PED-DATA: A Privacy-Preserving Framework for Data-Driven, Pediatric Multi-Center Studies.

Abstract

Introduction: Data-driven analysis of clinical databases is an efficient method for clinical knowledge generation, which is especially suitable when exceptional ethical and practical restrictions apply, such as in pediatrics. In the multi-center PEDREF 2.0 study, we are analyzing children's laboratory test results, diagnoses, and procedures from more than 20 German tertiary care centers to establish pediatric reference intervals. The PEDREF 2.0 study uses the framework of the German Medical Informatics Initiative, but the specific study needs require the development of a customized module for distributed pediatric analyses.

Methods: We developed the Pediatric Distributed Analysis, Anonymization, and Aggregation Module (PED-DATA), which is a containerized application that we deployed to all participating centers. PED-DATA transforms the input datasets to a harmonized internal representation and enables their decentralized analysis in compliance with data protection rules, resulting in an anonymous output dataset that is transferred for central analysis.

Results: In a preliminary analysis of data from 15 centers, we analyzed 52,807,236 laboratory test results from 753,774 different patients (323,943 to 4,338,317 test results per laboratory test), enabling us to establish pediatric reference intervals with previously unmatched precision.

Conclusion: PED-DATA facilitates the implementation of pediatric data-driven multicenter studies in a decentralized and privacy-respecting manner, and its use throughout German University Hospitals in the PEDREF 2.0 study demonstrates its usefulness in a real-world use case.