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Enregistrement W3096699547 · doi:10.1182/blood-2020-139008

Decision Making Factors That Influence Treatment Options for an Autologous Stem Cell Transplant for Older Adults (aged 65-75) with Newly Diagnosed Multiple Myeloma: A Mixed Methods Study

2020· article· en· W3096699547 sur OpenAlexaffabout
Owais Mian, Martine Puts, Arleigh McCurdy, Tanya M. Wildes, Mark A. Fiala, Matthew Kang, Mary Salib, Shabbir M.H. Alibhai, Hira Mian

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensMcMaster UniversityUniversity Health NetworkUniversity of TorontoPROTO Manufacturing (Canada)Ottawa HospitalJoseph Brant HospitalUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésMedicineThematic analysisMultiple myelomaFamily medicineReferralCohortInternal medicineQualitative researchGerontologyOncology

Résumé

récupéré en direct d'OpenAlex

Background Multiple myeloma (MM) is an incurable hematological malignancy of older adults. Autologous stem cell transplant (ASCT) remains a standard of care with multiple retrospective and registry cohort studies demonstrating its efficacy in MM patients including older adults with the disease. Despite this favourable data, there remains wide heterogeneity in the utilization of ASCT, particularly among older adults with MM. We conducted a mixed methods study from the perspective of both oncologists and older adults with MM to: 1) identify decision making factors that influence ASCT eligibility and 2) to explore any barriers to ASCT utilization. Methods We conducted a mixed methods study at two academic centres and two community centres in Ontario, Canada. Older adults with MM (aged 65-75) who were within one year of treatment decision making regarding ASCT were invited to complete a survey from outpatient clinics. Oncologists (both community & academic) were recruited via email. Semi-structured interviews were conducted with all participants who agreed to an interview. Thematic analysis was conducted to identify themes from the transcripts using NVivo (qualitative analytical software). The initial 3 transcripts were independently coded by two investigators, to develop a codebook. Any discrepancies were resolved using consensual validation. Once consensus was reached, the codes were then applied to the rest of the transcripts by one coder. A convergent parallel approach was used in combining the results of the qualitative and quantitative sections of the study. Results A total of 15 oncologists and 18 patients with MM completed the surveys. Baseline patient and oncologist characteristics are listed in Table 1. The majority of patients were offered an ASCT (78%) and among those offered, 79% went ahead with ASCT. Most patients were happy with the decision to either go ahead or refuse the transplant as indicated by a low decisional regret score (median of 5 and IQR of 0-19 out of 100, with a lower score indicating less regret with the decision). With regards to oncologists, 80% stated they were aware of geriatric tools to help with treatment risk stratification; however, the majority (75%) used none of these tools and relied on the 'eye-ball' test for decision making. Nine oncologists and 9 patients completed the semi-structured interview. Summarized themes identified are shown in Figure 1. From the perspective of patients, factors that most affected ASCT decision making were: strong trusting relationship with their oncologist (n=9), family support (n=9) and wanting the best treatment available (n=6). Top reasons to refuse ASCT were: fear of not recovering to baseline (n=2) and prolonged hospital stay (n=2). Oncologists identified using their clinical judgement (n=7), the belief that transplant was the best option (n=7) and lack of medical comorbidities (n=8), as the most important factors when recommending treatment. The lack of high quality randomized controlled trial data (n=9), local guidelines (n=5) and targeted assessment tools (n=7) were identified as barriers to ASCT. Notably, both patients (n=7) and oncologists (n=7) felt that ASCT decision making should not rely on chronological age alone. The findings of the qualitative and quantitative parts of the study concurred with each other and showed similar patterns. Conclusion To our knowledge, our study is the first to analyze contextual factors from the perspective of oncologists and older adults with MM that influence ASCT decision making and utilization. Despite guidelines supporting ASCT efficacy and safety among older adults with MM, our results demonstrate that the decision to undergo ASCT in older adults with MM is complex and variable both from the perspective of the patient and oncologist. Future incorporation of patient decision aids in parallel with enrollment of older adults in ASCT clinical studies and targeted geriatric assessments tools may provide an opportunity to enhance shared decision making and local guideline developments. Disclosures McCurdy: Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Sanofi: Honoraria. Wildes:Carevive Systems: Consultancy; Janssen: Research Funding; Seattle Genetics: Consultancy. Mian:Sanofi: Consultancy; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy; Takeda: Consultancy, Honoraria.

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)0,021
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,060
Score d'incertitude au seuil0,119

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,030
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,001
Communication savante0,0030,002
Science ouverte0,0020,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,054
Tête enseignante GPT0,357
Écart entre enseignants0,303 · 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'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations2
Publié2020
Routes d'admission2
Résumé présentoui

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