial Conversion

Missed opportunities for IV to PO antimicrobial conversion had the largest number of significant associations with organizational variables: 14 variables were associated with fewer missed opportunities, while 5 were associated with greater missed opportunities. Variables associated with the largest reductions in missed opportunities for IV to PO conversion included having guidelines for antimicrobial duration (12.8%), participating in regional stewardship collaboratives (8.1%), number of antimicrobial-specific order sets (6.0% per order set), ID training of the ASP pharmacist (4.9%), and VA facility complexity designation (4.2% per quarter increase in score indicating greater complexity).²³ Variables associated with more missed opportunities included stop orders (11.7%), overall perceived receptiveness to antimicrobial stewardship among clinical services (9.4%), the degree of engagement with ASTF online resources (6.9% per quarter increase in factor score range), educational programs for prudent antimicrobial use (4.1%), and hospital size (1.0% per 50-bed increase).

 

Missed Opportunities for Avoidance of Double Anaerobic Coverage

Four variables were associated with more avoidance of double anaerobic coverage: ID training of the lead ASP pharmacist (8.8%), presence of pharmacists and/or ID attendings on acute care ward teams (6.2% per quarter increase in index score), degree of ID pharmacist involvement in antimicrobial approvals, ranging from not at all (score=0) to both weekdays and nights/weekends (score=2; 4.3% per ordinal increase), and the number of antimicrobial-specific order sets (1.5% per order set). No variables were associated with less avoidance of double anaerobic coverage.

Variables Associated with Multiple Favorable or Unfavorable Antimicrobial Utilization Measures

To better assess the consistency of the relationship between organizational variables and measures of antimicrobial use, we tabulated variables that were associated with at least 3 potentially favorable (ie, reduced overall or noninfectious antimicrobial use or fewer missed opportunities) measures. Altogether, 5 variables satisfied this criterion: the presence of postgraduate physician/pharmacy training programs, the number of antimicrobial-specific order sets, frequency of systematic de-escalation review, the presence of pharmacists and/or ID attendings on acute care ward teams, and formal ID training of the lead ASP pharmacist (Table 3). Three other variables were associated with at least 2 unfavorable measures: hospital size, the degree to which the facility engaged with ASTF online resources, and presence of antimicrobial stop orders.

DISCUSSION

Variability in ASP implementation across VA allowed us to assess the relationship between ASP and facility elements and baseline patterns of antimicrobial utilization. Hospitalists and hospital policy-makers are becoming more and more engaged in inpatient antimicrobial stewardship. While our results suggest that having pharmacists and/or physicians with formal ID training participate in everyday inpatient activities can favorably improve antimicrobial utilization, considerable input into stewardship can be made by hospitalists and policy makers. In particular, based on this work, the highest yield from an organizational standpoint may be in working to develop order sets within the electronic medical record and systematic efforts to promote de-escalation of broad-spectrum therapy, as well as encouraging hospital administration to devote specific physician and pharmacy salary support to stewardship efforts.

While we noted that finding the ASTF online resources helpful was associated with potentially unfavorable antimicrobial utilization, we speculate that this may represent reverse causality due to facilities recognizing that their antimicrobial usage is suboptimal and thus seeking out sample ASTF policies to implement. The association between the presence of automatic stop orders and potentially unfavorable antimicrobial utilization is less clear since the timeframe was not specified in the survey; it may be that setting stop orders too far in advance may promote an environment in which critical thinking about antimicrobial de-escalation is not encouraged or timely. The larger magnitude of association between ASP characteristics and antimicrobial usage among patients without infectious discharge diagnoses versus overall antimicrobial usage also suggests that clinical situations where infection was of low enough suspicion to not even have the providers eventually list an infectious diagnosis on their discharge summaries may be particularly malleable to ASP interventions, though further exploration is needed in determining how useful this utilization measure may be as a marker for inappropriate antimicrobial use.

Our results complement those of Pakyz et al.²⁴ who surveyed 44 academic medical facilities in March 2013 to develop an ASP intensity score and correlate this score and its specific components to overall and targeted antimicrobial use. This study found that the overall ASP intensity score was not significantly associated with total or targeted antimicrobial use. However, ASP strategies were more associated with decreased total and targeted antimicrobial use than were specific ASP resources. In particular, the presence of a preauthorization strategy was associated with decreased targeted antimicrobial use. Our particular findings that order set establishment and de-escalation efforts are associated with multiple antibiotic outcomes also line up with the findings of Schuts et al, who performed a meta-analysis of the effects of meeting antimicrobial stewardship objectives and found that achieving guideline concordance (such as through establishment of order sets) and successfully de-escalating antimicrobial therapy was associated with reduced mortality.^25,26 This meta-analysis, however, was limited by low rigor of its studies and potential for reverse causality. While our study has the advantages of capturing an entire national network of 130 acute care facilities with a 100% response rate, it, too, is limited by a number of issues, most notably by the fact that the survey was not specifically designed for the analysis of antimicrobial utilization measures, patient-level risk stratification was not available, the VA population does not reflect the U.S. population at-large, recall bias, and that antimicrobial prescribing and stewardship practices have evolved in VA since 2012. Furthermore, all of the antimicrobial utilization measures studied are imperfect at capturing inappropriate antibiotic use; in particular, our reliance on principal ICD-9 codes for noninfectious outcomes requires prospective validation. Many survey questions were subjective and subject to misinterpretation; other unmeasured confounders may also be present. Causality cannot be inferred from association. Nevertheless, our findings support many core indicators for hospital ASP recommended by the CDC and the Transatlantic Taskforce on Antimicrobial Resistance,^3,4 most notably, having personnel with ID training involved in stewardship and establishing a formal procedure for ASP review for the appropriateness of an antimicrobial at or after 48 hours from the initial order.

In summary, the VA has made efforts to advance the practice of antimicrobial stewardship system-wide, including a 2014 directive that all VA facilities have an ASP,²⁷ since the 2012 HAIG assessment reported considerable variability in antimicrobial utilization and antimicrobial stewardship activities. Our study identifies areas of stewardship that may correlate with, positively or negatively, antimicrobial utilization measures that will require further investigation. A repeat and more detailed antimicrobial stewardship survey was recently completed and will help VA gauge ongoing effects of ASTF activities. We hope to re-evaluate our model with newer data when available.

 

Acknowledgments

The authors wish to thank Michael Fletcher, Jaime Lopez, and Catherine Loc-Carrillo for their administrative and organizational support of the project and Allison Kelly, MD, for her pivotal role in survey development and distribution. This work was supported by the VA Health Services Research and Development Service Collaborative Research to Enhance and Advance Transformation and Excellence (CREATE) Initiative; Cognitive Support Informatics for Antimicrobial Stewardship project (CRE 12-313).

Disclosure

 The authors report no financial conflicts of interest.

