Evaluating patient preferences for chronic lymphocytic leukemia (CLL) in Korea: A discrete choice experiment
Notice bibliographique
Résumé
Abstract Introduction: CLL disease control and survival outcomes have improved since the introduction of Bruton tyrosine kinase inhibitors (BTKis). Since CLL often requires treatments, understanding patient preferences is critical in optimizing adherence and overall satisfaction. However, there are limited published data on how Korean patients prioritize treatment efficacy, safety and the convenience of administration. To address this gap, a discrete choice experiment (DCE) methodology was applied in a comprehensive quantitative analysis to evaluate patient preferences for BTKi treatment attributes among patients with CLL in Korea. Methods: Adult patients (≥18 years of age) with a confirmed diagnosis of CLL in Korea were recruited by The Korea Blood Disease & Cancer Association to participate in an online DCE survey questionnaire from April 30, 2025 to May 22, 2025. Treatment attributes and levels were identified based on published literature and clinical inputs. Efficacy attributes included progression-free survival (PFS); safety attributes included impacts of adverse events (AEs), including diarrhea, headache, atrial fibrillation and hypertension, on quality of life (QoL); convenience attributes included formulation type (including tablet or capsule) and dosing frequency (including once daily or twice daily). Preference data was analyzed using a conditional logistic regression. Relative importance of BTKi treatment attributes were calculated to measure the importance of each attribute in treatment decision-making. Additionally, subgroup analyses were conducted among patients aged <60 years and ≥60 years, those who received 2 or more lines (2L+) of treatments, and those who experienced AEs from CLL treatment. Results: A total of 57 patients completed the survey. The mean age was 61 years, with 46% aged <60 years and 54% aged ≥60 years. Female patients comprise 35% of the sample. Around 33% had a high school education or less, and 23% were employed full-time. More than half (54%) reported no comorbid conditions, and 70% were diagnosed ≥5 years ago (26% diagnosed 1 to <5 years ago; 4% diagnosed less than a year ago). In terms of treatment experience, 11% were treatment naive, while 89% received ≥1 treatment (61% first-line, 21% second-line and 7% third-line or later). In the context of CLL treatment preferences, patients showed a significant preference (P < .05) for therapies that minimized the impact of AEs on QoL, offered longer PFS, and required less frequent dosing. Formulation type did not have a statistically significant impact on treatment choice. Overall, patients placed greater importance on how treatment-related attributes affect their QoL and dosing frequency. The highest-ranked factors were the impact of headache (22%) and diarrhea (21%) on QoL, followed by dosing frequency (19%), the impact of atrial fibrillation (16%) and hypertension (14%) on QoL, PFS (7%), and formulation type (0.6%). Results from the subgroup analyses were generally consistent with the full sample analysis, with impacts of AEs on QoL being the most important factors for treatment decision-making, especially among older patients, although the relative ranking of impacts of AEs varied by age groups and treatment experience. Regarding efficacy, younger patients (<60 years of age) valued PFS slightly more than older patients (≥60 years of age), ranking it 4th and 6th out of the 10 attributes, respectively. Additionally, preference on dosing frequency was more important for 2L+ patients, reflecting the treatment burden experienced from previous regimens. Conclusions: This study provides valuable insights into the treatment preferences of Korean patients with CLL, highlighting the importance of QoL considerations for treatment decision-making. Among the evaluated attributes, patients placed the greatest weight on minimizing the impacts of AEs, particularly headache and diarrhea, on daily functioning, followed by dosing frequency. These preferences underscore the need for patient-centered care that prioritizes not only clinical efficacy but also tolerability and convenience. These insights can inform shared decision-making and support the development of personalized treatment strategies that better align with patient needs and expectations in Korea.
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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 | 0,015 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».