Functional Status Predicts Acute Care Readmissions from Inpatient Rehabilitation in the Stroke Population

Disparities in transitions of acute stroke care: The transitions of care stroke disparities study methodological report

OBJECTIVE

The Transitions of Stroke Care Disparities Study (TCSD-S) is an observational study designed to determine race-ethnic and sex disparities in post-hospital discharge transitions of stroke care and stroke outcomes and to develop hospital-level initiatives to reduce these disparities to improve stroke outcomes.

MATERIALS AND METHODS

Here, we present the study rationale, describe the methodology, report preliminary outcomes, and discuss a critical need for the development, implementation, and dissemination of interventions for successful post-hospital transition of stroke care. The preliminary outcomes describe the demographic, stroke risk factor, socioeconomic, and acute care characteristics of eligible participants by race-ethnicity and sex. We also report on all-cause and vascular-related death, readmissions, and hospital/emergency room representations at 30- and 90-days after hospital discharge.

RESULTS

The preliminary sample included data from 1048 ischemic stroke and intracerebral hemorrhage discharged from 10 comprehensive stroke centers across the state of Florida. The overall sample was 45% female, 22% Non-Hispanic Black and 21% Hispanic participants, with an average age of 64 ± 14 years. All cause death, readmissions, or hospital/emergency room representations are 10% and 19% at 30 and 90 days, respectively. One in 5 outcomes was vascular-related.

CONCLUSIONS

This study highlights the transition from stroke hospitalization as an area in need for considerable improvement in systems of care for stroke patients discharged from hospital. Results from our preliminary analysis highlight the importance of investigating race-ethnic and sex differences in post-stroke outcomes.

Frequency and reasons for unplanned transfer to the primary acute care service of inpatient rehabilitation glioblastoma multiforme patients

PURPOSE

To determine the percentage of and factors associated with unplanned transfer to the acute care service of glioblastoma multiforme acute rehabilitation inpatients.

METHODS

Retrospective review of glioblastoma multiforme acute rehabilitation inpatients admitted 4/1/2016-3/31/2020 at a National Cancer Institute Comprehensive Cancer Center.

RESULTS

One hundred thirty-nine consecutive admissions of unique glioblastoma multiforme acute rehabilitation inpatients were analyzed. Fifteen patients (10.7%, 95% confidence interval 6.5-17.1%) were transferred to the acute care service for unplanned reasons. The most common reasons for transfer back were neurosurgical complication 6/15(40%), neurologic decline due to mass effect 4/15(26.7%), and pulmonary embolism 2/15(13.3%). Older age (p = 0.010), infection prior to acute inpatient rehabilitation transfer (p = 0.020), and lower activity measure of post-acute care 6-click basic mobility scores (p = 0.048) were significantly associated with transfer to the acute care service. Patients who transferred to the acute care service had significantly lower overall survival than patients who did not transfer off (log-rank test p = 0.001).

CONCLUSION

Acute inpatient physiatrists should closely monitor patients for neurosurgical and neurologic complications. The variables significantly associated with transfer to the acute care service may help identify patients at increased risk for medical complications who may require closer observation.

Development of a 30-Day Readmission Risk Calculator for the Inpatient Rehabilitation Setting

OBJECTIVES

Readmission to acute care from the inpatient rehabilitation facility (IRF) setting is potentially preventable and an important target of quality improvement and cost savings. The objective of this study was to develop a risk calculator to predict 30-day all-cause readmissions from the IRF setting.

DESIGN

Retrospective database analysis using the Uniform Data System for Medical Rehabilitation (UDSMR) from 2015 through 2019.

SETTING AND PARTICIPANTS

In total, 956 US inpatient rehabilitation facilities and 1,849,768 IRF discharges comprising patients from 14 impairment groups.

METHODS

Logistic regression models were developed to calculate risk-standardized 30-day all-cause hospital readmission rates for patients admitted to an IRF. Models for each impairment group were assessed using 12 common clinical and demographic variables and all but 4 models included various special variables. Models were assessed for discrimination (c-statistics), calibration (calibration plots), and internal validation (bootstrapping). A readmission risk scoring system was created for each impairment group population and was graphically validated.

RESULTS

The mean age of the cohort was 68.7 (15.2) years, 50.7% were women, and 78.3% were Caucasian. Medicare was the primary payer for 73.1% of the study population. The final models for each impairment group included between 4 and 13 total predictor variables. Model c-statistics ranged from 0.65 to 0.70. There was good calibration represented for most models up to a readmission risk of 30%. Internal validation of the models using bootstrap samples revealed little bias. Point systems for determining risk of 30-day readmission were developed for each impairment group.

CONCLUSIONS AND IMPLICATIONS

Multivariable risk factor algorithms based upon administrative data were developed to assess 30-day readmission risk for patients admitted from IRF. This report represents the development of a readmission risk calculator for the IRF setting, which could be instrumental in identifying high risk populations for readmission and targeting resources towards a diverse group of IRF impairment groups.

Readmission to an Acute Care Hospital During Inpatient Rehabilitation After Stroke

OBJECTIVE

The aim of the study was to identify causes for readmission to acute care of patients admitted to inpatient rehabilitation facility after stroke.

DESIGN

The institutional Uniform Data System for Medical Rehabilitation database was used to identify stroke patients who experienced readmission to acute care and an equal number of age-/sex-matched group of patients who successfully completed their inpatient rehabilitation facility stay during 2005-2018. Retrospective chart review was used to extract clinical data. The two study groups were compared using univariate and multivariate analyses.

RESULTS

The rate of readmission to acute care was 4.7% (n = 89; age = 65 ± 14 yrs; 37% female; 65% White; 73% ischemic stroke). The most common indications for transfer were neurological (31%) and cardiovascular (28%). Compared with control group, the readmission to acute care group had statistically higher rates of comorbid conditions, lower median (interquartile range) Functional Independence Measure score on inpatient rehabilitation facility admission (55 [37-65] vs. 64 [51-78], P < 0.001), and a higher rate of sedative/hypnotic prescription (82% vs. 23%, P < 0.001).

