Development of a Risk-adjustment Model for the Inpatient Rehabilitation Facility Discharge Self-care Functional Status Quality Measure

Background Medicare data from acute hospitals do not contain information on functional status. This lack of information limits the ability to conduct rehabilitation-related health services research. Objective The purpose of this study was to examine the associations between 5 comorbidity indexes derived from acute care claims data and functional status assessed at admission to an inpatient rehabilitation facility (IRF). Comorbidity indexes included tier comorbidity, Functional Comorbidity Index (FCI), Charlson Comorbidity Index, Elixhauser Comorbidity Index, and Hierarchical Condition Category (HCC). Design This was a retrospective cohort study. Methods Medicare beneficiaries with stroke, lower extremity joint replacement, and lower extremity fracture discharged to an IRF in 2011 were studied (N=105,441). Data from the beneficiary summary file, Medicare Provider Analysis and Review (MedPAR) file, and Inpatient Rehabilitation Facility–Patient Assessment Instrument (IRF-PAI) file were linked. Inpatient rehabilitation facility admission functional status was used as a proxy for acute hospital discharge functional status. Separate linear regression models for each impairment group were developed to assess the relationships between the comorbidity indexes and functional status. Base models included age, sex, race/ethnicity, disability, dual eligibility, and length of stay. Subsequent models included individual comorbidity indexes. Values of variance explained (R2) with each comorbidity index were compared. Results Base models explained 7.7% of the variance in motor function ratings for stroke, 3.8% for joint replacement, and 7.3% for fracture. The R2 increased marginally when comorbidity indexes were added to base models for stroke, joint replacement, and fracture: Charlson Comorbidity Index (0.4%, 0.5%, 0.3%), tier comorbidity (0.2%, 0.6%, 0.5%), FCI (0.4%, 1.2%, 1.6%), Elixhauser Comorbidity Index (1.2%, 1.9%, 3.5%), and HCC (2.2%, 2.1%, 2.8%). Limitation Patients from 3 impairment categories were included in the sample. Conclusions The 5 comorbidity indexes contributed little to predicting functional status. The indexes examined were not useful as proxies for functional status in the acute settings studied.

Download Full-text

Adjusting Client-Level Risks Impacts on Home Care Organization Ranking

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115502 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5502

Author(s):

Aylin Wagner ◽

René Schaffert ◽

Julia Dratva

Keyword(s):

Risk Factors ◽

Home Care ◽

Risk Adjustment ◽

Care Quality ◽

Assessment Instrument ◽

Care Organization ◽

Discrimination Power ◽

Adjustment Model ◽

Risk Adjustment Model ◽

The Impact

Quality indicators (QIs) based on the Resident Assessment Instrument-Home Care (RAI-HC) offer the opportunity to assess home care quality and compare home care organizations’ (HCOs) performance. For fair comparisons, providers’ QI rates must be risk-adjusted to control for different case-mix. The study’s objectives were to develop a risk adjustment model for worsening or onset of urinary incontinence (UI), measured with the RAI-HC QI bladder incontinence, using the database HomeCareData and to assess the impact of risk adjustment on quality rankings of HCOs. Risk factors of UI were identified in the scientific literature, and multivariable logistic regression was used to develop the risk adjustment model. The observed and risk-adjusted QI rates were calculated on organization level, uncertainty addressed by nonparametric bootstrapping. The differences between observed and risk-adjusted QI rates were graphically assessed with a Bland-Altman plot and the impact of risk adjustment examined by HCOs tertile ranking changes. 12,652 clients from 76 Swiss HCOs aged 18 years and older receiving home care between 1 January 2017, and 31 December 2018, were included. Eight risk factors were significantly associated with worsening or onset of UI: older age, female sex, obesity, impairment in cognition, impairment in hygiene, impairment in bathing, unsteady gait, and hospitalization. The adjustment model showed fair discrimination power and had a considerable effect on tertile ranking: 14 (20%) of 70 HCOs shifted to another tertile after risk adjustment. The study showed the importance of risk adjustment for fair comparisons of the quality of UI care between HCOs in Switzerland.

Download Full-text

Inpatient rehabilitation facility discharge destination among younger adults with traumatic brain injury: differences by race and ethnicity

Brain Injury ◽

10.1080/02699052.2021.1895317 ◽

2021 ◽

pp. 1-14

Author(s):

Tolu O. Oyesanya ◽

Gabrielle Harris ◽

Qing Yang ◽

Lindsey Byom ◽

Michael P Cary Jr ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Race And Ethnicity ◽

Inpatient Rehabilitation ◽

Discharge Destination ◽

Younger Adults ◽

Rehabilitation Facility ◽

Inpatient Rehabilitation Facility

Download Full-text

Abstract 11372: A Risk-Adjustment Model for Patients Presenting to Hospitals with Out-of-Hospital Cardiac Arrest and ST-Elevation Myocardial Infarction

Circulation ◽

10.1161/circ.144.suppl_2.11372 ◽

2021 ◽

Vol 144 (Suppl_2) ◽

Author(s):

Andy T Tran ◽

Anthony Hart ◽

John Spertus ◽

Philip Jones ◽

Bryan McNally ◽

...

Keyword(s):

Cardiac Arrest ◽

Hospital Mortality ◽

Risk Adjustment ◽

Quality Measures ◽

Coronary Intervention ◽

Spontaneous Circulation ◽

Logistic Regression Models ◽

Adjustment Model ◽

Risk Adjustment Model ◽

Hospital Cardiac Arrest

Background: Given the diversity of patients resuscitated from out-of-hospital cardiac arrest (OHCA) complicated by STEMI, adequate risk adjustment is needed to account for potential differences in case-mix to reflect the quality of percutaneous coronary intervention. Objectives: We sought to build a risk-adjustment model of in-hospital mortality outcomes for patients with OHCA and STEMI requiring emergent angiography. Methods: Within the Cardiac Arrest Registry to Enhance Survival, we included adult patients with OHCA and STEMI who underwent angiography within 2 hours from January 2013 to December 2019. Using pre-hospital patient and arrest characteristics, multivariable logistic regression models were developed for in-hospital mortality. We then described model calibration, discrimination, and variability in patients’ unadjusted and adjusted mortality rates. Results: Of 2,999 hospitalized patients with OHCA and STEMI who underwent emergent angiography (mean age 61.2 ±12.0, 23.1% female, 64.6% white), 996 (33.2%) died. The final risk-adjustment model for mortality included higher age, unwitnessed arrest, non-shockable rhythms, not having sustained return of spontaneous circulation upon hospital arrival, and higher total resuscitation time on scene ( C -statistic, 0.804 with excellent calibration). The risk-adjusted proportion of patients died varied substantially and ranged from 7.8% at the 10 th percentile to 74.5% at the 90 th percentile (Figure). Conclusions: Through leveraging data from a large, multi-site registry of OHCA patients, we identified several key factors for better risk-adjustment for mortality-based quality measures. We found that STEMI patients with OHCA have highly variable mortality risk and should not be considered as a single category in public reporting. These findings can lay the foundation to build quality measures to further optimize care for the patient with OHCA and STEMI.

Download Full-text