Development, Validation, and Deployment of an Interpretable Machine Learning Model for In-Hospital Mortality Prediction in Critically Ill Patients with COPD and Sepsis: A Multicenter Cohort Study

Abstract

Background Critically ill patients with chronic obstructive pulmonary disease (COPD) and sepsis are at high risk of in-hospital mortality because of respiratory and organ dysfunction. Early risk assessment may be useful for clinical decision-making. This study aimed to develop an interpretable machine learning model for this population. Methods Data were extracted from MIMIC-IV and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV cohort was split in a 7:3 ratio into training and internal test sets. Features were selected using least absolute shrinkage and selection operator regression and Boruta. Eight models were trained using 10-fold cross-validation and grid search. The best-performing model was externally validated using eICU-CRD, interpreted using Shapley Additive Explanations (SHAP), and compared with the Sequential Organ Failure Assessment (SOFA) score. Results Among 4,900 MIMIC-IV patients, in-hospital mortality was 19.2%. In the internal test set, the naive Bayes (NB) model achieved an area under the receiver operating characteristic curve (AUC) of 0.743 (95% CI, 0.711–0.775), outperforming the SOFA score (AUC, 0.696, 95% CI, 0.663–0.730, P < 0.001). In external validation, AUC decreased to 0.681 (95% CI, 0.607–0.755), an absolute reduction of 0.062. SHAP identified Simplified Acute Physiology Score II and SOFA as predictors, followed by the anion gap and urine output. The model was deployed across web, desktop, and mobile platforms. Conclusion An NB model was successfully developed to predict in-hospital mortality in critically ill patients with COPD and sepsis. Further prospective multicenter validation is warranted before routine clinical use.