A multinational study to explore patient preferences for chronic myeloid leukaemia treatments
Notice bibliographique
Résumé
Abstract INTRODUCTION: Tyrosine kinase inhibitors (TKIs) enable most patients with chronic phase chronic myeloid leukaemia (CP-CML) to achieve near normal life expectancy. However, they are associated with a range of side effects that can impact treatment adherence and eventually, efficacy. Maintaining tolerability is essential for sustaining optimal outcomes. Understanding how people with CP-CML weigh efficacy against toxicity is critical to ensuring treatment decisions reflect what matters most to them. METHODS: This study quantified preferences using a discrete choice experiment (DCE). Adults with CP-CML are recruited by patient advocacy groups from three regions: Europe (United Kingdom, France, Germany, Italy, Spain), North America (United States, Canada) and East Asia (China, Japan). The online DCE survey included 12 choice tasks, each asking participants to choose between two unlabelled, hypothetical TKIs described by six attributes. The six attributes that patients highlighted as important in qualitative interviews, and that were supported by published clinical data on existing TKIs were selected for the DCE and captured aspects of efficacy, toxicity and convenience: 1) severity of fatigue, 2) risk of gastrointestinal (GI) problems, 3) risk of respiratory problems, 4) risk of long-term cardiovascular (CV) problems, 5) chance of achieving deep molecular response (DMR) within two years and 6) mode of administration (MOA; combination of frequency of tablets +/- fasting requirements). The attribute levels were informed by efficacy and safety outcomes from recent TKI trials. The choice data were analysed using conditional logit models, and relative attribute importance (RAI) scores were calculated to capture the impact of each attribute on overall preferences. Benefit-risk trade-offs were estimated by dividing each attribute-level coefficient by the (continuous) DMR coefficient, to assess the trade-offs that patients may be willing to make. This pooled multinational analysis is interim; final results based on the full study sample will be ready and presented at the conference. RESULTS: As of 28 July 2025, a total of 423 respondents completed the survey in Europe and North America (333 and 90, respectively) and were included in the analysis. Overall, 66% of respondents were female and approximately 28% of respondents were aged between 18 and 44 inclusive, with 25% aged 65 and above. 46% of respondents were on first line treatment and 14% were in treatment-free remission. The most to least important attributes, as indicated by the RAI scores (in parentheses) were severity of fatigue (25.9%), chance of achieving DMR within two years (20.9%), risk of GI problems (17.7%), risk of respiratory problems (12.5%), MOA (11.6%), and risk of long-term CV problems (11.3%). There were no statistically significant differences in RAI scores by region (Europe vs. North America). Benefit-risk trade-off estimates showed that, on average, respondents would need a TKI to offer a 30-percentage point (pp) higher chance of achieving DMR within two years to accept a 75% risk of GI problems (compared to a 15% risk). A 21pp increase would be required to accept a 40% risk of respiratory problems (vs. 5%) and a 20pp increase would be required to accept a 25% risk of long-term CV problems (vs. 5%).CONCLUSIONS: In this multinational preference study of people living with CP-CML, attributes reflecting common symptoms and side effects (e.g., fatigue and GI problems) had a substantial impact on (hypothetical) TKI choice. Risk of long-term CV problems was not as important a concern as other side effects, potentially reflecting patients' experiences with these issues and their desire to avoid them. The chance of achieving DMR was a highly important factor in choices, however, the results indicate that patients are willing to accept a lower chance of achieving DMR to avoid side effects. These results highlight the importance of informed and shared decision-making for CP-CML patients in the context of their treatment decisions.
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 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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».