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Enregistrement W4411321377 · doi:10.1002/hon.70096_549

549 | PROSPECTIVE PATIENT PREFERENCE STUDY FOR CHRONIC LYMPHOCYTIC LEUKEMIA TREATMENT ATTRIBUTES IMPACTING PATIENT SHARED‐DECISION MAKING

2025· article· en· W4411321377 sur OpenAlexaff
Sikander Ailawadhi, Swetha Challagulla, Dominic Pilon, Todor Totev, Yan Meng, Lilián Díaz, Zhiguo Chen, Kehu Yang

Notice bibliographique

RevueHematological Oncology · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesBeiGene
Mots-clésChronic lymphocytic leukemiaPreferenceMedicineLeukemiaOncologyIntensive care medicineInternal medicineStatistics

Résumé

récupéré en direct d'OpenAlex

Introduction: Treatments for CLL differ in efficacy, safety, treatment duration and monitoring needs, all of which can impact overall treatment experience and outcomes. To better understand patient preferences for various treatment attributes, a comprehensive quantitative analysis was conducted. Methods: A patient survey with a discrete choice experiment (DCE) design was conducted from December 2024 to February 2025 among adults (≥ 18 years) from the United States with confirmed diagnosis of CLL, recruited through online patient panels, physician referrals, and support groups. Treatment attributes were selected based on results of a targeted literature review and clinical inputs. Patients responded to DCE questions on attributes related to efficacy (PFS), safety (impacts of diarrhea, headache, atrial fibrillation, hypertension and tumor lysis syndrome [TLS]/kidney dysfunction on quality of life [QoL]) and treatment convenience (treatment duration: continuous vs. fixed duration with monitoring/hospitalization requirements). A conditional logistic regression model was used to calculate the relative importance of each attribute. Results: A total of 199 patients with CLL completed the survey (median age: 60 years; 91% White; 46% female; 66% with a bachelor’s degree or above; 46% employed; 54% commercially insured; 86% suburban/urban residence; 49% diagnosed ≥ 5 years ago). While 30% of patients had received ≥ 3 lines of therapy, 23% of all patients were treatment-naïve, 25% received 1 line of prior therapy, and 23% received 2 lines of prior therapy. Most patients (88%) reported having experienced ≥ 1 AE from treatment previously, with the most common AEs being headache (53%), fatigue (53%), diarrhea (44%), and nausea and/or vomiting (34%). Based on DCE preference results, patients favored treatments with longer PFS and less impact of headache, atrial fibrillation and TLS/kidney dysfunction on QoL (p < 0.001). Impact of diarrhea and hypertension on QoL and treatment convenience did not have a statistically significant influence on treatment preferences. The top 3 treatment attributes with the highest relative importance to patients were PFS (30%), impact of headache on QoL (26%) and impact of atrial fibrillation on QoL (24%), followed by impact of TLS/kidney dysfunction (10%) on QoL, treatment convenience (5%), and impact of diarrhea (4%) and hypertension (1%) on QoL (Figure). Conclusions: This patient preference survey showed that efficacy measured by PFS, the impact of headache, and impact of atrial fibrillation on QoL, were the most important attributes of treatment for patients with CLL. To support patient-centered care, shared decision-making in CLL treatment selection should incorporate a comprehensive discussion on AEs in addition to efficacy endpoints, as patients may prioritize treatments with less impact of AEs on their QoL. Future studies should assess the impact of shared decision-making on treatment adherence and outcomes. Research funding declaration: This study was funded by BeiGene, Ltd. Keywords: quality of life, late effects, survivorship care; patient and family-centered care; chronic lymphocytic leukemia (CLL) Potential sources of conflict of interest: S. Ailawadhi Consultant or advisory role: GSK, Sanofi, BMS, Takeda, Beigene, Pharmacyclics, Amgen, Janssen, Regeneron, Cellectar, Pfizer Other remuneration: Research funding to institution: GSK, BMS, Pharmacyclics, Amgen, Janssen, Cellectar, AbbVie, and Ascentage S. Challagulla Employment or leadership position: BeiGene USA, Inc. Stock ownership: BeiGene USA, Inc. D. Pilon Employment or leadership position: Analysis Group, Inc. T. I. Totev Employment or leadership position: Analysis Group, Inc. Y. Meng Employment or leadership position: Analysis Group, Inc. L. Diaz Employment or leadership position: Analysis Group, Inc. Z. Chen Employment or leadership position: Analysis Group, Inc. K. Yang Employment or leadership position: BeiGene USA, Inc. Stock ownership: BeiGene USA, Inc.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,807
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,071
Tête enseignante GPT0,399
Écart entre enseignants0,328 · 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 tête enseignante, pas un consensus.

Devis d'étudeAutre devis
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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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