Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease

This is the authors' abstract. We don't add key points for this paper.

While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalization for Chronic Obstructive Pulmonary Disease (COPD). We identified patients aged ≥40 years with unplanned hospitalization due to COPD in the Diagnosis Procedure Combination database, an administrative claims database in Japan, from 2011 through 2016 (index hospitalizations). COPD was defined by ICD-10-CM diagnostic codes, according to Centers for Medicare and Medicaid Services (CMS) readmission measures. The primary outcome was any readmission within 30 days after index hospitalization. In the training set (randomly-selected 70% of sample), patient characteristics and inpatient care data were used as predictors to derive a conventional logistic regression model and two machine learning models (lasso regression and deep neural network). In the test set (remaining 30% of sample), the prediction performances of the machine learning models were examined by comparison with the reference model based on CMS readmission measures. Among 44,929 index hospitalizations for COPD, 3413 (7%) were readmitted within 30 days after discharge. The reference model had the lowest discrimination ability (C-statistic: 0.57 [95% confidence interval (CI) 0.56–0.59]). The two machine learning models had moderate, significantly higher discrimination ability (C-statistic: lasso regression, 0.61 [95% CI 0.59–0.61], p = 0.004; deep neural network, 0.61 [95% CI 0.59–0.63], p = 0.007). Tube feeding duration, blood transfusion, thoracentesis use, and male sex were important predictors. In this study using nationwide administrative data in Japan, machine learning models improved the prediction of 30-day readmission after COPD hospitalization compared with a conventional model.