CONCLUSIONS

Readmission to acute care is not common in our cohort. Patients who experienced readmission to acute care had higher medical complexity and were prescribed more sedative/hypnotic medications than the control group. Practitioners should be vigilant in patients who meet these criteria.

Acute inpatient rehabilitation improves function independent of comorbidities in medically complex patients

Objectives

(1) To evaluate the benefits of acute inpatient rehabilitation for a medically complex patient population and (2) to assess the effect of comorbid conditions on rehabilitation outcomes.

Design

Retrospective cohort study.

Setting

Freestanding inpatient rehabilitation facility.

Participants

A total of 270 medically complex adult patients including those with cardiac, pulmonary, and orthopedic conditions, with mean age of 73.6 years; 52.6% were female and 47.4% were male (N=270).

Interventions

Not applicable.

Main Outcome Measures

Functional Independence Measure (FIM) gain, FIM efficiency, rehabilitation length of stay (RLOS), home discharge rate, and readmission to acute care (RTAC).

Results

Among 270 medically complex patients, mean total FIM gain, mean RLOS, and mean FIM efficiency with SD were 26.0±13.6 points, 12.6±5.9 days, and 2.3±1.6, respectively. A total of 71.9% of patients were discharged to home, 12.2% for RTAC, and 15.9% to a skilled nursing facility (SNF). Hypertension (HTN) was the only comorbidity significantly associated with FIM gain (53.7% [total FIM gain ≥27] vs 67.2% [total FIM gain <27]; P=.024) and FIM efficiency (53.3% [FIM efficiency ≥2.12] vs 67.4% [FIM efficiency <2.12]; P=.025), independent of age, body mass index, sex, race, ethnicity, insurance type, and Charlson Comorbidity Index. The 5 most common reasons for RTAC were cardiac, pulmonary, acute blood loss anemia, infection, and neurologic conditions.

Conclusions

Among 270 medically complex patients, 71.9% were discharged to home, 15.9% to an SNF, and 12.2% for RTAC with a mean RLOS 12.6 days, mean total FIM gain of 26 points, and mean FIM efficiency of 2.3, which were all better than those of all admissions at our facility in 2018. Furthermore, RLOS, total FIM gain and FIM efficiency in this study were all better than their corresponding eRehabData weighted national benchmarks (RLOS, 15.82 days; FIM gain, 25.57; FIM efficiency, 2.13) for a total of 202,520 discharges in 2018. These findings support acute inpatient rehabilitation for this patient population. With the exception of HTN, no medical comorbidities or demographic variables were associated with rehabilitation outcomes.

Real-world outcomes associated with the use of the EmboTrap revascularization device for ischemic stroke in the United States

BACKGROUND

Mechanical thrombectomy (MT) has become the standard of care for the treatment of acute ischemic stroke (AIS). The EmboTrap revascularization device (CERENOVUS, Johnson & Johnson Medical Devices, Irvine, California, USA) has an innovative, dual layer feature designed to facilitate thrombus retrieval.

OBJECTIVE

To investigate the real-world clinical and economic outcomes among patients with AIS undergoing MT using the EmboTrap device in the United States (US).

METHODS

Adult patients (≥18 years) who underwent MT for AIS using the EmboTrap device between July 2018 and December 2020 were identified from the Premier Healthcare Database. Patient outcomes included discharge status (including in-hospital mortality), mean length of stay (LOS), intracranial hemorrhage (ICH), mean hospital costs, and 30-day readmissions (all-cause, cardiovascular (CV)-related, and AIS-related).

RESULTS

A total of 318 patients (mean age 68.5±14.6 years) with AIS treated with the EmboTrap device as the only stent retriever used were identified. Approximately 25% of patients were discharged to home/home health organization, and the in-hospital mortality rate was 10.7%. The rate of ICH was 16.7%. Mean hospital LOS was 9.9±11.3 days, and the mean hospital costs were US$47 367±30 297. The 30-day readmission rate was 9.6% for all-causes, 5.9% for CV-related causes, and 2.6% for AIS-related causes.

CONCLUSIONS

This is the first study in the US to report real-world outcomes sourced by retrospective database analysis among patients with AIS undergoing MT using the EmboTrap device. Further research is needed to better understand performance of the EmboTrap device in real-world settings.

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing

Background and Purpose: This study aims to determine whether machine learning (ML) and natural language processing (NLP) from electronic health records (EHR) improve the prediction of 30-day readmission after stroke.

Methods: Among index stroke admissions between 2011 and 2016 at an academic medical center, we abstracted discrete data from the EHR on demographics, risk factors, medications, hospital complications, and discharge destination and unstructured textual data from clinician notes. Readmission was defined as any unplanned hospital admission within 30 days of discharge. We developed models to predict two separate outcomes, as follows: (1) 30-day all-cause readmission and (2) 30-day stroke readmission. We compared the performance of logistic regression with advanced ML algorithms. We used several NLP methods to generate additional features from unstructured textual reports. We evaluated the performance of prediction models using a five-fold validation and tested the best model in a held-out test dataset. Areas under the curve (AUCs) were used to compare discrimination of each model.

Results: In a held-out test dataset, advanced ML methods along with NLP features out performed logistic regression for all-cause readmission (AUC, 0.64 vs. 0.58; p < 0.001) and stroke readmission prediction (AUC, 0.62 vs. 0.52; p < 0.001).

Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.

Variation in 30-Day Readmission Rates from Inpatient Rehabilitation Facilities to Acute Care Hospitals

OBJECTIVES

To quantify the rate of readmission from inpatient rehabilitation facilities (IRFs) to acute care hospitals (ACHs) during the first 30 days of rehabilitation stay. To measure variation in 30-day readmission rate across IRFs, and the extent that patient and facility characteristics contribute to this variation.

DESIGN

Retrospective analysis of an administrative database.

