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Identifying Patients with Polycythemia Vera at Risk of Thrombosis after Hydroxyurea Initiation: The Polycythemia Vera—Advanced Integrated Models (PV-AIM) Project

Affiliation
Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
Verstovsek, Srdan;
ORCID
0000-0002-8642-305X
Affiliation
Department of Internal Medicine, General Hospital of Sibenik-Knin County, 22000 Sibenik, Croatia
Krečak, Ivan;
ORCID
0000-0003-2438-1955
Affiliation
Hematology, Oncology, Stem Cell Transplantation and Palliative Care, Internal Medicine C, University Medicine Greifswald, 17475 Greifswald, Germany
Heidel, Florian H.;
ORCID
0000-0002-5178-5827
Affiliation
Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica, Fondazione Policlinico A. Gemelli IRCCS, 00168 Roma, Italy
De Stefano, Valerio;
Affiliation
Novartis Ireland Limited, Dublin 4, D04 A9N6 Dublin, Ireland
Bryan, Kenneth;
Affiliation
Novartis Pharma AG, CH-4056 Basel, Switzerland
Zuurman, Mike W.;
Affiliation
Novartis Pharma AG, CH-4056 Basel, Switzerland
Zaiac, Michael;
Affiliation
Novartis Farma SpA, 21040 Origgio, Italy
Morelli, Mara;
Affiliation
Novartis Pharma AG, CH-4056 Basel, Switzerland
Smyth, Aoife;
Affiliation
Novartis Farmaceutica, S.A., 28033 Madrid, Spain
Redondo, Santiago;
Affiliation
The Boston Consulting Group, Boston, MA 02210, USA
Bigan, Erwan;
Affiliation
The Boston Consulting Group, Boston, MA 02210, USA
Ruhl, Michael;
Affiliation
The Boston Consulting Group, Boston, MA 02210, USA
Meier, Christoph;
Affiliation
The Boston Consulting Group, Boston, MA 02210, USA
Beffy, Magali;
Affiliation
Centre d’Investigations Cliniques (INSERM CIC 1427), Université de Paris, Hôpital Saint-Louis, AP-HP, 75010 Paris, France
Kiladjian, Jean-Jacques

Patients with polycythemia vera (PV) are at significant risk of thromboembolic events (TE). The PV-AIM study used the Optum ® de-identified Electronic Health Record dataset and machine learning to identify markers of TE in a real-world population. Data for 82,960 patients with PV were extracted: 3852 patients were treated with hydroxyurea (HU) only, while 130 patients were treated with HU and then changed to ruxolitinib (HU-ruxolitinib). For HU-alone patients, the annualized incidence rates (IR; per 100 patients) decreased from 8.7 (before HU) to 5.6 (during HU) but increased markedly to 10.5 (continuing HU). Whereas for HU-ruxolitinib patients, the IR decreased from 10.8 (before HU) to 8.4 (during HU) and was maintained at 8.3 (after switching to ruxolitinib). To better understand markers associated with TE risk, we built a machine-learning model for HU-alone patients and validated it using an independent dataset. The model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as key markers of TE risk, and optimal thresholds for these markers were established, from which a decision tree was derived. Using these widely used laboratory markers, the decision tree could be used to identify patients at high risk for TE, facilitate treatment decisions, and optimize patient management.

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