Neural networks as a tool to predict syncope risk in the Emergency Department

There is no universally accepted tool for the risk stratification of syncope patients in the Emergency Department. The aim of this study was to investigate the short-term predictive accuracy of an artificial neural network (ANN) in stratifying the risk in this patient group.

We analysed individual level data from three prospective studies, with a cumulative sample size of 1844 subjects. Each dataset was reanalysed to reduce the heterogeneity among studies defining abnormal electrocardiogram (ECG) and serious outcomes according to a previous consensus. Ten variables from patient history, ECG, and the circumstances of syncope were used to train and test the neural network. Given the exploratory nature of this work, we adopted two approaches to train and validate the tool. One approach used 4/5 of the data for the training set and 1/5 for the validation set, and the other approach used 9/10 for the training set and 1/10 for the validation set. The sensitivity, specificity, and area under the receiver operating characteristic curve of ANNs in identifying short-term adverse events after syncope were 95% [95% confidence interval (CI) 80-98%], 67% (95% CI 62-72%), 0.69 with the 1/5 approach and 100% (95% CI 84-100%), 79% (95% CI 72-85%), 0.78 with the 1/10 approach.

The results of our study suggest that ANNs are effective in predicting the short-term risk of patients with syncope. Prospective studies are needed in order to compare ANNs' predictive capability with existing rules and clinical judgment.