Ivana Croghan, PhD

Incidental findings create both medical and logistical challenges regarding communication.^1,2 Pulmonary nodules are among the most frequent and medically relevant incidental findings, being noted in up to 8.4% of abdominal computed tomography (CT) scans.³ There are guidelines regarding proper follow-up and management of such incidental pulmonary nodules, but appropriate evidence-based surveillance imaging is often not performed, and many patients remain uninformed. Collins et al.⁴ reported that, before initiation of a standardized protocol, only 17.7% of incidental findings were communicated to patients admitted to the trauma service; after protocol initiation, the rate increased to 32.4%. The hospital discharge summary provides an opportunity to communicate incidental findings to patients and their medical care providers, but Kripalani et al.⁵ raised questions regarding the current completeness and accuracy of discharge summaries, reporting that 65% of discharge summaries omitted relevant diagnostic testing, and 30% omitted a follow-up plan.

We conducted a study to determine how often incidental pulmonary nodules found on abdominal CT are documented in the discharge summary, and to identify factors associated with pulmonary nodule inclusion.

This was a retrospective cohort study of hospitalized patients ≥35 years of age who underwent in-patient abdominal CT between January 1, 2012 and December 31, 2014. Patients were identified by cross-referencing hospital admissions with Current Procedural Terminology (CPT) codes indicating abdominal CT (74176, 74177, 74178, 74160, 74150, 74170). Patients with chest CT (CPT codes 71260, 71250, 71270) during that hospitalization or within 30 days before admission were excluded to ensure that pulmonary nodules were incidental and asymptomatic. The index hospitalization was defined as the first hospitalization during which the patient was diagnosed with an incidental pulmonary nodule on abdominal CT, or the first hospitalization during the study period for patients without pulmonary nodules. All patient charts were manually reviewed, and baseline age, sex, and smoking status data collected.

Radiology reports were electronically screened for the words nodule and nodules and then confirmed through manual review of the full text reports. Nodules described as tiny (without other size description) were assumed to be <4 mm in size, per manual review of a small sample. Nodules were deemed as falling outside the Fleischner Society criteria guidelines (designed for indeterminate pulmonary nodules), and were therefore excluded, if any of seven criteria were met: The nodule was (1) cavitary, (2) associated with a known metastatic disease, (3) associated with a known granulomatous disease, (4) associated with a known inflammatory process, (5) reported likely to represent atelectasis, (6) reported likely to be a lymph node, or (7) previously biopsied.⁴

For each patient with pulmonary nodules, a personal history of cancer was obtained. Nodule size, characteristics, and stability compared with available prior imaging were recorded. Radiology reports were reviewed to determine if pulmonary nodules were mentioned in the summary headings of the reports or in the body of the reports and whether specific follow-up recommendations were provided. Hospital discharge summaries were reviewed for documentation of pulmonary nodule(s) and follow-up recommendations. Discharging service (medical/medical subspecialty, surgical/surgical subspecialty) was noted, along with the patients’ condition at discharge (alive, alive on hospice, deceased).

The frequency of incidental pulmonary nodules on abdominal CT during hospitalization and the frequency of nodules requiring follow-up were reported using a point estimate and corresponding 95% confidence interval (CI). The χ² test was used to compare the frequency of pulmonary nodules across patient groups. In addition, for patients found to have incidental nodules requiring follow-up, the χ² test was used to compare across groups the percentage of patients with discharge documentation of the incidental nodule. In all cases, 2-tailed Ps are reported, with P ≤ 0.05 considered statistically significant.

Between January 1, 2012 and December 31, 2014, 7173 patients ≥35 years old underwent in-patient abdominal CT without concurrent chest CT. Of these patients, 62.2% were ≥60 years old, 50.6% were men, and 45.5% were current or former smokers. Incidental pulmonary nodules were noted in 402 patients (5.6%; 95% CI, 5.1%-6.2%), of whom 68.7% were ≥60 years old, 56.5% were men, and 46.3% were current or former smokers. Increasing age (P = 0.004) and male sex (P = 0.015) were associated with increased frequency of incidental pulmonary nodules, but smoking status (P = 0.586) was not. Of patients with incidental nodules, 71.6% had solitary nodules, and 58.5% had a maximum nodule size of ≤4 mm (Table 1). Based on smoking status, nodule size, and reported size stability, 208 patients (2.9%; 95% CI, 2.5%-3.3%) required follow-up surveillance as per 2005 Fleischner Society guidelines. Among solitary pulmonary nodules requiring further surveillance (n = 147), the mean risk of malignancy based on the Mayo Clinic solitary pulmonary nodule risk calculator was 7.9% (interquartile range, 3.0%-10.5%), with 28% having a malignancy risk of ≥10%.⁶

Of the 208 patients with nodules requiring further surveillance, only 48 (23%) received discharge summaries documenting the nodule; 34 of these summaries included a recommendation for nodule follow-up, with 19 of the recommendations including a time frame for repeat CT. Three factors were positively associated with documentation of the pulmonary nodule in the discharge summary: mention of the pulmonary nodule in the summary headings of the radiology report (P < 0.001), radiologist recommendation for further surveillance (P < 0.001), and medical discharging service (P = 0.016) (Table 2). The highest rate of pulmonary nodule inclusion in the discharge summary (42%) was noted among patients for whom the radiology report included specific recommendations.

The frequency of incidental pulmonary nodules reported on abdominal CT in our study (5.6%) is consistent with frequencies reported in similar studies. Wu et al.⁷ (reviewing 141,406 abdominal CT scans) and Alpert et al.⁸ (reviewing 12,287 abdominal CT scans) reported frequencies of 2.5% and 3%, respectively, while Rinaldi et al.³ (reviewing 243 abdominal CT scans) reported a higher frequency, 8.4%. Variation likely results from patient factors and the individual radiologist’s attention to incidental pulmonary findings. Rinaldi et al. suggested that up to 39% of abdominal CT scans include pulmonary nodules on independent review, raising the possibility of significant underreporting. In our study, we focused on pulmonary nodules included in the radiology report to tailor the relevance of our study to the hospital medicine community. We also included only those incidental nodules falling within the purview of the Fleischner Society criteria in order to analyze only findings with established follow-up guidelines.

The rate of pulmonary nodule documentation in our study was low overall (23%) but consistent with the literature. Collins et al.,⁴ for example, reported that only 17.7% of patients with trauma were notified of incidental CT findings by either the discharge summary or an appropriate specialist consultation. Various contributing factors can be hypothesized. First, incidental pulmonary nodules are discovered largely in the context of evaluation for other symptomatic conditions, which can overshadow their importance. Second, the lack of clear patient-friendly education materials regarding incidental pulmonary nodules can complicate discussions with patients. Third, many electronic health record (EHR) systems cannot automatically pull incidental findings into the discharge summary and instead rely on provider vigilance.

As our study does, the literature highlights the importance of the radiology report in communicating incidental findings. In a review of >1000 pulmonary angiographic CT studies, Blagev et al.⁹ reported an overall follow-up rate of 29% (28/96) among patients with incidental pulmonary nodules, but none of the 12 patients with pulmonary nodules mentioned in the body of the report (rather than in the summary headings) received adequate follow-up. Similarly, in Shuaib et al.,¹⁰ radiology reports that included follow-up recommendations were more likely to change patient treatment than reports without follow-up recommendations (70% vs 2%). However, our data also show that radiologist recommendations alone are insufficient to ensure adequate communication of incidental findings.

The literature regarding the most cost-effective means of addressing this quality gap is limited. Some institutions have integrated their EHR systems to allow radiologists to flag incidental findings for auto-population in a dedicated section of the discharge summary. Although these efforts can be helpful, documentation alone does not save lives without appropriate follow-up and intervention. Some institutions have hired dedicated nursing staff as incidental finding coordinators. For high-risk incidental findings, Sperry et al.¹¹ reported that hiring an incidental findings coordinator helped their level I trauma center achieve nearly complete documentation, patient notification, and confirmation of posthospital follow-up appointments. Such solutions, however, are labor-intensive and still rely on appropriate primary care follow-up.

Strengths of our study include its relatively large size and particular focus on the issues and decisions facing hospital medicine providers. By focusing on incidental pulmonary nodules reported on abdominal CT, and excluding patients with concurrent chest CT, we avoided including patients with symptomatic or previously identified pulmonary findings. Study limitations include the cross-sectional, retrospective design, which did not include follow-up data regarding such outcomes as rates of appropriate follow-up surveillance and subsequent lung cancer diagnoses. Our single-center study findings may not apply to all hospital practice settings, though they are consistent with the literature with comparison data.

Our study results highlight the need for a multidisciplinary systems-based approach to incidental pulmonary nodule documentation, communication, and follow-up surveillance.

Disclosure

Nothing to report.

Incidental findings create both medical and logistical challenges regarding communication.^1,2 Pulmonary nodules are among the most frequent and medically relevant incidental findings, being noted in up to 8.4% of abdominal computed tomography (CT) scans.³ There are guidelines regarding proper follow-up and management of such incidental pulmonary nodules, but appropriate evidence-based surveillance imaging is often not performed, and many patients remain uninformed. Collins et al.⁴ reported that, before initiation of a standardized protocol, only 17.7% of incidental findings were communicated to patients admitted to the trauma service; after protocol initiation, the rate increased to 32.4%. The hospital discharge summary provides an opportunity to communicate incidental findings to patients and their medical care providers, but Kripalani et al.⁵ raised questions regarding the current completeness and accuracy of discharge summaries, reporting that 65% of discharge summaries omitted relevant diagnostic testing, and 30% omitted a follow-up plan.

We conducted a study to determine how often incidental pulmonary nodules found on abdominal CT are documented in the discharge summary, and to identify factors associated with pulmonary nodule inclusion.

This was a retrospective cohort study of hospitalized patients ≥35 years of age who underwent in-patient abdominal CT between January 1, 2012 and December 31, 2014. Patients were identified by cross-referencing hospital admissions with Current Procedural Terminology (CPT) codes indicating abdominal CT (74176, 74177, 74178, 74160, 74150, 74170). Patients with chest CT (CPT codes 71260, 71250, 71270) during that hospitalization or within 30 days before admission were excluded to ensure that pulmonary nodules were incidental and asymptomatic. The index hospitalization was defined as the first hospitalization during which the patient was diagnosed with an incidental pulmonary nodule on abdominal CT, or the first hospitalization during the study period for patients without pulmonary nodules. All patient charts were manually reviewed, and baseline age, sex, and smoking status data collected.

Radiology reports were electronically screened for the words nodule and nodules and then confirmed through manual review of the full text reports. Nodules described as tiny (without other size description) were assumed to be <4 mm in size, per manual review of a small sample. Nodules were deemed as falling outside the Fleischner Society criteria guidelines (designed for indeterminate pulmonary nodules), and were therefore excluded, if any of seven criteria were met: The nodule was (1) cavitary, (2) associated with a known metastatic disease, (3) associated with a known granulomatous disease, (4) associated with a known inflammatory process, (5) reported likely to represent atelectasis, (6) reported likely to be a lymph node, or (7) previously biopsied.⁴

For each patient with pulmonary nodules, a personal history of cancer was obtained. Nodule size, characteristics, and stability compared with available prior imaging were recorded. Radiology reports were reviewed to determine if pulmonary nodules were mentioned in the summary headings of the reports or in the body of the reports and whether specific follow-up recommendations were provided. Hospital discharge summaries were reviewed for documentation of pulmonary nodule(s) and follow-up recommendations. Discharging service (medical/medical subspecialty, surgical/surgical subspecialty) was noted, along with the patients’ condition at discharge (alive, alive on hospice, deceased).

The frequency of incidental pulmonary nodules on abdominal CT during hospitalization and the frequency of nodules requiring follow-up were reported using a point estimate and corresponding 95% confidence interval (CI). The χ² test was used to compare the frequency of pulmonary nodules across patient groups. In addition, for patients found to have incidental nodules requiring follow-up, the χ² test was used to compare across groups the percentage of patients with discharge documentation of the incidental nodule. In all cases, 2-tailed Ps are reported, with P ≤ 0.05 considered statistically significant.

Between January 1, 2012 and December 31, 2014, 7173 patients ≥35 years old underwent in-patient abdominal CT without concurrent chest CT. Of these patients, 62.2% were ≥60 years old, 50.6% were men, and 45.5% were current or former smokers. Incidental pulmonary nodules were noted in 402 patients (5.6%; 95% CI, 5.1%-6.2%), of whom 68.7% were ≥60 years old, 56.5% were men, and 46.3% were current or former smokers. Increasing age (P = 0.004) and male sex (P = 0.015) were associated with increased frequency of incidental pulmonary nodules, but smoking status (P = 0.586) was not. Of patients with incidental nodules, 71.6% had solitary nodules, and 58.5% had a maximum nodule size of ≤4 mm (Table 1). Based on smoking status, nodule size, and reported size stability, 208 patients (2.9%; 95% CI, 2.5%-3.3%) required follow-up surveillance as per 2005 Fleischner Society guidelines. Among solitary pulmonary nodules requiring further surveillance (n = 147), the mean risk of malignancy based on the Mayo Clinic solitary pulmonary nodule risk calculator was 7.9% (interquartile range, 3.0%-10.5%), with 28% having a malignancy risk of ≥10%.⁶

Of the 208 patients with nodules requiring further surveillance, only 48 (23%) received discharge summaries documenting the nodule; 34 of these summaries included a recommendation for nodule follow-up, with 19 of the recommendations including a time frame for repeat CT. Three factors were positively associated with documentation of the pulmonary nodule in the discharge summary: mention of the pulmonary nodule in the summary headings of the radiology report (P < 0.001), radiologist recommendation for further surveillance (P < 0.001), and medical discharging service (P = 0.016) (Table 2). The highest rate of pulmonary nodule inclusion in the discharge summary (42%) was noted among patients for whom the radiology report included specific recommendations.

The frequency of incidental pulmonary nodules reported on abdominal CT in our study (5.6%) is consistent with frequencies reported in similar studies. Wu et al.⁷ (reviewing 141,406 abdominal CT scans) and Alpert et al.⁸ (reviewing 12,287 abdominal CT scans) reported frequencies of 2.5% and 3%, respectively, while Rinaldi et al.³ (reviewing 243 abdominal CT scans) reported a higher frequency, 8.4%. Variation likely results from patient factors and the individual radiologist’s attention to incidental pulmonary findings. Rinaldi et al. suggested that up to 39% of abdominal CT scans include pulmonary nodules on independent review, raising the possibility of significant underreporting. In our study, we focused on pulmonary nodules included in the radiology report to tailor the relevance of our study to the hospital medicine community. We also included only those incidental nodules falling within the purview of the Fleischner Society criteria in order to analyze only findings with established follow-up guidelines.

The rate of pulmonary nodule documentation in our study was low overall (23%) but consistent with the literature. Collins et al.,⁴ for example, reported that only 17.7% of patients with trauma were notified of incidental CT findings by either the discharge summary or an appropriate specialist consultation. Various contributing factors can be hypothesized. First, incidental pulmonary nodules are discovered largely in the context of evaluation for other symptomatic conditions, which can overshadow their importance. Second, the lack of clear patient-friendly education materials regarding incidental pulmonary nodules can complicate discussions with patients. Third, many electronic health record (EHR) systems cannot automatically pull incidental findings into the discharge summary and instead rely on provider vigilance.

As our study does, the literature highlights the importance of the radiology report in communicating incidental findings. In a review of >1000 pulmonary angiographic CT studies, Blagev et al.⁹ reported an overall follow-up rate of 29% (28/96) among patients with incidental pulmonary nodules, but none of the 12 patients with pulmonary nodules mentioned in the body of the report (rather than in the summary headings) received adequate follow-up. Similarly, in Shuaib et al.,¹⁰ radiology reports that included follow-up recommendations were more likely to change patient treatment than reports without follow-up recommendations (70% vs 2%). However, our data also show that radiologist recommendations alone are insufficient to ensure adequate communication of incidental findings.

The literature regarding the most cost-effective means of addressing this quality gap is limited. Some institutions have integrated their EHR systems to allow radiologists to flag incidental findings for auto-population in a dedicated section of the discharge summary. Although these efforts can be helpful, documentation alone does not save lives without appropriate follow-up and intervention. Some institutions have hired dedicated nursing staff as incidental finding coordinators. For high-risk incidental findings, Sperry et al.¹¹ reported that hiring an incidental findings coordinator helped their level I trauma center achieve nearly complete documentation, patient notification, and confirmation of posthospital follow-up appointments. Such solutions, however, are labor-intensive and still rely on appropriate primary care follow-up.

Strengths of our study include its relatively large size and particular focus on the issues and decisions facing hospital medicine providers. By focusing on incidental pulmonary nodules reported on abdominal CT, and excluding patients with concurrent chest CT, we avoided including patients with symptomatic or previously identified pulmonary findings. Study limitations include the cross-sectional, retrospective design, which did not include follow-up data regarding such outcomes as rates of appropriate follow-up surveillance and subsequent lung cancer diagnoses. Our single-center study findings may not apply to all hospital practice settings, though they are consistent with the literature with comparison data.

Our study results highlight the need for a multidisciplinary systems-based approach to incidental pulmonary nodule documentation, communication, and follow-up surveillance.

Disclosure

Nothing to report.

Incidental findings create both medical and logistical challenges regarding communication.^1,2 Pulmonary nodules are among the most frequent and medically relevant incidental findings, being noted in up to 8.4% of abdominal computed tomography (CT) scans.³ There are guidelines regarding proper follow-up and management of such incidental pulmonary nodules, but appropriate evidence-based surveillance imaging is often not performed, and many patients remain uninformed. Collins et al.⁴ reported that, before initiation of a standardized protocol, only 17.7% of incidental findings were communicated to patients admitted to the trauma service; after protocol initiation, the rate increased to 32.4%. The hospital discharge summary provides an opportunity to communicate incidental findings to patients and their medical care providers, but Kripalani et al.⁵ raised questions regarding the current completeness and accuracy of discharge summaries, reporting that 65% of discharge summaries omitted relevant diagnostic testing, and 30% omitted a follow-up plan.

We conducted a study to determine how often incidental pulmonary nodules found on abdominal CT are documented in the discharge summary, and to identify factors associated with pulmonary nodule inclusion.

This was a retrospective cohort study of hospitalized patients ≥35 years of age who underwent in-patient abdominal CT between January 1, 2012 and December 31, 2014. Patients were identified by cross-referencing hospital admissions with Current Procedural Terminology (CPT) codes indicating abdominal CT (74176, 74177, 74178, 74160, 74150, 74170). Patients with chest CT (CPT codes 71260, 71250, 71270) during that hospitalization or within 30 days before admission were excluded to ensure that pulmonary nodules were incidental and asymptomatic. The index hospitalization was defined as the first hospitalization during which the patient was diagnosed with an incidental pulmonary nodule on abdominal CT, or the first hospitalization during the study period for patients without pulmonary nodules. All patient charts were manually reviewed, and baseline age, sex, and smoking status data collected.

