Title: Understanding the effectiveness, cost-effectiveness and current use of cancer diagnostic tools to aid decision-making in primary care
Funder: Department of Health and Social Care

Abstract: Design: We will systematically review evidence on the effectiveness and cost-effectiveness of cancer diagnostic tools used in primary care (Systematic Review 1), supplemented by a systematic review of studies reporting the development, validation or accuracy of diagnostic models (Systematic Review 2). We will use a decision analytic model to explore uncertainty in the effectiveness and cost-effectiveness of using these tools in colorectal cancer, using evidence from an updated systematic review on associations between cancer diagnosis and/or treatment interval and patient outcomes (Update Systematic Review). We will also conduct a GP survey to estimate the proportion using existing tools, and their subsequent impact on 2 week wait referrals. Literature review strategy: Systematic Reviews 1 and 2 will be conducted in line with good practice guidelines, with defined inclusion/exclusion criteria. Included studies will be assessed using the Cochrane Risk of Bias Tool, or adaptation, and the PROBAST checklist. To inform the decision modelling, we will identify relevant evidence based on its appropriateness to the research question, prioritising recent evidence specific to the NHS. The Update Systematic Review will follow the inclusion/exclusion criteria and risk of bias assessment from the original. Population: Patients presenting to primary care with self-reported symptoms, or symptoms identified within primary care via routine testing (such as anaemia from a full blood count test). Health Technologies: Diagnostic tools are diagnostic prediction models used in clinical practice, hence we define the health technology in two steps: 1) Any model predicting the probability or risk of a patient currently having cancer based on a combination of symptoms (and may include patient characteristics in addition to symptoms) for a patient. Examples are the models by Hippisley-Cox and Hamilton. 2) Any tool that allows the diagnostic prediction model to be incorporated into clinical practice, e.g. mouse-mats for the Hamilton models, Qcancer electronic system for the Hippisley-Cox models. Any model or tool used for cancer screening of asymptomatic patients, or to predict future risk of cancer in asymptomatic patients will be excluded. Measured costs and outcomes: The decision model will be from the perspective of the NHS and include all relevant patient outcomes and costs. Costs will be sought from NHS reference costs and the published literature. Sample size: For the GP survey a sample size of 392 GP practices gives a 95% confidence interval with a margin of error of no more than 5%. Assuming a response rate of 40%(9), the questionnaire will be sent to 900 GP practices. Project timetables: 12 months duration. Systematic Reviews 1 and 2 will run in tandem from month 1-6. The GP survey will run from month 1-9. The Update Systematic Review will run from month 3-8. Development of clinical pathways and adjustment/updating of the decision model will be months 1-4, and analysis and interpretation in months 7-10. Writing and dissemination activities will be in months 11 and 12. Expertise in team: JP, AML, RL, BG and CH are experienced systematic reviewers. CC is a highly experience information scientist. JP, AML, AS and CH are proficient in decision analytic modelling. SP and WH have extensive experience in methods to develop cancer diagnostic tools. WH and RN have highly relevant clinical and research experience.

Lay abstract: Aims: To understand whether the use of diagnostic tools designed to help GPs identify patients who may have cancer leads to patients living longer or having a better quality of life. We will find out whether GPs have access to these tools and how widely they are used. We will also look at the impact on NHS resources, for example, whether the use of these tools leads to an increase in hospital referrals, or a greater number of tests and investigations being carried out. We will consider whether the benefit to patients of identifying cancer earlier is sufficient to be worth the increased cost. Background: For many types of cancer, survival is lower in the UK than for most European countries. Identifying and referring patients who have symptoms that could indicate cancer promptly could enable it to be diagnosed and treated at an earlier stage. This might lead to improved quality of life and survival. Diagnostic tools combine multiple predictors, including symptoms and test results, in order to estimate the probability of the presence or absence of a disease within an individual patient. Tools that can predict the risk of current cancer in patients with symptoms have been developed and are available to GPs to use in clinical practice. However, there has been no summary of the evidence on whether use of these tools improves patient quality of life or survival, and what impact their use might have on NHS resources. Design and methods: We will use systematic review methods to identify published and unpublished studies that evaluate the impact of these tools on the timing of cancer diagnosis, the extent and severity of cancer at diagnosis, patients’ quality of life, survival, or NHS resources. We will also review studies reporting the development, validation or accuracy of any tools that could be used to help GPs diagnose cancer. A computer-based simulation model will be used to link evidence on the short-term impacts of GPs using these tools (such as quicker cancer diagnoses) with longer-term impacts (such as improved quality of life or probability of survival). The model will consider the impact on patients, such as reduced anxiety from a quicker diagnosis, as well as the impacts on NHS resources, such as the costs of additional tests and referrals. Patient and public involvement: is essential to this study; the overarching aim is to ensure that the study trajectories are informed by patients’ perspectives as well as those of other stakeholders. This will help us to ensure that the reviews and simulation model capture patient-relevant outcomes. We will recruit, train and support a lay member to the research team, who will also join our expert advisory group to assist on areas of uncertainty that may occur throughout the project. Dissemination: As the findings of this project will be important to a number of different groups including patients, GPs, hospital clinicians, cancer charities, and researchers, we will use a variety of routes to disseminate our work. This will include publication of our findings in high-quality open-access journals, and presentation and networking at national and international conferences. We will also use existing links and networks via our expert advisory group, the GPs participating in the survey, and links co-applicants have with key organisations including the Royal College of General Practitioners, Cancer Research UK, Macmillan, Department of Health and NICE.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS