{"title":"Performance of the Elixhauser Comorbidity Index in Predicting Mortality Among a National US Sample of Hospitalized Homeless Adults.","authors":"Jack Tsai, Youngran Kim","doi":"10.1097/MLR.0000000000002019","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The Elixhauser Comorbidity Index (ECI) is widely used, but its performance in homeless populations has not been evaluated.</p><p><strong>Objectives: </strong>Using a national sample of inpatients, this study compared homeless and nonhomeless inpatients on common clinical diagnoses and evaluated ECI performance in predicting mortality among homeless inpatients.</p><p><strong>Research design: </strong>A retrospective study was conducted using 2019 National Inpatient Sample (NIS) data, the largest publicly available all-payer inpatient health care database in the United States.</p><p><strong>Subjects: </strong>Among 4,347,959 hospitalizations, 78,819 (weighted 1.8%) were identified as homeless.</p><p><strong>Measures: </strong>The ECI consists of 38 medical conditions; homelessness was defined using the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM) diagnostic code, and clinical conditions were based on the Clinical Classifications Software Refined (CCSR) for ICD-10-CM.</p><p><strong>Results: </strong>Leading clinical diagnoses for homeless inpatients included schizophrenia and other psychotic disorders (13.3%), depressive disorders (9.4%), and alcohol-related disorders (7.2%); leading diagnoses for nonhomeless inpatients were septicemia (10.2%), heart failure (5.2%), and acute myocardial infarction (3.0%). Metastatic cancer and liver disease were the most common ECI diagnoses for both homeless and nonhomeless inpatients. ECI indicators and summary scores were predictive of in-hospital mortality for homeless and nonhomeless inpatients, with all models yielding concordance statistics above 0.80, with better performance found among homeless inpatients.</p><p><strong>Conclusions: </strong>These findings underlie the high rates of behavioral health conditions among homeless inpatients and the strong performance of the ECI in predicting in-hospital mortality among homeless inpatients, supporting its continued use as a case-mix control method and predictor of hospital readmissions.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MLR.0000000000002019","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The Elixhauser Comorbidity Index (ECI) is widely used, but its performance in homeless populations has not been evaluated.
Objectives: Using a national sample of inpatients, this study compared homeless and nonhomeless inpatients on common clinical diagnoses and evaluated ECI performance in predicting mortality among homeless inpatients.
Research design: A retrospective study was conducted using 2019 National Inpatient Sample (NIS) data, the largest publicly available all-payer inpatient health care database in the United States.
Subjects: Among 4,347,959 hospitalizations, 78,819 (weighted 1.8%) were identified as homeless.
Measures: The ECI consists of 38 medical conditions; homelessness was defined using the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM) diagnostic code, and clinical conditions were based on the Clinical Classifications Software Refined (CCSR) for ICD-10-CM.
Results: Leading clinical diagnoses for homeless inpatients included schizophrenia and other psychotic disorders (13.3%), depressive disorders (9.4%), and alcohol-related disorders (7.2%); leading diagnoses for nonhomeless inpatients were septicemia (10.2%), heart failure (5.2%), and acute myocardial infarction (3.0%). Metastatic cancer and liver disease were the most common ECI diagnoses for both homeless and nonhomeless inpatients. ECI indicators and summary scores were predictive of in-hospital mortality for homeless and nonhomeless inpatients, with all models yielding concordance statistics above 0.80, with better performance found among homeless inpatients.
Conclusions: These findings underlie the high rates of behavioral health conditions among homeless inpatients and the strong performance of the ECI in predicting in-hospital mortality among homeless inpatients, supporting its continued use as a case-mix control method and predictor of hospital readmissions.