Is Risk-Standardized In-Hospital Stroke Mortality an Adequate Proxy for Risk-Standardized 30-Day Stroke Mortality Data? Findings From Get With The Guidelines-Stroke

Mathew J Reeves; Gregg C Fonarow; Haolin Xu; Roland A Matsouaka; Ying Xian; Jeffrey Saver; Lee Schwamm; Eric E Smith

doi:10.1161/CIRCOUTCOMES.117.003748

Is Risk-Standardized In-Hospital Stroke Mortality an Adequate Proxy for Risk-Standardized 30-Day Stroke Mortality Data? Findings From Get With The Guidelines-Stroke

Circ Cardiovasc Qual Outcomes. 2017 Oct;10(10):e003748. doi: 10.1161/CIRCOUTCOMES.117.003748.

Authors

Mathew J Reeves¹, Gregg C Fonarow², Haolin Xu², Roland A Matsouaka², Ying Xian², Jeffrey Saver², Lee Schwamm², Eric E Smith²

Affiliations

¹ From the Department of Epidemiology and Biostatistics, Michigan State University, East Lansing (M.J.R.); Division of Cardiology (G.C.F.), and Department of Neurology (J.S.), Geffen School of Medicine, University of California, Los Angeles; Duke Clinical Research Institute, Durham, NC (H.X., R.A.M., Y.X.); Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (R.A.M.); Department of Neurology, Duke University Medical Center, Durham, NC (Y.X.); Massachusetts General Hospital, Boston, MA (L.S.); and Hotchkiss Brain Institute, University of Calgary, Canada (E.E.S.). reevesm@msu.edu.
² From the Department of Epidemiology and Biostatistics, Michigan State University, East Lansing (M.J.R.); Division of Cardiology (G.C.F.), and Department of Neurology (J.S.), Geffen School of Medicine, University of California, Los Angeles; Duke Clinical Research Institute, Durham, NC (H.X., R.A.M., Y.X.); Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (R.A.M.); Department of Neurology, Duke University Medical Center, Durham, NC (Y.X.); Massachusetts General Hospital, Boston, MA (L.S.); and Hotchkiss Brain Institute, University of Calgary, Canada (E.E.S.).

PMID: 29021333
DOI: 10.1161/CIRCOUTCOMES.117.003748

Abstract

Background: Hospital profiling is typically undertaken using risk-standardized 30-day mortality, but obtaining these data for hospitals can be difficult. We sought to determine whether risk-standardized in-hospital mortality could serve as an adequate proxy for risk-standardized 30-day mortality data for the purposes of identifying outlier hospitals.

Methods and results: Acute ischemic stroke cases entered into GWTG (Get With The Guidelines)-Stroke between 2003 and 2013 were linked to fee-for-service Medicare files to obtain 30-day mortality. Risk-standardized mortality rates (RSMR) for in-hospital and 30-day mortality were generated using previously developed risk score models, and the proportion of hospitals classified as statistical outliers compared. We also assessed the impact of using the combined outcome of in-hospital mortality or discharge to hospice. A total of 535 332 ischemic stroke patients from 1494 GWTG-Stroke hospitals were included; mean age was 80 years, 59% female, and 19% nonwhite. At the hospital level, mean in-hospital RSMRs and 30-day RSMRs were 6.0% and 14.6%, respectively, but the correlation between the 2 was modest (r=0.53). Overall agreement in the designation of outlier hospitals between in-hospital and 30-day RSMRs was 78%, but chance-corrected agreement was only fair (κ=0.29). However, when using the combined outcome of in-hospital mortality or discharge to hospice (risk-standardized mean =11.8%), the correlation with 30-day RSMR was much stronger (r= 0.83) and outlier agreement improved substantially (κ=0.60).

Conclusions: When used to identify outlier hospitals with high or low mortality, the agreement between risk-standardized in-hospital mortality and 30-day mortality was modest. However, the combined outcome of in-hospital mortality or discharge to hospice showed much better agreement with 30-day mortality. This composite outcome could serve as a proxy for 30-day mortality when used to identify low- or high-performing hospitals.

Keywords: Medicare; hospital profiling; mortality/survival; registry; stroke.

MeSH terms

Administrative Claims, Healthcare
Aged
Aged, 80 and over
Brain Ischemia / diagnosis
Brain Ischemia / mortality*
Brain Ischemia / therapy
Databases, Factual
Female
Guideline Adherence / standards*
Hospice Care
Hospital Mortality*
Hospitals / standards*
Humans
Male
Medicare / standards
Patient Discharge
Practice Guidelines as Topic / standards*
Quality Improvement / standards*
Quality Indicators, Health Care / standards*
Risk Assessment
Risk Factors
Stroke / diagnosis
Stroke / mortality*
Stroke / therapy
Time Factors
United States