What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420)
David P. French,
Susan Astley,
Adam R. Brentnall,
Jack Cuzick,
Richard Dobrashian,
Stephen W. Duffy,
Louise S. Gorman,
Elaine F. Harkness,
Fiona Harrison,
Michelle Harvie,
Anthony Howell,
Andrew Jerrison,
Matthew Machin,
Anthony J. Maxwell,
Lorna McWilliams,
Katherine Payne,
Nadeem Qureshi,
Helen Ruane,
Sarah Sampson,
Paula Stavrinos,
Emma Thorpe,
Fiona Ulph,
Tjeerd van Staa,
Victoria Woof,
D. Gareth Evans
Affiliations
David P. French
Manchester Centre of Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, University of Manchester
Susan Astley
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Adam R. Brentnall
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London
Jack Cuzick
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London
Richard Dobrashian
East Lancashire Hospitals NHS Trust, Royal Blackburn Hospital
Stephen W. Duffy
Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London
Louise S. Gorman
The Nightingale and Prevent Breast Cancer Centre, Manchester University NHS Foundation Trust
Elaine F. Harkness
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Fiona Harrison
Patient representative
Michelle Harvie
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Anthony Howell
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Andrew Jerrison
Research IT, IT Services, University of Manchester
Matthew Machin
Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester
Anthony J. Maxwell
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Lorna McWilliams
Manchester Centre of Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, University of Manchester
Katherine Payne
Division of Population Health, Health Services Research & Primary Care, School of Health Sciences, University of Manchester
Nadeem Qureshi
School of Medicine, University of Nottingham, University Park
Helen Ruane
The Nightingale and Prevent Breast Cancer Centre, Manchester University NHS Foundation Trust
Sarah Sampson
The Nightingale and Prevent Breast Cancer Centre, Manchester University NHS Foundation Trust
Paula Stavrinos
The Nightingale and Prevent Breast Cancer Centre, Manchester University NHS Foundation Trust
Emma Thorpe
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Fiona Ulph
Manchester Centre of Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, University of Manchester
Tjeerd van Staa
Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester
Victoria Woof
Manchester Centre of Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, University of Manchester
D. Gareth Evans
NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals NHS Foundation Trust
Abstract Background 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. Methods 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. Discussion 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. Trial registration Retrospectively registered with clinicaltrials.gov ( NCT04359420 ).