Consensus-Based Best Practice Recommendations for Myelofibrosis Management in Routine Clinical Practice with a Focus on Patients with Cytopenias: Systematic Literature Review and Clinical Practice Recommendations from a Global Consensus Group
Notice bibliographique
Résumé
Background: Myelofibrosis (MF) is a BCR-ABL1-negative myeloproliferative neoplasm (MPN) characterized by progressive bone marrow fibrosis, splenomegaly, and cytopenias due to impaired hematopoiesis (Passamonti and Mora. Blood 2022). Despite the availability of comprehensive national and international guidelines for diagnosing and managing MF, gaps remain in translating these guidelines into clinical practice, particularly for patients with cytopenias, non-response or intolerance to Janus kinase (JAK) inhibitor treatment, and those ineligible for clinical trial enrollment. Consequently, an international expert consensus group was established with the objective of augmenting the existing guidance. Objectives: To develop a set of evidence- and consensus-based recommendations for managing MF in routine clinical practice, with a focus on patients with cytopenias, including practical strategies and tools to support clinicians. Methods : Modified Delphi methodology was utilized to achieve consensus (Figure 1). A Steering Committee (SC) of 9 expert hematologists (the authors) was established. During an initial meeting, the SC proposed 25 clinical questions that addressed key issues across 5 consensus themes: 1) Defining the thresholds for anemia and when to initiate/modify treatment; 2) defining the threshold for thrombocytopenia and when to initiate/modify treatment; 3) defining JAK inhibitor failure and what would warrant switching treatment; 4) how and when to determine prognosis in patients with MF; and, 5) unmet needs in MF clinical trials. An extended faculty (EF) was then enlisted, comprising hematologists and patient advocacy groups, who voted on the importance of the questions to address. The 15 highest scoring questions were selected for the consensus program (Table 1). To gather scientific evidence around the questions, a systematic literature review (SLR) was conducted using the PubMed and Embase databases, adhering to a PICO (Population, Intervention, Comparison, Outcome) framework. In a subsequent meeting, recommendations were formulated to address the questions using evidence from the SLR and the expert clinical experience of the SC. An online voting platform was then used for both the SC and EF to provide an agreement score for each recommendation. Consensus was achieved when 75% of the respondents agreed within the range of 7-9 on a 9-point scale (1=strongly disagree, 9=strongly agree). Results: Consensus was achieved among voters (hematologists [n=29] and patients [n=9] from Europe, the United States, Canada, Australia, and Israel) for all 15 recommendations. Recommendations in theme 1 emphasize the importance of comprehensive evaluation, exclusion of other causes of anemia, therapy dose optimization, and consideration of additional treatments for managing anemia in patients with MF. Recommendations in theme 2 highlight the complexity of managing splenomegaly, symptoms, and anemia in patients with low platelet counts, and factors guiding therapy selection for these patients. Theme 3 recommendations discuss criteria used to determine JAK inhibitor failure, including relapse, refractoriness, or intolerance, and guidance for distinguishing between these. In theme 4, recommendations address the use of validated prognostic scores at diagnosis and during the disease course, and transplantation risk assessment. The limitations and appropriate utilization of these scores are emphasized, and the need to develop prognostic scores in pre-primary MF is highlighted. Theme 5 recommendations cover the importance of addressing unmet needs in MF clinical trials, and include striving for inclusivity by removing barriers to the participation of underserved patient populations and focusing efforts on validating additional endpoints beyond traditional measures. Conclusions: An international panel of physicians with expertise in MF, together with a diverse EF, was able to achieve a high level of consensus across a wide range of critical gaps in MF management. These recommendations provide a valuable framework to support clinicians in optimizing care for patients with MF.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,196 | 0,321 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,011 |
| Bibliométrie | 0,028 | 0,017 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,008 | 0,010 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».