 

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

 

METHODS

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

 

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

RESULTS

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

DISCUSSION

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

 

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

 

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

 

METHODS

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

 

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

RESULTS

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

DISCUSSION

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

 

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

 

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Older adults commonly experience insomnia and agitation during hospitalization. Unfortunately, the use of benzodiazepines and sedative hypnotics (BSH) to treat these conditions can be ineffective and expose patients to significant adverse effects.^1,2 Choosing Wisely^® is a campaign that promotes dialogue to reduce unnecessary medical tests, procedures, or treatments. This international campaign has highlighted BSHs as potentially harmful and has recommended against their use as first-line treatment of insomnia and agitation.^3-5 Examples of harm with benzodiazepine use include cognitive impairment, impaired postural stability, and an increased incidence of falls and hip fractures in both community and acute care settings.^6-8 In addition, prescriptions initiated in hospital appear to be associated with a higher risk of falls and unplanned readmission.^9,10 The newer nonbenzodiazepine sedative hypnotics, commonly referred to as “z-drugs”, were initially marketed as a safer alternative in older adults due to their more favorable pharmacokinetics. Evidence has emerged that they carry similar risks.^6,11,12 A study comparing benzodiazepines and zolpidem found relatively greater risk of fractures requiring hospitalization with the use of zolpidem compared to lorazepam.¹³

The use of benzodiazepines in the acute care setting has been evaluated in a number of studies and ranges from 20% to 45%.^14-16 Few studies focus on the initiation of these medications in BSH-naïve hospitalized patients; however, reports range from 18% to 29%.^17,18 Factors found to be associated with potentially inappropriate prescriptions (PIP) include Hispanic ethnicity, residing in an assisted care setting, and a greater number of BSH prescriptions prior to admission.^16,19 Additionally, Cumbler et al.¹⁵ found that the presence of dementia was associated with fewer prescriptions for sleep aids in hospital. To our knowledge, there are no published studies that have investigated prescriber factors associated with the use of BSH.

The purpose of our study was to determine the frequency of PIPs of BSH in our academic hospital. Additionally, we aimed to identify patient and prescriber factors that were associated with increased likelihood of prescriptions to help guide future quality improvement initiatives.

 

METHODS

Study Design and Setting

This was a retrospective observational study conducted at Mount Sinai Hospital (MSH) in Toronto over a 4-month period from January 2013 to April 2013. The hospital is a 442-bed acute care academic health science center affiliated with the University of Toronto. The MSH electronic health record contains demographic data, medications and allergies, nursing documentation, and medical histories from prior encounters. It also includes computerized physician order entry (CPOE) and a detailed medication administration record. This system is integrated with an electronic pharmacy database used to monitor and dispense medications for each patient.

Patient and Medication Selection

We included inpatients over the age of 65 who were prescribed a BSH during the study period from the following services: general internal medicine, cardiology, general surgery, orthopedic surgery, and otolaryngology. To identify new exposure to BSHs, we excluded patients who were regularly prescribed a BSH prior to admission to hospital. The medications of interest included all benzodiazepines and the nonbenzodiazepine sedative hypnotic, zopiclone. Zopiclone is the most commonly used nonbenzodiazepine sedative hypnotic in Canada and the only 1 available on our hospital formulary. These were selected based on the strength of evidence to recommend against their use as first-line agents in older adults and in consultation with our geriatric medicine consultation team pharmacist.²⁰

Data Collection

The hospital administrative database provided patient demographic information, admission service, admitting diagnosis, length of stay, and the total number of patients discharged from the study units over the study period. We then searched the pharmacy electronic database for all benzodiazepines and zopiclone prescribed during the study period for patients who met the inclusion criteria. Manual review of paper and electronic health records for this cohort of patients was conducted to extract additional variables. We used a standardized form to record data elements. Dr. Pek collected all data elements. Dr. Remfry reviewed a random sample of patient records (10%) to ensure accuracy. The agreement between reviewers was 100%.

In compliance with hospital accreditation standards, a clinical pharmacist documents a best possible medication history (BPMH) on every inpatient on admission. We used the BPMH to identify and exclude patients who were prescribed a BSH prior to hospitalization. Because all medications were ordered through the CPOE system, as-needed medication prescriptions required the selection of a specified indication. Available options included ‘agitation/anxiety’ and necessitated combining these 2 indications into 1 category. Indications were primarily extracted through electronic order entry reviews. Paper charts were reviewed when further clarification was needed.

We identified ordering physicians’ training level and familiarity with the service from administrative records obtained from medical education offices, hospital records, and relevant call schedules. Fellows were defined as trainees with a minimum of 6 years of postgraduate training.

Our primary outcome of interest was the proportion of eligible patients age 65 and older who received a PIP for a BSH. Patient variables of interest included age, sex, comorbid conditions, and a pre-admission diagnosis of dementia. Comorbid conditions and age were used to calculate the Carlson Comorbidity Index for each patient.²¹ Prescription variables included the medication prescribed, time of first prescription (“overnight hours” refer to prescriptions ordered after 7:00 PM and before 7:00 AM), and whether the medication was ordered as part of an admission or postoperative order set. To determine whether patients were discharged home with a prescription for a BSH, we reviewed electronic discharge prescriptions of BSH-naïve patients who received a sedative in hospital. Only medical and cardiology inpatients receive electronic discharge prescriptions, and these were available for 189 patients in our cohort. Provider variables included training level, service, and familiarity with patients. We used the provider’s training program or department of appointment to define the ‘physician on-service’ variable. As an example, a resident registered in internal medicine is defined as ‘on-service’ when prescribing sedatives for a medical inpatient. In contrast, a psychiatry resident would be considered “off-service” if he prescribed a sedative for a surgical inpatient. The familiarity of a provider was categorized as ‘regular’ if they were responsible for a patient’s care on a day-to-day basis and ‘covering’ if they were only covering on call. Other variables included admitting service and hospital length of stay.

Appropriateness Criteria

Criteria for potentially inappropriate use were modified from the American and Canadian Geriatrics Societies’ Choosing Wisely recommendations,^4,5 and included insomnia and agitation. These recommendations are in line with other evidence based guidelines for safe prescribing in older adults.²⁰ For the purposes of our study, prescriptions for “agitation/anxiety”, “agitation”, or “insomnia/sleep” were considered potentially inappropriate. Appropriate indications included alcohol withdrawal, end-of-life symptom control, preprocedural sedation, and seizure.⁵ Patients who were already using a BSH prior to admission for any indication, including a psychiatric diagnosis, were excluded.

 

Statistical Analyses

We determined the proportion of patients with at least one PIP, as well as the proportion of all prescribing events that were potentially inappropriate. We used the Chi-square statistic and 2-sample t tests to compare the unadjusted associations between patient-level characteristics and receipt of at least 1 inappropriate prescription and prescribing event-level factors with inappropriate prescriptions. Given that first-year residents are more likely to be working overnight when most PIPs are prescribed, we performed a simple logistic regression of potentially inappropriate prescribing by level of training stratified by time of prescription. A multivariable random-intercept logistic regression model was used to assess the adjusted association between patient- and prescribing event-level characteristics with inappropriate prescribing, adjusting for clustering of prescribing events within patients. Characteristics of interest were identified a priori and those with significant bivariate associations with potentially inappropriate were selected for inclusion in the model. Additionally, we included time of prescription in our model to control for potential confounding. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc, Cary, North Carolina). The MSH Research Ethics Board approved the study.

RESULTS

Description of Patients Prescribed a Benzodiazepine Sedative Hypnotic

There were 1540 patients over the age of 65 discharged during the 4-month study period. We excluded the 232 patients who had been prescribed a BSH prior to admission. Of the remaining eligible 1308 BSH-naïve patients, 251 (19.2%) were prescribed a new BSH in hospital and were included in the study. Of this cohort of 251 patients, 193 (76.9%) patients were prescribed a single BSH during their admission while 58 (23.1%) received 2 or more. Of all eligible patients, 208 (15.9%) were prescribed at least 1 PIP. Approximately half of the cohort was admitted to the general internal medicine service, and the most common reason for admission was cardiovascular disease (Table 1).

