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.

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, 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.