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Identifying Patients with Polycythemia Vera at Risk of Thrombosis after Hydroxyurea Initiation: The Polycythemia

Srdan Verstovsek1, Ivan Krečak2,3, Florian H Heidel4

  • 1Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Biomedicines
|July 29, 2023
PubMed

Insights

Patients with polycythemia vera (PV) face high risks of thromboembolic events (TE). Machine learning identified lymphocyte percentage, neutrophil percentage, and red cell distribution width as key TE risk markers in PV patients.

Area of Science:

  • Hematology
  • Oncology
  • Data Science

Background:

  • Polycythemia vera (PV) patients have a high risk of thromboembolic events (TE).
  • Real-world data is crucial for understanding TE risk factors in PV.
  • Treatment strategies for PV aim to mitigate TE risk.

Purpose of the Study:

  • To identify key markers associated with TE risk in a large, real-world PV population.
  • To develop a predictive model for TE risk using machine learning.
  • To establish optimal thresholds and a decision tree for identifying high-risk patients.

Main Methods:

  • Utilized the Optum® de-identified Electronic Health Record dataset for 82,960 PV patients.
  • Applied machine learning models to identify TE risk markers.
  • Validated the model using an independent dataset.

Main Results:

  • Analyzed incidence rates of TE in patients treated with hydroxyurea (HU) alone versus HU followed by ruxolitinib.
  • Machine learning model identified lymphocyte percentage (LYP), neutrophil percentage (NEP), and red cell distribution width (RDW) as significant TE risk markers.
  • Optimal thresholds for LYP, NEP, and RDW were established, leading to a derived decision tree.

Conclusions:

  • A machine learning-derived decision tree using LYP, NEP, and RDW can identify PV patients at high risk for TE.
  • These laboratory markers can aid in treatment decisions and optimize patient management for PV.
  • The findings support the use of readily available lab markers for proactive TE risk assessment in PV.

Keywords:
biomarkershydroxyureamachine learningpolycythemia verareal-world evidenceruxolitinibthromboembolic events

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