当前位置： SCI文献检索 > JOURNAL OF GENERAL INTERNAL MEDICINE期刊下所有文献 > Impact of Instrumental Activities of Daily Living Limitations on Hospital Readmission: an Observational Study Using Machine Learning.

Impact of Instrumental Activities of Daily Living Limitations on Hospital Readmission: an Observational Study Using Machine Learning.

Abstract：

BACKGROUND:Limitations in instrumental activities of daily living (IADL) hinder a person's ability to live independently in the community and self-manage their conditions, but its impact on hospital readmission has not been firmly established. OBJECTIVE:To test the importance of IADL dependency as a predictor of 30-day readmissions and quantify its impact relative to other morbidities. DESIGN:A retrospective cohort study of the population-based Health and Retirement Study linked to Medicare claims data. Random forest was used to rank each predictor variable in terms of its ability to predict readmission. Classification and regression tree (CART) was used to identify complex multimorbidity combinations associated with high or low risk of readmission. Generalized linear regression was used to estimate the adjusted relative risk of readmission for IADL limitations. SUBJECTS:Hospitalizations of adults age 65 and older (n = 20,007), from 6617 unique subjects. MAIN MEASURES:The main outcome was 30-day all-cause unplanned readmission. The main predictor of interest was self-reported IADL limitation. Other key predictors were self-reported complex multimorbidity including chronic diseases, geriatric syndromes, and activities of daily living (ADL) limitations, along with demographic, socioeconomic, and behavioral factors. KEY RESULTS:The overall 30-day readmission rate in the study was 16.4%. Random forest analysis ranked ADLs and IADL limitations as the two most important predictors of 30-day readmission. CART identified hospitalizations of patients with IADL limitations and diabetes as a subgroup at the highest risk of readmission (26% readmitted). Multivariable regression analyses showed that ADL limitations were associated with 1.17 (1.06-1.29) times higher risk of readmission even after adjusting for other patient covariates. Risk prediction was modest though for even the best model (AUC = 0.612). CONCLUSIONS:IADL limitations are key predictors of 30-day readmission as demonstrated using several machine learning methods. Routine assessment of functional abilities in hospital settings could help identify those most at risk.

journal_name

J Gen Intern Med

journal_title

Journal of general internal medicine

authors

Schiltz NK,Dolansky MA,Warner DF,Stange KC,Gravenstein S,Koroukian SM

doi

10.1007/s11606-020-05982-0

subject

Has Abstract

pub_date

2020-10-01 00:00:00

pages

2865-2872

issue

eissn

0884-8734

issn

1525-1497

pii

10.1007/s11606-020-05982-0

journal_volume

pub_type

杂志文章

在线工具

Impact of Instrumental Activities of Daily Living Limitations on Hospital Readmission: an Observational Study Using Machine Learning.