Prognostic models in the myeloproliferative neoplasms.

Essential thrombocythemia Genomics Myelofibrosis Myeloproliferative neoplasms Polycythemia vera Prognostic modelling

Journal

Blood reviews
ISSN: 1532-1681
Titre abrégé: Blood Rev
Pays: England
ID NLM: 8708558

Informations de publication

Date de publication:
07 2020
Historique:
received: 28 12 2019
revised: 25 02 2020
accepted: 27 05 2020
pubmed: 14 6 2020
medline: 20 1 2021
entrez: 14 6 2020
Statut: ppublish

Résumé

The management of myelofibrosis (MF) is predominantly supportive, with the use of JAK2 inhibitors or allogeneic stem cell transplantation reserved for patients predicted to have poor overall survival. Identification of these patients is aided by a number of prognostic scoring systems, foremost among them the Dynamic International Prognostic Scoring System (DIPSS). Similarly, the use of cytoreductive therapies in essential thrombocytosis (ET) and polycythemia vera (PV) is targeted to patients identified as at highest risk of thrombosis. In this context, age and history of prior thrombosis are the strongest risk factors. Several studies have sought to improve the accuracy of prognostic modelling by encorporating a wider range of clinical and genomic variables, while also assessing those predictive for other endpoints, such as disease transformation. This review aims to summarise and evaluate existing prognostic models in the myeloproliferative neoplasms, and examine ways in which they can be further refined.

Identifiants

pubmed: 32532453
pii: S0268-960X(20)30063-1
doi: 10.1016/j.blre.2020.100713
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

100713

Informations de copyright

Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Jacob Grinfeld (J)

Department of Paediatric Haematology, Leeds General Infirmary, Great George St, Leeds LS1 3EX, UK. Electronic address: jacob.grinfeld@nhs.net.

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Classifications MeSH