Radiology reports were electronically screened for the words nodule and nodules and then confirmed through manual review of the full text reports. Nodules described as tiny (without other size description) were assumed to be <4 mm in size, per manual review of a small sample. Nodules were deemed as falling outside the Fleischner Society criteria guidelines (designed for indeterminate pulmonary nodules), and were therefore excluded, if any of seven criteria were met: The nodule was (1) cavitary, (2) associated with a known metastatic disease, (3) associated with a known granulomatous disease, (4) associated with a known inflammatory process, (5) reported likely to represent atelectasis, (6) reported likely to be a lymph node, or (7) previously biopsied.⁴

For each patient with pulmonary nodules, a personal history of cancer was obtained. Nodule size, characteristics, and stability compared with available prior imaging were recorded. Radiology reports were reviewed to determine if pulmonary nodules were mentioned in the summary headings of the reports or in the body of the reports and whether specific follow-up recommendations were provided. Hospital discharge summaries were reviewed for documentation of pulmonary nodule(s) and follow-up recommendations. Discharging service (medical/medical subspecialty, surgical/surgical subspecialty) was noted, along with the patients’ condition at discharge (alive, alive on hospice, deceased).

The frequency of incidental pulmonary nodules on abdominal CT during hospitalization and the frequency of nodules requiring follow-up were reported using a point estimate and corresponding 95% confidence interval (CI). The χ² test was used to compare the frequency of pulmonary nodules across patient groups. In addition, for patients found to have incidental nodules requiring follow-up, the χ² test was used to compare across groups the percentage of patients with discharge documentation of the incidental nodule. In all cases, 2-tailed Ps are reported, with P ≤ 0.05 considered statistically significant.

Between January 1, 2012 and December 31, 2014, 7173 patients ≥35 years old underwent in-patient abdominal CT without concurrent chest CT. Of these patients, 62.2% were ≥60 years old, 50.6% were men, and 45.5% were current or former smokers. Incidental pulmonary nodules were noted in 402 patients (5.6%; 95% CI, 5.1%-6.2%), of whom 68.7% were ≥60 years old, 56.5% were men, and 46.3% were current or former smokers. Increasing age (P = 0.004) and male sex (P = 0.015) were associated with increased frequency of incidental pulmonary nodules, but smoking status (P = 0.586) was not. Of patients with incidental nodules, 71.6% had solitary nodules, and 58.5% had a maximum nodule size of ≤4 mm (Table 1). Based on smoking status, nodule size, and reported size stability, 208 patients (2.9%; 95% CI, 2.5%-3.3%) required follow-up surveillance as per 2005 Fleischner Society guidelines. Among solitary pulmonary nodules requiring further surveillance (n = 147), the mean risk of malignancy based on the Mayo Clinic solitary pulmonary nodule risk calculator was 7.9% (interquartile range, 3.0%-10.5%), with 28% having a malignancy risk of ≥10%.⁶

Of the 208 patients with nodules requiring further surveillance, only 48 (23%) received discharge summaries documenting the nodule; 34 of these summaries included a recommendation for nodule follow-up, with 19 of the recommendations including a time frame for repeat CT. Three factors were positively associated with documentation of the pulmonary nodule in the discharge summary: mention of the pulmonary nodule in the summary headings of the radiology report (P < 0.001), radiologist recommendation for further surveillance (P < 0.001), and medical discharging service (P = 0.016) (Table 2). The highest rate of pulmonary nodule inclusion in the discharge summary (42%) was noted among patients for whom the radiology report included specific recommendations.

The frequency of incidental pulmonary nodules reported on abdominal CT in our study (5.6%) is consistent with frequencies reported in similar studies. Wu et al.⁷ (reviewing 141,406 abdominal CT scans) and Alpert et al.⁸ (reviewing 12,287 abdominal CT scans) reported frequencies of 2.5% and 3%, respectively, while Rinaldi et al.³ (reviewing 243 abdominal CT scans) reported a higher frequency, 8.4%. Variation likely results from patient factors and the individual radiologist’s attention to incidental pulmonary findings. Rinaldi et al. suggested that up to 39% of abdominal CT scans include pulmonary nodules on independent review, raising the possibility of significant underreporting. In our study, we focused on pulmonary nodules included in the radiology report to tailor the relevance of our study to the hospital medicine community. We also included only those incidental nodules falling within the purview of the Fleischner Society criteria in order to analyze only findings with established follow-up guidelines.

The rate of pulmonary nodule documentation in our study was low overall (23%) but consistent with the literature. Collins et al.,⁴ for example, reported that only 17.7% of patients with trauma were notified of incidental CT findings by either the discharge summary or an appropriate specialist consultation. Various contributing factors can be hypothesized. First, incidental pulmonary nodules are discovered largely in the context of evaluation for other symptomatic conditions, which can overshadow their importance. Second, the lack of clear patient-friendly education materials regarding incidental pulmonary nodules can complicate discussions with patients. Third, many electronic health record (EHR) systems cannot automatically pull incidental findings into the discharge summary and instead rely on provider vigilance.

As our study does, the literature highlights the importance of the radiology report in communicating incidental findings. In a review of >1000 pulmonary angiographic CT studies, Blagev et al.⁹ reported an overall follow-up rate of 29% (28/96) among patients with incidental pulmonary nodules, but none of the 12 patients with pulmonary nodules mentioned in the body of the report (rather than in the summary headings) received adequate follow-up. Similarly, in Shuaib et al.,¹⁰ radiology reports that included follow-up recommendations were more likely to change patient treatment than reports without follow-up recommendations (70% vs 2%). However, our data also show that radiologist recommendations alone are insufficient to ensure adequate communication of incidental findings.

The literature regarding the most cost-effective means of addressing this quality gap is limited. Some institutions have integrated their EHR systems to allow radiologists to flag incidental findings for auto-population in a dedicated section of the discharge summary. Although these efforts can be helpful, documentation alone does not save lives without appropriate follow-up and intervention. Some institutions have hired dedicated nursing staff as incidental finding coordinators. For high-risk incidental findings, Sperry et al.¹¹ reported that hiring an incidental findings coordinator helped their level I trauma center achieve nearly complete documentation, patient notification, and confirmation of posthospital follow-up appointments. Such solutions, however, are labor-intensive and still rely on appropriate primary care follow-up.

Strengths of our study include its relatively large size and particular focus on the issues and decisions facing hospital medicine providers. By focusing on incidental pulmonary nodules reported on abdominal CT, and excluding patients with concurrent chest CT, we avoided including patients with symptomatic or previously identified pulmonary findings. Study limitations include the cross-sectional, retrospective design, which did not include follow-up data regarding such outcomes as rates of appropriate follow-up surveillance and subsequent lung cancer diagnoses. Our single-center study findings may not apply to all hospital practice settings, though they are consistent with the literature with comparison data.

