Prosthesis preferences for those with upper limb loss: Discrete choice study of PULLTY & REG; for use in regulatory decisions

Leslie Wilson, Dan Dohan, Matthew Garibaldi,David Szeto, Molly Timmerman,Johnny Matheny

Journal of rehabilitation and assistive technologies engineering(2023)

引用 0|浏览4
暂无评分
摘要
IntroductionThe patient's voice in shared decision-making has progressed from physician's office to regulatory decision-making for medical devices with FDA's Patient Preference Initiative. A discrete-choice preference measure for upper limb prosthetic devices was developed to investigate patient's risk/benefit preference choices for regulatory decision making.MethodsRapid ethnographic procedures were used to design a discrete-choice measure describing risk and benefits of osseointegration with myoelectric control and test in a pilot preference study in adults with upper limb loss. Primary outcome is utility of each choice based conjoint (CBC) attribute using mixed-effects regression. Utilities with and without video, and between genders were compared.ResultsStrongest negative preference was for avoiding infection risk (B = -1.77, p < 0.001) and chance of daily pain (B = -1.22, p, 0.001). Strongest positive preference was for attaining complete independence when cooking dinner (B = 1.62, p < 0.001) and smooth grip patterns at all levels (B = 1.62, B = 1.28, B = 1.26, p < 0.001). Trade-offs showed a 1% increase in risk of serious/treatable infection resulted in a 1.77 decrease in relative preference. There were gender differences, and where video was used, preferences were stronger.ConclusionsStrongest preferences were for attributes of functionality and independence versus connectedness and sensation but showed willingness to make risk-benefit trade-offs. Findings provide valuable information for regulatory benefit-risk decisions for prosthetic device innovations.
更多
查看译文
关键词
Prosthesis,conjoint analysis,decision making,devices,discrete choice,federal drug administration,myoelectric control,osseointegration,patient choice,regulatory,validity
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要