Prediction of spontaneous circulation recovery in out-of-hospital cardiac arrest cardiopulmonary resuscitation patients using machine learning algorithms.

Abstract

Background: In this study, we develop a machine learning algorithm for the prediction of spontaneous circulation recovery in out-of-hospital cardiac arrest (OHCA) and cardiopulmonary resuscitation (CPR) patients. This will provide data support for the improvement of CPR success rates in OHCA patients. Methods and Results: We identified 463 patients who had undergone CPR following OHCA between September 2018 and April 2022 from the emergency digital intelligence platform(EDIP).The study endpoint was ROSC,defined as the restoration of a palpable pulse and an autonomous cardiac rhythm lasting for at least 20 minutes after the completion or cessation of CPR.The data were preprocessed with Pandas in python,and the 14 variables with the highest accuracy were determined in combination with clinical characteristics. 75% of the samples were divided into training sets to build the model,and the data were trained and tested using four machine learning algorithms:Logistic regression, XGBClassifier, Gradient Boosting Trees, and Random forest. 25% of the samples were divided into test sets for verification, and the performance of the model was evaluated according to the accuracy,precision,recall,relative operating characteristic curve (ROC curve) of the subjects,and the appropriate model was selected for impact factor analysis. The area under the curve (AUC) values of the four learning models of Logical regression, Random forest, XGBClassifier and Gradient Boosting Trees are 0.73,0.87,0.90 and 0.86 respectively.Select the Random forest(accuracy 0.89, precision 0.90,recall 0.89,AUC 0.87) to calculate the importance of each characteristic value,we concluded that the main predictors of autonomic circulation recovery in OHCA patients are age,speed of CPR initiation,history of cardiopulmonary conditions,another person is present when cardiac arrest occurs,chest compressions and defibrillation. Conclusions: Machine learning has the potential to predict the recovery of spontaneous circulation in OHCA patients treated with CPR. A Random Forest model was found to provide the most accurate predictions for this purpose. This can be used to provide data support and as a reference source to improve the success rate of CPR.