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

Abstract

Matthew Bonomo,1,* Michael G Hermsen,1,* Samuel Kaskovich,1 Maximilian J Hemmrich,1 Juan C Rojas,2 Kyle A Carey,3 Laura Ruth Venable,4 Matthew M Churpek,5 Valerie G Press3,6 1Pritzker School of Medicine, University of Chicago, Chicago, IL, USA; 2Department of Medicine, Section of Pulmonary/Critical Care, University of Chicago, Chicago, IL, USA; 3Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL, USA; 4Department of Medicine, Section of Hospitalist Medicine, University of Chicago, Chicago, IL, USA; 5Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin-Madison, Madison, WI, USA; 6Department of Pediatrics, Section of Academic Pediatrics, University of Chicago, Chicago, IL, USA*These authors contributed equally to this workCorrespondence: Valerie G Press, University of Chicago, 5841 S Maryland, MC 2007, Chicago, IL, 60637, USA, Tel [phone], Email [email protected]: Chronic 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.Objective: We 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).Design: Retrospective cohort study.Participants: Adult 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.Methods: Random 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.Results: Among 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].Conclusion: Our 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.Keywords: chronic obstructive lung disease, COPD, readmissions, machine learning

Keywords

• chronic obstructive lung disease
• copd
• readmissions
• machine learning