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The American Journal of Managed Care April 2015
Clinical Provider Perceptions of Proactive Medication Discontinuation
Amy Linsky, MD, MSc; Steven R. Simon, MD, MPH; Thomas B. Marcello, BA; and Barbara Bokhour, PhD
Optimizing the Use of Telephone Nursing Advice for Upper Respiratory Infection Symptoms
Rosalind Harper, PhD, RN; Tanya Temkin, MPH; and Reena Bhargava, MD
Redefining and Reaffirming Managed Care for the 21st Century
David Blumenthal, MD, MPP; and David Squires, MA
Managing Specialty Care in an Era of Heightened Accountability: Emphasizing Quality and Accelerating Savings
John W. Peabody, MD, PhD, DTM&H; Xiaoyan Huang, MD; Riti Shimkhada, PhD; and Meredith Rosenthal, PhD
Antibiotic Prescribing for Respiratory Infections at Retail Clinics, Physician Practices, and Emergency Departments
Ateev Mehrotra, MD, MPH; Courtney A. Gidengil, MD, MPH; Claude M. Setodji, PhD; Rachel M. Burns, MPH; and Jeffrey A. Linder, MD, MPH
Persistent High Utilization in a Privately Insured Population
Wenke Hwang, PhD; Michelle LaClair, MPH; Fabian Camacho, MS; and Harold Paz, MD, MS
Self-Efficacy in Insurance Decision Making Among Older Adults
Kathleen Kan, MD; Andrew J. Barnes, PhD; Yaniv Hanoch, PhD; and Alex D. Federman, MD, MPH
Limited Effects of Care Management for High Utilizers on Total Healthcare Costs
Brent C. Williams, MD, MPH
Observation Encounters and Subsequent Nursing Facility Stays
Anita A. Vashi, MD, MPH, MHS; Susannah G. Cafardi, MSW, LCSW, MPH; Christopher A. Powers, PharmD; Joseph S. Ross, MD, MHS; and William H. Shrank, MD, MSHS
Elderly Veterans With Dual Eligibility for VA and Medicare Services: Where Do They Obtain a Colonoscopy?
Ashish Malhotra, MD, MS; Mary Vaughan-Sarrazin, PhD; and Gary E. Rosenthal, MD
Costs of Venous Thromboembolism Associated With Hospitalization for Medical Illness
Kevin P. Cohoon, DO, MSc; Cynthia L. Leibson, PhD; Jeanine E. Ransom, BA; Aneel A. Ashrani, MD, MS; Tanya M. Petterson, MS; Kirsten Hall Long, PhD; Kent R. Bailey, PhD; and John A. Heit, MD
Binary Measures for Associating Medication Adherence and Healthcare Spending
Pamela N. Roberto, MPP; and Eberechukwu Onukwugha, PhD
Currently Reading
Functional Status and Readmissions in Unilateral Hip Fractures
Paul Gerrard, MD; Richard Goldstein, PhD; Margaret A. DiVita, PhD; Chloe Slocum, MD; Colleen M. Ryan, MD; Jacqueline Mix, MPH; Paulette Niewczyk, PhD, MPH; Lewis Kazis, ScD; Ross Zafonte, DO; and Jeffrey C. Schneider, MD

Functional Status and Readmissions in Unilateral Hip Fractures

Paul Gerrard, MD; Richard Goldstein, PhD; Margaret A. DiVita, PhD; Chloe Slocum, MD; Colleen M. Ryan, MD; Jacqueline Mix, MPH; Paulette Niewczyk, PhD, MPH; Lewis Kazis, ScD; Ross Zafonte, DO; and Jeffrey C. Schneider, MD
Functional status is an important predictor of an acute care readmission in patients who have had a unilateral hip fracture.
Objectives: To test whether functional status is a robust predictor of acute care readmission risk in patients who have been discharged to an inpatient rehabilitation facility (IRF) following a unilateral hip fracture.
Study Design: Retrospective database study using a large administrative data set.
Methods: A retrospective analysis of data from the Uniform Data System for Medical Rehabilitation from the years 2002 to 2011 was performed, examining patients with an impairment of unilateral hip fracture. A basic prediction model based on functional status was compared with competing models incorporating medical comorbidities. C statistics were compared to evaluate model performance.
Results: There were a total of 433,154 patients: 32,783 (7.87%) patients were transferred back to an acute hospital, including 7937 (1.91%) transferred within 3 days, 16,150 (3.88%) transferred within 7 days, and 32,607 (7.83%) transferred within 30 days after IRF admission. The C statistics for the Basic Model are 0.710, 0.674, and 0.667 at days 3, 7, and 30, respectively. Compared with the Basic Model, the best performing Basic-Plus model was the Basic + Elixhauser Model with C statistic differences of +0.013, +0.014, and +0.019, and the best performing Age-Comorbidity Model was the Age + Elixhauser Model with C statistic differences of –0.110, –0.079, and –0.065 at days 3, 7, and 30, respectively.
Conclusions: Functional status is a robust and potentially modifiable risk factor for patients admitted to IRFs following a unilateral hip fracture.
Am J Manag Care. 2015;21(4):e282-e287
Take-Away Points
  • A high volume of previously published readmissions research has had little impact on readmission rates.
  • Hospital readmission rates, risk-adjusted for demographic and medical characteristics of patients, are being monitored by health policy makers with planned financial penalties for hospitals with high readmission rates.
  • Functional status, a risk factor not currently routinely in use by hospitals or CMS’ risk adjustment models, is an important risk factor for an acute care readmission in patients who have had a unilateral hip fracture.
Hip fracture is a significant source of morbidity and mortality in older adults.1-4 Recent epidemiological studies among Medicare beneficiaries have shown that hospital lengths of stay for patients with hip fractures have been decreasing in recent years, yet there has been a rise in the average comorbidity burden among this population1 in diagnoses such as congestive heart failure, 1 which has been identified by CMS as a high-volume diagnosis for which the 30-day readmission rate may result in hospital payment penalties of 1% to 3% payment reduction.5