Description of Prescriptions of Benzodiazepine Sedative Hypnotic

We reviewed 328 prescriptions for BSH during the study period. The majority of these, 254 (77.4%) were potentially inappropriate (Table 2). The most common PIPs were zopiclone (167; 65.7%) and lorazepam (82; 32.3%). The PIPs were most frequently ordered on an as-needed basis (219; 86%), followed by one-time orders (30; 12%), and standing orders (5; 2%). The majority of PIPs (222; 87.4%) was prescribed for insomnia with a minority (32; 12.6%) prescribed for agitation and/or anxiety.

Most PIP were prescribed during overnight hours (159; 62.6%) and when an in-house pharmacist was unavailable (211; 83.1%). These variables were highly correlated with prescription of sleep aid, which was defined in our criteria as potentially inappropriate. Copies of discharge prescriptions were available for 189 patients. Of these 189 patients, 19 (10.1%) were sent home with a prescription for a new sedative.

Association Between Patient/Provider Variables and Prescriptions

Patient factors associated with fewer PIPs in our bivariate analyses included older age and dementia (Table 1). A greater proportion of nighttime prescriptions were PIPs; however, this finding was not statistically significant (P = 0.067). The majority of all prescriptions was prescribed by residents in their first year of training (64.9%; Table 2), and there was a significant difference in rates of PIP across level of training (P = 0.0007). When stratified by time of prescription, there was no significant difference by level of training for nighttime prescriptions. Among daytime prescriptions, second-year residents and staff (attending physicians and fellows) were less likely to prescribe a PIP than first-year residents (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.09-0.66 and OR, 0.39; 95% CI, 0.14-1.13, respectively; Table 3); however, the association between staff and first-years only approached statistical significance (P = 0.08). Interestingly, 20.4% of all PIPs were ordered routinely as part of an admission or postoperative order set.

In our regression model, admission to a specialty or surgical service, compared to the general internal medicine service, was associated with a significantly higher likelihood of a PIP (OR, 6.61; 95% CI, 2.70-16.17; Table 4). Additionally, compared to cardiovascular admission diagnoses, neoplastic admitting diagnoses were associated with a higher likelihood of a PIP (OR, 4.43; 95% CI, 1.23-15.95). Time of prescription was a significant predictor in our multivariable regression model with nighttime prescriptions having increased odds of a PIP (OR, 4.48; 95% CI, 2.21-9.06,). When comparing prescribers at the extremes of training, attending physicians and fellows were much less likely to prescribe a PIP compared to first-year residents (OR, 0.23; 95% CI, 0.08-0.69; Table 4). However, there were no other significant differences across training levels after adjusting for patient and prescribing event characteristics.

DISCUSSION

We found that the majority of newly prescribed BSH in hospital was for the potentially inappropriate indications of insomnia and agitation/anxiety. Medications for insomnia were primarily initiated during overnight hours. Training level of prescribers and admitting service were found to be associated with appropriateness of prescriptions.

 

Our study showed that 15.9% of hospitalized older adults were newly prescribed a PIP during their admission. Of all new in hospital prescriptions, 77% were deemed potentially inappropriate. These numbers are similar to those reported by other centers; however, wide ranges exist.^16,19 This is likely the result of differences in appropriate use and inclusion criteria. Gillis et al.¹⁷ focused their investigation on sleep aids and showed that 26% of all admitted patients and 18% of BSH naïve patients received a prescription for insomnia. While this is similar to our findings, more than half of these patients were under the age of 65, and additional medications, such as trazodone, antihistamines, and antipsychotics were included.¹⁷ Other studies did not exclude patients who used a BSH regularly prior to admission. For example, 21% of veterans admitted to an acute care facility received a prescription for potentially inappropriate indications, but this included continuation of prior home medications.¹⁹ In contrast, we chose to focus on older adults in whom BSH pose a greater risk of harm. Exclusion of patients who regularly used a BSH prior to admission allowed us to better understand the circumstances surrounding the initiation of these medications in hospital. Furthermore, abrupt cessation of benzodiazepines can cause withdrawal and worsen confusion.²²

We found that 10% of patients newly prescribed a BSH in hospital were discharged with a prescription for a BSH. The accuracy of this is limited by the lack of availability of electronic discharge prescriptions on our surgical wards; however, it is likely an underrepresentation of the true effect given the high rates of PIPs on these wards. Our study highlights the concerning practice of continuing newly prescribed BSH following discharge from hospital.

Sleep disruption and poor quality sleep in hospital is a common issue that leads to significant use of BSH.¹⁵ Nonpharmacologic interventions in older adults can be effective in improving sleep quality and reducing the need for BSH; however, they can be time-consuming to implement.²³ With the exception of preventative strategies used on our Acute Care for Elders unit, formal nonpharmacologic interventions for sleep are not practiced in our hospital. We found that the majority of PIPs were prescribed as sleep aids in the overnight hours. This suggests that disruptions in sleep are leading patients and nursing staff to request pharmacologic treatments and highlights an area with significant room for improvement. Work is underway to implement and evaluate safe sleep protocols for older adults.

To our knowledge, we are the first to report an association between training level and PIP of BSH in older adults. The highest rates of PIPs were found among the first-year residents and, after controlling for patient and prescribing event characteristics, such as time of prescription, first-year residents were significantly more likely to prescribe a PIP. First-year residents are more likely to respond first to issues on the wards. There may be pressure on first-year trainees to prescribe sleep aids, as many patients and nurses may seek pharmacologic solutions for symptom management. Knowledge gaps may also be a contributing factor early in their training. A survey of physicians found that residents were more likely than attending physicians to list lack of formal education as a barrier to appropriate prescribing.²⁴

Similarities are seen in a study of antibiotic appropriateness, where residents demonstrated gaps in knowledge of treatment of asymptomatic bacteriuria that seemed to vary by specialty.²⁵ Interestingly, we found that patients admitted to general internal medicine were prescribed fewer PIPs. This service includes our Acute Care for Elders unit, which is staffed by trained geriatric nurses and other allied health professionals. Residents who rotated on internal medicine are also likely to have received informal teaching about medication safety in older adults. Educational interventions highlighting adverse effects of BSH and promoting nonpharmacologic solutions should be targeted at first-year residents. However, an interprofessional team approach to sleep disturbance in hospital, in combination with decision support for appropriate BSH use will achieve greater impact than education alone.

Several limitations of this study merit discussion. First, findings from a single academic center may lack generalizability. However, the demographics of our patient population and our rates of BSH use were similar to those reported in previous studies. Second, our study may be subject to observer bias, as the data collectors were not blinded. To minimize this, a strict template and clear appropriateness criteria were developed. Additionally, a second reviewer independently conducted data validation with 100% agreement among reviewers. Third, we studied prescribing patterns rather than medication administration and lacked data on filling of new BSH prescriptions in the postdischarge period. However, our primary goal is to determine risk of exposure to a BSH to minimize it. Fourth, although BSH are discouraged as “first choice for insomnia, anxiety or delirium,”⁴ they may be appropriate in limited situations where all nonpharmacologic strategies have failed and patient or staff safety is at risk. In our chart reviews, we were unable to determine whether all nonpharmacologic strategies were exhausted prior to prescription initiation. However, more than 20% of all PIP were routinely prescribed as part of an admission or postoperative order set, suggesting a reflexive rather than reflective approach to sedative use. Furthermore, the indications of anxiety and agitation were combined as they appear in the CPOE as a combination indication, thus leaving us unable to determine the true proportion for each indication. However, more than 87% of all PIPs were for insomnia, reflecting a clear opportunity to improve sleep management in hospital. Last, the lack of a power calculation may have resulted in the study being underpowered and thus affected the ability to detect a significant effect of covariates that have real differences on the likelihood of sedative prescriptions. For example, the low number of prescribing events by second-year residents and staff may have resulted in a type II error when comparing PIP rates with first-year residents.

