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Enregistrement W2083073031 · doi:10.1111/bjh.12985

Use of <scp>JAK</scp> inhibitors in the management of myelofibrosis: a revision of the <scp>B</scp>ritish <scp>C</scp>ommittee for <scp>S</scp>tandards in <scp>H</scp>aematology <scp>G</scp>uidelines for <scp>I</scp>nvestigation and <scp>M</scp>anagement of <scp>M</scp>yelofibrosis 2012

2014· letter· en· W2083073031 sur OpenAlexaff
John T. Reilly, Mary Frances McMullin, Philip Beer, Nauman M. Butt, Eibhlin Conneally, Andrew Duncombe, Anthony R. Green, G. Mikhaeel, Maria Gilleece, Steven Knapper, Adam J. Mead, Ruben A. Mesa, Mallika Sekhar, Claire Harrison

Notice bibliographique

RevueBritish Journal of Haematology · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueMyeloproliferative Neoplasms: Diagnosis and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesMedical Research CouncilPublic Health Agency
Mots-clésRuxolitinibMyelofibrosisMedicineHazard ratioInternal medicineCalreticulinConfidence intervalHematologyOncologyBiologyBone marrowGenetics

Résumé

récupéré en direct d'OpenAlex

The British Committee for Standards in Haematology (BCSH) Guidelines for myelofibrosis were produced in 2012 (Reilly et al, 2012), but since then Ruxolitinib, a JAK1/JAK2 inhibitor, has been approved for use in the European Union and highly prevalent mutations in the Calreticulin gene (CALR) have been described. We therefore wish to revise the existing guideline (Reilly et al, 2012) to accommodate this important data. Current diagnostic criteria should be modified to incorporate testing for the CALR mutations into major criteria A2 alongside JAK2 V617F, as shown in Table 1 (Evidence grade 1A). Patients with CALR mutations may have a better prognosis (Klampfl et al, 2013), but this has not formally been assessed and incorporated into prognostic scores. Substantial data are now available concerning responses to JAK inhibitor therapies including beneficial effects upon survival (Verstovsek et al, 2012, 2013; Cervantes et al, 2013). For example, at 144 weeks in the COMFORT-II study the median of overall survival had not been reached in either arm. A total of 29 (19·9%) and 22 (30·1%) patients died during the study in the ruxolitinib and best available therapy (BAT) arms, respectively, of which deaths on treatment were reported for 13 (8·9%) in the ruxolitinib arm, and 5 (6·8%) in the BAT arm (one death occurred after crossover to ruxolitinib). There was a 52% reduction in risk of death in the ruxolitinib treatment arm compared to the BAT arm (Hazard Ratio = 0·48, 95% confidence interval 0·28–0·85). The estimated probability of being alive at 144 weeks was 81% in ruxolitinib arm and 61% in BAT arm. The P-value for the log-rank test stratified by the baseline risk category was 0·009, (Cervantes et al, 2013). Furthermore, data from these randomized studies suggest that standard therapies are comparable to placebo in terms of spleen and symptom responses. The previous guideline (Reilly et al, 2012) recommended consideration of JAK inhibitor therapy for patients who have failed hydroxycarbamide therapy and are not presently suitable for bone marrow transplantation, or for patients with severe constitutional symptoms. In view of new evidence we now formally recommend ruxolitinib as first line therapy for symptomatic splenomegaly and/or myelofibrosis-related constitutional symptoms regardless of JAK2 V617F mutation status (evidence grade 1A) where the balance between need to resolve the latter outweighs risk of side effects and, in particular, we make the following recommendations: Whilst treatment with ruxolitinib is suggested to confer a survival advantage treatment with this agent in asymptomatic patients and/or those who lack bothersome splenomegaly is not currently recommended. For patients failing or intolerant of ruxolitinib, additional JAK inhibitors are being assessed in clinical trials and may be approved in the future. The content was reviewed and approved by all authors, the manuscript was written by CH. John T. Reilly has acted as consultant or been paid on the speakers bureau for Novartis and Shire. Mary Frances McMullin has acted as a consultant or been on the speakers bureau for Novartis, Sanofi, Shire and Gilead pharmaceuticals. Philip A. Beer, none. Nauman Butt has received sponsorship to attend educational meetings from Novartis and Shire Pharmaceuticals, and acted as a speaker for educational meeting sponsored by Novartis and Bristol-Myers Squibb. Eibhlin Conneally has acted as an advisory board member for Novartis, Bristol-Myers Squibb and Pfizer Pharmaceuticals. Andrew Duncombe has acted as an advisory board or speaker bureau member for Novartis, Sanofi, Amgen, Roche and Baxter. Anthony R. Green, none. N. George Mikhaeel, none. Marie H. Gilleece, none. Steven Knapper has acted as a consultant for Novartis and has received funding for overseas conference travel from Novartis, Shire. Adam Mead has received consultancy fees from Novartis and Sanofi Aventis and research funding from Novartis. Ruben A. Mesa has received research support from Incyte, Genentech, Sanofi, Lilly, NS pharma and Gilead and consultancy fees from Novartis. Mallika Sekhar has received research funding from Novartis. Claire Harrison has received research funding from Novartis pharmaceuticals, acted as a consultant or been on the speakers bureau for Novartis, Sanofi, Shire, Celgene, YMBioscience, SBio, CTI and Gilead pharmaceuticals.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,002
Intégrité de la recherche0,0030,008
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,278
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations38
Publié2014
Routes d'admission1
Résumé présentoui

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