Unmet Needs in Multiple Myeloma and Potential Solutions: A Systemic Literature Review
Notice bibliographique
Résumé
Introduction: Unmet needs refer to unaddressed resources, unachieved health/survival outcomes or needs necessary for optimal well-being of patients. There has been no systematic reporting of the unmet needs in multiple myeloma (MM). We conducted this systematic review to highlight the unmet needs in MM and their proposed solutions in the literature and provide direction for future research. Methods: PubMed, Embase, and ClinicalTrials.gov were searched following the PRISMA guidelines using the relevant keywords and MeSH terms related to “multiple myeloma” and “unmet” needs without restrictions capturing studies from 2004 to Jan 2024. They were further screened to select the studies reporting unmet needs to develop a timeline of their reporting, identify the article types and the proposed solutions. Results: Out of 1214 articles, 668 (58% abstracts and 42% full manuscripts) were included. The use of the term “unmet” increased through the years [2004-2009 (1%), 2010-2017 (27%) and 2018-2024 (72%)]. None of the articles defined the term “unmet” or “unmet need”. Twenty-three articles (3%) specified the unmet needs of a particular country/region (China 7, Canada 3, Brazil 2, India 2, US 2 and 1 each for Italy, Korea, Lebanon, Portugal, Ukraine, Turkey, and Latin America). Narrative reviews (24%) were the most common article types followed by retrospective studies (21%), and clinical trials (19%). Among various aspects of MM, “treatment” was consistently the most common (73%) reported unmet need which included treatment of relapsed/refractory MM (RRMM) (38%). Treatment of bone disease (2%) and treatment cost (1%) were also consistently reported treatment-related unmet needs through this period. Other unmet needs reported were patient and family experience/supportive care (11%), pathophysiology (4%), diagnosis/response assessment (4%), toxicity management/prevention (3%) and poor prognosis (4%). Unmet needs that were reported starting from 2010 [and their proposed solutions] include treatment of high-risk cytogenetic features (4%) [elotuzumab/isatuximab/melflufen/selinexor/anti-BCMA conjugates], treatment of plasma cell leukemia/extramedullary disease (3%) [utilizing MM drugs (daratumumab/boretezemib/melfufen/frontline ASCT etc.)], prevention/management of toxicities (mucositis, neuropathy, nephropathy and GVHD) (1%) [CR4056/amifostine/cannabidiol], treatment/prevention of thromboembolism (1%) [risk assessment model for thromboembolism/studies on anticoagulation], treatment of frail/elderly (1%) [SEA BCMA/melflufen], immune response in MM (<1%) [immunological studies], and measures to assess response to treatment (<1%) [heavy light chain ratio/DW imaging/BH3 profiling]. Unmet needs [and proposed solutions] reported specifically after 2018 include CAR-T refractory MM (<1%) [bispecific antibodies/antibody-drug conjugates/combination therapies/allogeneic HCT], steroid-free myeloma regimens (<1%) [vactosertib], MM with high baseline LDH (<1%) [alternative therapy to AHCT], MM in young (<1%) [standardized definition for young and studies focusing this age], tools to evaluate the risk of death at first relapse (<1%) [an algorithm to stratify patients by defining patient-specific risk and combine both frailty and disease aggressiveness into a single tool], noninvasive blood-based MRD assays (<1%) [ctDNA/NGF/EasyM assay], COVID-19 in MM (<1%) [investigating humoral/T-cell response to SARS-CoV-2 vaccination in MM patients] etc. Talquetamab (2022), ciltacaptagene (2021), elranatamab (2021), teclistamab (2020), idecabtagene (2020), isatuximab (2017), melflufen (2017) etc. were some of the most recent options reported to address the unmet need for treatment of RRMM. Conclusion: Our study identified the use of term “unmet” to indicate diverse needs related to MM and a broad range of solutions reported for these needs inferring a need to standardize the use of the term “unmet need”. Treatment of RRMM remains the most reported unmet need with a satisfactory solution yet to be uncovered with future research. With advancements, more unmet needs are being uncovered/reported with time. The dynamic nature of these needs should be considered while addressing gaps with future research. Our study only captured studies that used the term “unmet” and might have missed unmet needs reported by articles that didn't specifically use this term.
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 | 0,017 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,006 |
| Bibliométrie | 0,027 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».