We found that the majority of newly prescribed BSH among older adults in hospital were potentially inappropriate. They were most frequently prescribed by first-year residents overnight in response to insomnia. Our findings demonstrate BSH overuse remains prevalent and is associated with poor sleep in hospital. Future work will focus on implementing and evaluating safe sleep protocols and educational interventions aimed at first-year residents.

 

Acknowledgments

Elisabeth Pek had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ciara Pendrith conducted and is responsible for the statistical analysis.

Disclosure

The authors report no financial conflicts of interest.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

METHODS

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

 

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

RESULTS

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

 

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

DISCUSSION

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

 

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

METHODS

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

 

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

RESULTS

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

 

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

DISCUSSION

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

 

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Hospital-acquired anemia (HAA) is defined as having a normal hemoglobin value upon admission but developing anemia during the course of hospitalization. The condition is common, with an incidence ranging from approximately 25% when defined by using the hemoglobin value prior to discharge to 74% when using the nadir hemoglobin value during hospitalization.^1-5 While there are many potential etiologies for HAA, given that iatrogenic blood loss from phlebotomy may lead to its development,^6,7 HAA has been postulated to be a hazard of hospitalization that is potentially preventable.⁸ However, it is unclear whether the development of HAA portends worse outcomes after hospital discharge.

The limited number of studies on the association between HAA and postdischarge outcomes has been restricted to patients hospitalized for acute myocardial infarction (AMI).^3,9,10 Among this subpopulation, HAA is independently associated with greater morbidity and mortality following hospital discharge.^3,9,10 In a more broadly representative population of hospitalized adults, Koch et al.² found that the development of HAA is associated with greater length of stay (LOS), hospital charges, and inpatient mortality. However, given that HAA was defined by the lowest hemoglobin level during hospitalization (and not necessarily the last value prior to discharge), it is unclear if the worse outcomes observed were the cause of the HAA, rather than its effect, since hospital LOS is a robust predictor for the development of HAA, as well as a major driver of hospital costs and a prognostic marker for inpatient mortality.^3,9 Furthermore, this study evaluated outcomes only during the index hospitalization, so it is unclear if patients who develop HAA have worse clinical outcomes after discharge.

Therefore, in this study, we used clinically granular electronic health record (EHR) data from a diverse cohort of consecutive medicine inpatients hospitalized for any reason at 1 of 6 hospitals to: 1) describe the epidemiology of HAA; 2) identify predictors of its development; and 3) examine its association with 30-day postdischarge adverse outcomes. We hypothesized that the development of HAA would be independently associated with 30-day readmission and mortality in a dose-dependent fashion, with increasing severity of HAA associated with worse outcomes.

METHODS

Study Design, Population, and Data Sources

We conducted a retrospective observational cohort study using EHR data collected from November 1, 2009 to October 30, 2010 from 6 hospitals in the north Texas region. One site was a university-affiliated safety-net hospital; the remaining 5 community hospitals were a mix of teaching and nonteaching sites. All hospitals used the Epic EHR system (Epic Systems Corporation, Verona, Wisconsin). Details of this cohort have been published.^11,12This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice. For individuals with multiple eligible hospitalizations during the study period, we included only the first hospitalization. We also excluded those discharged to hospice, given that this population of individuals may have intentionally desired less aggressive care.

 

Definition of Hospital-Acquired Anemia

HAA was defined as having a normal hematocrit value (36% or greater for women and 40% or greater for men) within the first 24 hours of admission and a hematocrit value at the time of hospital discharge lower than the World Health Organization’s sex-specific cut points.¹³ If there was more than 1 hematocrit value on the same day, we chose the lowest value. Based on prior studies, HAA was further categorized by severity as mild (hematocrit greater than 33% and less than 36% in women; and greater than 33% and less than 40% in men), moderate (hematocrit greater than 27% and 33% or less for all), or severe (hematocrit 27% or less for all).^2,14

Characteristics

We extracted information on sociodemographic characteristics, comorbidities, LOS, procedures, blood transfusions, and laboratory values from the EHR. Hospitalizations in the 12 months preceding the index hospitalization were ascertained from the EHR and from an all-payer regional hospitalization database that captures hospitalizations from 75 acute care hospitals within a 100-mile radius of Dallas-Fort Worth. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis codes were categorized according to the Agency for Healthcare Research and Quality (AHRQ) Clinical Classifications Software (CCS).¹⁵ We defined a diagnosis for hemorrhage and coagulation, and hemorrhagic disorder as the presence of any ICD-9-CM code (primary or secondary) that mapped to the AHRQ CCS diagnoses 60 and 153, and 62, respectively. Procedures were categorized as minor diagnostic, minor therapeutic, major diagnostic, and major therapeutic using the AHRQ Healthcare Cost and Utilization Procedure Classes tool.¹⁶

Outcomes

The primary outcome was a composite of death or readmission within 30 days of hospital discharge. Hospital readmissions were ascertained at the index hospital and at any of 75 acute care hospitals in the region as described earlier. Death was ascertained from each of the hospitals’ EHR and administrative data and the Social Security Death Index. Individuals who had both outcomes (eg, a 30-day readmission and death) were considered to have only 1 occurrence of the composite primary outcome measure. Our secondary outcomes were death and readmission within 30 days of discharge, considered as separate outcomes.

Statistical Analysis

We used logistic regression models to evaluate predictors of HAA and to estimate the association of HAA on subsequent 30-day adverse outcomes after hospital discharge. All models accounted for clustering of patients by hospital. For the outcomes analyses, models were adjusted for potential confounders based on prior literature and our group’s expertise, which included age, sex, race/ethnicity, Charlson comorbidity index, prior hospitalizations, nonelective admission status, creatinine level on admission, blood urea nitrogen (BUN) to creatinine ratio of more than 20:1 on admission, LOS, receipt of a major diagnostic or therapeutic procedure during the index hospitalization, a discharge diagnosis for hemorrhage, and a discharge diagnosis for a coagulation or hemorrhagic disorder. For the mortality analyses, given the limited number of 30-day deaths after hospital discharge in our cohort, we collapsed moderate and severe HAA into a single category. In sensitivity analyses, we repeated the adjusted model, but excluded patients in our cohort who had received at least 1 blood transfusion during the index hospitalization (2.6%) given its potential for harm, and patients with a primary discharge diagnosis for AMI (3.1%).¹⁷

The functional forms of continuous variables were assessed using restricted cubic splines and locally weighted scatterplot smoothing techniques. All analyses were performed using STATA statistical software version 12.0 (StataCorp, College Station, Texas). The University of Texas Southwestern Medical Center institutional review board approved this study.

