Construction of prediction model for poor prognosis of neurological function in cardiac arrest patients after the return of spontaneous circulation based on Random Forest and XGBoost algorithms

Sang Zhenzhen; Cui Jie; Yan Han; Wang Weifeng; Pang Xiuyan

doi:10.3969/j.issn.1002-1949.2024.07.006

Abstract：Objective To construct a predictive model of poor neurological outcome in cardiac arrest(CA)patients with the return of spontaneous circulation(ROSC)after cardiopulmonary resuscitation(CPR)by using machine learning algorithms,and to explore the factors related to outcome.Methods A total of 481 CA patients with ROSC after CPR admitted to Cangzhou Central Hospital from January 2016 to January 2024 were retrospectively collected as the study objects.Clinical data were collected and the patients were divided into a good neurologic outcome group(GNO,n=158)and a poor neurologic outcome group(PNO,n=323)according to Glasgow-Pittsburgh cerebral performance category(CPC)scores at the time the patient was transferred out of the ICU.The 481 patients were randomly divided into training set(n=338)and test set(n=143)by a ratio of 7∶3.The training set was used to construct the model,and the test set was used to evaluate the model efficacy.Firstly,two machine learning algorithms,eXtreme Gradient Boosting(XGBoost)and Random Forest(RF),were used to construct a prediction model of poor neurological function prognosis of the patients,and the variables affecting the neurological function prognosis of the patients were obtained respectively.The interpretability of XGBoost model was analyzed by Shapley additive explanations(SHAP).Secondly,the intersection of variables obtained by XGBoost and RF algorithms was screened,and the intersection variables were analyzed by multivariate Logistic regression to obtain the variables with significant differences,and then the decision tree model was constructed.Finally,receiver operating characteristic(ROC)curve and the area under curve(AUC)were used to evaluate the predictive performance of the proposed decision tree model on the training set and the test set.Results XGBoost model obtained 15 variables associated with poor neurological function prognosis,Random Forest model obtained 14 variables associated with poor neurological function prognosis.The intersection of the two models obtained 11 intersection variables associated with poor neurological outcomes[change rate of optic nerve sheath diameter(ONSD),neuron-specific enolase(NSE),ONSD day 3,CA-CPR time,ROSC time,acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ),serum creatinine,albumin,length of ICU stay,blood lactic acid,age].Multivariate Logistic regression analysis was performed on these 11 intersection variables,and the results showed that there were statistical significances in 5 variables such as change rate of ONSD,NSE,ONSD day 3,ROSC time and age between PNO group and GNO group(P＜0.05).Using these 5 important variables to build a decision tree model,three variables(NSE,ROSC time,change rate of ONSD)correlated with poor neurological function prognosis were obtained.The decision tree model on the training set predicted that the AUC of ROSC patients with poor neurological prognosis after CPR was 0.857(95％CI 0.809-0.903,P＜0.001),and the AUC on the test set was 0.834(95％CI0.761-0.906,P＜0.001).Conclusions The decision tree model based on two machine learning algorithms,XGBoost and Random Forest,can more accurately evaluate the poor prognosis of nerve function after ROSC in CA patients,and the evaluation indicators may be simplified to NSE,ROSC time and change rate of ONSD.