SETTING AND PARTICIPANTS

Adult IRF discharges from 944 US IRFs captured in the Uniform Data System for Medical Rehabilitation database between October 1, 2015 and December 31, 2017.

METHODS

Multilevel logistic regression was used to calculate adjusted rates of readmission within 30 days of IRF admission and examine variation in IRF readmission rates, using patient and facility-level variables as predictors.

RESULTS

There were a total of 104,303 ACH readmissions out of a total of 1,102,785 IRFs discharges. The range of 30-day readmission rates to ACHs was 0.0%‒28.9% (mean = 8.7%, standard deviation = 4.4%). The adjusted readmission rate variation narrowed to 2.8%‒17.5% (mean = 8.7%, standard deviation = 1.8%). Twelve patient-level and 3 facility-level factors were significantly associated with 30-day readmission from IRF to ACH. A total of 82.4% of the variance in 30-day readmission rate was attributable to the model predictors.

CONCLUSIONS AND IMPLICATIONS

Fifteen patient and facility factors were significantly associated with 30-day readmission from IRF to ACH and explained the majority of readmission variance. Most of these factors are nonmodifiable from the IRF perspective. These findings highlight that adjusting for these factors is important when comparing readmission rates between IRFs.

Clinical impact of functional independent measure (FIM) on 180-day readmission and mortality in elderly patients hospitalized with acute decompensated heart failure

Activities of daily living (ADL) are important prognostic factors for heart failure. The functional independent measure (FIM) has emerged as a comprehensive valid measure of ADL from both physical and cognitive perspectives. This study aimed to investigate the prognostic impact of the FIM score on clinical outcomes in hospitalized patients with acute decompensated heart failure (ADHF). We retrospectively analyzed 473 ADHF patients, with available pre-discharge FIM scores, admitted to our institution between May 2018 and May 2020. Primary outcome measures, defined as a composite of 180-day all-cause deaths and readmissions, were compared among three tertiles. The median FIM score was 102 (interquartile range: 85-115). Tertile 1 corresponded to an FIM score > 111 (n = 154), Tertile 2 to that of 90-111 (n = 167), and Tertile 3 to that of < 90 (n = 152). During follow-up, 28 deaths and 114 readmissions occurred. Patients with lower FIM scores were associated with a graded increase in the risk of primary outcome measure (p = 0.001). Even after multivariable adjustment, the results remained significant [Tertile 1 vs 3; adjusted hazard ratio: 3.28 (95% confidence interval: 1.72-6.56), p < 0.001; Tertile 2 vs 3; 2.32 (1.27-4.47), p = 0.006]. FIM scores were significantly associated with readmission or death within 180 days of discharge in hospitalized ADHF patients.

Association Between Use of Rehabilitation in the Acute Care Hospital and Hospital Readmission or Mortality in Patients With Stroke

OBJECTIVE

To examine the association between the number of physical and occupational therapist visits received in the acute care hospital and the risk of hospital readmission or death.

DESIGN

Retrospective cohort study of electronic health records and administrative claims data collected for 2.25 years (January 1, 2016-March 30, 2018).

SETTING

Twelve acute care hospitals in a large health care system in western Pennsylvania.

PARTICIPANTS

Adults (N=8279) discharged with a primary stroke diagnosis.

INTERVENTIONS

The exposure was number of physical and occupational therapist visits during the acute care stay.

MAIN OUTCOME MEASURES

Generalized linear mixed models were estimated to examine the relationship between therapy use and 30- and 7-day hospital readmission or death (outcome), controlling for patient demographic and clinical characteristics.

RESULTS

The 30- and 7-day readmission or death rates were 16.0% and 5.7%, respectively. The number of therapist visits was inversely related to the risk of 30-day readmission or death. Relative to no therapist visits, the odds of readmission or death were 0.70 (95% confidence interval [CI], 0.54-0.90) for individuals who received 1-2 visits, 0.59 (95% CI, 0.43-0.81) for 3-5 visits, and 0.57 (95% CI, 0.39-0.81) for >5 visits. A similar pattern was seen for the 7-day outcome, with slightly larger effect sizes. Effects were also greater in individuals with more mobility limitations on admission and for those discharged to a postacute care facility vs home.

CONCLUSIONS

There was an inverse relationship between the number of therapist visits and risk for readmission or death for patients with stroke discharged from an acute care hospital. Effects differed by time to the event (30d vs 7d), discharge location, and mobility limitations on admission.

Risk of Hospital Readmission among Older Patients Discharged from the Rehabilitation Unit in a Rural Community Hospital: A Retrospective Cohort Study

Rehabilitation for hospitalized older people can improve their independence for performing activities of daily living (ADL), but determining its appropriateness can be challenging because of inherent limitations in their ADL and short life expectancy. Thus, we aimed to clarify the benefit of rehabilitation among older Japanese patients. We retrospectively evaluated consecutive older patients (age > 65 years) admitted to the rehabilitation unit of a rural community hospital between 1 April 2016 and 31 March 2020. The primary outcome measure was readmission for acute conditions. Of the 732 patients evaluated, 311 patients (42.5%) were readmitted. Readmission was significantly associated with body mass index (BMI) (p < 0.001), dependent condition (p < 0.001), higher cognitive domain scores in the functional independence measure (FIM) (p = 0.019), and polypharmacy (p = 0.026). The most frequent cause of readmission was pyelonephritis (11.9%), followed by pneumonia (10.9%), compression fracture (10.6%), heat stroke (8.4%), and cerebral stroke (8.0%). In conclusion, older Japanese patients discharged from rehabilitation units have lower readmission rates than those previously reported. Thus, better nutritional control, a multidisciplinary approach to the management of cognitive dysfunction, and a decrease in polypharmacy could be associated with improved outcomes among discharged older patients.

