Development and validation of an interpretable machine learning model for early prediction of mortality in ICU patients with chronic obstructive pulmonary disease and heart failure

Chronic obstructive pulmonary disease and heart failure frequently coexist, and the mortality risk for patients with these comorbidities is significantly elevated in the intensive care unit (ICU) setting. Therefore, developing an interpretable model for early mortality prediction is crucial to improving the prognosis and management of these patients. This study developed models based on the Medical Information Mart for Intensive Care IV database (3167 patients) and performed external validation using the eICU Collaborative Research Database (1781 patients). Least absolute shrinkage and selection operator regression combined with the Boruta algorithm was used to screen clinical features within the first 24 hours of ICU admission. Based on the selected features, 5 machine learning models (eXtreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine, Random Forest, and Support Vector Machine) were constructed and compared for their performance in predicting 28-day all-cause mortality. The optimal model was interpreted using the Shapley Additive Explanations framework. In internal validation, the Categorical Boosting model demonstrated the best overall performance, achieving an area under the receiver operating characteristic curve of 0.788, with a sensitivity of 0.482 and a specificity of 0.900. In independent external validation, the model’s area under the receiver operating characteristic curve was 0.739. Feature selection identified 14 key predictive variables. Shapley Additive Explanations analysis revealed that the Sequential Organ Failure Assessment score, Simplified Acute Physiology Score II score, anion gap, vasopressor use, and respiratory rate were the top 5 features contributing most to the model’s predictions. This study developed and validated an interpretable machine learning model based on multicenter data for the early prediction of 28-day mortality risk in ICU patients with chronic obstructive pulmonary disease and heart failure. The model demonstrates good discriminative ability and reasonable generalizability, with its interpretability analysis clarifying core predictive factors to aid in clinical risk stratification and intervention.