Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation.

Content Provider	Europe PMC
Author	Bonomo, Matthew Hermsen, Michael G Kaskovich, Samuel Hemmrich, Maximilian J Rojas, Juan C Carey, Kyle A Venable, Laura Ruth Churpek, Matthew M Press, Valerie G
Copyright Year	2022
Abstract	BackgroundChronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients’ readmission risk during index hospitalizations.ObjectiveWe used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD).DesignRetrospective cohort study.ParticipantsAdult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or −10 criteria consistent with AE-COPD were included.MethodsRandom forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients’ index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score.ResultsAmong 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79].ConclusionOur models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk.
Page Count	9
ISSN	11769106
Volume Number	17
PubMed Central reference number	PMC9590342
PubMed reference number	36299799
Journal	International Journal of Chronic Obstructive Pulmonary Disease [Int J Chron Obstruct Pulmon Dis]
e-ISSN	11782005
DOI	10.2147/COPD.S379700
Language	English
Publisher	Dove
Publisher Date	2022-10-20
Access Restriction	Open
Rights License	This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). © 2022 Bonomo et al.
Subject Keyword	chronic obstructive lung disease COPD readmissions machine learning
Content Type	Text
Resource Type	Article
Subject	Pulmonary and Respiratory Medicine Public Health, Environmental and Occupational Health Health Policy