A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis

Valerio De Stefano, F. Passamonti, T. Giorgino, B. Mora, P. Guglielmelli, E. Rumi, M. Maffioli, A. Rambaldi, M. Caramella, R. Komrokji, J. Gotlib, J. J. Kiladjian, F. Cervantes, T. Devos, F. Palandri, M. Ruggeri, R. T. Silver, G. Benevolo, F. Albano, D. CaramazzaM. Merli, D. Pietra, R. Casalone, G. Rotunno, T. Barbui, M. Cazzola, A. M. Vannucchi

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116 Citations (Scopus)

Abstract

Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms with variable risk of evolution into post-PV and post-ET myelofibrosis, from now on referred to as secondary myelofibrosis (SMF). No specific tools have been defined for risk stratification in SMF. To develop a prognostic model for predicting survival, we studied 685 JAK2, CALR, and MPL annotated patients with SMF. Median survival of the whole cohort was 9.3 years (95% CI: 8-not reached-NR-). Through penalized Cox regressions we identified negative predictors of survival and according to beta risk coefficients we assigned 2 points to hemoglobin level o 11 g/dl, to circulating blasts 3%, and to CALR-unmutated genotype, 1 point to platelet count o 150 × 109/l and to constitutional symptoms, and 0.15 points to any year of age. Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) allocated SMF patients into four risk categories with different survival (P o 0.0001): low (median survival NR; 133 patients), intermediate-1 (9.3 years, 95% CI: 8.1-NR; 245 patients), intermediate-2 (4.4 years, 95% CI: 3.2–7.9; 126 patients), and high risk (2 years, 95% CI: 1.7–3.9; 75 patients). Finally, we found that the MYSEC-PM represents the most appropriate tool for SMF decision-making to be used in clinical and trial settings.
Original languageEnglish
Pages (from-to)2726-2731
Number of pages6
JournalLeukemia
Volume31
DOIs
Publication statusPublished - 2017

Keywords

  • Cancer Research, Secondary Myelofibrosis, Prognostic Model

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