A simple scoring rule to predict survival to discharge after out of hospital cardiac arrest at the time of ED arrival

It is important to be able to predict the chance of survival to hospital discharge upon ED arrival in order to determine whether to continue or terminate resuscitation efforts after out of hospital cardiac arrest. This study was conducted to develop and validate a simple scoring rule that could predict survival to hospital discharge at the time of ED arrival. This was a multicenter retrospective cohort study based on a nationwide registry (Korean Cardiac Arrest Research Consortium) of out of hospital cardiac arrest (OHCA). The study included adult OHCA patients older than 18 years old, who visited one of 33 tertiary hospitals in South Korea from September 1st, 2015 to June 30th, 2020. Among 12,321 screened, 5471 patients were deemed suitable for analysis after exclusion. Pre-hospital ROSC, pre-hospital witness, shockable rhythm, initial pH, and age were selected as the independent variables. The dependent variable was set to be the survival to hospital discharge. Multivariable logistic regression (LR) was performed, and the beta-coefficients were rounded to the nearest integer to formulate the scoring rule. Several machine learning algorithms including the random forest classifier (RF), support vector machine (SVM), and K-nearest neighbor classifier (K−NN) were also trained via 5-fold cross-validation over a pre-specified grid, and validated on the test data. The prediction performances and the calibration curves of each model were obtained. Pre-processing of the registry was done using R, model training & optimization using Python. A total of 5471 patients were included in the analysis. The AUROC of the scoring rule over the test data was 0.7620 (0.7311–0.7929). The AUROCs of the machine learning classifiers (LR, SVM, k-NN, RF) were 0.8126 (0.7748–0.8505), 0.7920 (0.7512–0.8329), 0.6783 (0.6236–0.7329), and 0.7879 (0.7465–0.8294), respectively. A simple scoring rule consisting of five, binary variables could aid in the prediction of the survival to hospital discharge at the time of ED arrival, showing comparable results to conventional machine learning classifiers.