RESULTS

Of 53,995 consecutive medicine hospitalizations among adults age 18 years or older during our study period, 11,309 index hospitalizations were included in our study cohort (Supplemental Figure 1). The majority of patients excluded were because of having documented anemia within the first 24 hours of admission (n=24,950). With increasing severity of HAA, patients were older, more likely to be female, non-Hispanic white, electively admitted, have fewer comorbidities, less likely to be hospitalized in the past year, more likely to have had a major procedure, receive a blood transfusion, have a longer LOS, and have a primary or secondary discharge diagnosis for a hemorrhage or a coagulation or hemorrhagic disorder (Table 1).

Epidemiology of HAA

Among this cohort of patients without anemia on admission, the median hematocrit value on admission was 40.6 g/dL and on discharge was 38.9 g/dL. One-third of patients with normal hematocrit value at admission developed HAA, with 21.6% developing mild HAA, 10.1% developing moderate HAA, and 1.4% developing severe HAA. The median discharge hematocrit value was 36 g/dL (interquartile range [IQR]), 35-38 g/dL) for the group of patients who developed mild HAA, 31 g/dL (IQR, 30-32 g/dL) for moderate HAA, and 26 g/dL (IQR, 25-27 g/dL) for severe HAA (Supplemental Figure 2). Among the severe HAA group, 135 of the 159 patients (85%) had a major procedure (n=123, accounting for 219 unique major procedures), a diagnosis for hemorrhage (n=30), and/or a diagnosis for a coagulation or hemorrhagic disorder (n=23) during the index hospitalization. Of the 219 major procedures among patients with severe HAA, most were musculoskeletal (92 procedures), cardiovascular (61 procedures), or digestive system-related (41 procedures). The most common types of procedures were coronary artery bypass graft (36 procedures), hip replacement (25 procedures), knee replacement (17 procedures), and femur fracture reduction (15 procedures). The 10 most common principal discharge diagnoses of the index hospitalization by HAA group are shown in Supplemental Table 1. For the severe HAA group, the most common diagnosis was hip fracture (20.8%).

 

Predictors of HAA

Compared to no or mild HAA, female sex, elective admission status, serum creatinine on admission, BUN to creatinine ratio greater than 20 to 1, hospital LOS, and undergoing a major diagnostic or therapeutic procedure were predictors for the development of moderate or severe HAA (Table 2). The model explained 23% of the variance (McFadden’s pseudo R²).

Incidence of Postdischarge Outcomes by Severity of HAA

The severity of HAA was associated with a dose-dependent increase in the incidence of 30-day adverse outcomes, such that patients with increasing severity of HAA had greater 30-day composite, mortality, and readmission outcomes (P < 0.001; Figure). The 30-day postdischarge composite outcome was primarily driven by hospital readmissions given the low mortality rate in our cohort. Patients who did not develop HAA had an incidence of 9.7% for the composite outcome, whereas patients with severe HAA had an incidence of 16.4%. Among the 24 patients with severe HAA but who had not undergone a major procedure or had a discharge diagnosis for hemorrhage or for a coagulation or hemorrhagic disorder, only 3 (12.5%) had a composite postdischarge adverse outcome (2 readmissions and 1 death). The median time to readmission was similar between groups, but more patients with severe HAA had an early readmission within 7 days of hospital discharge than patients who did not develop HAA (6.9% vs. 2.9%, P = 0.001; Supplemental Table 2).

Association of HAA and Postdischarge Outcomes

In unadjusted analyses, compared to not developing HAA, mild, moderate, and severe HAA were associated with a 29%, 61%, and 81% increase in the odds for a composite outcome, respectively (Table 3). After adjustment for confounders, the effect size for HAA attenuated and was no longer statistically significant for mild and moderate HAA. However, severe HAA was significantly associated with a 39% increase in the odds for the composite outcome and a 41% increase in the odds for 30-day readmission (P = 0.008 and P = 0.02, respectively).

In sensitivity analyses, the exclusion of individuals who received at least 1 blood transfusion during the index hospitalization (n=298) and individuals who had a primary discharge diagnosis for AMI (n=353) did not substantively change the estimates of the association between severe HAA and postdischarge outcomes (Supplemental Tables 3 and 4). However, because of the fewer number of adverse events for each analysis, the confidence intervals were wider and the association of severe HAA and the composite outcome and readmission were no longer statistically significant in these subcohorts.

DISCUSSION

In this large and diverse sample of medical inpatients, we found that HAA occurs in one-third of adults with normal hematocrit value at admission, where 10.1% of the cohort developed moderately severe HAA and 1.4% developed severe HAA by the time of discharge. Length of stay and undergoing a major diagnostic or therapeutic procedure were the 2 strongest potentially modifiable predictors of developing moderate or severe HAA. Severe HAA was independently associated with a 39% increase in the odds of being readmitted or dying within 30 days after hospital discharge compared to not developing HAA. However, the associations between mild or moderate HAA with adverse outcomes were attenuated after adjusting for confounders and were no longer statistically significant.

To our knowledge, this is the first study on the postdischarge adverse outcomes of HAA among a diverse cohort of medical inpatients hospitalized for any reason. In a more restricted population, Salisbury et al.³ found that patients hospitalized for AMI who developed moderate to severe HAA (hemoglobin value at discharge of 11 g/dL or less) had greater 1-year mortality than those without HAA (8.4% vs. 2.6%, P < 0.001), and had an 82% increase in the hazard for mortality (95% confidence interval, hazard ratio 1.11-2.98). Others have similarly shown that HAA is common among patients hospitalized with AMI and is associated with greater mortality.^5,9,18 Our study extends upon this prior research by showing that severe HAA increases the risk for adverse outcomes for all adult inpatients, not only those hospitalized for AMI or among those receiving blood transfusions.

Despite the increased harm associated with severe HAA, it is unclear whether HAA is a preventable hazard of hospitalization, as suggested by others.^6,8 Most patients in our cohort who developed severe HAA underwent a major procedure, had a discharge diagnosis for hemorrhage, and/or had a discharge diagnosis for a coagulation or hemorrhagic disorder. Thus, blood loss due to phlebotomy, 1 of the more modifiable etiologies of HAA, was unlikely to have been the primary driver for most patients who developed severe HAA. Since it has been estimated to take 15 days of daily phlebotomy of 53 mL of whole blood in females of average body weight (and 20 days for average weight males) with no bone marrow synthesis for severe anemia to develop, it is even less likely that phlebotomy was the principal etiology given an 8-day median LOS among patients with severe HAA.^19,20 However, since the etiology of HAA can be multifactorial, limiting blood loss due to phlebotomy by using smaller volume tubes, blood conservation devices, or reducing unnecessary testing may mitigate the development of severe HAA.^21,22 Additionally, since more than three-quarters of patients who developed severe HAA underwent a major procedure, more care and attention to minimizing operative blood loss could lessen the severity of HAA and facilitate better recovery. If minimizing blood loss is not feasible, in the absence of symptoms related to anemia or ongoing blood loss, randomized controlled trials overwhelmingly support a restrictive transfusion strategy using a hemoglobin value threshold of 7 mg/dL, even in the postoperative setting.^23-25

The implications of mild to moderate HAA are less clear. The odds ratios for mild and moderate HAA, while not statistically significant, suggest a small increase in harm compared to not developing HAA. Furthermore, the upper boundary of the confidence intervals for mild and moderate HAA cannot exclude a possible 30% and 56% increase in the odds for the 30-day composite outcome, respectively. Thus, a better powered study, including more patients and extending the time interval for ascertaining postdischarge adverse events beyond 30 days, may reveal a harmful association. Lastly, our study assessed only the association of HAA with 30-day readmission and mortality. Examining the association between HAA and other patient-centered outcomes such as fatigue, functional impairment, and prolonged posthospitalization recovery time may uncover other important adverse effects of mild and moderate HAA, both of which occur far more frequently than severe HAA.

Our findings should be interpreted in the context of several limitations. First, although we included a diverse group of patients from a multihospital cohort, generalizability to other settings is uncertain. Second, as this was a retrospective study using EHR data, we had limited information to infer the precise mechanism of HAA for each patient. However, procedure codes and discharge diagnoses enabled us to assess which patients underwent a major procedure or had a hemorrhage or hemorrhagic disorder during the hospitalization. Third, given the relatively few number of patients with severe HAA in our cohort, we were unable to assess if the association of severe HAA differed by suspected etiology. Lastly, because we were unable to ascertain the timing of the hematocrit values within the first 24 hours of admission, we excluded both patients with preexisting anemia on admission and those who developed HAA within the first 24 hours of admission, which is not uncommon.²⁶ Thus, we were unable to assess the effect of acute on chronic anemia arising during hospitalization and HAA that develops within the first 24 hours, both of which may also be harmful.^18,27,28

In conclusion, severe HAA occurs in 1.4% of all medical hospitalizations and is associated with increased odds of death or readmission within 30 days. Since most patients with severe HAA had undergone a major procedure or had a discharge diagnosis of hemorrhage or a coagulation or hemorrhagic disorder, it is unclear if severe HAA is potentially preventable through preventing blood loss from phlebotomy or by reducing iatrogenic injury during procedures. Future research should assess the potential preventability of severe HAA, and examine other patient-centered outcomes potentially related to anemia, including fatigue, functional impairment, and trajectory of posthospital recovery.

 

Acknowledgments

The authors would like to acknowledge Ruben Amarasingham, MD, MBA, President and Chief Executive Officer of the Parkland Center for Clinical Innovation, and Ferdinand Velasco, MD, Chief Health Information Officer at Texas Health Resources, for their assistance in assembling the 6 hospital cohort used in this study. The authors would also like to thank Valy Fontil, MD, MAS, Assistant Professor of Medicine at the University of California San Francisco School of Medicine, and Elizabeth Rogers, MD, MAS, Assistant Professor of Internal Medicine and Pediatrics at the University of Minnesota Medical School, for their constructive feedback on an earlier version of this manuscript.

Disclosures

This work was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01); the Commonwealth Foundation (#20100323); the UT Southwestern KL2 Scholars Program supported by the National Institutes of Health (KL2 TR001103); the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006); and the National Institute on Aging (K23AG052603). The study sponsors had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors have no financial conflicts of interest to disclose.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

METHODS

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

 

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

RESULTS

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

 

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Results of the logistic regression models testing the association between patient characteristics and radiologic IFs are displayed in Table 2. Only age and repeat admissions remained significantly associated with radiologic IFs in the fully adjusted model (adjusted odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.06 and 2.68; 95% CI, 1.60-4.44, respectively).

Median LOS was 2 days (IQR=1) for patients with no IFs and 2 days (IQR=2) for patient with radiologic IFs (P = 0.08). Unadjusted negative binomial regression analysis revealed that identification of any radiologic IFs during admission (vs. none) was associated with an increased LOS by 24% (unadjusted IRR, 1.24; 95% CI, 1.06-1.45). After adjustment for confounders, identification of any radiologic IFs during admission remained significantly associated with a longer LOS (adjusted IRR, 1.26; 95% CI, 1.07-1.49). Results remained significant on a sensitivity analysis excluding admissions lasting longer than 10 days (adjusted IRR, 1.21; 95% CI, 1.03-1.42; Supplement Table 5).

Incidental findings of minor and moderate clinical significance were associated with increase in LOS on multiple negative binomial regression (adjusted IRR, 1.27; 95% CI, 1.03-1.57 and 1.24; 95% CI, 1.02-1.52, respectively; Table 3); however, upon dropping length of hospitalization outliers, only radiologic IFs with major clinical significance were associated with increase in length of hospitalization (adjusted IRR, 1.39; 95% CI, 1.04-1.87; Table 3).

Supplemental chart review revealed that 26 patients accounted for the 27 radiologic IFs of major clinical significance. This group had 54% women, median LOS remained 2 days (IQR 2) and, on average, had about 3 diagnostic tests performed per patient. Cardiac testing was performed less on these patients compared to others (Supplement Table S6). Review also revealed that, of the 26 patients, 2 had abnormal labs, 2 had drug abuse/psychiatric issues, and another 2 had radiologic IFs that warranted further consultations, imaging, and longer LOS.

DISCUSSION

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

 

CONCLUSION

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

METHODS

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

 

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

RESULTS

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

 

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Results of the logistic regression models testing the association between patient characteristics and radiologic IFs are displayed in Table 2. Only age and repeat admissions remained significantly associated with radiologic IFs in the fully adjusted model (adjusted odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.06 and 2.68; 95% CI, 1.60-4.44, respectively).

Median LOS was 2 days (IQR=1) for patients with no IFs and 2 days (IQR=2) for patient with radiologic IFs (P = 0.08). Unadjusted negative binomial regression analysis revealed that identification of any radiologic IFs during admission (vs. none) was associated with an increased LOS by 24% (unadjusted IRR, 1.24; 95% CI, 1.06-1.45). After adjustment for confounders, identification of any radiologic IFs during admission remained significantly associated with a longer LOS (adjusted IRR, 1.26; 95% CI, 1.07-1.49). Results remained significant on a sensitivity analysis excluding admissions lasting longer than 10 days (adjusted IRR, 1.21; 95% CI, 1.03-1.42; Supplement Table 5).

Incidental findings of minor and moderate clinical significance were associated with increase in LOS on multiple negative binomial regression (adjusted IRR, 1.27; 95% CI, 1.03-1.57 and 1.24; 95% CI, 1.02-1.52, respectively; Table 3); however, upon dropping length of hospitalization outliers, only radiologic IFs with major clinical significance were associated with increase in length of hospitalization (adjusted IRR, 1.39; 95% CI, 1.04-1.87; Table 3).

Supplemental chart review revealed that 26 patients accounted for the 27 radiologic IFs of major clinical significance. This group had 54% women, median LOS remained 2 days (IQR 2) and, on average, had about 3 diagnostic tests performed per patient. Cardiac testing was performed less on these patients compared to others (Supplement Table S6). Review also revealed that, of the 26 patients, 2 had abnormal labs, 2 had drug abuse/psychiatric issues, and another 2 had radiologic IFs that warranted further consultations, imaging, and longer LOS.

DISCUSSION

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

 

CONCLUSION

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.

Diagnostic imaging is an integral part of patient evaluation in acute care settings. The use of imaging for presenting complaints of chest pain, abdominal pain, and injuries has increased in emergency departments across the United States without an increase in detection of acute pathologic conditions.^1,2 An unintended consequence of this increase in diagnostic imaging is the discovery of incidental findings (IFs).