Association of Pain on Hospital Discharge with the Risk of 30-Day Readmission in Patients with Total Hip and Knee Replacement

BACKGROUND

It is not clear if there is a risk of 30-day readmissions following total hip and knee arthroplasty in patients reporting high levels of pain at hospital discharge. We examined the relationship between post-surgical pain on the day of discharge and 30-day readmission in patients who received total knee and hip arthroplasty.

METHODS

Retrospective cohort study was conducted of patients who received total knee (n = 155,284) or hip arthroplasty (n = 89,283) from 2011 to 2018 using electronic health records from the Optum database. Four categories of pain at discharge were created, from none to severe. Multivariate logistic regression models to predict 30-day all-cause readmission were adjusted for patient and clinical characteristics and built separately for knee and hip arthroplasty patients.

RESULTS

Mean ages for hip and knee patients were 64.4 (standard deviation 11.3) and 65.7 (standard deviation 9.7) years, respectively. The majority of patients were female (hip: 54.4%; knee: 61.5%). The unadjusted rate of 30-day readmission was 3.54% for hip replacement and 3.66% for knee replacement. In models adjusted for patient and clinical characteristics, for patients with total hip replacement, the odds of 30-day readmission for those with severe pain score at discharge vs those with no pain at discharge were 1.60 (95% confidence interval 1.33-1.92). Similarly, readmission likelihood increased as pain at discharge increased (severe pain vs no pain) for patients with total knee arthroplasty (odds ratio 1.38, 95% confidence interval 1.19-1.59).

CONCLUSION

Our findings demonstrated that the pain scores on the day of discharge are associated with 30-day hospital readmission.

A Machine Learning Approach to Predicting Readmission or Mortality in Patients Hospitalized for Stroke or Transient Ischemic Attack

Readmissions after stroke are not only associated with greater levels of disability and a higher risk of mortality but also increase overall medical costs. Predicting readmission risk and understanding its causes are thus essential for healthcare resource allocation and quality improvement planning. By using machine learning techniques on initial admission data, this study aimed to develop prediction models for readmission or mortality after stroke. During model development, resampling methods were implemented to balance the class distribution. Two-layer nested cross-validation was used to build and evaluate the prediction models. A total of 3422 patients were included for analysis. The 90-day rate of readmission or mortality was 17.6%. This study identified several important predictive factors, including age, prior emergency department visits, pre-stroke functional status, stroke severity, body mass index, consciousness level, and use of a nasogastric tube. The Naïve Bayes model with class weighting to compensate for class imbalance achieved the highest discriminatory capacity in terms of the area under the receiver operating characteristic curve (0.661). Despite having room for improvement, the prediction models could be used for early risk assessment of patients with stroke. Identification of patients at high risk for readmission or mortality immediately after admission has the potential of enabling early discharge planning and transitional care interventions.

Predictors, causes and outcome of 30-day readmission among acute ischemic stroke

BACKGROUND AND PURPOSE

Readmission within 30 days of index acute ischemic stroke (AIS) after hospitalization increases the burden on patients and healthcare expense. The purpose of our study was to investigate predictors and causes of 30-day readmission after AIS and investigate hospitalization expenses, length of stay (LOS) and in-hospital mortality of 30-day readmission.

METHODS

This is a multicenter retrospective study. AIS were captured by International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes, patients with readmitted within 30 days after discharge were identified as readmission group. Multivariable logistic regression was used to identify independent predictors of 30-day readmissions. Hospitalization expenses, LOS and in-hospital mortality were compared for index admission and readmission.

RESULTS

We identified 2371 patients with AIS, 176 patients died before discharge, 504(23.0%) patients were admitted within 30 days. Older age, prior stroke, non-neurology floor during index admission, indwelling urinary catheter and diabetes were independently associated with increased risk of 30-day readmission (P<0.05). The most common causes for 30-day readmission were infection (28.8%) and recurrent stroke and TIA (22.8%). Patients with 30-day readmission have longer LOS and higher hospitalization expenses on readmission compared with the mean of these metrics on index admission (P<0.001). The in-hospital mortality after a within 30-day readmission was higher than index admission (13.1% vs 8.0%; OR 1.88, 95% CI 2.5-5.3; P<0.001).

CONCLUSIONS

Older age, stroke severity, prior stroke, diabetes, indwelling urinary catheter and admission to non-neurology floor during index admission were associated with 30-day readmission. 30-readmission after AIS increased hospitalization expenses, LOS and in-hospital mortality.

Inpatient rehabilitation facilities' hospital readmission rates for medicare beneficiaries treated following a stroke

BACKGROUND

Stroke is the leading cause for admission to the nearly 1,200 Inpatient Rehabilitation Facilities (IRFs) nationally in the US. For many patients, post-acute care is an important component of their rehabilitation. Several quality measures have been publicly reported for post-acute care providers, including hospital readmissions. However, to date none have focused on specific medical conditions, limiting the usability for patients and quality improvement.

OBJECTIVE

To assess hospital readmission rates for Medicare patients receiving inpatient rehabilitation following stroke and to identify risk factors in order to evaluate the feasibility of a stroke-specific hospital readmission measure.

METHODS

Observational study analyzing national Medicare inpatient claims and administrative data to assess hospital readmissions. Using logistic regression, we calculated unadjusted and risk-standardized readmission rates, which adjusted for patient characteristics, including type of stroke and admission function, to capture stroke severity.

RESULTS

Our national study included 116,073 fee-for-service Medicare beneficiary discharged from IRFs in 2013-2014 following stroke from 1,162 IRFs nationally. The observed hospital readmission rate among IRF patients following stroke was 11.6% and varied by patients' admission motor function. Patients with greater functional dependence had higher readmission rates on average. Lower admission function, hemorrhagic and other stroke types (relative to ischemic) were significantly associated with higher odds of hospital readmission.

CONCLUSION

Results suggest it is feasible to assess hospital readmission rates among a stroke-cohort treated in IRFs. Stroke-focused quality measures would be useful to patients in selecting a provider and for providers in evaluating their stroke rehabilitation program outcomes. Secondary results suggest that admission function (FIM) capture stroke severity, a limitation with other claims-based stroke measures.

