# Advanced machine learning models for prediction of readmission and mortality risks in patients with chronic obstructive pulmonary disease using routine clinical data

## TL;DR

This study developed machine learning models to better predict COPD patients' risk of readmission or death compared to existing tools.

## Contribution

New machine learning models using EHR data outperform the CODEX model for COPD risk prediction.

## Key findings

- The Top64 model achieved an AUC of 0.769, outperforming the CODEX model's AUC of 0.587.
- The 11-feature model had an AUC of 0.746 and better sensitivity than CODEX.
- Calibration curves showed good agreement between predicted and observed outcomes for both models.

## Abstract

To develop a comprehensive machine learning model incorporating various clinical factors, including frailty and comorbidities, to predict 30-day readmission and mortality risk in patients with chronic obstructive pulmonary disease (COPD).

This retrospective cohort study used electronic health records (EHR) from Fujita Health University Hospital (2004–2019) for 1294 patients with COPD and 3499 hospitalization or death events. The EHR contained longitudinal patient data (demographics, diagnoses, test results, clinical records). We developed two eXtreme Gradient Boosting models, the comprehensive Top64 and practical 11-feature models. We compared these with the Comorbidity, Obstruction, Dyspnea, and Previous Exacerbations index (CODEX) model, a widely used tool for predicting hospital readmission or death in patients with COPD. The area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI), sensitivity, and specificity were used to evaluate the model performance.

The Top64 (AUC: 0.769, 95% CI: 0.747–0.791) and practical 11-feature (AUC: 0.746, 95% CI: 0.730–0.762) models performed better than the CODEX model (AUC: 0.587, 95% CI: 0.563–0.611). The Top64 model showed 0.978 sensitivity and 0.341 specificity, and the practical 11-feature model achieved 0.955 sensitivity and 0.361 specificity. The calibration curves showed good agreement between the observed and predicted results for both models.

A machine learning approach based on clinical data readily available from the EHR performed better than existing models in predicting 30-day readmission and mortality risks in patients with COPD. A comprehensive risk prediction tool may enhance individualized care strategies and improve patient outcomes in COPD management.