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Machine-learning algorithms to diagnose PV
Results from a retrospective study comparing machine learning (ML) algorithms for predicting polycythemia vera (PV) in patients with elevated hemoglobin (Hb; N = 1,484) were recently published in the Journal of Clinical Pathology by Haskul et al.
Key data: Platelet (PLT) count was the most influential parameter for PV prediction (42.4%), followed by hematocrit (HCT; 26.7%), white blood cells (WBCs; 18.7%), and Hb (12.0%). Significant differences were observed between PV and non-PV groups for all complete blood count (CBC) parameters (p < 0.001).
Key learning: ML algorithms can diagnose PV using CBC parameters with high accuracy. This approach may reduce the dependence on costly diagnostic methods such as JAK2, bone marrow biopsy, and erythropoietin (EPO).
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A 68-year-old male with primary myelofibrosis received ruxolitinib 10 mg twice daily for 7 months. His spleen size and symptoms are controlled, but Hb remains <8 g/dL. EPO, 210 mU/mL; platelets, 85,000/µL. What is your next step?