Weekend Admission to Inpatient Rehabilitation Facilities Is Associated With Transfer to Acute Care in a Nationwide Sample of Patients With Stroke

OBJECTIVE

The aim of the study was to determine the impact of weekend versus weekday admission to an inpatient rehabilitation facility on the risk of acute care transfer in patients with stroke.

DESIGN

This was a retrospective analysis using the Uniform Data System for Medical Rehabilitation, a national database comprising data from 70% of US inpatient rehabilitation facilities. A total of 1,051,436 adult (age ≥18 yrs) stroke cases were identified between 2002 and 2014 that met inclusion criteria. Logistic regression models were developed to test for associations between weekend (Friday-Sunday) versus weekday (Monday-Thursday) inpatient rehabilitation facility admission and transfer to acute care (primary outcome) and inpatient rehabilitation facility length of stay (secondary outcome), adjusting for relevant patient, medical, and facility variables. A secondary analysis examined acute care transfer from 2002 to 2009 before passage of the Affordable Care Act (ACA), 2010 to 2012 post-Affordable Care Act, and 2013 to 2014 after implementation of the Hospital Readmissions Reduction Program.

RESULTS

Weekend inpatient rehabilitation facility admission was associated with increased odds of acute care transfer (odds ratio = 1.06, 95% confidence interval = 1.04-1.08) and slightly shorter inpatient rehabilitation facility length of stay (P < 0.001). Overall, the risk of acute care transfer decreased after the ACA and Hospital Readmissions Reduction Program.

CONCLUSIONS

Weekend admission to inpatient rehabilitation facility may pose a modest increase in the risk of transfer to acute care in patients with stroke.

TO CLAIM CME CREDITS

Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES: Upon completion of this article, the reader should be able to: (1) Understand disparities in obesity rates among adolescents with mobility disabilities; (2) Describe limitations of current clinical screening methods of obesity in children with mobility disabilities; and (3) Identify potential alternatives for obesity screening in children with mobility disabilities.

LEVEL

Advanced ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Resuscitation status and characteristics and outcomes of patients transferred from subacute care to acute care hospitals: A multi-site prospective cohort study

AIMS AND OBJECTIVES

To examine the relationship between resuscitation status and (i) patient characteristics; (ii) transfer characteristics; and (iii) patient outcomes following an emergency inter-hospital transfer from a subacute to an acute care hospital.

BACKGROUND

Patients who experience emergency inter-hospital transfers from subacute to acute care hospitals have high rates of acute care readmission (81%) and in-hospital mortality (15%).

DESIGN

This prospective, exploratory cohort study was a subanalysis of data from a larger case-time-control study in five Health Services in Victoria, Australia. There were 603 transfers in 557 patients between August 2015 and October 2016. The study was conducted in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology guidelines.

METHODS

Data were extracted by medical record audit. Three resuscitation categories (full resuscitation; limitation of medical treatment (LOMT) orders; or not-for-cardiopulmonary resuscitation (CPR) orders) were compared using chi-square or Kruskal-Wallis tests. Stratified multivariable proportional hazard Cox regression models were used to account for health service clustering effect.

FINDINGS

Resuscitation status was 63.5% full resuscitation; 23.1% LOMT order; and 13.4% not-for-CPR. Compared to patients for full resuscitation, patients with not-for-CPR or LOMT orders were more likely to have rapid response team calls during acute care readmission or to die during hospitalisation. Patients who were not-for-CPR were less likely to be readmitted to acute care and more likely to return to subacute care.

CONCLUSIONS

Two-thirds of patients in subacute care who experienced an emergency inter-hospital transfer were for full resuscitation. Although the proportion of patients with LOMT and not-for-CPR orders increased after transfer, there were deficiencies in the documentation of resuscitation status and planning for clinical deterioration for subacute care patients.

RELEVANCE TO CLINICAL PRACTICE

As many subacute care patients experience clinical deterioration, patient preferences for care need to be discussed and documented early in the subacute care admission.

Sepsis Survivors Transitioned to Home Health Care: Characteristics and Early Readmission Risk Factors

OBJECTIVE

To profile the characteristics of growing numbers of sepsis survivors receiving home healthcare (HHC) by type of sepsis before, during, and after a sepsis hospitalization and identify characteristics significantly associated with 7-day readmission.

DESIGN

Cross-sectional descriptive study. Data sources included the Outcome and Assessment Information Set (OASIS) and Medicare administrative and claims data.

SETTING AND PARTICIPANTS

National sample of Medicare beneficiaries hospitalized for sepsis who were discharged to HHC between July 1, 2013 and June 30, 2014 (N = 165,228).

METHODS

We used an indicator distinguishing among 3 types of sepsis: explicitly coded sepsis diagnosis without organ dysfunction; severe sepsis with organ dysfunction; and septic shock. We compared these subgroups' demographic, clinical and functional characteristics, comorbidities, risk factors for rehospitalization, characteristics of the index hospital stay, and predicted 7-day hospital readmission.

RESULTS

The majority (80.7%) had severe sepsis, 5.7% had septic shock, and 13.6% had sepsis without acute organ system dysfunction. The medical diagnoses recorded at HHC admission identified sepsis or blood infection only 7% of the time, potentially creating difficulty identifying the sepsis survivor in HHC. Among sepsis types, septic shock survivors had the greatest illness burden profile. This study describes 12 key variables, each of which individually raises the relative 7-day readmission risk by as much as 60%. Increased risk of 7-day rehospitalization was found among those with septic shock, 3 or more previous inpatient stays, index hospital length of stay of >8 days, dyspnea, >6 functional dependencies, and other risk factors.

CONCLUSIONS AND IMPLICATIONS

Implications for practice include using our findings to identify sepsis survivors who are at risk for early readmission. Assessment for these factors may profile the at-risk patient, thereby triggering the call for additional acute care intervention such as delayed discharge, or post-acute intervention such as early home visit and outpatient follow-up.

