Key result
A Random Forest machine learning model accurately predicted the return of spontaneous circulation in out-of-hospital cardiac arrest patients with an area under the curve of 0.87 and accuracy of 0.89.
Why the study?
To develop machine learning algorithms to predict spontaneous circulation recovery in OHCA patients undergoing CPR and provide data support to improve CPR success rates.
Can machine learning algorithms accurately predict the return of spontaneous circulation in out-of-hospital cardiac arrest patients treated with CPR?
Observational (n=463)
Yes
Can machine learning algorithms accurately predict the return of spontaneous circulation in out-of-hospital cardiac arrest patients treated with CPR?
Effect estimate: AUC 0.87
Absolute Event Rate: 0.87% vs 0.73%
A Random Forest machine learning model can accurately predict the return of spontaneous circulation in out-of-hospital cardiac arrest patients, identifying age and speed of CPR initiation as key predictors.
ML models may aid ROSC prediction in OHCA cohorts; leaves open prospective validation before clinical use.
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.
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Chen et al. (2023) conducted an observational in Out-of-hospital cardiac arrest (OHCA) (n=463). Random Forest machine learning model vs. Logistic Regression model was evaluated on Prediction of return of spontaneous circulation (ROSC) measured by Area Under the Curve (AUC) (AUC 0.87). A Random Forest machine learning model accurately predicted the return of spontaneous circulation in out-of-hospital cardiac arrest patients with an area under the curve of 0.87 and accuracy of 0.89.
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