When platelets reach a million: insights from hematologists.
- 1Cerrahpaşa Faculty of Medicine, Department of Internal Medicine, Division of Hematology, Istanbul University-Cerrahpaşa, Fatih, Istanbul, Türkiye.
- 2Cerrahpaşa Faculty of Medicine, Department of Internal Medicine, Division of Hematology, Istanbul University-Cerrahpaşa, Fatih, Istanbul, Türkiye. [email protected].
- 3Division of Hematology, Department of Internal Medicine, Cerrahpaşa Faculty of Medicine, Istanbul University-Cerrahpaşa, Istanbul, Türkiye. [email protected].
Abstract
The management of patients with essential thrombocythemia (ET) and extreme thrombocytosis (ExT) remains plagued by several unresolved issues. Therefore, we conducted a web-based survey to better understand hematologists' clinical approaches to ExT and to compare them with responses generated by artificial intelligence (AI) programs. A total of 56 hematologists participated. Cytoreductive treatment was preferred by 41.1% of respondents at a platelet threshold of ≥ 1000 × 10⁹/L and by 83.9% at ≥ 1500 × 10⁹/L. Assessment of vWF: RCo at diagnosis was reported by 78.6% of hematologists. In the presence of cardiovascular risk factors and the absence of acquired von Willebrand disease (vWD), aspirin therapy was favored by 91.1% and 78.6% of respondents for platelet counts ≥ 1000 × 10⁹/L and ≥ 1500 × 10⁹/L, respectively. Prevention of thrombosis, prevention of bleeding, and symptom control were cited with similar frequencies as reasons for initiating cytoreductive treatment. Artificial intelligence (AI)-generated recommendations supported vWF testing, favored cytoreductive treatment, particularly at platelet counts ≥ 1500 × 10⁹/L, and generally recommended aspirin only for patients with platelet counts ≥ 1000 × 10⁹/L in the absence of acquired vWD. Overall, ExT appeared to be an important driver of treatment decisions, particularly at platelet counts ≥ 1500 × 10⁹/L. Comparisons with AI-generated recommendations revealed few differences in treatment approaches across some clinical scenarios, supporting the exploratory nature of the AI analysis.
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BibTeX
@article{sonmez2026when,
title = {{When platelets reach a million: insights from hematologists.}},
author = {Özge Sönmez and Ahmet Emre Eşkazan},
journal = {Annals of hematology},
year = {2026},
month = sep,
doi = {10.1007/s00277-026-07183-x},
url = {https://europepmc.org/article/MED/42961495},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - When platelets reach a million: insights from hematologists.
AU - Özge Sönmez
AU - Ahmet Emre Eşkazan
PY - 2026
DA - 2026/09/26
JO - Annals of hematology
DO - 10.1007/s00277-026-07183-x
UR - https://europepmc.org/article/MED/42961495
AB - The management of patients with essential thrombocythemia (ET) and extreme thrombocytosis (ExT) remains plagued by several unresolved issues. Therefore, we conducted a web-based survey to better understand hematologists' clinical approaches to ExT and to compare them with responses generated by artificial intelligence (AI) programs. A total of 56 hematologists participated. Cytoreductive treatment was preferred by 41.1% of respondents at a platelet threshold of ≥ 1000 × 10⁹/L and by 83.9% at ≥ 1500 × 10⁹/L. Assessment of vWF: RCo at diagnosis was reported by 78.6% of hematologists. In the presence of cardiovascular risk factors and the absence of acquired von Willebrand disease (vWD), aspirin therapy was favored by 91.1% and 78.6% of respondents for platelet counts ≥ 1000 × 10⁹/L and ≥ 1500 × 10⁹/L, respectively. Prevention of thrombosis, prevention of bleeding, and symptom control were cited with similar frequencies as reasons for initiating cytoreductive treatment. Artificial intelligence (AI)-generated recommendations supported vWF testing, favored cytoreductive treatment, particularly at platelet counts ≥ 1500 × 10⁹/L, and generally recommended aspirin only for patients with platelet counts ≥ 1000 × 10⁹/L in the absence of acquired vWD. Overall, ExT appeared to be an important driver of treatment decisions, particularly at platelet counts ≥ 1500 × 10⁹/L. Comparisons with AI-generated recommendations revealed few differences in treatment approaches across some clinical scenarios, supporting the exploratory nature of the AI analysis.
SN - 0939-5555
SN - 1432-0584
ER -