Predictors of Readmission to Acute Care from Inpatient Rehabilitation: An Integrative Review

Readmission to acute care (RTAC) from inpatient rehabilitation can have negative consequences for individuals and associated financial costs are increasing. Consequently, preventing avoidable RTAC represents a target for improvement in quality of care. The aim of this integrative review was to identify predictors of RTAC from inpatient rehabilitation. A systematic search of MEDLINE, EMBASE, ProQuest, and CINAHL databases was used. Thematic analysis was used to examine extracted data. Strong evidence indicating that the principal predictors of RTAC are lower functional status on admission to rehabilitation, a more severe injury and a higher number of comorbidities was identified in this review. This is despite the heterogeneous nature of impairment groups and factors/measures examined. However, the relevance of some predictors of RTAC (such as patient demographics, invasive devices and primary diagnoses) may be dependent on rehabilitation setting, impairment group or time between rehabilitation admission and RTAC (eg, below 3 vs 30 days). Consequently, findings of this integrative review highlight that RTAC is a complex, multifactorial patient issue with a complex interplay between the predictors and reasons for RTAC. LEVEL OF EVIDENCE: IV.

Characteristics and outcomes of emergency interhospital transfers from subacute to acute care for clinical deterioration

OBJECTIVE

To describe characteristics and outcomes of emergency interhospital transfers from subacute to acute hospital care and develop an internally validated predictive model to identify features associated with high risk of emergency interhospital transfer.

DESIGN

Prospective case-time-control study.

SETTING

Acute and subacute healthcare facilities from five health services in Victoria, Australia.

PARTICIPANTS

Cases were patients with an emergency interhospital transfer from subacute to acute hospital care. For every case, two inpatients from the same subacute care ward on the same day of emergency transfer were randomly selected as controls. Admission episode was the unit of measurement and data were collected prospectively.

MAIN OUTCOME MEASURES

Patient and admission characteristics, transfer characteristics and outcomes (cases), serious adverse events and mortality.

RESULTS

Data were collected for 603 transfers in 557 patients and 1160 control patients. Cases were significantly more likely to be male, born in a non-English speaking country, have lower functional independence, more frequent vital sign assessments and experience a serious adverse event during first acute care or subacute care admissions. When adjusted for health service, cases had significantly higher inpatient mortality, were more likely to have unplanned intensive care unit admissions and rapid response team calls during their entire hospital admission.

CONCLUSIONS

Patients who require an emergency interhospital transfer from subacute to acute hospital care have hospital admission rates and in-hospital mortality. Clinical instability during the first acute care admission (serious adverse events or increased surveillance) may prompt reassessment of patient suitability for movement to a separate subacute care hospital.

The Impact of Ischaemic Stroke Subtype on 30-day Hospital Readmissions

Background

Stroke aetiology may affect the risk and causes of readmission after ischaemic stroke (IS) and transient ischaemic attack (TIA) due to differences in risk factors, functional outcome, and treatment. We aimed to examine frequencies, causes, and risk of 30-day readmission by stroke subtype, determine predictors of 30-day readmission, and study the impact of 30-day readmissions on one-year mortality.

Methods

All surviving patients admitted with IS or TIA from July 2007 to December 2013 were followed by review of medical records for all unplanned readmissions within 30 days after discharge. Stroke subtype was classified as large-artery atherosclerosis (LAA), cardioembolism (CE), small vessel occlusion (SVO), stroke of other determined aetiology (SOE), or stroke of undetermined aetiology (SUE). Cox regression analyses were performed to assess the risk of 30-day readmission for the stroke subtypes and identify predictors of 30-day readmission, and its impact on one-year mortality.

Results

Of 1874 patients, 200 (10.7%) were readmitted within 30 days [LAA 42/244 (17.2%), CE 75/605 (12.4%), SVO 12/205 (5.9%), SOE 6/32 (18.8%), SUE 65/788 (8.3%)]. The most frequent causes of readmissions were stroke-related event, infection, recurrent stroke/ TIA, and cardiac disease. After adjusting for age, sex, functional outcome, length of stay, and the risk factor burden, patients with LAA and SOE subtype had significantly higher risks of readmission for any cause, recurrent stroke or TIA, and stroke-related events. Predictors of 30-day readmission were higher age, peripheral arterial disease, enteral feeding, and LAA subtype. Thirty-day readmission was an independent predictor of one-year mortality.

Conclusions

Patients with LAA or SOE have a high risk of 30-day readmission, possibly caused by an increased risk of recurrent stroke and stroke-related events. Awareness of the risk of readmission for different causes and appropriate handling according to stroke subtype may be useful for preventing some readmissions after stroke.

Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management

Objective

We sought to assess the current state of risk prediction and segmentation models (RPSM) that focus on whole populations.

Materials

Academic literature databases (ie MEDLINE, Embase, Cochrane Library, PROSPERO, and CINAHL), environmental scan, and Google search engine.

Methods

We conducted a critical review of the literature focused on RPSMs predicting hospitalizations, emergency department visits, or health care costs.

Results

We identified 35 distinct RPSMs among 37 different journal articles (n = 31), websites (n = 4), and abstracts (n = 2). Most RPSMs (57%) defined their population as health plan enrollees while fewer RPSMs (26%) included an age-defined population (26%) and/or geographic boundary (26%). Most RPSMs (51%) focused on predicting hospital admissions, followed by costs (43%) and emergency department visits (31%), with some models predicting more than one outcome. The most common predictors were age, gender, and diagnostic codes included in 82%, 77%, and 69% of models, respectively.

Discussion

Our critical review of existing RPSMs has identified a lack of comprehensive models that integrate data from multiple sources for application to whole populations. Highly depending on diagnostic codes to define high-risk populations overlooks the functional, social, and behavioral factors that are of great significance to health.

