Title: Primary care models for the detection of clinically significant prostate cancer: the ProsDetect study
Funder: Department of Health and Social Care

Abstract: Research question What is the diagnostic performance of Prostate Specific Antigen (PSA) for the identification of clinically significant prostate cancer in primary care and can this be improved by incorporating additional information within a multivariable model? Background Prostate cancer is the most common form of cancer in UK men, with over 40,000 new cases diagnosed each year. Up to 15% of patients are diagnosed with indolent forms of the disease which do not cause harm or death and which do not require radical treatment. A key challenge is how to identify men with clinically significant forms of prostate cancer early, while avoiding overdiagnosis of indolent disease and over-investigation of those with benign disease. In the UK, most men with prostate cancer are diagnosed after visiting their GP and being tested for PSA. However, the diagnostic performance of PSA for the detection of clinically significant prostate cancer (or all prostate cancer) in primary care is unknown. Objectives 1) To determine the diagnostic accuracy of PSA in men presenting to UK primary care for the identification of (a) clinically significant prostate cancer, and (b) all prostate cancer. 2) To develop multivariable models, incorporating PSA, symptoms, demographics, and risk factors to determine the probability of (a) clinically significant forms of prostate cancer, and (b) all prostate cancer in men presenting to UK primary care. Methods This retrospective cohort study will use routinely collected primary care and cancer registry data from over 220,000 men who underwent PSA testing in primary care in England (2010-2015). The diagnostic accuracy of PSA for the detection of clinically significant prostate cancer and all prostate cancer will be determined, applying National Institute for Health and Care Excellence (NICE) advocated age specific cut-offs. Multivariate logistic regression will be used to develop diagnostic prediction models containing the most predictive variables available within GP records. These models will be used to estimate the probability of undiagnosed clinically significant prostate cancer and all prostate cancer in individual patients. A bootstrap validation approach will be used to calculate optimism-corrected measures of model performance. The sensitivity and specificity of models, at a range of risk thresholds, will be compared with that of PSA at age specific cut-offs used in current practice. Anticipated Impact and Dissemination We anticipate developing models which are able to detect clinically significant prostate cancer in men presenting to primary care with greater accuracy than the current method (PSA at age specific cut-offs). If implemented within the diagnostic pathway, such models could benefit patients and the healthcare system by reducing unnecessary specialist referrals, investigations and treatment, and targeting urgent specialist investigations at patients at greatest risk of having undiagnosed clinically significant prostate cancer. We plan to disseminate study findings through national conference presentations, academic publications and a public engagement program. On study completion, we aim to perform follow-up research to externally validate our models, integrate them into GP IT software within an e-Clinical Decision Support (eCDS) tool, and assess the health economic implications of applying model thresholds within UK primary care.

Lay abstract: Prostate cancer is the most common cancer in men in the UK, with 40,000 men diagnosed each year. However, not all types of prostate cancer cause harm. A key challenge is how to distinguish men with harmful forms of prostate cancer (that require urgent treatment) from men with less harmful forms of prostate cancer (that do not require invasive treatment) and men with conditions affecting the prostate that are not cancer. Most men with prostate cancer are identified after visiting their GP and being tested for Prostate Specific Antigen (PSA); a simple blood test which is used to help patients and GPs decide whether prostate cancer might be present. There is very little research on how good PSA is at catching harmful forms of prostate cancer. However, we do know that it is not a perfect test: it can sometimes be normal in men with prostate cancer and abnormal in men without prostate cancer. This means the test misses some men with prostate cancer so the disease is picked up later (when it is harder to cure). Also, some men without cancer end up having further unnecessary tests (such as biopsy). We believe that by using PSA together with other simple blood tests and medical information in an electronic tool, GPs will be better able to catch harmful prostate cancer early and reduce unnecessary tests and treatments for patients. In this study, we will use anonymised information from the GP records of over 220,000 patients to determine how good PSA is at finding prostate cancer in men who visit their GP and have the test. We will then develop and test a new tool which will include information, such as blood test results and the patient s body mass index, alongside PSA. This new tool will be part of the GP practice s computer system and linked to a patient s healthcare records, making it easy to use. It will provide patients and GPs with an estimate of how likely a man is to have harmful prostate cancer to help them make informed choices about whether to have further tests, such as scans and biopsies. Our aim is to achieve better care for the hundreds of thousands of men who are tested for prostate cancer each year when they visit their GP. Patient representatives have played a key role in developing this project, and a patient representative with personal family experience of prostate cancer is part of the team. We will ensure patients are at the core of this research from start to finish, and plan to heavily involve patients in sharing our findings and planning the next steps in testing out this new tool in practice.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS