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December 8, 2025Blood

Real-world temporal evaluation of static risk prediction in polycythemia vera

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Authors

YSYushu ShiGAGhaith Abu‐Zeinah

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Overview

Real-world assessment reveals genomic data impact and highlights age as key factor in leukemic transformation.

Key Points

  • The study aims to assess the performance of prognostic models for polycythemia vera over time.
  • Evaluated model performance in a cohort of 475 polycythemia vera patients after treatment.
  • Analyzed genomic data and reapplication of models every 5 years to track evolving risks.
  • Used Kaplan-Meier methods and Cox proportional-hazards models to estimate outcomes.
  • Age emerged as a dominant factor in predicting overall survival and leukemic transformation.
  • Reapplying models showed decreased prognostic significance over time, emphasizing temporal factors.
  • Genomic data revealed connections to risk stratification but required context in model applications for accuracy.

Cite This Study

Shi et al. (2025) studied this question.

synapsesocial.com/papers/69362f744fa91c937236e293https://doi.org/10.1182/blood-2025-5594
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