Significant research in recent years has been devoted to identifying patients who are at risk for an acute care readmission,6 but despite this attention, the rate of readmissions has remained relatively unchanged, suggesting that this research has not resulted in actionable information that clinicians can use to reduce this rate.7 This may be partly because studies have tended to repeatedly examine the same risk factors, such as medical comorbidities, which is the most often included risk factor in readmission prediction models6 and also not modifiable. Conversely, many risk factors that are potentially modifiable have not been studied, such as functional status, which has been shown to be predictive of acute care readmissions in the burn and stroke populations,8-10 despite it being one of the least studied predictors.6

Another limitation of prior readmissions work is that the populations of interest tend to not be well defined. For example, 30-day readmission rates have been commonly examined,6 probably because 30 days is the time frame used by CMS. However, patients may return to the hospital within a much shorter time frame, such as a week or even a few days; these “bouncebacks” are likely a distinct population from patients who take almost a month to return to the hospital. Additionally, following hospitalization, patients may be discharged to markedly different and distinct levels of care, ranging from self-care at home to post acute care environments with around-the-clock nursing and possibly even daily physician visits. Among patients who have had a hip fracture, about 90% are discharged from an acute care hospital to a post acute care facility—about 20% of whom are transferred to an acute inpatient rehabilitation facility (IRF) from the hospital.11

In this study, we sought to examine the role of functional status as a predictor of acute care readmissions in the unilateral hip fracture population at IRFs within multiple time windows of IRF admission (3, 7, and 30 days). There are no large scale prior studies examining risk factors for readmissions in this population, but based on research in other populations8-10,12 and a much smaller study that included orthopedic patients in general,13 we hypothesized that functional status as measured by the FIM instrument—a proxy measure for the burden of care14-18—can be used to create relatively simple and strong models for predicting the risk of acute care readmissions in the unilateral hip fracture population at IRFs.


We analyzed data from the Uniform Data System for Medical Rehabilitation (UDSMR), a data repository of IRF patients discharged from 2002 to 2011, which contains demographic, functional, medical, and facility data from approximately 70% of the IRFs in the United States. This data is routinely collected as part of the IRF Patient Assessment Instrument, as required by CMS. Inclusion criteria were for the subject to be 18 years or older with an impairment code of unilateral hip fracture and admission to IRF. Subjects were excluded if they were not transferred directly from acute care to an IRF, if they died at the IRF, or if they came from a zero-onset facility.19 The primary outcome variable in this study was the probability of a discharge from the IRF to an acute care hospital.

The FIM instrument (“FIM”), a valid and reliable tool for assessing functional status in the IRF setting,20-25 has 2 components: motor and cognitive. The motor domain, which was used in this study, consists of 13 items, including eating, dressing, grooming, bathing, toileting, sphincter control, bowel and bladder management, transfers, and locomotion. It is typically administered to patients by a combination of nursing and therapy staff. Each item is rated with a 7-level ordinal scale from completely dependent (1) to independent (7), with a FIM motor total score range of 13 to 91.

