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Updated: Jan 15, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A practical tool for predicting outcomes in essential thrombocythemia: Triple A risk model and beyond
Ozge Sonmez1, Esiyla Oyur2, Ipek Yonal-Hindilerden3
1Department of Internal Medicine, Cerrahpaşa Faculty of Medicine, Istanbul University Cerrahpaşa, Istanbul, Turkey.
Abstract:
The management of essential thrombocythemia (ET) relies on risk stratification; therefore, easily applicable risk scores with improved prognostic value are in demand. The Triple A risk score is a novel model incorporating age, absolute neutrophil, and absolute lymphocyte counts (AAA model). In this study, we aimed to evaluate the predictive performance of Triple A score in ET for survival and its complications in an independent cohort, in addition to refining the model by incorporating absolute monocyte count and neutrophil-to-lymphocyte ratio (NLR). Demographic, clinical, and laboratory data of 565 patients with ET were retrospectively collected. Based on Triple A score, 250 patients were classified as low risk, 228 as intermediate-1 risk, 37 as intermediate-2 risk, and 50 as high risk. Over a median follow-up of 6 years, 10.3% patients developed thrombosis, 4.4% experienced bleeding, 5.1% had post-ET myelofibrosis, and 10.9% died. There were significant differences in overall survival and thrombosis-free survival across risk groups. Monocytosis (>0.8 × 109/L) was associated with increased mortality and its prevalence increased progressively with higher Triple A scores. Elevated NLR was also linked to a higher risk of mortality, and moreover, NLR-monocyte-age-based risk score demonstrated significant differences in survival risk. Consequently, Triple A score is an easy-to-use and reliable tool for predicting survival and thrombosis in ET. Incorporating monocytosis into Triple A (AAA+A model) may further enhance its prognostic accuracy. NLR also demonstrated prognostic value both independently and as component of NLR-monocyte-age-based model, highlighting its potential as a robust tool for risk stratification.
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