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Enregistrement W2982997998 · doi:10.1182/blood-2019-124660

Treatment Decision Making in AML: Factors of Importance to Clinicians, AML Patients and Their Family

2019· article· en· W2982997998 sur OpenAlexaffabout
Thomas W. LeBlanc, Roland B. Walter, Loriana Hernandez-Aldama, Kate Sully, Timothy Bell, Chloe Johnson, François Péloquin, Adam Gater, Verna Welch, Louise O’Hara, Ryan Hohman, Diana M. Merino, Nigel H. Russell, Neil Horikoshi, Dawn Maze

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensPrincess Margaret Cancer CentrePfizer (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineQuality of life (healthcare)Myeloid leukemiaFamily medicineAffect (linguistics)Internal medicinePediatricsPsychologyNursing

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Acute myeloid leukemia (AML) is a rare hematologic cancer primarily affecting older people, with a median age at diagnosis of 67 years old (Almeida & Ramos, 2016). Heterogeneity in the presentation, functional status and presence of comorbidities among AML patients presents unique challenges for treatment. Intensive chemotherapy (IC), although the best option for prolonging survival, carries a risk of early death and other trade-offs, including significant time spent in the hospital. The toxicity of IC treatment and the requirement for prolonged hospitalizations may negatively affect patients' physical functioning and health-related quality of life (HRQoL). Novel, low-intensity treatments can be administered in the clinic and may pose less risk of immediate toxicities but may be associated with reduced efficacy. Each patient and family will approach these trade-offs differently, yet few studies examine the process of shared decision-making in AML. We aimed to better understand this process by eliciting patient/family and physician narratives about expectations and attitudes towards AML treatments. METHODS: Ten physicians in the US (n=4), UK (n=3) and Canada (n=3) and 12 AML patients (all US) and a member of their family took part in an individual, 60-minute qualitative telephone interview. Further interviews are scheduled and any additional data at the time of presentation will also be reported. The interviews followed a semi-structured guide comprising open-ended questions. The overarching aim of the interviews was to understand the value of living longer for AML patients who are not candidates for standard IC and to explore the treatment decision process from the patient, family and physician perspective. During the interviews, each participant (patient, family member or physician) completed a rating exercise in which they were asked to rate a list of pre-defined factors (9-10 factors) on a scale of 0 (not at all important) to 3 (very important) to determine their importance in AML treatment decisions. As well as providing a numerical rating for each factor, each participant was asked why they selected their rating and which three factors they would consider the most important. RESULTS: Across all three groups, relief in AML symptoms (namely fatigue and pain), longer survival and better QoL were equally considered the three most important factors when making a treatment decision. All three groups described the interaction between QoL and longer survival, explaining that any increase in survival would be important, but only if QoL (time spent with family, maintaining hobbies/interests) was maintained or improved. Physician's advice was also important to AML patients when making decisions regarding treatment. Other treatment-related factors within the rating task were rated as very important for at least a subset of patients, with no factors widely considered to be of limited importance. All participants noted that while relief of AML symptoms, longer survival and quality of life were all important, these had to be considered in light of treatment side effects and risk of infection. Likelihood of being hospitalized was important to family members, as they wanted to spend quality time at home with their loved ones, while patients and physicians considered hospitalization as an inevitable consequence of the disease and treatment. All participants reported that AML patients would consider taking any form of treatment if suitable and effective, regardless of the mode of administration. However, it was acknowledged that oral treatments would be more convenient and less invasive. The relative importance of location of care/treatment delivery was influenced by proximity to resources and whether the participant was based in an urban or rural setting. The ability to receive treatment at home was considered beneficial. CONCLUSIONS: The results from this rating exercise and qualitative interviews showed convergence across all stakeholders, indicating that AML patients, family and physicians have similar priorities regarding treatment decisions, prioritizing symptom relief, survival, and quality of life. The predominant treatment pathways in AML each require trade-offs in these factors, demonstrating the importance of shared decision making in ensuring the most appropriate treatment is selected for a given individual, in accordance with their values, goals, and preferences. Figure Disclosures LeBlanc: Jazz Pharmaceuticals: Research Funding; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Otsuka: Consultancy, Membership on an entity's Board of Directors or advisory committees; Heron: Membership on an entity's Board of Directors or advisory committees; Helsinn: Consultancy; Flatiron: Consultancy; NINR/NIH: Research Funding; Duke University: Research Funding; Astra Zeneca: Consultancy, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Medtronic: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Research Funding; Celgene: Honoraria; AbbVie: Membership on an entity's Board of Directors or advisory committees; Pfizer Inc: Consultancy; American Cancer Society: Research Funding; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CareVive: Consultancy. Walter:Seattle Genetics: Research Funding; Agios: Consultancy; Amgen: Consultancy; Amphivena Therapeutics: Consultancy, Equity Ownership; Aptevo Therapeutics: Consultancy, Research Funding; Argenx BVBA: Consultancy; Astellas: Consultancy; BioLineRx: Consultancy; BiVictriX: Consultancy; Boehringer Ingelheim: Consultancy; Boston Biomedical: Consultancy; Covagen: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmaceuticals: Consultancy; Kite Pharma: Consultancy; New Link Genetics: Consultancy; Pfizer: Consultancy, Research Funding; Race Oncology: Consultancy. Hernandez-Aldama:Pfizer Inc: Consultancy. Sully:Pfizer Inc: Consultancy; Adelphi Values Ltd: Employment. Bell:Pfizer Inc.: Employment, Equity Ownership. Johnson:Pfizer Inc: Consultancy; Adelphi Values Ltd: Employment. Peloquin:Pfizer Inc: Employment, Equity Ownership. Gater:Pfizer Inc: Consultancy; Adelphi Values Ltd: Employment. Welch:Pfizer Inc: Employment, Equity Ownership. O'Hara:Adelphi Values Ltd: Employment; Pfizer Inc: Consultancy. Russell:Astellas: Consultancy, Honoraria, Speakers Bureau; Pfizer Inc: Consultancy, Honoraria, Speakers Bureau; Jazz: Consultancy, Honoraria, Speakers Bureau; DSI: Consultancy, Honoraria, Speakers Bureau. Horikoshi:Pfizer Inc: Consultancy. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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)0,007
score de la tête « metaresearch » (Gemma)0,039
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,039
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,003
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,024
Tête enseignante GPT0,317
Écart entre enseignants0,293 · 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'étudeObservationnel
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

Citations3
Publié2019
Routes d'admission2
Résumé présentoui

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