This study intends to screen serological indicators related to weaning outcomes and 30-day mortality in severe intracerebral hemorrhage (ICH) patients, use machine learning (ML) algorithms to construct predictive models, and explore their predictive value. Data for this study were derived from the Medical Information Mart for Intensive Care (MIMIC)-IV database, which was divided into training and testing sets at a 7:3 ratio. Random Forest (RF) feature importance ranking was used to evaluate feature importance. Nine ML algorithms were applied to construct predictive models for successful weaning/30-day mortality. A total of 1,058 participants were enrolled in this study, among whom 242 achieved successful weaning. In the testing set, among the 9 ML models for predicting successful weaning, extreme Gradient Boosting (XGBoost) showed the best predictive performance, with an AUC of 0.580, an accuracy of 0.582, a sensitivity of 0.522, and a specificity of 0.598. Among the 9 ML models for predicting 30-day mortality, RF showed the best predictive performance, with an AUC of 0.693, an accuracy of 0.642, a sensitivity of 0.627, and a specificity of 0.656. In the DCA curve for predicting successful weaning, within the threshold range of 20%–30%, the clinical net benefits of Decision Tree (DT), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), XGBoost, and Ridge were all higher than those of the “treat all” and “treat none” models. XGBoost and RF exhibited relatively better performance among tested models in predicting successful weaning and 30-day mortality, respectively. Weight, glucose, WBC, MCHC, and temperature were the five key influencing factors for successful weaning. Age, APS III, glucose, WBC, and LODS were the most critical prognostic factors for 30-day mortality. This study provides a concise and practical reference tool for clinical risk stratification and decision-making.
Yang et al. (Wed,) studied this question.