Development and multi-database validation of machine learning models for 28-day mortality prediction in ICU patients with COPD and pulmonary hypertension

Early mortality prediction in critically ill patients with chronic obstructive pulmonary disease (COPD) coexisting pulmonary hypertension (PH) remains challenging, particularly because prediction models developed in intensive care unit (ICU) database may not perform consistently across other healthcare systems.

This retrospective multicenter study employed two large-scale databases to establish and validate ML models for predicting early mortality in ICU patients with COPD and PH. The MIMIC-IV database served as the model-development cohort (2555 patients), with external validation conducted on eICU CRD (292 patients) database. Six ML algorithms were adopted: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Support Vector Machines (SVM), and Artificial Neural Network (ANN).The baseline variables were screened to identify potential predictors via least absolute shrinkage and selection operator (Lasso) regression. Model interpretability was evaluated via SHapley Additive exPlanations (SHAP) analysis.

The overall cohort consisted of 2,847 ICU patients with COPD and PH. Twelve consistently crucial clinical features were identified, including acute kidney injury, pneumonia, age, weight, heart rate, red blood cell distribution width (RDW), white blood cell (WBC), anion gap, chloride, creatinine, base excess, potential of hydrogen (pH). The Random Forest algorithm achieved the optimal performance, with a training Area Under Curve (AUC) of 0.938, internal validation AUC of 0.748, and eICU CRD external validation AUC of 0.800. SHAP analysis indicated that acute kidney injury was the most influential predictor, followed by base excess and RDW. Additionally, a web tool was developed in this study to facilitate the application of early death risk prediction in clinical practice.

This study is the multi-database validation of ML models for early mortality prediction in ICU patients with COPD and PH. The Random Forest model integrated with SHAP interpretability offers a reliable tool for clinical decision-making support, with consistent validity verified across two databases, including our internal validation cohort. This work provides a decision support tool to assist in identifying high-risk individuals within this complex comorbid population, and establishes a foundation for future studies with a more dynamic and personalized prognostic approach.