Gender and survival in essential thrombocythemia: A two-center study of 1,494 patients.
Ayalew TefferiSilvia BettiDaniela BarracoMythri MudireddySahrish ShahCurtis A HansonRhett P KetterlingAnimesh PardananiNaseema GangatGiacomo ColtroPaola GuglielmelliAlessandro M VannucchiPublished in: American journal of hematology (2017)
Based on suggestive information from recent epidemiologic data and earlier retrospective studies, we revisited the effect of gender on survival in 1,494 patients with essential thrombocythemia (ET). The primary study population included 904 patients from the Mayo Clinic (median age 58 years; 65% females); risk distribution, according to the international prognostic score for ET (IPSET), was 23% high, 42% intermediate and 35% low. Multivariable analysis that included IPSET-relevant risk factors identified male sex (HR 1.6, 95% CI 1.3-2.0), age ≥60 years (HR 4.3, 95% CI 3.4-5.4) and leukocyte count ≥11 × 10(9)/L (HR 1.5, 95% CI 1.3-1.9) as independent predictors of shortened survival. These findings were confirmed by analysis of a separate cohort of 590 ET patients (65% females) from the University of Florence, Italy, with corresponding HRs (95% CI) of 1.6 (1.1-2.5), 4.6 (2.2-9.5) and 1.8 (1.1-2.8). The independent prognostic effect of gender was further corroborated by a separate multivariable analysis against IPSET risk categories; HR (95% CI) for the Mayo Clinic/Florence cohorts were 1.5/1.6 (1.2/1.1-1.8/2.5) for male sex, 6.8/7.5 (5.0/3.1-9.3/18.3) for IPSET high risk and 2.8/4.1 (2.1/1.8-3.8/9.5) for IPSET intermediate risk. Furthermore, the survival disadvantage in men was most apparent in IPSET high risk category and in patients older than 60 years. In both patient cohorts, thrombosis history garnered significance in univariate, but not in multivariable analysis. The observations from the current study suggest that women with ET live longer than their male counterparts and that gender might supersede thrombosis history as a risk variable for overall survival.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- risk factors
- chronic kidney disease
- prognostic factors
- mental health
- primary care
- healthcare
- pulmonary embolism
- magnetic resonance
- case report
- free survival
- machine learning
- computed tomography
- magnetic resonance imaging
- big data
- electronic health record
- patient reported
- artificial intelligence
- deep learning
- data analysis