Risk stratification of patients with syncope in the emergency department using ECG based artificial intelligence models.

Syncope is a common presentation in the emergency department, yet risk stratification for adverse events remains a substantial clinical challenge. In this study, the use of artificial intelligence (AI) models for identification of patients at high risk of 1-year cardiovascular death was investigated. The study included 39,735 patients aged 18 years or older who presented to a Danish emergency department with a discharge diagnosis of syncope and had an electrocardiogram recorded on the same day. Using multiple electrocardiogram parameters four different models were developed - artificial neural network, logistic regression, random forest, and extreme gradient boosting. Additionally, the models’ ability to stratify patients into low- and high-risk groups of 1-year cardiovascular death was assessed. The mean area under the receiver operating characteristic curves was 0.85 (0.01), while the mean area under the precision-recall curve was 0.11 (0.01). Stratification into low- and high-risk groups showed hazard ratios ranging from 10.80 (95% CI, 8.55–13.63) to 17.97 (95% CI, 12.32–26.20) in univariate Cox Proportional Hazards regression analysis across the four models. These findings demonstrate that AI-based models can successfully distinguish high-risk from low-risk patients with syncope, allowing for targeted evaluation and efficient clinical decision making.