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Patient Preferences and Treatment Decision Drivers in IDH-Mutant Grade 2 Glioma: An International Discrete Choice
Ashley Parham Ghiaseddin1, Kismet Hossain-Ibrahim2, Antje Wick3
1University of Florida Health, Gainesville, FL, USA.
Background:
Grade 2 isocitrate dehydrogenase (IDH)-mutant (mIDH) glioma imposes significant health-related quality of life (HRQoL) and economic burden.
Objective:
The aim of this study was to explore patient preferences and socioeconomic burden in grade 2 mIDH glioma and its treatment.
Methods:
A discrete choice experiment (DCE) was administered to patients across the US, UK, Canada and Germany. Attributes included life expectancy, time until tumour progression, side effects, future risk of challenges performing work/usual activities and treatment modality. DCE data were analysed using a mixed-effects logit model. Relative attribute importance scores and trade-offs between attributes were estimated.
Results:
A total of 118 participants (56% male; mean age 41.9 years; 67.8% employed) were included in the final analysis. Four of the five attributes were independent drivers of patients' treatment preferences. Participants preferred treatments with additional years of life expectancy and tumour-free progression and were averse to treatments with higher risks of side effects and future challenges performing work/usual activities. Treatment modality did not significantly influence preferences. Based on 10% risk increments, relative attribute importance scores showed life expectancy was the most important driver of choice (26.5%), followed by side effects (21.6%). Side effects (31.9-37.9%) and future issues with work/usual activities (20.6-24.4%) became relatively more important when expressed as 20-30% increments. Patients were willing to trade life expectancy to avoid increased risk of side effects.
Conclusions:
Patients placed the greatest value on gains in survival. Avoidance of risks of side effects or impacts to daily activities were also significant predictors of choice, with patients willing to trade life expectancy to avoid risk attributes. Incorporating patient preferences is essential to support shared treatment decision making and ensure treatment strategies align with patients' values and expectations.
