Privacy-preserving synthetic data from a national CIDP registry: validation against real-world evidence.

Authors

Alessio Signori, Erika Schirinzi, Fiore Manganelli, Dario Cocito, Yuri Matteo Falzone, Chiara Briani, Anna Mazzeo, Angelo Schenone, Vincenzo Di Stefano, Giuseppe Cosentino, Girolama Alessandra Marfia, Luana Benedetti, Marinella Carpo, Massimiliano Filosto, Luca Leonardi, Marco Luigetti, Sabrina Matà, Giuseppe Piscosquito, Tiziana Rosso, Marta Lucchetta, Gabriele Siciliano, Giuseppe Lauria Pinter, Maurizio Inghilleri, Teresa Anna Cantisani, Francesca Notturno, Dario Ricciardi, Francesco Habetswallner, Elisa Vegezzi, Federica Ginanneschi, Antonio Todisco, Roberta Brusa, Stefano Sotgiu, Paolo Solla, Alberto De Lorenzo, Claudia Lozi, Alessandro Salvalaggio, Adele Ratti, Luca Gentile, Giorgia Mataluni, Filippo Brighina, Barbara Risi, Loris Poli, Francesca Forcina, Francesca Vitali, Federica Moret, Fabrizio Canale, Emanuele Cassano, Eleonora Dalla Bella, Stefania Casali, Vincenzo Di Lazzaro, Alessandro Cesare Prelle, Alessandra Carta, Filippo De Mattei, Giuseppe De Biasi, Eduardo Nobile-Orazio, Pietro Emiliano Doneddu

Published in

Frontiers in neurology. Volume 17. Pages 1839538. Epub Oct 13, 2026.

Abstract

Research in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is constrained by low prevalence, limited data sharing, and privacy regulations. Disease registries mitigate some of these barriers, yet the reuse of individual-level data remains challenging. Artificial intelligence- and regression-based synthetic data (SD) generation may offer a privacy-preserving solution, provided analytical validity is maintained. We aimed to validate SD derived from the Italian CIDP Registry using a published case-control study as a reference framework.
We analyzed a matched case-control dataset including 195 CIDP cases and 195 controls to examine lifestyle and dietary factors associated with CIDP risk. SD were generated using sequential conditional models stratified by sex. Fidelity was assessed by comparing real and SD in terms of marginal distributions, data distinguishability (propensity score mean squared error), and preservation of associations with CIDP risk using univariable and multivariable logistic regression.
SD preserved the original case-control balance and sex distribution. Marginal distributions showed high concordance between real and SD, with minor smoothing in a small number of multi-level dietary variables. Distinguishability analyses indicated low separability between real and synthetic records. Univariable associations with CIDP risk were closely replicated, including the protective effects of higher fish and rice intake. Multivariable models demonstrated highly concordant effect estimates between datasets.
SD derived from the Italian CIDP Registry accurately reproduces key analytical properties of real-world data while preserving patient privacy. This approach enables reproducible and shareable CIDP research and may support future applications, including synthetic standard-of-care comparator cohorts and precision medicine-oriented investigations.

PMID:
42947862
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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