Conclusion

More emphasis on including nonbilling data and providing holistic perspectives of individuals is needed in RPSMs. Nursing-generated data could be beneficial in addressing this gap, as they are structured, frequently generated, and tend to focus on key health status elements like functional status and social/behavioral determinants of health.

Functional status predicts acute care readmission in the traumatic spinal cord injury population

CONTEXT/OBJECTIVE

Acute care readmission has been identified as an important marker of healthcare quality. Most previous models assessing risk prediction of readmission incorporate variables for medical comorbidity. We hypothesized that functional status is a more robust predictor of readmission in the spinal cord injury population than medical comorbidities.

DESIGN

Retrospective cross-sectional analysis.

SETTING

Inpatient rehabilitation facilities, Uniform Data System for Medical Rehabilitation data from 2002 to 2012.

PARTICIPANTS

traumatic spinal cord injury patients.

OUTCOME MEASURES

A logistic regression model for predicting acute care readmission based on demographic variables and functional status (Functional Model) was compared with models incorporating demographics, functional status, and medical comorbidities (Functional-Plus) or models including demographics and medical comorbidities (Demographic-Comorbidity). The primary outcomes were 3- and 30-day readmission, and the primary measure of model performance was the c-statistic.

RESULTS

There were a total of 68,395 patients with 1,469 (2.15%) readmitted at 3 days and 7,081 (10.35%) readmitted at 30 days. The c-statistics for the Functional Model were 0.703 and 0.654 for 3 and 30 days. The Functional Model outperformed Demographic-Comorbidity models at 3 days (c-statistic difference: 0.066-0.096) and outperformed two of the three Demographic-Comorbidity models at 30 days (c-statistic difference: 0.029-0.056). The Functional-Plus models exhibited negligible improvements (0.002-0.010) in model performance compared to the Functional models.

CONCLUSION

Readmissions are used as a marker of hospital performance. Function-based readmission models in the spinal cord injury population outperform models incorporating medical comorbidities. Readmission risk models for this population would benefit from the inclusion of functional status.

Predictors of readmission to acute care during inpatient rehabilitation for non-traumatic spinal cord injury

OBJECTIVES

To investigate the frequency of and reasons for readmissions to acute care (RTAC) during inpatient rehabilitation (IPR) after non-traumatic spinal cord injury (NT-SCI). To develop a predictive model for RTAC using identified risk factors.

DESIGN

Retrospective case-control.

SETTING

Academic IPR hospital.

PARTICIPANTS

Individuals with NT-SCI admitted to an academic SCI rehabilitation unit from January 2014-December 2015.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Readmissions to acute care services from IPR.

RESULTS

Thirty-seven participants (20%) experienced a RTAC for a total of 39 episodes. Thirty-five experienced 1 RTAC, while two had 2. The most common medical reasons for RTAC were infection (27%), neurological (27%), and noninfectious respiratory (16%). Multivariable logistic regression was used to develop a model to predict RTAC. Paraplegia was associated with 3.2 times increase in the odds of RTAC (P = 0.03). For every unit increase in FIM-Motor, there was a 5% reduction in the odds of RTAC (P = 0.03) Body mass index less than 30 decreased odds of RTAC by 61% (P = 0.004).

CONCLUSION

RTACs were associated with body mass index greater than 30, decreased FIM-Motor subscore on admission, and paraplegia. Physiatrists caring for the non-traumatic SCI patient need be more circumspect of individuals with these parameters to potentially prevent the problems necessitating acute care transfer.

Effects of Age and Sex on Hospital Readmission in Traumatic Brain Injury

OBJECTIVE

To investigate the effects of age and sex on 30-, 60-, and 90-day hospital readmission after acute hospital discharge for individuals with traumatic brain injury (TBI).

DESIGN

Retrospective cohort study.

SETTING

Acute hospitals and postacute discharge settings.

PARTICIPANTS

Individuals (N=52,877) with Diagnosis Related Group codes of TBI, who were divided into 4 age groups: 18 to 40, 41 to 65, 66 to 75, and ≥76 years.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

All-cause hospital readmission.

RESULTS

Sex differences in 30-, 60-, and 90-day hospital readmission were found among all age groups (P<.05 for all). The largest sex differences in hospital readmission were in the 2 oldest groups (66-75 and ≥76y). For both sexes, the oldest group (≥76y) had the highest adjusted 90-day readmission risk (eg, 90-d readmission: odds ratio, 2.32 [95% confidence interval, 2.01-2.69] for men; odds ratio, 1.96 [95% confidence interval, 1.59-2.43] for women). Among those readmitted within 90 days, the youngest group (18-40y) had the highest cumulative readmission percentage (35% for both sexes) within the first week of hospital discharge.

CONCLUSIONS

Age and sex were significantly associated with hospital readmission during the first 90 days postdischarge in our TBI sample. Specifically, those aged 66 to 75 or ≥76 years had the highest readmission risk over 90 days for both sexes. The findings suggest that clinicians should consider age and sex in discharge planning and for the entire episode of care for the population with TBI.

Geographic Region and Profit Status Drive Variation in Hospital Readmission Outcomes Among Inpatient Rehabilitation Facilities in the United States

OBJECTIVE

To examine whether there are differences in inpatient rehabilitation facilities' (IRFs') all-cause 30-day postdischarge hospital readmission rates vary by organizational characteristics and geographic regions.

DESIGN

Observational study.

SETTING

IRFs.

PARTICIPANTS

Medicare fee-for-service beneficiaries discharged from all IRFs nationally in 2013 and 2014 (N = 1166 IRFs).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

We applied specifications for an existing quality measure adopted by Centers for Medicare & Medicaid Services for public reporting that assesses all-cause unplanned hospital readmission measure for 30 days postdischarge from inpatient rehabilitation. We estimated facility-level observed and risk-standardized readmission rates and then examined variation by several organizational characteristics (facility type, profit status, teaching status, proportion of low-income patients, size) and geographic factors (rural/urban, census division, state).

