Interpretable Machine Learning Models for Predicting In-Hospital Mortality in Patients with Chronic Critical Illness and Heart Failure: A Multicenter Study (Preprint)

BACKGROUND Heart failure (HF) is a leading cause of morbidity and mortality among patients in intensive care units (ICUs), particularly those with chronic critical illness (CCI). OBJECTIVE We aimed to develop and validate a machine learning (ML) model to predict in-hospital mortality for in CCI patients with CCI and HF. METHODS Retrospective data encompassing medical records from over 200 hospitals were obtained from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU Collaborative Research Database (eICU-CRD). Patients diagnosed with CCI and HF at their first ICU admission were included. The MIMIC-III and -IV datasets were used as a derivation cohort, while that from eICU-CRD was employed as a validation cohort. Key predictive features were identified utilizing the recursive feature elimination with 10-fold cross-validation method. Subsequently, multiple ML algorithms were evaluated, including Random Forest, K-Nearest Neighbors, Support Vector Machine (SVM), Extreme Gradient Boosting, Naive Bayes, Light Gradient Boosting Machine, and Adaptive Boosting. The performance of the models was assessed via metrics such as area under the receiver operating characteristic curve (AUROC), decision curve analysis, accuracy, sensitivity, specificity, and F1 score. Furthermore, model interpretability was enhanced by applying the SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods, providing insights into the contribution of individual features to the predictive outcomes. RESULTS A total of 780 (males: 451 57.8%) and 610 (males: 343 56.2%) patients with CCI and HF were allocated to the derivation and validation cohorts, respectively. Eleven features were selected to develop the prediction models. Among all models, the SVM algorithm-based model demonstrated high predictive accuracy (derivation cohort: AUROC, 0.781; sensitivity, 0.739; specificity, 0.691; and F1 score, 0.613; validation cohort: AUROC, 0.683; accuracy, 0.645; sensitivity, 0.607; specificity, 0.656; and F1 score, 0.44). The SHAP and LIME analyses evaluated the feature contributions, highlighting Sequential Organ Failure Assessment score, oxyhemoglobin saturation, diastolic blood pressure, and systolic blood pressure as significant predictors of in-hospital mortality. CONCLUSIONS The SVM model developed in this study effectively predicts in-hospital mortality in patients with CCI and HF and can serve as a reliable tool for early intervention and improved patient management. Furthermore, this ML model combines high accuracy with interpretability, thereby substantially contributing to clinical predictive analytics.