Our study results highlight the need for a multidisciplinary systems-based approach to incidental pulmonary nodule documentation, communication, and follow-up surveillance.

Disclosure

Nothing to report.

From the Mayo Clinic Center for Innovation (Dr. Romero-Brufau) Department of Medicine (Drs. Manning, Borrud, Keller, Kashiwagi, Huddleston, and Croghan) Department of Health Sciences Research (Mr. Cha), Mayo Clinic, Rochester, MN.

Abstract

• Objective: To determine whether a score of 8 or greater on the I-MOVE, a bedside instrument that evaluates the need for assistance in turning, sitting, standing, transferring from bed to a chair, and ambulating, predicts lower risk for 30-day readmission or mortality.
• Design: Retrospective cohort study of patients discharged from 2003 to 2011 from a referral hospital in Southeastern Minnesota. We used a convenience sample of 426 inpatients who had at least one documented calculation of the I-MOVE score performed as part of the clinical process during the study.
• Results: Overall 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. After controlling for confounding variables, an I-MOVE score ≥ 8 was a significant predictive factor for 30-day mortality (OR = 0.136, P < 0.01) but not 30-day readmission (OR = 1.143, P = 0.62) or the combined outcome death/readmission (OR = 0.682, P = 0.13).
• Conclusion: The clinical information provided by a patient's I-MOVE score before discharge does not provide information about readmission risk but may offer incremental information about 30-day mortality risk.

Risk factors for hospital 30-day readmission have been studied by Hasan et al [1], van Walraven et al [2], Allaudeen et al [3], and more recently, Donze et al [4]. Risk factors found to be associated with readmission include race, length of stay, and number of hospitalizations in the last 12 months. Additionally, patients identified “feeling unprepared for discharge” and “difficulty performing activities of daily living” as top issues contributing to readmission. The Affordable Care Act established the Value-Based Purchasing (VBP) model for defined hospital illnesses such as acute myocardial infarction, heart failure, and community acquired pneumonia. This has focused more attention on post-discharge 30-day mortality and readmissions as publicly reported metrics that in part determine the Centers for Medicare and Medicaid Services care reimbursement rates [5].

In our hospital, over 400 inpatients have been evaluated since 2004 using the I-MOVE scoring system in the course of their usual care. I-MOVE was most commonly employed by geriatricians in the division of hospital internal medicine, who collectively endorsed the tool in their practice meetings, especially for elderly patients returning to home alone whose mobility independence was uncertain.

Although it was initially designed to help clinicians understand the mobility independence of a patient before discharge, it may provide incremental value discerning risk of 30-day readmission and/or death. We therefore hypothesized that an I-MOVE score of less than 8 (not being able to transfer from a bed to a chair without assistance) would be a significant predictor of 30-day readmission and/or death.

Methods

Study Design

We performed a retrospective cohort study using a convenience sample including the patients in which the I-MOVE score had been calculated as part of the clinical process of care.

Setting and Participants

Participants were any inpatients discharged from the general medicine unit at Mayo Clinic Rochester from January 2003 to May 2011 who had at least one documented calculation of the I-MOVE score performed as part of the clinical process. Patients in the general medicine unit are adults not requiring subspecialty cardiovascular or neurology, coronary care unit, surgical, psychiatry, or rehabilitation. Patients were excluded if there was missing key outcome information or if they died during the hospitalization. For patients with more than one I-MOVE assessment, only the one closest to discharge was used. Data were abstracted from the electronic medical records between July and August 2011.

Variables

Outcome variables were 30-day readmission, 30-day mortality, and the combined outcome of mortality or readmission. We used the last I-MOVE score as a dichotomous variable with a cut-off of 8, which corresponds to the ability to transfer from bed to a chair unaided, for predicting the 2 outcomes. Only readmissions to the study hospital were captured. Deaths were identified from the electronic medical record. Mayo Clinic patient records are updated monthly with external reports of confirmed, actuarial records of deaths reported from public databases.

To control for possible confounding variables, we included the following covariates: age, gender, race/ethnicity, dates of admission and discharge, insurance (Medicare, Medicaid, self-pay, or private), marital status (currently married/not currently married), length of hospital stay, emergent admission, number of hospital admissions in the last 12 months, number of visits to the emergency department in the last 6 months and Charlson Index. All variables were abstracted from the electronic medical record.

Sample

A search was performed in the electronic medical record to find clinical documents (admission notes, progress notes, and hospital summaries) that mentioned the term “I-MOVE.” Manual review of the records was performed to confirm inclusion criteria.

Statistical Analysis

Separate analyses were performed for the 2 outcomes considered. First, a univariate analysis was performed with all covariates for variable selection. Variables that were significantly predictive with P < 0.1 were included in the multivariate model. Variables included in the first run of the multivariate model were excluded from the final multivariate model if they were not independently significant with P < 0.05. The I-MOVE variable was then added to that model to check its predictive power beyond that of the included covariates.

Results

Patient Characteristics

For the final dataset of 426 patients, 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. A total of 6 patients were readmitted and died within 30 days after the initial discharge. The number of patients that had an I-MOVE score greater than or equal to 8 was 232 (54.4%). Table 2 presents the mean, standard deviation, and median I-MOVE score by patient discharge destination. Patients discharged home had an average I-MOVE score of 11.98, versus 7.24 for patients discharge to a skilled nursing facility (P = 0.2).

Analysis

Table 3 presents the odds ratios and coefficient estimate of the models. In the univariate analysis, an I-MOVE score greater than or equal to 8 was significantly correlated with 30-day mortality (P < 0.001), and the combined outcome (P = 0.044) but not with 30-day readmission (P = 0.76). After controlling for confounding variables, I-MOVE greater than or equal to 8 was a significant predictor of 30-day mortality (P < 0.01) but not 30-day readmission (P = 0.75) 

Discussion

An I-MOVE score of less than 8 (inability to transfer from bed to a chair unassisted) is a statistically significant predictor of 30-day post-discharge mortality but not readmission or the combined outcome of death/readmission.

