Authors
Robert Dewor, Michal J Dabrowski, Łukasz Więcek, Wojciech Amtmański, Wojciech Woszczek, Mikołaj Mróz, Paweł Turczyn, Grzegorz Helbig, Patryk Węglarz, Szymon Fornagiel, Jacek Krzanowski, Katarzyna Stach-Nowosiad, Marta Sobas, Łukasz Szukalski, Mirosław Markiewicz, Sylwia Kot, Agnieszka Gala-Błądzińska, Sebastian Bróż, Jarosław Piszcz, Natalia Leończuk, Wojciech Homenda, Anita Rutkowska, Anna Lasoń, Anna Meryn, Aleksandra Jurczuk, Michał Konopelko, Aleksandra Bator, Kinga Marciniak, Marek Dudziński, Paweł Dubiela, Karol Lis, Grzegorz Basak
Published in
NPJ digital medicine. Jul 09, 2026. Epub Jul 09, 2026.
Abstract
Paroxysmal nocturnal haemoglobinuria (PNH) is a rare, life-threatening hematologic disease with diagnostic delays exceeding 5 years in 24% of cases. We developed and deployed an artificial intelligence algorithm analyzing structured and unstructured electronic health record data across 14 healthcare organizations in Poland. Screening of 1,307,140 patients identified 356 high-risk individuals; of 119 referred for flow cytometry, 13 were diagnosed (positive predictive value: 10.92%; 95% CI, 9.68%-12.30%), comparing favourably to 6.9% conventional screening hit rate. High-risk patients were significantly older (median 69.5 years) with elevated rates of fatigue (76.4% vs 29.19%), anaemia (72.2% vs 7.61%), and myelodysplastic syndrome (49.2% vs 0.24%; all p < 0.001). Only 2.25% presented with haemoglobinuria versus 45-62% in registry cohorts. Retrospective analysis revealed potentially preventable diagnostic delays of 74-1337 days. Monte Carlo feature selection identified Coombs-negative haemolysis and visit frequency as strongest predictors, supporting the potential utility of AI-assisted screening for identifying atypical PNH presentations.
PMID:
42426209
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.
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