Construction and evaluation of prognostic models of ECMO in elderly patients with cardiogenic shock based on BP neural network, random forest, and decision tree

Objective: To analyze the predictive effect of a back propagation (BP) neural network, random forest (RF) and decision tree model on the prognosis of elderly patients with cardiogenic shock after extracorporeal membrane oxygenation (ECMO). Methods: This is a retrospective analysis of the clinical data of elderly patients with cardiogenic shock (258 cases) who underwent ECMO in People's Hospital of Guangxi Zhuang Autonomous Region from January 2016 to January 2022. All patients were followed up for 6 months after ECMO treatment. The prognosis was evaluated, and the prognostic factors were analyzed. BP neural network, RF and decision tree were used to establish predictive models, and the predictive performance of the models was evaluated. Results: Among the 258 elderly patients with cardiogenic shock, 52 (20.16%) died 6 months after the ECMO treatment. Based on BP neural network, RF, and decision tree, predictive models for the prognosis and death of elderly patients with cardiogenic shock were constructed. A test set was used to predict the performance of the three models. The results showed that the predictive performances of the three models were all more than 80.00%. The accuracy, sensitivity, and specificity of the RF model were 0.987, 1.000, and 0.929 respectively, which were higher than those of the decision tree model. The area under the receiver operating characteristic curve (AUC) of the RF model was 1.000, which was higher than 0.916 for the decision tree model. DeLong test showed that there was a significant difference in the AUC of the RF model compared to the decision tree test set (D=-2.063, P=0.042 < 0.05). Conclusion: The predictive performance is good in all the three models, which have a high application value for prognosis of ECMO in elderly patients with cardiogenic shock. In clinical practice, predictive models should be selected according to the actual situation, so clinicians and patients can make decisions.