Predicting In-hospital of Death of Patients with Acute Stroke in the ICU Using Stacking Model

Objective: To establish the in-hospital death prediction model of acute stroke patients in ICU based on 8 kinds of machine learning algorithms (SVC, Logistics, RandomForest, XGboost, GBDT, LightGBM, Catboost, MLP). Methods: The data of 1882 acute stroke patients in ICU of the Second Affiliated Hospital of Nanchang University from November 2006 to October 2022 were collected, Lasso regression was used to screen the features, multifactorial Logistics regression algorithm was utilized to mine the risk factors of acute stroke death in ICU, and eight machine learning algorithms were utilized to build ICU patient death prediction models, and selecting the four optimal algorithms as the Stacking model base learner, as well as selecting the optimal algorithms as the Stacking model meta-learners to construct ICU stroke death prediction models. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) of the subjects, accuracy, sensitivity, and specificity, and the gain value of the model was evaluated using the decision curve. Result: The multi-factorial logistics regression analysis showed that atrial fibrillation, pulmonary infection, coma, high creatinine, international normalized ratio(INR) of prothrombin time, serum sodium, neutrophil count and low platelet count were independent risk factors for in-hospital death in stroke patients (P<0.05). In the training set, validation set, and external validation set, the AUC values of the Stacking prediction model were 0.878, 0.871, and 0.809, respectively. The sensitivity values were 0.82, 0.85, and 0.87, respectively. The specificity values were 0.87, 0.84, and 0.68, respectively. The top four AUC values in the eight algorithms were MLP, XGBoost, GBDT, and CatBoost with correspondingly test set AUC values of 0.829, 0.786, 0.78, and 0.777. The decision curve showed that when the probability threshold predicted by the Stacking prediction model was greater than 0.1, the model had a positive net benefit. Conclusion: The Stacking model has a better prediction effect on ICU in-hospital death in stroke patients and can be applied to early prediction of death in ICU stroke patients, providing a basis for early clinical intervention.