The Dynamic International Prognostic Scoring System (DIPSS) for primary myelofibrosis (PMF) uses five risk factors to predict survival: age older than 65 years, hemoglobin lower than 10 g/dL, leukocytes higher than 25 × 10(9)/L, circulating blasts ≥ 1%, and constitutional symptoms. The main objective of this study was to refine DIPSS by incorporating prognostic information from karyotype, platelet count, and transfusion status. PATIENTS AND METHODS: Mayo Clinic databases for PMF were used to identify patients with available bone marrow histologic and cytogenetic information. RESULTS: Seven hundred ninety-three consecutive patients were selected and divided into two groups based on whether or not their referral occurred within (n = 428; training set) or after (n = 365; test set) 1 year of diagnosis. Multivariable analysis identified DIPSS, unfavorable karyotype, platelets lower than 100 × 10(9)/L, and transfusion need as independent predictors of inferior survival. Hazard ratio (HR)-weighted adverse points were assigned to these variables to develop a composite prognostic model using the training set. The model was subsequently validated in the test set, and its application to all 793 patients resulted in median survivals of 185, 78, 35, and 16 months for low, intermediate-1 (HR, 2.2; 95% CI, 1.4 to 3.6), intermediate-2 (HR, 4.9; 95% CI, 3.2 to 7.7), and high-risk groups (HR, 10.7; 95% CI, 6.8 to 16.9), respectively (P < .001). Leukemia-free survival was predicted by the presence of thrombocytopenia or unfavorable karyotype (10-year risk of 31% v 12%; HR, 3.3; 95% CI, 1.9 to 5.6). CONCLUSION: DIPSS plus effectively combines prognostic information from DIPSS, karyotype, platelet count, and transfusion status to predict overall survival in PMF. In addition, unfavorable karyotype or thrombocytopenia predicts inferior leukemia-free survival.

DIPSS plus: a refined Dynamic International Prognostic Scoring System for primary myelofibrosis that incorporates prognostic information from karyotype, platelet count, and transfusion status.

PASSAMONTI, FRANCESCO;
2011-01-01

Abstract

The Dynamic International Prognostic Scoring System (DIPSS) for primary myelofibrosis (PMF) uses five risk factors to predict survival: age older than 65 years, hemoglobin lower than 10 g/dL, leukocytes higher than 25 × 10(9)/L, circulating blasts ≥ 1%, and constitutional symptoms. The main objective of this study was to refine DIPSS by incorporating prognostic information from karyotype, platelet count, and transfusion status. PATIENTS AND METHODS: Mayo Clinic databases for PMF were used to identify patients with available bone marrow histologic and cytogenetic information. RESULTS: Seven hundred ninety-three consecutive patients were selected and divided into two groups based on whether or not their referral occurred within (n = 428; training set) or after (n = 365; test set) 1 year of diagnosis. Multivariable analysis identified DIPSS, unfavorable karyotype, platelets lower than 100 × 10(9)/L, and transfusion need as independent predictors of inferior survival. Hazard ratio (HR)-weighted adverse points were assigned to these variables to develop a composite prognostic model using the training set. The model was subsequently validated in the test set, and its application to all 793 patients resulted in median survivals of 185, 78, 35, and 16 months for low, intermediate-1 (HR, 2.2; 95% CI, 1.4 to 3.6), intermediate-2 (HR, 4.9; 95% CI, 3.2 to 7.7), and high-risk groups (HR, 10.7; 95% CI, 6.8 to 16.9), respectively (P < .001). Leukemia-free survival was predicted by the presence of thrombocytopenia or unfavorable karyotype (10-year risk of 31% v 12%; HR, 3.3; 95% CI, 1.9 to 5.6). CONCLUSION: DIPSS plus effectively combines prognostic information from DIPSS, karyotype, platelet count, and transfusion status to predict overall survival in PMF. In addition, unfavorable karyotype or thrombocytopenia predicts inferior leukemia-free survival.
2011
http://www.ncbi.nlm.nih.gov/pubmed/21149668
myelofibrosis; prognosis
Gangat, N; Caramazza, D; Vaidya, R; George, G; Begna, K; Schwager, S; Van Dyke, D; Hanson, C; Wu, W; Pardanani, A; Cervantes, F; Passamonti, Francesco; Tefferi, A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2023366
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