Incidental findings are unexpected findings (eg, nodules) noted on diagnostic imaging that are not related to the presenting complaint.³ The increasing use of diagnostic imaging and increased sensitivity of these tests have led to a higher burden of radiologic IFs.⁴ In a tertiary level hospital, Lumbreras et al.⁵ found that the overall incidence of IFs for all radiologic imaging for inpatients and outpatients was 15%, while Orme et al.⁶ found that the incidence in imaging research was 39.8%. The existing evidence base suggests that the identification of radiologic IFs has financial,^5,7 clinical,⁶ ethical, and legal implications.⁸ Also, IFs increase workload for healthcare professionals, including that related to follow-up and surveillance.⁹

In the field of radiology, the burden of radiologic IFs is a well-accepted fact and various white papers have been published by the American College of Radiology on how to address them.^4,7 Hospitalized patients are a population that undergoes a substantial number of diagnostic tests. In the era of accountable care organizations¹⁰ with an emphasis on population health and high-value care, radiologic IFs pose a particular challenge to healthcare providers.

Chest pain is one of the most common reasons for emergency department visits in the United States.¹¹ In this study, we report on radiologic IFs and factors associated with these among patients hospitalized for chest pain of suspected cardiac origin, and we evaluate the hypothesis that radiologic IFs are associated with an increase in LOS in this population.

METHODS

We conducted a secondary analysis of data from the Chest Pain and Cocaine Study (CPAC). The CPAC study is a cross sectional study of all patients hospitalized with chest pain to our urban academic medical center. Medical records were reviewed to generate a database of all such patients during the study period. The main focus of CPAC was to look at healthcare disparities and resource utilization in patients with or without a concomitant diagnosis of cocaine use.¹²

Study Population

The Figure shows the selection of the study sample for this analysis. The CPaC Study identified 1811 consecutive admissions for chest pain/angina pectoris (based on admitting diagnosis ICD-9-CM codes: 411.x; 413.x, 414.x; and 786.5x) over 24 months. Per the CPaC Study protocol, patients older than 65 years were excluded (n=567 admissions). After chart review, all admissions diagnosed with acute myocardial infarction (n=97) or noncardiac chest pain (n=655) were excluded. For this analysis, we excluded 39 additional admissions of patients who had known prior radiologic IFs, leading to a sample size of 453 admissions. Three hundred and seventy six patients had accounted for 453 admissions during the study period, and we included1 of these admissions in the analysis using the following process: If a patient had a radiologic IF on any admission during the study period, that patient was included in the “IF” group for the analysis, and data from the first admission with an IF were used for the analysis. If a patient had no radiologic IFs on any admission during the study period, that patient was included in the “no IF” group, and the data from the first admission in the database were used for analysis.

 

Measurements

Data collection was completed retrospectively by medical record review using a standardized CPaC Study protocol. The database was created and maintained using REDCap (Research Electronic Data Capture; Vanderbilt University, Knoxville, Tennessee) electronic data capture tool hosted at Johns Hopkins University.¹³ All data were manually abstracted into REDCap from electronic medical records. All missing values and inconsistent data were reviewed by multiple physicians to ensure data integrity.

We defined all diagnostic (noninterventional; nonlaboratory) testing done during a patient’s hospitalization as “diagnostic” tests, except cardiac stress testing and echocardiogram. We defined diagnostic tests as “primary” tests if they were done in response to patients’ presenting complaint. We defined diagnostic tests as “secondary” tests if they were done by providers due to IFs. Cardiac computed tomography was included in diagnostic tests. Cardiac testing (echocardiogram, cardiac stress testing, cardiac catheterization and pacemaker placement) was considered separate from the “diagnostic tests” since these were focused cardiac imaging that are interventional in nature with low yield on extra-cardiac radiologic IFs.

Incidental findings were defined as any unexpected findings on diagnostic imaging unrelated to the reason for admission, and were classified based on organ systems and their clinical significance as major, moderate, or minor using a classification previously published by Lumbreras et al.¹⁴ All radiologic IFs data underwent sequential dual review by investigators for accuracy of documentation. Individuals with multiple radiologic IFs belonging to more than one category of clinical significance were categorized with the IFs group of highest clinical significance. Ten percent of the patients with no IFs were reviewed again, and no errors found.

Demographic variables at the time of admission included age, sex, race, level of education, employment status, insurance status, body mass index (BMI), and smoking status. Comorbid conditions at the time of admission consisted of the following: hypertension, diabetes mellitus, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), history of myocardial infarction, cerebrovascular accident (CVA), congestive heart failure (CHF), drug use and malignancy or history of it. Initial laboratory values were extracted from electronic medical records and included hemoglobin, creatinine, blood urea nitrogen (BUN), aspartate transaminase, alanine transaminase, and alkaline phosphatase. We calculated the estimated glomerular filtration rate (eGFR) using the MDRD (Modification of Diet in Renal Disease) equation.¹⁵ Admission and discharge information as well as whether the patient had a primary care provider, were obtained from medical records. The length of hospital stay was calculated by subtracting date of admission from date of discharge.

Statistical Analysis

We conducted 2 main analyses: 1) a descriptive analysis of the association between patient characteristics (independent variables) and identification of IFs during admission (primary outcome) and 2) an analysis of the association between identification of incidental findings during admission (independent variable) and LOS (primary outcome).

For the descriptive analysis of radiologic IFs, we compared the characteristics of patients with and without radiologic IFs during admission using a t-test (for normally distributed continuous variables) or Mann-Whitney test (for nonnormally distributed continuous variables) and a chi-square or Fisher exact test for categorical variables based on the number of observations. We included variables significantly associated with the occurrence of radiologic IFs (P < 0.05) in a multiple logistic regression model to identify characteristics independently associated with presence of radiologic IFs.

Length of stay was right-skewed even after natural logarithm transformation and, therefore, we used negative binomial regression for the analysis of the association between the identification of radiologic IFs during admission and LOS. We included potential confounding variables in the multiple negative binomial regression model based on plausibility of confounding and association with both the exposure (identification of radiologic IFs during admission) and outcome (LOS) at a level of P < 0.3. Age, education level, history of drug use, history of CHF, history of CKD, lower eGFR, higher serum creatinine/BUN, hemoglobin, occurrence of cardiac catheterization, stress testing, and multiple admissions during the study period were identified as confounders. For correlated variables (eg, hemoglobin and hematocrit), the variable with the strongest statistical association (lowest P value) was included in the model. In sensitivity analysis, we dropped patients with extreme LOS (longer than 10 days). All analyses were performed using STATA 13 (Stata Statistical Software: Release 13; StataCorp., College Station, Texas).

RESULTS

Table 1 shows the characteristics of the 376 patients included in this study. Overall mean age was 50.5 years, 40% were females, 62% were Caucasian, 66% were unemployed, 84% identified a primary care provider upon admission, and 68% were cared for by a hospitalist. Overall median LOS was 2 days (interquartile range [IQR] = 2). Of the 376 patients in the study, 197 (52%) had new radiologic IFs. Comparing the patients with radiologic IFs and no IFs, it was evident that more radiological tests were performed in the IF group (2.2 tests per patient) in comparison with the no IF group (1.26 tests per patient). Looking at patient characteristics, patients with radiologic IFs were older (52 years vs. 48.8 years; P < 0.001), reported a lower education level and lower hemoglobin levels on admission (12.0 gm/dL vs. 13.4 gm/dL; P = 0.029), but were more likely to be unemployed (72% vs. 59%; P = 0.009), have COPD (19% vs. 10%; P = 0.007), and a history of malignancy (7% vs. 2%, P = 0.04). In addition, patients in the radiologic IF group had lower rates of cardiac catheterization (18% vs. 28%; P = 0.02), were more likely to be readmitted more than once during the study period (17% vs. 7%; P = 0.02) and be discharged by hospitalists (75% vs. 60%; P = 0.003; Supplemental Table 1).

 

Overall, 658 diagnostic tests were performed in the study population; of these, 268 (40.7%) tests revealed 364 new radiologic IFs (Supplement Table 2). Of these radiologic IFs, 27 (7.4%) were of major clinical significance, 154 (42%) were of moderate clinical significance, and 183 (50%) were of minor clinical significance (Supplement Table 3). Computed tomography (CT) scans yielded more IFs compared to any other imaging modalities. Of the radiologic IFs of major clinical significance, 3 malignant/premalignant lesions were found. While pulmonary nodules were the most common moderate clinically significant findings, atelectasis and spinal degenerative changes were the most common radiologic IFs of minor clinical significance (Supplement Table 4).

Results of the logistic regression models testing the association between patient characteristics and radiologic IFs are displayed in Table 2. Only age and repeat admissions remained significantly associated with radiologic IFs in the fully adjusted model (adjusted odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.06 and 2.68; 95% CI, 1.60-4.44, respectively).

Median LOS was 2 days (IQR=1) for patients with no IFs and 2 days (IQR=2) for patient with radiologic IFs (P = 0.08). Unadjusted negative binomial regression analysis revealed that identification of any radiologic IFs during admission (vs. none) was associated with an increased LOS by 24% (unadjusted IRR, 1.24; 95% CI, 1.06-1.45). After adjustment for confounders, identification of any radiologic IFs during admission remained significantly associated with a longer LOS (adjusted IRR, 1.26; 95% CI, 1.07-1.49). Results remained significant on a sensitivity analysis excluding admissions lasting longer than 10 days (adjusted IRR, 1.21; 95% CI, 1.03-1.42; Supplement Table 5).

Incidental findings of minor and moderate clinical significance were associated with increase in LOS on multiple negative binomial regression (adjusted IRR, 1.27; 95% CI, 1.03-1.57 and 1.24; 95% CI, 1.02-1.52, respectively; Table 3); however, upon dropping length of hospitalization outliers, only radiologic IFs with major clinical significance were associated with increase in length of hospitalization (adjusted IRR, 1.39; 95% CI, 1.04-1.87; Table 3).

Supplemental chart review revealed that 26 patients accounted for the 27 radiologic IFs of major clinical significance. This group had 54% women, median LOS remained 2 days (IQR 2) and, on average, had about 3 diagnostic tests performed per patient. Cardiac testing was performed less on these patients compared to others (Supplement Table S6). Review also revealed that, of the 26 patients, 2 had abnormal labs, 2 had drug abuse/psychiatric issues, and another 2 had radiologic IFs that warranted further consultations, imaging, and longer LOS.

DISCUSSION

Radiologic IFs in patients admitted with chest pain of suspected cardiac origin are a common occurrence as shown in our study. Similar to prior studies, 41% of all radiologic tests done in our study population revealed IFs.⁶ The majority of the IFs were of minor to moderate clinical significance and, as reported in the literature, were more common with older age and CT imaging.^14,16 In addition, an IF diagnosed during admission for chest pain was associated with a 26% increase in length of hospital stay.

To our knowledge, we present the first study on the impact of identification of radiologic IFs in hospitalized patients on length of hospital stay and specifically in patients hospitalized with chest pain of suspected cardiac origin. Trends over the past decade have shown a decrease in LOS and hospitalizations but with an increase in health resource utilization.^17,18 Association of radiologic IFs with increase in LOS is significant as this potentially increases hospital-acquired conditions such as infections and resource utilization leading to increase in costs of hospitalizations.¹⁹ This in return is a concern for patient safety.

The positive association between LOS and radiologic IFs, interestingly, continued to exist despite sensitivity analysis. Incidental findings of major clinical significance were associated with longer LOS in the sensitivity analysis. Supplemental chart review of patients with major clinical findings suggested more extra-cardiac workup compared to patients with minor/moderate radiologic IFs. This could indicate that the presence of clinically significant radiologic IFs could have led to further inpatient work-up and consultations. The downstream healthcare expenditure associated with workup of IFs in individual radiologic tests is well established.²⁰ In case of cardiac CT, Goehler et al.²¹ found that the healthcare expenditure was high following incidentally detected pulmonary nodules with an overall small reduction in lung cancer mortality. Incidental findings also increase the burden of reporting and concern for medico-legal issues for providers.⁴ These concerns are likely valid for hospitalized patients as well.

The socioeconomic trends in the study population were consistent with data from the Bureau of Labor Statistics in that low education is associated with higher unemployment.²² Although, overall, gender, race and insurance mix were similar in both groups, we did see trends of socioeconomic differences in the patients with radiologic IFs of major clinical significance that might not have been statistically significant owing to the small sample size. Despite the population being relatively of younger age (given our cut off age was 65 years) there was still a positive association with age and presence of radiologic IFs. The higher number of patients with COPD or history of malignancy in the radiologic IF group suggests that an association with IFs could exist for these disease cohorts; however, after adjustment for multiple covariates, such an association did not transpire. Interestingly, patients with no radiologic IFs underwent cardiac catheterization or stress testing more often than patients with discovered IFs. This speaks of 2 possibilities; first, that both tests probably do not yield many extra-cardiac IFs, or, secondly, that these patients did not require further workup. More patients in the IF group had more than 1 admission during the study period, and this was associated with increased odds of detecting radiologic IFs. We hypothesize that this might have occurred because of the diagnostic dilemma in these patients who have multiple admissions for the same reason leading to wider array of diagnostic workup. Indeed, we did not note upon chart review alternative diagnoses in these patients but only more IFs. There are several study limitations to consider. First, the fact that this is a single center study sets limitations to interpretation and generalizability of the data. Second, we cannot exclude the possibility of residual confounding. Third, the small number of patients included in this study precludes definitive identification of more factors potentially associated with IFs. However, this study sheds light on a yet unidentified problem within the realm of inpatient management especially for the internists and hospitalists. We tried to limit bias to the extent possible by including only 1 presenting complaint and age-restricting the population.

 

CONCLUSION

Incidental findings are both clinical and financial challenges to the medical field. This study attempted to shed light on impact of radiologic IFs on care and resource utilization in patients admitted with chest pain of suspected cardiac origin. The positive association between radiologic IFs and length of hospital stay implies that the presence of IFs is associated with increase in LOS and indirectly a likely increase in overall healthcare expenditure. Given the high incidence of radiologic IFs, assuming that these will be present on radiologic tests, should be more a norm than an exception. Providers should know that radiologic testing, especially CT, is associated with detection of IFs.¹⁶ By avoiding inappropriate ordering of imaging, the issue of IFs could be mitigated.

While radiologists have recommendations about necessary follow-up for some IFs,⁷ no clear follow-up guidelines exist for most IFs arising in hospitalized patients. Further prospective and cost analysis studies are needed to assess the overall impact of IFs on other hospitalized patient populations and on the healthcare system in general.

Disclosure

The authors report no conflicts of interest.