A recent review that evaluated published models that attempted to predict readmissions concluded that most current readmission risk prediction models designed for either comparative or clinical purposes perform poorly and that efforts are needed to improve their performance as use becomes more widespread [8]. Health care providers’ ability to predict which patients would be readmitted within 30 days was also shown by a recent study to be very poor, with C-statistics around 0.60 [9]. This inability of both experts and statistical methods to accurately predict readmissions may reflect some inherent randomness or unpredictability of readmissions, or the fact that a paradigm shift is still needed in the identification of the most important risk factors for readmissions. Along the same line, a recent evaluation of interventions aimed at reducing readmissions found that none of those identified in the literature managed to consistently reduce readmission rates long-term [10]. In addition, hospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Conceptually, readmissions are an example of what is called “complexity science,” where many agents or factors (including the patient’s underlying illness, quality of care delivered, continuity and coordination of care, and resources available in the patient’s environment) and their interactions all play a role in the outcome [11,12]. Since I-MOVE primarily evaluates the physical capacity of the patient, and not any of the other variables that strongly affect readmission, it is perhaps not surprising that it did not predict readmission. It can be argued, on the other hand, that short term (30-day) mortality is more dependent on the patient’s physical and functional status [13] and so more likely to correlate with a measure such as I-MOVE. Inouye et al [13] found that pre-hospital, self-reported need for assistance in 7 basic “activities of daily living” (among which are transfers and ambulation) correlated with 90-day, and 2-year, post-hospital mortality.

The study has the advantage of a relatively large sample size, and the fact that the I-MOVE score was assessed before discharge eliminates the possibility of assessor bias. However, it has some limitations. We used a convenience sample, which may have introduced selection bias. Although we have no data on how providers selected patients for I-MOVE assessment, it would be reasonable to assume that patients were selected from among those whose activity level was, in terms of independence, doubtful or uncertain. That is, those who were not clearly vigorous (up and walking easily), nor clearly debilitated (in need of great assistance) may have been more likely to be assessed using I-MOVE. A more systematic selection of subjects might increase or decrease the predictive performance of the I-MOVE assessment. In addition, although we attempted to control for potential confounders, it is possible that additional confounders were left out of our analysis.

In summary, although the predictive performance of I-MOVE still needs to be confirmed by prospective studies with a comprehensive selection of subjects, the I-MOVE score at discharge appears to be associated with 30-day post-discharge mortality.

Acknowledgments: We thank the Department of Medicine’s clinical research office for their help in study design, data acquisition, and statistical analysis. 

Corresponding author: Santiago Romero-Brufau, MD, Mayo Clinic Center for Innovation,  200 First St. SW, Rochester, MN 55905,  [email protected].

Funding/support: This publication was supported by grant number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.  

From the Mayo Clinic Center for Innovation (Dr. Romero-Brufau) Department of Medicine (Drs. Manning, Borrud, Keller, Kashiwagi, Huddleston, and Croghan) Department of Health Sciences Research (Mr. Cha), Mayo Clinic, Rochester, MN.

Abstract

• Objective: To determine whether a score of 8 or greater on the I-MOVE, a bedside instrument that evaluates the need for assistance in turning, sitting, standing, transferring from bed to a chair, and ambulating, predicts lower risk for 30-day readmission or mortality.
• Design: Retrospective cohort study of patients discharged from 2003 to 2011 from a referral hospital in Southeastern Minnesota. We used a convenience sample of 426 inpatients who had at least one documented calculation of the I-MOVE score performed as part of the clinical process during the study.
• Results: Overall 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. After controlling for confounding variables, an I-MOVE score ≥ 8 was a significant predictive factor for 30-day mortality (OR = 0.136, P < 0.01) but not 30-day readmission (OR = 1.143, P = 0.62) or the combined outcome death/readmission (OR = 0.682, P = 0.13).
• Conclusion: The clinical information provided by a patient's I-MOVE score before discharge does not provide information about readmission risk but may offer incremental information about 30-day mortality risk.

Risk factors for hospital 30-day readmission have been studied by Hasan et al [1], van Walraven et al [2], Allaudeen et al [3], and more recently, Donze et al [4]. Risk factors found to be associated with readmission include race, length of stay, and number of hospitalizations in the last 12 months. Additionally, patients identified “feeling unprepared for discharge” and “difficulty performing activities of daily living” as top issues contributing to readmission. The Affordable Care Act established the Value-Based Purchasing (VBP) model for defined hospital illnesses such as acute myocardial infarction, heart failure, and community acquired pneumonia. This has focused more attention on post-discharge 30-day mortality and readmissions as publicly reported metrics that in part determine the Centers for Medicare and Medicaid Services care reimbursement rates [5].

In our hospital, over 400 inpatients have been evaluated since 2004 using the I-MOVE scoring system in the course of their usual care. I-MOVE was most commonly employed by geriatricians in the division of hospital internal medicine, who collectively endorsed the tool in their practice meetings, especially for elderly patients returning to home alone whose mobility independence was uncertain.

Although it was initially designed to help clinicians understand the mobility independence of a patient before discharge, it may provide incremental value discerning risk of 30-day readmission and/or death. We therefore hypothesized that an I-MOVE score of less than 8 (not being able to transfer from a bed to a chair without assistance) would be a significant predictor of 30-day readmission and/or death.

Methods

Study Design

We performed a retrospective cohort study using a convenience sample including the patients in which the I-MOVE score had been calculated as part of the clinical process of care.

Setting and Participants

Participants were any inpatients discharged from the general medicine unit at Mayo Clinic Rochester from January 2003 to May 2011 who had at least one documented calculation of the I-MOVE score performed as part of the clinical process. Patients in the general medicine unit are adults not requiring subspecialty cardiovascular or neurology, coronary care unit, surgical, psychiatry, or rehabilitation. Patients were excluded if there was missing key outcome information or if they died during the hospitalization. For patients with more than one I-MOVE assessment, only the one closest to discharge was used. Data were abstracted from the electronic medical records between July and August 2011.

Variables

Outcome variables were 30-day readmission, 30-day mortality, and the combined outcome of mortality or readmission. We used the last I-MOVE score as a dichotomous variable with a cut-off of 8, which corresponds to the ability to transfer from bed to a chair unaided, for predicting the 2 outcomes. Only readmissions to the study hospital were captured. Deaths were identified from the electronic medical record. Mayo Clinic patient records are updated monthly with external reports of confirmed, actuarial records of deaths reported from public databases.

To control for possible confounding variables, we included the following covariates: age, gender, race/ethnicity, dates of admission and discharge, insurance (Medicare, Medicaid, self-pay, or private), marital status (currently married/not currently married), length of hospital stay, emergent admission, number of hospital admissions in the last 12 months, number of visits to the emergency department in the last 6 months and Charlson Index. All variables were abstracted from the electronic medical record.

Sample

A search was performed in the electronic medical record to find clinical documents (admission notes, progress notes, and hospital summaries) that mentioned the term “I-MOVE.” Manual review of the records was performed to confirm inclusion criteria.

Statistical Analysis

Separate analyses were performed for the 2 outcomes considered. First, a univariate analysis was performed with all covariates for variable selection. Variables that were significantly predictive with P < 0.1 were included in the multivariate model. Variables included in the first run of the multivariate model were excluded from the final multivariate model if they were not independently significant with P < 0.05. The I-MOVE variable was then added to that model to check its predictive power beyond that of the included covariates.

Results

Patient Characteristics

For the final dataset of 426 patients, 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. A total of 6 patients were readmitted and died within 30 days after the initial discharge. The number of patients that had an I-MOVE score greater than or equal to 8 was 232 (54.4%). Table 2 presents the mean, standard deviation, and median I-MOVE score by patient discharge destination. Patients discharged home had an average I-MOVE score of 11.98, versus 7.24 for patients discharge to a skilled nursing facility (P = 0.2).

Analysis

Table 3 presents the odds ratios and coefficient estimate of the models. In the univariate analysis, an I-MOVE score greater than or equal to 8 was significantly correlated with 30-day mortality (P < 0.001), and the combined outcome (P = 0.044) but not with 30-day readmission (P = 0.76). After controlling for confounding variables, I-MOVE greater than or equal to 8 was a significant predictor of 30-day mortality (P < 0.01) but not 30-day readmission (P = 0.75) 

Discussion

An I-MOVE score of less than 8 (inability to transfer from bed to a chair unassisted) is a statistically significant predictor of 30-day post-discharge mortality but not readmission or the combined outcome of death/readmission.

A recent review that evaluated published models that attempted to predict readmissions concluded that most current readmission risk prediction models designed for either comparative or clinical purposes perform poorly and that efforts are needed to improve their performance as use becomes more widespread [8]. Health care providers’ ability to predict which patients would be readmitted within 30 days was also shown by a recent study to be very poor, with C-statistics around 0.60 [9]. This inability of both experts and statistical methods to accurately predict readmissions may reflect some inherent randomness or unpredictability of readmissions, or the fact that a paradigm shift is still needed in the identification of the most important risk factors for readmissions. Along the same line, a recent evaluation of interventions aimed at reducing readmissions found that none of those identified in the literature managed to consistently reduce readmission rates long-term [10]. In addition, hospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Conceptually, readmissions are an example of what is called “complexity science,” where many agents or factors (including the patient’s underlying illness, quality of care delivered, continuity and coordination of care, and resources available in the patient’s environment) and their interactions all play a role in the outcome [11,12]. Since I-MOVE primarily evaluates the physical capacity of the patient, and not any of the other variables that strongly affect readmission, it is perhaps not surprising that it did not predict readmission. It can be argued, on the other hand, that short term (30-day) mortality is more dependent on the patient’s physical and functional status [13] and so more likely to correlate with a measure such as I-MOVE. Inouye et al [13] found that pre-hospital, self-reported need for assistance in 7 basic “activities of daily living” (among which are transfers and ambulation) correlated with 90-day, and 2-year, post-hospital mortality.

The study has the advantage of a relatively large sample size, and the fact that the I-MOVE score was assessed before discharge eliminates the possibility of assessor bias. However, it has some limitations. We used a convenience sample, which may have introduced selection bias. Although we have no data on how providers selected patients for I-MOVE assessment, it would be reasonable to assume that patients were selected from among those whose activity level was, in terms of independence, doubtful or uncertain. That is, those who were not clearly vigorous (up and walking easily), nor clearly debilitated (in need of great assistance) may have been more likely to be assessed using I-MOVE. A more systematic selection of subjects might increase or decrease the predictive performance of the I-MOVE assessment. In addition, although we attempted to control for potential confounders, it is possible that additional confounders were left out of our analysis.

In summary, although the predictive performance of I-MOVE still needs to be confirmed by prospective studies with a comprehensive selection of subjects, the I-MOVE score at discharge appears to be associated with 30-day post-discharge mortality.

Acknowledgments: We thank the Department of Medicine’s clinical research office for their help in study design, data acquisition, and statistical analysis. 

Corresponding author: Santiago Romero-Brufau, MD, Mayo Clinic Center for Innovation,  200 First St. SW, Rochester, MN 55905,  [email protected].

Funding/support: This publication was supported by grant number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.  

From the Mayo Clinic Center for Innovation (Dr. Romero-Brufau) Department of Medicine (Drs. Manning, Borrud, Keller, Kashiwagi, Huddleston, and Croghan) Department of Health Sciences Research (Mr. Cha), Mayo Clinic, Rochester, MN.

Abstract

• Objective: To determine whether a score of 8 or greater on the I-MOVE, a bedside instrument that evaluates the need for assistance in turning, sitting, standing, transferring from bed to a chair, and ambulating, predicts lower risk for 30-day readmission or mortality.
• Design: Retrospective cohort study of patients discharged from 2003 to 2011 from a referral hospital in Southeastern Minnesota. We used a convenience sample of 426 inpatients who had at least one documented calculation of the I-MOVE score performed as part of the clinical process during the study.
• Results: Overall 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. After controlling for confounding variables, an I-MOVE score ≥ 8 was a significant predictive factor for 30-day mortality (OR = 0.136, P < 0.01) but not 30-day readmission (OR = 1.143, P = 0.62) or the combined outcome death/readmission (OR = 0.682, P = 0.13).
• Conclusion: The clinical information provided by a patient's I-MOVE score before discharge does not provide information about readmission risk but may offer incremental information about 30-day mortality risk.

Risk factors for hospital 30-day readmission have been studied by Hasan et al [1], van Walraven et al [2], Allaudeen et al [3], and more recently, Donze et al [4]. Risk factors found to be associated with readmission include race, length of stay, and number of hospitalizations in the last 12 months. Additionally, patients identified “feeling unprepared for discharge” and “difficulty performing activities of daily living” as top issues contributing to readmission. The Affordable Care Act established the Value-Based Purchasing (VBP) model for defined hospital illnesses such as acute myocardial infarction, heart failure, and community acquired pneumonia. This has focused more attention on post-discharge 30-day mortality and readmissions as publicly reported metrics that in part determine the Centers for Medicare and Medicaid Services care reimbursement rates [5].

