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Background: Myeloproliferative neoplasms represent a heterogeneous group of acquired hematopoietic stem cell diseases in which chronic inflammation is essential for both clonal evolution and thrombotic complications. The neutrophil-to-lymphocyte ratio (NLR), reflecting the imbalance between systemic inflammation and immunity, is emerging as a prognostic biomarker in several diseases, including hematological ones., Methods: A total of 473 patients with essential thrombocythemia (ET), the relationship between NLR value at diagnosis and the risk of thrombotic events in the follow-up, in addition to conventional clinical and biological variables, were retrospectively analyzed., Results: A total of 78 thrombotic events were reported for an incidence rate of 1.8 × 100 patients/year. In multivariate analysis, NLR value ≥4 at diagnosis was associated with higher cumulative thrombotic risk (hazard ratio [HR], 2.05; 95% CI, 1.29-2.28; p = .0001) as well International Prognostic Score for Thrombosis in Essential Thrombosis score intermediate-high (HR, 2.69; 95% CI, 1.27-5.72; p = .01) and diabetes (HR, 2.49; 95% CI, 1.23-3.05; p = .010). Concerning arterial thrombotic events, in multivariate analysis, NLR value at diagnosis ≥4 was predictive for thrombosis (HR, 2.13; 95% CI, 1.31-4.04; p = .001 as well diabetes (HR, 2.44; 95% CI, 1.05-5.68; p = .04) and hypertension (HR, 2.46; 95% CI, 1.05-5.68; p = .01). About venous thrombotic events, NLR value ≥5 was a marker predictive for venous thrombosis (HR, 2.99; 95% CI, 2.45-6.48; p = .01) as well age >60 years old (HR, 2.26; 95% CI, 1.0-5.10; p = .05)., Conclusion: NLR value is a simple, cost-effective, and easy-to-obtain inflammatory marker that can predict a diagnosis the risk of thrombosis in ET. Our results suggest that NLR value could be integrated into conventional cardiovascular risk scores, to better classify high-risk patients who are candidates for cytoreductive therapy. Further larger and prospective studies are warranted., (© 2024 American Cancer Society.)

In a first-of-its-kind study, we assessed the capabilities of large language models (LLMs) in making complex decisions in haematopoietic stem cell transplantation. The evaluation was conducted not only for Generative Pre-trained Transformer 4 (GPT-4) but also conducted on other artificial intelligence models: PaLm 2 and Llama-2. Using detailed haematological histories that include both clinical, molecular and donor data, we conducted a triple-blind survey to compare LLMs to haematology residents. We found that residents significantly outperformed LLMs (p = 0.02), particularly in transplant eligibility assessment (p = 0.01). Our triple-blind methodology aimed to mitigate potential biases in evaluating LLMs and revealed both their promise and limitations in deciphering complex haematological clinical scenarios., (© 2023 The Authors. British Journal of Haematology published by British Society for Haematology and John Wiley & Sons Ltd.)