Cargando…
Leukemic transformation among 1306 patients with primary myelofibrosis: risk factors and development of a predictive model
Among 1306 patients with primary myelofibrosis (PMF), we sought to identify risk factors that predicted leukemic transformation (LT) in the first 5 years of disease and also over the course of the disease. 149 (11%) LT were documented; patients who subsequently developed LT (n = 149), compared to th...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347609/ https://www.ncbi.nlm.nih.gov/pubmed/30683837 http://dx.doi.org/10.1038/s41408-019-0175-y |
Sumario: | Among 1306 patients with primary myelofibrosis (PMF), we sought to identify risk factors that predicted leukemic transformation (LT) in the first 5 years of disease and also over the course of the disease. 149 (11%) LT were documented; patients who subsequently developed LT (n = 149), compared to those who remained in chronic phase disease (n = 1,157), were more likely to be males (p = 0.02) and display higher circulating blasts (p = 0.03), ASXL1 (p = 0.01), SRSF2 (p = 0.001) and IDH1 (p = 0.02) mutations. Logistic regression analysis identified IDH1, ASXL1 and SRSF2 mutations, very high-risk karyotype, age > 70 years, male sex, circulating blasts ≥ 3%, presence of moderate or severe anemia and constitutional symptoms, as predictors of LT in the first 5 years of diagnosis. Time-to-event Cox analysis confirmed LT prediction for IDH1 mutation (HR 4.3), circulating blasts ≥ 3% (HR 3.3), SRSF2 mutation (HR 3.0), age > 70 years (HR 2.1), ASXL1 mutation (HR 2.0) and presence of moderate or severe anemia (HR 1.9). HR-based risk point allocation resulted in a three-tiered LT risk model: high-risk (LT incidence 57%; HR 39.3, 95% CI 10.8–114), intermediate-risk (LT incidence 17%; HR 4.1, 95% CI 2.4–7.3) and low-risk (LT incidence 8%). The current study provides a highly discriminating LT predictive model for PMF. |
---|