Federated learning for predicting critical intervention and poor clinical outcomes at emergency department triage stage

Background: Early detection and timely intervention of patients at risk during the triage stage can significantly improve patient outcomes. This study aimed to predict requirements for critical respiratory or cardiovascular intervention and poor clinical outcome using federated learning (FL). Methods: Patients of two tertiary hospitals who visited the emergency department (ED) were included. Local models for each hospital and FL models to predict high flow nasal cannula or endotracheal intubation (model 1), central venous catheter insertion or vasopressor administration (model 2), and admission to intensive care unit or cardiac arrest during ED stay (model 3) were developed and internally validated with data from 2017 to 2020. These models were then externally validated with data from 2021. Available information such as underlying disease, recent blood test results, age, sex, and initial vital signs at triage stage were used as input variables. Performances of models were evaluated using area under the receiver operating characteristic (AUROC) with 95% confidence interval. Results: A total of 262,283 and 180,261 ED visits from Samsung Medical Center (hospital A) and Korea University ANAM Hospital (hospital B) respectively, were included. AUROC values of three local and three FL models in both hospitals all exceeded 0.85 in internal validation. For hospital B, local models showed better performance than the FL model, including model 2 (0.942 (0.938–0.946) vs. 0.890 (0.884–0.896)) and model 3 (0.910 (0.905–0.914) vs. 0.886 (0.881–0.891)). AUROC values of local and FL models also exceeded 0.85 in external validation. The FL model showed comparable performance except model 3 of hospital B. Conclusions: Federated learning models demonstrated comparable performance to local models in predicting critical interventions and poor clinical outcomes at triage.