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ASH 2022 | PV-AIM: using machine learning to predict resistance to hydroxyurea therapy in patients with PV
Srdan Verstovsek, MD, The University of Texas MD Anderson Cancer Center, Houston, TX, shares insights into the the ‘Polycythemia Vera: A Machine Learning Study’ (PV-AIM) study which used electronic medical records to predict study which used electronic medical records to predict resistance to treatment with hydroxyurea in patients with polycythemia vera (PV). Prof. Verstovsek explains that two parameters – hemoglobin and red-cell distribution width (RDW) – can be used to effectively predict the outcomes of patients with PV treated with hydroxyurea. This interview took place at the 64th ASH Annual Meeting and Exposition congress in New Orleans, LA.
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Disclosures
NS Pharma: Research Funding; AstraZeneca: Research Funding; ItalPharma: Research Funding; Novartis: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Promedior: Research Funding; CTI BioPharma Corp.: Research Funding; Gilead: Research Funding; Sierra Oncology: Consultancy, Research Funding; Roche: Research Funding; Protagonist Therapeutics: Research Funding; PharmaEssentia: Research Funding; Blueprints Medicines Corp.: Research Funding; Celgene: Consultancy, Research Funding; Genentech: Research Funding; Pragmatist: Consultancy; Constellation Pharmaceuticals: Consultancy.
