Suppr超能文献

用于靶向筛查和预防的卵巢癌风险预测的人群研究。

Population Study of Ovarian Cancer Risk Prediction for Targeted Screening and Prevention.

作者信息

Gaba Faiza, Blyuss Oleg, Liu Xinting, Goyal Shivam, Lahoti Nishant, Chandrasekaran Dhivya, Kurzer Margarida, Kalsi Jatinderpal, Sanderson Saskia, Lanceley Anne, Ahmed Munaza, Side Lucy, Gentry-Maharaj Aleksandra, Wallis Yvonne, Wallace Andrew, Waller Jo, Luccarini Craig, Yang Xin, Dennis Joe, Dunning Alison, Lee Andrew, Antoniou Antonis C, Legood Rosa, Menon Usha, Jacobs Ian, Manchanda Ranjit

机构信息

Wolfson Institute of Preventative Medicine, Barts CRUK Cancer Centre, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.

Department of Gynaecological Oncology, St Bartholomew's Hospital, Barts Health NHS Trust, London EC1A 7BE, UK.

出版信息

Cancers (Basel). 2020 May 15;12(5):1241. doi: 10.3390/cancers12051241.

Abstract

UNLABELLED

Unselected population-based personalised ovarian cancer (OC) risk assessment combining genetic/epidemiology/hormonal data has not previously been undertaken. We aimed to perform a feasibility study of OC risk stratification of general population women using a personalised OC risk tool followed by risk management. Volunteers were recruited through London primary care networks.

INCLUSION CRITERIA

women ≥18 years.

EXCLUSION CRITERIA

prior ovarian/tubal/peritoneal cancer, previous genetic testing for OC genes. Participants accessed an online/web-based decision aid along with optional telephone helpline use. Consenting individuals completed risk assessment and underwent genetic testing (, OC susceptibility single-nucleotide polymorphisms). A validated OC risk prediction algorithm provided a personalised OC risk estimate using genetic/lifestyle/hormonal OC risk factors. Population genetic testing (PGT)/OC risk stratification uptake/acceptability, satisfaction, decision aid/telephone helpline use, psychological health and quality of life were assessed using validated/customised questionnaires over six months. Linear-mixed models/contrast tests analysed impact on study outcomes.

MAIN OUTCOMES

feasibility/acceptability, uptake, decision aid/telephone helpline use, satisfaction/regret, and impact on psychological health/quality of life. In total, 123 volunteers (mean age = 48.5 (SD = 15.4) years) used the decision aid, 105 (85%) consented. None fulfilled NHS genetic testing clinical criteria. OC risk stratification revealed 1/103 at ≥10% (high), 0/103 at ≥5%-<10% (intermediate), and 100/103 at <5% (low) lifetime OC risk. Decision aid satisfaction was 92.2%. The telephone helpline use rate was 13% and the questionnaire response rate at six months was 75%. Contrast tests indicated that overall depression ( = 0.30), anxiety ( = 0.10), quality-of-life ( = 0.99), and distress ( = 0.25) levels did not jointly change, while OC worry ( = 0.021) and general cancer risk perception ( = 0.015) decreased over six months. In total, 85.5-98.7% were satisfied with their decision. Findings suggest population-based personalised OC risk stratification is feasible and acceptable, has high satisfaction, reduces cancer worry/risk perception, and does not negatively impact psychological health/quality of life.

摘要

未标注

此前尚未开展基于未选择人群的个性化卵巢癌(OC)风险评估,该评估结合了遗传/流行病学/激素数据。我们旨在使用个性化OC风险工具对普通人群女性进行OC风险分层的可行性研究,随后进行风险管理。志愿者通过伦敦初级保健网络招募。

纳入标准

年龄≥18岁的女性。

排除标准

既往有卵巢/输卵管/腹膜癌,既往进行过OC基因的基因检测。参与者可使用在线/基于网络的决策辅助工具,并可选择使用电话帮助热线。同意参与的个体完成风险评估并接受基因检测(OC易感性单核苷酸多态性)。一种经过验证的OC风险预测算法使用遗传/生活方式/激素OC风险因素提供个性化的OC风险估计。在六个月的时间里,使用经过验证/定制的问卷评估人群基因检测(PGT)/OC风险分层的采用情况/可接受性、满意度、决策辅助工具/电话帮助热线的使用情况、心理健康和生活质量。线性混合模型/对比测试分析对研究结果的影响。

主要结果

可行性/可接受性、采用情况、决策辅助工具/电话帮助热线的使用情况、满意度/遗憾程度,以及对心理健康/生活质量的影响。共有123名志愿者(平均年龄 = 48.5(标准差 = 15.4)岁)使用了决策辅助工具,105名(85%)同意参与。没有人符合英国国家医疗服务体系(NHS)基因检测的临床标准。OC风险分层显示,103人中有1人终身OC风险≥10%(高风险),0人风险≥5%-<10%(中风险),100人风险<5%(低风险)。决策辅助工具的满意度为92.2%。电话帮助热线的使用率为13%,六个月时问卷的回复率为75%。对比测试表明,总体抑郁(= 0.30)、焦虑(= 0.10)、生活质量(= 0.99)和痛苦程度(= 0.25)水平没有共同变化,而OC担忧(= 0.021)和一般癌症风险认知(= 0.015)在六个月内有所下降。总体而言,85.5 - 98.7%的人对自己的决定感到满意。研究结果表明,基于人群的个性化OC风险分层是可行且可接受的,满意度高,可降低癌症担忧/风险认知,且不会对心理健康/生活质量产生负面影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a527/7281662/fb0f4f7748f7/cancers-12-01241-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验