| Objective To investigate the risk factors affecting the prognosis of severe COVID-19 patients, to establish and verify pre. dictive models, and then to accurately evaluate the poor prognosis of severe COVID-19 patients.Methods Clinical indicators and out. comes (death or survival within 28 days in hospital) of 526 patients with severe COVID-19 admitted to Cangzhou Central Hospital fromNovember 1, 2022 to July 1, 2023 were collected. For the R software "caret" package, 526 patients were randomly divided into 2 groups in a ratio of 7∶3: the training set (n=369) for model training and the test set (n=157) for model validation. Two machine learningalgorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were used to build the prediction model of patient clinicaloutcome, and SHAP was used to analyze the interpretability of XGBoost model. The variables affecting the prognosis of patients wereobtained respectively. The intersection of variables obtained by RF and XGBoost was used to obtain variables with significant differenc.es, and then the decision tree model is constructed. Finally, Receiver operating curve (ROC curve) and Area under curve (AUC) wereused to evaluate the predictive performance of the decision tree model on training set and test set.Results XGBoost model obtained 15 variables related to in-hospital death, and random forest model obtained 23 variables related to in-hospital death. At the intersectionof the two models, 13 important variables with the strongest correlation with nosocomial death were obtained (IL-6, NT-BNP, ALB, CT. NI, LYMPH, Lac, HBDH, CK-MB, PO2, Age, BUN, HB, LDH). A decision tree model was constructed with these 13 important vari.ables, and the 2 variables most related to patient death (IL-6, LYMPH) were obtained. The IL-6 level of patients in the death group was155.48 (42.81, 691.3) ng/L, significantly higher than that of the survival group, which was 15.38 (10.51, 31.11) ng/L(Z=37 387.50，P<0.001). The Lymphocyte count of patients in the death group was 5.4 (3.3, 12.6)%, significantly lower than that of the survival group,which was 13.5 (8.62, 22.28)%(Z=10 584.50，P<0.001). The AUC for death prediction of severe COVID-19 patients was 0.86 for the decision tree model on the training set, and 0.84 for the test set.Conclusion The decision tree model based on two machine learningmethods, XGBoost and random forest, can more accurately evaluate the poor prognosis of severe COVID-19 patients. |
