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

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.