A model study for the classification of high-risk groups for cardiac arrest in general ward patients using simulation techniques.

Currently, many hospitals use vital signs-based criteria such as modified early warning score (MEWS) and national early warning score (NEWS) to classify high-risk patients for cardiac arrest, but there are limitations in selecting high-risk patients with a possibility of cardiac arrest. The purpose of this study is to develop a cardiac arrest classification model to identify patients at high risk of cardiac arrest based on the patient family and past history, and blood test results after hospitalization, rather than vital signs. This study used electronic medical record (EMR) data from A university hospital, and patients in the high-risk group for cardiac arrest were defined as those who underwent cardio-pulmonary resuscitation (CPR) after cardiac arrest. Considering the use of the rapid response team of A university hospital, patients hospitalized in intensive care units (ICU), emergency medicine departments, psychiatric departments, pediatric departments, cardiology departments, and palliative care wards were excluded. This study included 325,534 patients, of which 3291 low-risk and 382 high-risk patients were selected for study. Data were split into training and validation data sets and univariate analysis was performed for 13 candidate risk factors. Then, multivariate analysis was performed using a bivariate logistic regression model, and an optimal model was selected using simulation analysis. In the training data set, it was calculated as sensitivity 75.25%, precision 21.59%, specificity 66.89%, accuracy 67.79%, F1 score 33.56, area under curve (AUC) 71.1 (95% confidence interval [CI] = 68.9-73.1 P value=<.001). In the validation data set, sensitivity 73.37%, precision 25.81%, specificity 75.03%, accuracy 74.86%, F1 score 38.19, AUC 74.2 (95% CI = 72.1-76.2, P value=<.001) were calculated. A model for classifying the high-risk group of cardiac arrest should be developed from various perspectives. In the future, in order to classify patients with high risk of cardiac arrest, a prospective study on the combined use of the model developed by this study and NEWS or MEWS should be conducted.