Medical care · 2024
Performance of the Elixhauser Comorbidity Index in Predicting Mortality Among a National US Sample of Hospitalized Homeless Adults.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tsai Jack, Kim Youngran
Аннотация
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.
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.