Lee Siang Ing, Qureshi Nadeem, Dutton Brittany, Kai Joe, Weng Stephen
Division of Primary Care, School of Medicine, University of Nottingham, Tower Building, University Park, Nottingham, NG7 2RD, UK.
J Community Genet. 2020 Jul;11(3):331-338. doi: 10.1007/s12687-020-00452-w. Epub 2020 Jan 22.
Family history of breast and related cancers can indicate increased breast cancer (BC) risk. In national familial breast cancer (FBC) guidelines, the risk is stratified to guide referral decisions. We aimed to identify characteristics associated with the recommendation for referral in a large cohort of women undergoing FBC risk assessment in a recent primary care study. Demographic, family history, psychological and behavioural factors were collected with family history questionnaires, psychological questionnaires and manual data extraction from general practice electronic health records. Participants were women aged 30-60 with no previous history of breast or ovarian cancer. Data from 1127 women were analysed with stepwise logistic regression. Two multivariable logistic models were developed to predict recommendations for referral: using the entire cohort (n = 1127) and in a subgroup with uncertain risks (n = 168). Model performance was assessed by the area under the receiver operating curve (AUC). In all 1127 women, a multivariable model incorporating five family history components (BC aged < 40, bilateral BC, prostate cancer, first degree relative with ovarian cancer, paternal family history of BC) and having a mammogram in the last 3 years, performed well (AUC = 0.86). For the 168 uncertain risk women, only paternal family history of BC remained significant (AUC = 0.71). Clinicians should pay particular attention to these five family history components when assessing FBC risk, especially prostate cancer which is not in the current national guidelines.
乳腺癌及相关癌症的家族史可能表明患乳腺癌（BC）的风险增加。在国家家族性乳腺癌（FBC）指南中，风险被分层以指导转诊决策。我们旨在确定在最近一项初级保健研究中接受FBC风险评估的大量女性队列中，与转诊建议相关的特征。通过家族史问卷、心理问卷以及从全科电子健康记录中手动提取数据，收集了人口统计学、家族史、心理和行为因素。参与者为年龄在30至60岁之间、既往无乳腺癌或卵巢癌病史的女性。对1127名女性的数据进行了逐步逻辑回归分析。开发了两个多变量逻辑模型来预测转诊建议：一个使用整个队列（n = 1127），另一个在风险不确定的亚组（n = 168）中。通过受试者操作特征曲线下面积（AUC）评估模型性能。在所有1127名女性中，一个纳入五个家族史成分（年龄<40岁的乳腺癌、双侧乳腺癌、前列腺癌、患有卵巢癌的一级亲属、父亲家族的乳腺癌病史）且在过去3年内进行过乳房X光检查的多变量模型表现良好（AUC = 0.86）。对于168名风险不确定的女性，只有父亲家族的乳腺癌病史仍然显著（AUC = 0.71）。临床医生在评估FBC风险时应特别关注这五个家族史成分，尤其是当前国家指南中未提及的前列腺癌。
