Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies

Abstract

BackgroundFederated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance.AimThis study aimed to evaluate four types of multicenter model development strategies for predicting 30-day mortality for patients undergoing transcatheter aortic valve implantation (TAVI): (1) central, learning one model from a centralized dataset of all hospitals; (2) local, learning one model per hospital; (3) federated averaging (FedAvg), averaging of local model coefficients; and (4) ensemble, aggregating local model predictions.MethodsData from all 16 Dutch TAVI hospitals from 2013 to 2021 in the Netherlands Heart Registration (NHR) were used. All approaches were internally validated. For the central and federated approaches, external geographic validation was also performed. Predictive performance in terms of discrimination [the area under the ROC curve (AUC-ROC, hereafter referred to as AUC)] and calibration (intercept and slope, and calibration graph) was measured.ResultsThe dataset comprised 16,661 TAVI records with a 30-day mortality rate of 3.4%. In internal validation the AUCs of central, local, FedAvg, and ensemble models were 0.68, 0.65, 0.67, and 0.67, respectively. The central and local models were miscalibrated by slope, while the FedAvg and ensemble models were miscalibrated by intercept. During external geographic validation, central, FedAvg, and ensemble all achieved a mean AUC of 0.68. Miscalibration was observed for the central, FedAvg, and ensemble models in 44%, 44%, and 38% of the hospitals, respectively.ConclusionCompared to centralized training approaches, FL techniques such as FedAvg and ensemble demonstrated comparable AUC and calibration. The use of FL techniques should be considered a viable option for clinical prediction model development.