[Predicting Intensive Care Unit Mortality in Patients With Heart Failure Combined With Acute Kidney Injury Using an Interpretable Machine Learning Model: A Retrospective Cohort Study].

Objective: Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predictions of short-term mortality. This study is focused on developing an interpretable machine learning model to enhance early prediction accuracy in such clinical scenarios. Methods: This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ, version 2.0) database. Data from the first 24 hours after admission to the ICU were extracted and divided into a training set (70%) and a validation set (30%). We utilized the SHapley Additive exPlanation (SHAP) method to interpret the workings of an extreme gradient boosting (XGBoost) model and identify key prognostic factors. The XGBoost model's predictive ability was evaluated against three other machine learning models using the area under the curve (AUC) metric, and its interpretation was enhanced using the SHAP method. Results: The study included 8028 patients with HF complicated by AKI. The XGBoost model outperformed the other models, achieving an AUC of 0.93 (95% confidence interval [CI]: 0.78-0.94; accuracy = 0.89), while neural network model showed the worst performance (AUC = 0.79, 95% CI: 0.77-0.82; accuracy = 0.82). Decision curve analysis showed the superior net benefit of the XGBoost model within the 9% to 60% threshold probabilities. SHAP analysis was performed to identify the top 20 predictors, with age (mean SHAP value 1.29) and Glasgow Coma Scale score (mean SHAP value 1.24) emerging as significant factors. Conclusions: Our interpretable model offers an enhanced ability to predict mortality risk in HF patients with AKI in ICUs. This model can be used to assist in formulating effective treatment plans and optimizing resource allocation.