Validation of syncope short-term outcomes prediction by machine learning models in an Italian emergency department cohort

Machine learning (ML) algorithms have the potential to enhance the prediction of adverse outcomes in patients with syncope. Recently, gradient boosting (GB) and logistic regression (LR) models have been applied to predict these outcomes following a syncope episode, using the Canadian Syncope Risk Score (CSRS) predictors. This study aims to externally validate these models and compare their performance with novel models. We included all consecutive non-low-risk patients evaluated in the emergency department for syncope between 2015 and 2017 at six Italian hospitals. The GB and LR models were trained and tested using previously validated CSRS predictors. Additionally, recently developed deep learning (TabPFN) and large language models (TabLLM) were validated on the same cohort. The area under the curve (AUC), Matthews correlation coefficient (MCC), and Brier score (BS) were compared for each model. A total of 257 patients were enrolled, with a median age of 71 years. Thirteen percent had adverse outcomes at 30 days. The GB model achieved the best performance, with an AUC of 0.78, an MCC of 0.36, and a BS of 0.42. Significant performance differences were observed compared with the TabPFN model (p < 0.01) and the TabLLM model (p = 0.01). The GB model performed only slightly better than the LR model. The predictive capability of the GB and LR models using CSRS variables was reduced when validated in an external syncope cohort characterized by a higher event rate.