Trial reportLeukemia2025
Prediction of resistance to hydroxyurea therapy in patients with polycythemia vera: a machine learning study (PV-AIM) validated in a prospective interventional phase IV trial (HU-F-AIM).
Trial report in Leukemia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Trial
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Response to the commentary by Xu et al. on the PV-AIM study.Leukemia · 2026Article
- Article
- Research progress of HIF-1a on immunotherapy outcomes in immune vascular microenvironment.Frontiers in immunology · 2025Review
- Clinical and Economic Implications of Hydroxyurea Intolerance in Polycythemia Vera in Routine Clinical Practice.Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Polycythemia vera (PV) is a myeloproliferative neoplasm associated with increased thromboembolic (TE) risk and hematologic complications. Hydroxyurea (HU) serves as the most frequently used first-line cytoreductive therapy worldwide; however, resistance to HU (HU-RES) develops in a significant subset of patients, leading to increased morbidity and necessitating alternative treatments. This study, part of the PV-AIM project, employed machine learning techniques on real-world evidence (RWE) from the Optum® EHR database containing 82.960 PV patients to identify baseline predictors of HU-RES within the first 6-9 months of therapy. Using a Random Forest model, the study analyzed data from 1850 patients, focusing on laboratory parameters and clinical characteristics. Key predictive markers included red cell distribution width (RDW) and hemoglobin (HGB), showing the strongest association with HU-RES. A synergistic interaction between RDW and HGB was identified, enabling TE risk stratification. This study provides a robust framework for early detection of HU-RES using readily available clinical data, facilitating timely intervention. These findings underscore the importance of personalized treatment approaches in managing PV and highlight the utility of machine learning in enhancing predictive accuracy and clinical outcomes. Based on the results of PV-AIM we initiated an open-label, prospective, single-arm, interventional, phase IV study (HU-F-AIM) evaluating HU-resistance/intolerance. Validation of predictive biomarkers may facilitate identification of patients at risk of HU resistance who may benefit from alternative treatment options, possibly preventing ongoing phlebotomy during HU treatment, a frequent therapeutic choice in high-risk PV associated with early disease progression and increased thromboembolic complications. We propose an updated terminology that differentiates between true molecular resistance and clinical resistance, that may indicate the requirement for alternative therapeutic strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.