Toward Successful International Pooling of Breast Implant Registry Data: The Role of Dataset Uniformity

Despite widespread clinical use, the long-term performance of breast implants remains debated. Current evidence is limited by small sample sizes and inconsistent methodologies. National breast device registries provide valuable real-world data, yet their impact is constrained by heterogeneity in variable definitions, data structures, and analytical approaches. The International Collaboration of Breast Registry Activities (ICOBRA) initiative established a harmonized dataset utilized by several countries.

Evaluate the implementation of the dataset of the ICOBRA across national registries, identify inconsistencies in data definitions and structures, and develop an updated international dataset supporting standardized, federated analyses.

Datasets from 5 national breast implant registries utilizing the harmonized dataset were systematically reviewed. Variations in data points and definitions were identified and discussed through a series of in-person and online consensus meetings. Structured voting was used to finalize revisions and establish the updated international core dataset.

Key discrepancies were identified in the classification of surgical indications, inclusion of implant types, and variable definitions across registries. As a result, more than half of the original core variables required revision, reclassification, or removal to enable multinational analyses. A federated analysis model was proposed to accommodate privacy regulations and registry diversity while supporting standardized, risk-adjusted analyses.

The updated international breast implant registry dataset represents a crucial step toward integrated global surveillance. Together with standardized statistical methods within a federated data model, it enables robust pooled analyses without requiring uniform registry structures. Countries are encouraged to adopt the revised dataset and contribute to a collaborative framework that enhances patient safety through structured, large-scale data collection and analysis.

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