Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Genomics Health Services Research Program, Li Ka Shing Knowledge Institute of St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Hum Genet. 2023 Apr;142(4):553-562. doi: 10.1007/s00439-023-02543-3. Epub 2023 Mar 21.
We aimed to describe patient preferences for a broad range of secondary findings (SF) from genomic sequencing (GS) and factors driving preferences. We assessed preference data within a trial of the Genomics ADvISER, (SF decision aid) among adult cancer patients. Participants could choose from five categories of SF: (1) medically actionable; (2) polygenic risks; (3) rare diseases; (4) early-onset neurological diseases; and (5) carrier status. We analyzed preferences using descriptive statistics and drivers of preferences using multivariable logistic regression models. The 133 participants were predominantly European (74%) or East Asian or mixed ancestry (13%), female (90%), and aged > 50 years old (60%). The majority chose to receive SF. 97% (129/133) chose actionable findings with 36% (48/133) choosing all 5 categories. Despite the lack of medical actionability, participants were interested in receiving SF of polygenic risks (74%), carrier status (75%), rare diseases (59%), and early-onset neurologic diseases (53%). Older participants were more likely to be interested in receiving results for early-onset neurological diseases, while those exhibiting lower decisional conflict were more likely to select all categories. Our results highlight a disconnect between cancer patient preferences and professional guidelines on SF, such as ACMG's recommendations to only return medically actionable secondary findings. In addition to clinical evidence, future guidelines should incorporate patient preferences.
我们旨在描述患者对基因组测序(GS)产生的广泛二级发现(SF)的偏好,以及驱动偏好的因素。我们在一项 Genomics ADvISER 试验(SF 决策辅助工具)中评估了成年癌症患者的偏好数据。参与者可以从以下五类 SF 中进行选择:(1)具有医学可操作性;(2)多基因风险;(3)罕见疾病;(4)早发性神经疾病;和(5)携带者状态。我们使用描述性统计分析偏好,使用多变量逻辑回归模型分析偏好的驱动因素。133 名参与者主要来自欧洲(74%)或东亚或混合血统(13%),女性(90%),年龄>50 岁(60%)。大多数参与者选择接受 SF。97%(129/133)选择具有可操作性的发现,其中 36%(48/133)选择了所有 5 类。尽管缺乏医学可操作性,但参与者仍对多基因风险(74%)、携带者状态(75%)、罕见疾病(59%)和早发性神经疾病(53%)的 SF 感兴趣。年龄较大的参与者更有可能对早发性神经疾病的结果感兴趣,而那些表现出较低决策冲突的参与者更有可能选择所有类别。我们的结果突显了癌症患者的偏好与 SF 的专业指南之间存在脱节,例如 ACMG 建议仅返回具有医学可操作性的二级发现。除了临床证据,未来的指南还应纳入患者的偏好。