The UDSMR data was analyzed using Stata version 12.1 (StataCorp, College Station, Texas). Logistic regression analysis was used to create all models. We first developed models based on functional status, called “Basic Models,” which included the Basic Model and Basic-Plus Models. The Basic Model used 2 predictors—FIM motor score and gender—and the odds of transfer to an acute care hospital was the dependent variable. Next, we compared the performance of the Basic Model with models that added comorbidity data (the Basic-Plus Models) and with models that included only gender and comorbidities (Gender-Comorbidity Models). Comorbidities are the most often included predictor in hospital readmission models, which is why models incorporating comorbidities were selected for comparison with the Basic Models. Three different comorbidity scoring systems were used in the analysis: the Elixhauser comorbidity index,26,27 the Deyo-Charlson comorbidity index,28,29 and the Medicare comorbidity tier system.30,31 Consequently, we developed 3 Basic-Plus Models and 3 Gender-Comorbidity Models. See Table 1 for a description of the predictors incorporated into each of the 7 models. For each model, we investigated performance at 3 days, 7 days, and 30 days into the rehabilitation stay. Thus, there were a total of 18 comparisons of the Basic Model with competing models incorporating comorbidity data (ie, 18 opportunities to reject our hypothesis). Generalizability of the models was tested with bootstrap resampling using 1000 samples rather than single subsample cross-validation. Model predictive ability was assessed using the C statistic for each model.

We hypothesized that the Basic Model would perform similarly to the Basic-Plus Models and better than the Gender-Comorbidity Models at all 3 time points. The area under the receiver operator curve (C statistic) was used to test model performance. A C statistic of 0.5 indicates that a model predicts an outcome no better than random chance, and a C statistic of 1 indicates a model has perfect discrimination. There are no established guidelines for the interpretation of C statistics, but the original readmission prediction models for CMS had C statistics in the range of 0.605 to 0.67632—similar to most readmission risk models published in the medical literature.6 We preselected a C statistic difference of 0.05 as a clinically meaningful difference in model discrimination ability. If any Basic-Plus Model met this C statistic threshold at any time point, it would be considered evidence opposed to our hypothesis. Likewise, any failure of the Basic Model to outperform any of the Gender-Comorbidity Models by at least +0.05 would be considered evidence against our hypothesis.


Patient Characteristics

The UDSMR database included 433,154 adult patients with unilateral hip fractures admitted for at least 1 night between 2002 and 2011. We excluded 10,133 patients who were not admitted to inpatient rehabilitation directly from an acute hospital; 626 patients who died in rehabilitation; and 5791 who were from zero-onset facilities. The final sample size was 416,604 patients from 1127 IRFs. Of these, 32,783 (7.87%) patients were transferred back to an acute hospital: they included 7937 (1.91%) transferred within 3 days, 16,150 (3.88%) transferred within 7 days, and 32,607 (7.83%) transferred within 30 days after IRF admission. Table 2 shows the study population’s demographic, medical, and facility data.

Regression Model Results

The coefficients for the logistic regressions of the Basic Model at each time point are shown in Table 3. Table 4 shows the C statistics for each model at each time point. The C statistics for the Basic Model are: 0.710, 0.674, and 0.667 at days 3, 7, and 30, respectively. The Basic-Plus Model C statistics were marginally better at each time point, though not by the threshold of 0.05 that was chosen a priori to establish superiority. The best performing comparison model was the Basic + Elixhauser Model at 3 days, which with a C statistic of 0.723, was only 0.013 greater than that of the Basic Model at this same time point. The Basic Model performed substantially better than the 3 Gender-Comorbidity Models at each time point. The best performing Gender-Comorbidity Model was the 30-day Gender + Elixhauser Model with a C statistic of 0.602, which was 0.065 lower than the 30-day Basic Model.



This study provides evidence that functional status can be used to create a robust readmission prediction model, and that models based on functional status outperform those based on comorbidity data in the unilateral hip fracture population admitted to an IRF. This study is unique not only because there are no other large-scale studies examining readmission risk factors in this particular patient population, but also because it relied on functional status, compared the functional status model with models based on medical comorbidities, and did this in multiple time frames following acute care discharge. The C statistics of the models based on functional status are as good or better than many of the previously published readmission models.6 While we do not have a method to study the reason that functional status predicts readmission risk, we suspect that it is at least in part because functional status is a proxy measure for health, and a better proxy measure than an enumeration of comorbidities alone.

One of the most recent studies examining readmission risks with one of the better predictive models found that comorbidities were not a significant predictor of readmission risk, and posited that this is because it is severity rather than presence of comorbidities that is important,33 a supposition that the findings in this study support. Our results are also consistent with prior research on readmission risk in the burn and stroke populations.8-10,12 Sicker patients are likely more disabled, and functional status as measured by the FIM instrument has been shown to be strongly related to the total hours of burden of care a patient requires.15 The findings in this study suggest both a novel approach to the clinical management and stratification of readmission risk, and a novel approach to adjusting readmission rates in CMS’s assessment of hospital quality.

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