Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage

BackgroundsEarly and accurate identification of patients with spontaneous intracerebral hemorrhage(sICH) who are at high risk of in-hospital death can help intensive care unit (ICU) physicians make optimal clinical decisions. The aim of this study was to develop a machine learning(ML)-based tool to predict the risk of in-hospital death in patients with sICH in ICU. MethodsWe conducted a retrospective administrative database study using the MIMIC-IV and Zhejiang Hospital database. The outcome of the study was in-hospital mortality. To develop and validate the final model, we employed the LASSO regression to screen and select relevant variables. Five algorithms, namely Logistic Regression (LR), K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were utilized. The selection of the best model was based on the area under the curve (AUC) in the validation cohort. Furthermore, we employ the SHapley Additive exPlanations (SHAP) methodology to elucidate the contributions of individual features to the model and analyze their impact on the models outputs. To facilitate accessibility, we also created a visual online calculation page for the model. ResultsIn the final cohort comprising 1596 patients from MIMIC-IV and Zhejiang Hospital, 367 individuals (23%) experienced in-hospital mortality during the inpatient follow-up period. After extracting 46 variables from the database, LASSO regression identified 14 predictor variables for further analysis. Among the five evaluated models, the XGBoost model demonstrated superior discriminative power in both the internal validation set (AUC = 0.907) and the external validation set (AUC = 0.787). Furthermore, through the SHAP technique, we identified the top 5 predictors in the feature importance rankings: Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA), anticoagulant medication, mannitol medication and oxygen saturation. ConclusionsAmong the five models, the XGBoost model exhibited superior performance in predicting mortality for patients with sICH in the ICU, indicating its potential significance in the development of early warning systems.