Interpretable machine learning models for predicting in-hospital mortality in patients with chronic critical illness and heart failure: A multicenter study

Background: Heart failure (HF) is a primary contributor to morbidity and mortality among patients in intensive care units (ICUs), particularly those experiencing chronic critical illness (CCI). This study aims to develop and validate a machine learning (ML) model for predicting in-hospital mortality in CCI patients with HF. Methods: Retrospective data from over 200 hospitals were sourced from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and the eICU Collaborative Research Database (eICU-CRD). Only patients diagnosed with both CCI and HF were included. The MIMIC datasets served as the derivation cohort, while the eICU-CRD dataset was used for external validation. Key predictive variables were identified through recursive feature elimination. A range of ML algorithms, including random forest, K-nearest neighbors, and support vector machine (SVM), were evaluated alongside four other models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). Model interpretability was enhanced through SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations. Results: A total of 780 and 610 patients with CCI and HF were assigned to the derivation and validation cohorts, respectively. Eleven features were selected for model development. The SVM model demonstrated substantial predictive accuracy, with AUROC values of 0.781 and 0.675 in the derivation and validation cohorts. Feature importance analysis using SHAP identified Sequential Organ Failure Assessment score, oxyhemoglobin saturation, and blood pressure as key predictors. Conclusion: The SVM model developed reliably predicts in-hospital mortality in patients with CCI and HF, offering a valuable tool for early intervention and enhanced patient management.