Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

Abstract

BackgroundThis study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH.MethodsIn the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques.ResultsThe incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819–0.845; external validation: AUC, 0.757 95% CI, 0.739–0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787–0.813; external validation: AUC, 0.729, 95% CI, 0.711–0.745).ConclusionHH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.