Developing federated time-to-event scores using heterogeneous real-world survival data.

OBJECTIVE: Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. MATERIALS AND METHODS: We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. RESULTS: In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6 %. Additionally, the federated score's time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. DISCUSSION: The model developed through our proposed method showed good local performance and is promising for future healthcare research. Sites participating in our proposed federated scoring model training can develop survival models with enhanced prediction accuracy and efficiency. CONCLUSION: This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.

Duke Scholars

Author Chuan Hong Biostatistics & Bioinformatics, Division of Translational Bi ...

Author Nan Liu Biostatistics & Bioinformatics, Division of Translational Bi ...