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<p>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 &lt; 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.</p>

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AI-driven diagnostic algorithm enhances early detection of paroxysmal nocturnal hemoglobinuria in real-world settings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • 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

摘要

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.