Prospective Patient Preference Study for Bruton Tyrosine Kinase Inhibitor (BTKi) Treatment Attributes and Factors Affecting Patient Shared Decision-Making in Chronic Lymphocytic Leukemia (CLL) and Small Lymphocytic Lymphoma (SLL) in the United States (USA)
Notice bibliographique
Résumé
Introduction: The prognosis for CLL/SLL has improved with the advent of novel therapeutic classes, including BTKis. While comparative data for several BTKis have been published, there are limited data on patient preferences in BTKi treatment selection. Understanding and integrating patient perspective in the BTKi treatment selection process is crucial to shared decision-making and attaining optimal treatment outcomes. To understand patients' priorities for different treatment attributes that impact their treatment decisions, a comprehensive quantitative analysis of patient preferences on BTKi treatment attributes was conducted. Methods: A patient survey with a discrete-choice experiment (DCE) design was conducted from March to June 2024 among USA adults (≥18 years) with confirmed diagnosis of CLL/SLL, recruited through online patient panels, physician referrals, and support groups. BTKi 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, safety (eg, impacts of diarrhea, headache, atrial fibrillation, and hypertension on quality of life [QoL]), formulation type, and dosing frequency. The impact of adverse events (AEs) on QoL was defined as the extent to which AEs caused interruptions in patients' ability to engage in their usual day-to-day activities. A conditional logistic regression model was used to calculate the relative importance of each attribute, as well as patient willingness to trade off specified BTKi treatment attributes. Results: A total of 200 patients with CLL/SLL completed the survey (median age: 61 years; 78% White; 55% female; 60% commercially insured; 82% suburban/urban residence). Less than half (43%) were diagnosed ≥5 years ago, and 61% received ≥3 lines of therapy. Almost all (89%) patients reported having experienced ≥1 AE from treatment previously, with the most common AEs being fatigue (76%), diarrhea (51%), headache (51%), and nausea and/or vomiting (51%). When considering the importance of efficacy measures, most patients prioritized CLL/SLL treatments that extended life expectancy (93%), followed by those that increased the likelihood of remission or cure (84%) and those that paused the progression of disease (67%). Patients preferred treatments with higher efficacy, less impact of AEs on QoL, and lower dosing frequency (P<.001). The top 3 treatment attributes with the highest relative importance to patients were impact of atrial fibrillation on QoL (24%), progression-free survival (PFS; 19%), and impact of headache on QoL (18%), followed by impact of diarrhea (14%) and hypertension (14%) on QoL, dosing frequency (9%), and formulation type (3%). On average, patients were willing to accept a reduction of 2.6, 1.9, 1.4, and 1.4 years of PFS to receive a treatment with less (none or mild vs significant) impact of atrial fibrillation, headache, diarrhea, and hypertension on QoL, respectively. Patients were willing to accept a reduction of 1.0 year of PFS to receive once-daily vs twice-daily treatment. Conclusions: Findings from this patient preference survey suggested that impact of atrial fibrillation on QoL, PFS, and impact of headache on QoL were the most important attributes of BTKi treatment for patients with CLL/SLL in the USA. Shared decision-making in CLL/SLL treatment selection should include an informed discussion about AEs, as besides efficacy comparisons, patients may prefer treatments with less impact of AEs on their QoL. Future prospective studies evaluating the effects of shared treatment decision-making on treatment adherence and outcomes are needed to better understand their impact on CLL/SLL patient care and inform clinical practice.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».