Prediction of spontaneous circulatory recovery in patients with cardiopulmonary resuscitation after out-of-hospital cardiac arrest based on machine learning algorithm
CHEN Ying1 ZHANG Jiaqi1 TIAN Jiangtao2 NIE Yingjie1 LI Ruiyue1 HAO Meilin1 LI Wentao1 ZHANG Yingqi1▲
1.Department of Emergency, the First Hospital of Hebei Medical University, Hebei Province, Shijiazhuang 050030, China; 2.China Mobile Communications Group Hebei Co., LTD, Hebei Province, Shijiazhuang 050011, China
Abstract：Objective To predict the recovery of spontaneous circulation in patients with out-of-hospital cardiac arrest and cardiopulmonary resuscitation (CPR) by machine learning algorithm, so as to provide data support for improving the success rate of CPR in patients with out-of-hospital cardiac arrest. Methods From the medical records of 1 201 patients with cardiac arrest and CPR in the emergency patient database (EPD) of Hebei Provincial Emergency Technology Innovation Center from September 2018 to April 2022, 463 patients with out-of-hospital cardiac arrest were screened out. The data were preprocessed with python language pandas and combined with clinical characteristics. The 14 variables with the highest accuracy were determined. 75% of the samples were divided into training sets to build a model, and four machine learning algorithms including logistic regression, parameter interpretation, gradient lifting, and random forest were used to train and test the data. 25% of the samples were divided into test sets for verification. The model performance was evaluated according to accuracy, accuracy, recall, receiver characteristic operating characteristic curve (ROC), etc., and the appropriate model was selected for influencing factor analysis. Results There were statistically significant differences in age, CPR start time, history of electrolyte disturbance, witness of cardiac arrest, CPR, defibrillation, and airway management using simple respirator between the return of spontaneous circulation(ROSC) group(57 cases) and failed group(406 cases) (P＜0.05). The area under the curve (AUC) values of logistic regression, random forest, parameter interpretation, and gradient lifting were 0.73, 0.87, 0.90, and 0.86, respectively. The random forest model (accuracy=0.89, accuracy=0.90, recall=0.89, AUC=0.87) was selected to calculate the importance of each characteristic value. The results showed that the main factors affecting the recovery of spontaneous circulation in patients with out-of-hospital cardiac arrest were age, start time of CPR, previous history, witnessing cardiac arrest, giving external chest compression, and electrical defibrillation. Conclusion Machine learning has the potential to predict spontaneous circulatory recovery in patients with out-of-hospital cardiac arrest and CPR, and the random forest model works best.
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