In our hospital, over 400 inpatients have been evaluated since 2004 using the I-MOVE scoring system in the course of their usual care. I-MOVE was most commonly employed by geriatricians in the division of hospital internal medicine, who collectively endorsed the tool in their practice meetings, especially for elderly patients returning to home alone whose mobility independence was uncertain.

Although it was initially designed to help clinicians understand the mobility independence of a patient before discharge, it may provide incremental value discerning risk of 30-day readmission and/or death. We therefore hypothesized that an I-MOVE score of less than 8 (not being able to transfer from a bed to a chair without assistance) would be a significant predictor of 30-day readmission and/or death.

Methods

Study Design

We performed a retrospective cohort study using a convenience sample including the patients in which the I-MOVE score had been calculated as part of the clinical process of care.

Setting and Participants

Participants were any inpatients discharged from the general medicine unit at Mayo Clinic Rochester from January 2003 to May 2011 who had at least one documented calculation of the I-MOVE score performed as part of the clinical process. Patients in the general medicine unit are adults not requiring subspecialty cardiovascular or neurology, coronary care unit, surgical, psychiatry, or rehabilitation. Patients were excluded if there was missing key outcome information or if they died during the hospitalization. For patients with more than one I-MOVE assessment, only the one closest to discharge was used. Data were abstracted from the electronic medical records between July and August 2011.

Variables

Outcome variables were 30-day readmission, 30-day mortality, and the combined outcome of mortality or readmission. We used the last I-MOVE score as a dichotomous variable with a cut-off of 8, which corresponds to the ability to transfer from bed to a chair unaided, for predicting the 2 outcomes. Only readmissions to the study hospital were captured. Deaths were identified from the electronic medical record. Mayo Clinic patient records are updated monthly with external reports of confirmed, actuarial records of deaths reported from public databases.

To control for possible confounding variables, we included the following covariates: age, gender, race/ethnicity, dates of admission and discharge, insurance (Medicare, Medicaid, self-pay, or private), marital status (currently married/not currently married), length of hospital stay, emergent admission, number of hospital admissions in the last 12 months, number of visits to the emergency department in the last 6 months and Charlson Index. All variables were abstracted from the electronic medical record.

Sample

A search was performed in the electronic medical record to find clinical documents (admission notes, progress notes, and hospital summaries) that mentioned the term “I-MOVE.” Manual review of the records was performed to confirm inclusion criteria.

Statistical Analysis

Separate analyses were performed for the 2 outcomes considered. First, a univariate analysis was performed with all covariates for variable selection. Variables that were significantly predictive with P < 0.1 were included in the multivariate model. Variables included in the first run of the multivariate model were excluded from the final multivariate model if they were not independently significant with P < 0.05. The I-MOVE variable was then added to that model to check its predictive power beyond that of the included covariates.

Results

Patient Characteristics

For the final dataset of 426 patients, 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. A total of 6 patients were readmitted and died within 30 days after the initial discharge. The number of patients that had an I-MOVE score greater than or equal to 8 was 232 (54.4%). Table 2 presents the mean, standard deviation, and median I-MOVE score by patient discharge destination. Patients discharged home had an average I-MOVE score of 11.98, versus 7.24 for patients discharge to a skilled nursing facility (P = 0.2).

Analysis

Table 3 presents the odds ratios and coefficient estimate of the models. In the univariate analysis, an I-MOVE score greater than or equal to 8 was significantly correlated with 30-day mortality (P < 0.001), and the combined outcome (P = 0.044) but not with 30-day readmission (P = 0.76). After controlling for confounding variables, I-MOVE greater than or equal to 8 was a significant predictor of 30-day mortality (P < 0.01) but not 30-day readmission (P = 0.75) 

Discussion

An I-MOVE score of less than 8 (inability to transfer from bed to a chair unassisted) is a statistically significant predictor of 30-day post-discharge mortality but not readmission or the combined outcome of death/readmission.

A recent review that evaluated published models that attempted to predict readmissions concluded that most current readmission risk prediction models designed for either comparative or clinical purposes perform poorly and that efforts are needed to improve their performance as use becomes more widespread [8]. Health care providers’ ability to predict which patients would be readmitted within 30 days was also shown by a recent study to be very poor, with C-statistics around 0.60 [9]. This inability of both experts and statistical methods to accurately predict readmissions may reflect some inherent randomness or unpredictability of readmissions, or the fact that a paradigm shift is still needed in the identification of the most important risk factors for readmissions. Along the same line, a recent evaluation of interventions aimed at reducing readmissions found that none of those identified in the literature managed to consistently reduce readmission rates long-term [10]. In addition, hospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.

Conceptually, readmissions are an example of what is called “complexity science,” where many agents or factors (including the patient’s underlying illness, quality of care delivered, continuity and coordination of care, and resources available in the patient’s environment) and their interactions all play a role in the outcome [11,12]. Since I-MOVE primarily evaluates the physical capacity of the patient, and not any of the other variables that strongly affect readmission, it is perhaps not surprising that it did not predict readmission. It can be argued, on the other hand, that short term (30-day) mortality is more dependent on the patient’s physical and functional status [13] and so more likely to correlate with a measure such as I-MOVE. Inouye et al [13] found that pre-hospital, self-reported need for assistance in 7 basic “activities of daily living” (among which are transfers and ambulation) correlated with 90-day, and 2-year, post-hospital mortality.

The study has the advantage of a relatively large sample size, and the fact that the I-MOVE score was assessed before discharge eliminates the possibility of assessor bias. However, it has some limitations. We used a convenience sample, which may have introduced selection bias. Although we have no data on how providers selected patients for I-MOVE assessment, it would be reasonable to assume that patients were selected from among those whose activity level was, in terms of independence, doubtful or uncertain. That is, those who were not clearly vigorous (up and walking easily), nor clearly debilitated (in need of great assistance) may have been more likely to be assessed using I-MOVE. A more systematic selection of subjects might increase or decrease the predictive performance of the I-MOVE assessment. In addition, although we attempted to control for potential confounders, it is possible that additional confounders were left out of our analysis.

In summary, although the predictive performance of I-MOVE still needs to be confirmed by prospective studies with a comprehensive selection of subjects, the I-MOVE score at discharge appears to be associated with 30-day post-discharge mortality.

Acknowledgments: We thank the Department of Medicine’s clinical research office for their help in study design, data acquisition, and statistical analysis. 

Corresponding author: Santiago Romero-Brufau, MD, Mayo Clinic Center for Innovation,  200 First St. SW, Rochester, MN 55905,  [email protected].

Funding/support: This publication was supported by grant number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.