RESULTS

IRFs' mean risk-standardized hospital readmission rate was 13.00%±0.77%. After controlling for organizational characteristics and practice patterns, we found substantial variation in IRFs' readmission rates: for-profit IRFs had significantly higher readmission rates than did not-for-profit IRFs (P<.001). We also found geographic variation: IRFs in the South Atlantic and South Central census regions had the highest hospital readmission rates than did IRFs in New England that had the lowest rates.

CONCLUSIONS

Our findings point to variation in quality of care as measured by risk-standardized hospital readmission rates after IRF discharge. Thus, monitoring of readmission outcomes is important to encourage quality improvement in discharge care planning, care transitions, and follow-up.

Current Risk Adjustment and Comorbidity Index Underperformance in Predicting Post-Acute Utilization and Hospital Readmissions After Joint Replacements: Implications for Comprehensive Care for Joint Replacement Model

OBJECTIVE

To compare the performances of 3 comorbidity indices, the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, and the Centers for Medicare & Medicaid Services (CMS) risk adjustment model, Hierarchical Condition Category (HCC), in predicting post-acute discharge settings and hospital readmission for patients after joint replacement.

METHODS

A retrospective study of Medicare beneficiaries with total knee replacement (TKR) or total hip replacement (THR) discharged from hospitals in 2009-2011 (n = 607,349) was performed. Study outcomes were post-acute discharge setting and unplanned 30-, 60-, and 90-day hospital readmissions. Logistic regression models were built to compare the performance of the 3 comorbidity indices using C statistics. The base model included patient demographics and hospital use. Subsequent models included 1 of the 3 comorbidity indices. Additional multivariable logistic regression models were built to identify individual comorbid conditions associated with high risk of hospital readmissions.

RESULTS

The 30-, 60-, and 90-day unplanned hospital readmission rates were 5.3%, 7.2%, and 8.5%, respectively. Patients were most frequently discharged to home health (46.3%), followed by skilled nursing facility (40.9%) and inpatient rehabilitation facility (12.7%). The C statistics for the base model in predicting post-acute discharge setting and 30-, 60-, and 90-day readmission in TKR and THR were between 0.63 and 0.67. Adding the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, or HCC increased the C statistic minimally from the base model for predicting both discharge settings and hospital readmission. The health conditions most frequently associated with hospital readmission were diabetes mellitus, pulmonary disease, arrhythmias, and heart disease.

CONCLUSION

The comorbidity indices and CMS-HCC demonstrated weak discriminatory ability to predict post-acute discharge settings and hospital readmission following joint replacement.

Venous Thromboembolism as Predictor of Acute Care Hospital Transfer and Inpatient Rehabilitation Length of Stay

OBJECTIVE

The aim of this study was to investigate the impact of venous thromboembolism (VTE) on rate of acute care hospital transfer, inpatient rehabilitation (IPR) length of stay (LOS), and functional outcomes.

DESIGN

This was a retrospective cohort study of 2312 consecutive patient discharges from a single IPR facility over an 18-month period.

RESULTS

When age, sex, reason for admission, and admission Functional Independence Measure (FIM) scores were controlled for, those with VTE had nearly 2 times greater odds for transfer to acute care hospital than did those without a diagnosis of VTE. Inpatient rehabilitation LOS was 4.700 days (95% confidence interval [CI], 2.956-6.445 days) longer for those with a diagnosis of VTE prior to IPR admission and 2.287 days (95% CI, 1.026-3.547 days) longer for those with a diagnosis of VTE during IPR admission compared with those without a diagnosis of VTE. There was no difference in FIM change based on VTE diagnosis. Venous thromboembolism diagnosis during IPR was associated with a significant decrease in FIM efficiency (-0.358; 95% CI, -0.654 to -0.062) if diagnosed during IPR admission, but there was no difference in FIM efficiency if VTE was diagnosed prior to IPR.

CONCLUSIONS

Patients with a diagnosis of VTE were more likely to be transferred to the acute care hospital and have longer IPR LOS independent of admission FIM scores. It is important to prevent development of VTE.

TO CLAIM CME CREDITS

Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES: Upon completion of this article, the reader should be able to: (1) understand the association of venous thromboembolism (VTE) diagnosis with acute hospital transfer and inpatient rehabilitation length of stay; (2) identify reasons for acute hospital transfer in patients diagnosed with VTE; and (3) understand the relationship between VTE diagnosis and functional outcomes during inpatient rehabilitation.

LEVEL

Advanced ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this activity for a maximum of 1.5 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Comparing Comorbidity Indices to Predict Post-Acute Rehabilitation Outcomes in Older Adults

OBJECTIVE

Compare 5 comorbidity indices to predict community discharge and functional status following post-acute rehabilitation.

DESIGN

This was a retrospective study of Medicare beneficiaries with stroke, lower-extremity fracture, and joint replacement discharged from inpatient rehabilitation in 2011 (N = 105,275). Community discharge and self-care, mobility, and cognitive function were compared using the Charlson, Elixhauser, Tier, Functional Comorbidity, and Hierarchical Condition Category comorbidity indices.

RESULTS

Of the patients, 64.4% were female, and 84.6% were non-Hispanic white. Mean age was 79.3 (SD, 7.5) years. Base regression models including sociodemographic and clinical variables explained 56.6%, 42.2%, and 23.0% of the variance (R) for discharge self-care; 47.4%, 30.9%, and 18.6% for mobility; and 62.0%, 55.3%, and 37.3% for cognition across the 3 impairment groups. R values for self-care, mobility, and cognition increased by 0.2% to 3.3% when the comorbidity indices were added to the models. The base model C statistics for community discharge were 0.58 (stroke), 0.61 (fracture), and 0.62 (joint replacement). The C statistics increased more than 25% with the addition of discharge functional status to the base model. Adding the comorbidity indices individually to the base model resulted in C-statistic increases of 1% to 2%.

CONCLUSION

Comorbidity indices were poor predictors of community discharge and functional status in Medicare beneficiaries receiving inpatient rehabilitation.