Manchester Centre of Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, University of Manchester, Coupland Street, Manchester, M13 9PL, England.
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust, Manchester, England.
BMC Cancer. 2020 Jun 18;20(1):570. doi: 10.1186/s12885-020-07054-2.
In principle, risk-stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) should produce a better balance of benefits and harms. The main benefit is the offer of NICE-approved more frequent screening and/ or chemoprevention for women who are at increased risk, but are unaware of this. We have developed BC-Predict, to be offered to women when invited to NHSBSP which collects information on risk factors (self-reported information on family history and hormone-related factors via questionnaire; mammographic density; and in a sub-sample, Single Nucleotide Polymorphisms). BC-Predict produces risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5 to < 8% 10-year) to have discussion of prevention and early detection options at Family History, Risk and Prevention Clinics. Despite the promise of systems such as BC-Predict, there are still too many uncertainties for a fully-powered definitive trial to be appropriate or ethical. The present research aims to identify these key uncertainties regarding the feasibility of integrating BC-Predict into the NHSBSP. Key objectives of the present research are to quantify important potential benefits and harms, and identify key drivers of the relative cost-effectiveness of embedding BC-Predict into NHSBSP.
A non-randomised fully counterbalanced study design will be used, to include approximately equal numbers of women offered NHSBSP (n = 18,700) and BC-Predict (n = 18,700) from selected screening sites (n = 7). In the initial 8-month time period, women eligible for NHSBSP will be offered BC-Predict in four screening sites. Three screening sites will offer women usual NHSBSP. In the following 8-months the study sites offering usual NHSBSP switch to BC-Predict and vice versa. Key potential benefits including uptake of risk consultations, chemoprevention and additional screening will be obtained for both groups. Key potential harms such as increased anxiety will be obtained via self-report questionnaires, with embedded qualitative process analysis. A decision-analytic model-based cost-effectiveness analysis will identify the key uncertainties underpinning the relative cost-effectiveness of embedding BC-Predict into NHSBSP.
We will assess the feasibility of integrating BC-Predict into the NHSBSP, and identify the main uncertainties for a definitive evaluation of the clinical and cost-effectiveness of BC-Predict.
Retrospectively registered with clinicaltrials.gov (NCT04359420).
原则上,将风险分层作为国民保健服务乳房筛查计划(NHSBSP)的常规部分,应该能够更好地平衡收益和危害。主要的益处是为处于较高风险但尚未意识到这一点的女性提供 NICE 批准的更频繁的筛查和/或化学预防。我们已经开发了 BC-Predict,在邀请女性参加 NHSBSP 时提供给她们,该系统收集有关风险因素的信息(通过问卷报告家族史和激素相关因素的自我报告信息;乳腺密度;以及在一个子样本中,单核苷酸多态性)。BC-Predict 会生成风险反馈信,邀请高风险(≥8% 10 年)或中风险(≥5 至 <8% 10 年)的女性在家族史、风险和预防诊所讨论预防和早期检测选项。尽管像 BC-Predict 这样的系统有很大的希望,但对于一个完全有效的确定性试验来说,仍然存在太多的不确定性,因此不太合适或不道德。本研究旨在确定将 BC-Predict 纳入 NHSBSP 的可行性方面的这些关键不确定性。本研究的主要目标是量化重要的潜在收益和危害,并确定将 BC-Predict 嵌入 NHSBSP 的相对成本效益的关键驱动因素。
将使用非随机完全平衡研究设计,从选定的筛查点(n=7)中纳入大约相等数量的接受 NHSBSP(n=18700)和 BC-Predict(n=18700)的女性。在最初的 8 个月时间内,符合 NHSBSP 条件的女性将在四个筛查点接受 BC-Predict。三个筛查点将为女性提供常规 NHSBSP。在接下来的 8 个月中,提供常规 NHSBSP 的研究点将切换到 BC-Predict,反之亦然。将为两组女性获得包括风险咨询、化学预防和额外筛查在内的关键潜在收益。通过自我报告问卷获得包括增加焦虑在内的关键潜在危害,同时嵌入定性过程分析。基于决策分析模型的成本效益分析将确定将 BC-Predict 嵌入 NHSBSP 的相对成本效益的主要不确定性。
我们将评估将 BC-Predict 纳入 NHSBSP 的可行性,并确定对 BC-Predict 的临床和成本效益进行确定性评估的主要不确定性。
在 clinicaltrials.gov 上进行了回顾性注册(NCT04359420)。