| Title |
A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis
|
|---|---|
| Published in |
Leukemia, May 2017
|
| DOI | 10.1038/leu.2017.169 |
| Pubmed ID | |
| Authors |
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, V De Stefano, M Ruggeri, R T Silver, G Benevolo, F Albano, D Caramazza, M Merli, D Pietra, R Casalone, G Rotunno, T Barbui, M Cazzola, A M Vannucchi |
| 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 <11 g/dl, to circulating blasts ⩾3%, and to CALR-unmutated genotype, 1 point to platelet count <150 × 10(9)/l and to constitutional symptoms, and 0.15 points to any year of age. MYSEC-PM (Myelofibrosis Secondary to PV and ET-Prognostic Model) allocated SMF patients into four risk categories with different survival (P<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.Leukemia accepted article preview online, 31 May 2017. doi:10.1038/leu.2017.169. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 146 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 13% |
| Researcher | 17 | 12% |
| Other | 13 | 9% |
| Professor | 12 | 8% |
| Student > Bachelor | 11 | 8% |
| Other | 27 | 18% |
| Unknown | 47 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 64 | 44% |
| Biochemistry, Genetics and Molecular Biology | 14 | 10% |
| Physics and Astronomy | 3 | 2% |
| Agricultural and Biological Sciences | 2 | 1% |
| Business, Management and Accounting | 2 | 1% |
| Other | 11 | 8% |
| Unknown | 50 | 34% |
Attention Score in Context
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