Cancer diagnostic tools to aid decision-making in primary care: mixed-methods systematic reviews and cost-effectiveness analysis

Abstract

Background Tools based on diagnostic prediction models are available to help general practitioners diagnose cancer. It is unclear whether or not tools expedite diagnosis or affect patient quality of life and/or survival. Objectives The objectives were to evaluate the evidence on the validation, clinical effectiveness, cost-effectiveness, and availability and use of cancer diagnostic tools in primary care. Methods Two systematic reviews were conducted to examine the clinical effectiveness (review 1) and the development, validation and accuracy (review 2) of diagnostic prediction models for aiding general practitioners in cancer diagnosis. Bibliographic searches were conducted on MEDLINE, MEDLINE In-Process, EMBASE, Cochrane Library and Web of Science) in May 2017, with updated searches conducted in November 2018. A decision-analytic model explored the tools’ clinical effectiveness and cost-effectiveness in colorectal cancer. The model compared patient outcomes and costs between strategies that included the use of the tools and those that did not, using the NHS perspective. We surveyed 4600 general practitioners in randomly selected UK practices to determine the proportions of general practices and general practitioners with access to, and using, cancer decision support tools. Association between access to these tools and practice-level cancer diagnostic indicators was explored. Results Systematic review 1 – five studies, of different design and quality, reporting on three diagnostic tools, were included. We found no evidence that using the tools was associated with better outcomes. Systematic review 2 – 43 studies were included, reporting on prediction models, in various stages of development, for 14 cancer sites (including multiple cancers). Most studies relate to QCancer® (ClinRisk Ltd, Leeds, UK) and risk assessment tools. Decision model In the absence of studies reporting their clinical outcomes, QCancer and risk assessment tools were evaluated against faecal immunochemical testing. A linked data approach was used, which translates diagnostic accuracy into time to diagnosis and treatment, and stage at diagnosis. Given the current lack of evidence, the model showed that the cost-effectiveness of diagnostic tools in colorectal cancer relies on demonstrating patient survival benefits. Sensitivity of faecal immunochemical testing and specificity of QCancer and risk assessment tools in a low-risk population were the key uncertain parameters. Survey Practitioner- and practice-level response rates were 10.3% (476/4600) and 23.3% (227/975), respectively. Cancer decision support tools were available in 83 out of 227 practices (36.6%, 95% confidence interval 30.3% to 43.1%), and were likely to be used in 38 out of 227 practices (16.7%, 95% confidence interval 12.1% to 22.2%). The mean 2-week-wait referral rate did not differ between practices that do and practices that do not have access to QCancer or risk assessment tools (mean difference of 1.8 referrals per 100,000 referrals, 95% confidence interval –6.7 to 10.3 referrals per 100,000 referrals). Limitations There is little good-quality evidence on the clinical effectiveness and cost-effectiveness of diagnostic tools. Many diagnostic prediction models are limited by a lack of external validation. There are limited data on current UK practice and clinical outcomes of diagnostic strategies, and there is no evidence on the quality-of-life outcomes of diagnostic results. The survey was limited by low response rates. Conclusion The evidence base on the tools is limited. Research on how general practitioners interact with the tools may help to identify barriers to implementation and uptake, and the potential for clinical effectiveness. Future work Continued model validation is recommended, especially for risk assessment tools. Assessment of the tools’ impact on time to diagnosis and treatment, stage at diagnosis, and health outcomes is also recommended, as is further work to understand how tools are used in general practitioner consultations. Study registration This study is registered as PROSPERO CRD42017068373 and CRD42017068375. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 24, No. 66. See the NIHR Journals Library website for further project information.

Predictive values for different cancers and inflammatory bowel disease of 6 common abdominal symptoms among more than 1.9 million primary care patients in the UK: A cohort study

“…Further, our study also includes both “all-cancer” and cancer site–specific evidence, whereas most prior literature in this field considered associations between specific abdominal symptoms and individual cancer sites in isolation. Our findings can be used in addition to other cancer risk tools in current use [ 30 , 31 ].…”

Development and Experience with Cancer Risk Prediction Models Using Federated Databases and Electronic Health Records

“…However, few have progressed past the development and initial validation stages to prospective real-time validation in the USA. The reasons behind this are complex, but much can be learned from the attempts at implementation of similar models in Europe, where several risk assessment tools have been integrated into practice, most notably the 'QCancer' and Risk Assessment Tool (RAT) models (9). These were tested as decision support tools based solely on a generated risk score, for multiple cancers (including PDAC and ovarian adenocarcinoma), and while reporting fair performance, their adoption by primary care physicians has been slow (9).…”

“…The reasons behind this are complex, but much can be learned from the attempts at implementation of similar models in Europe, where several risk assessment tools have been integrated into practice, most notably the 'QCancer' and Risk Assessment Tool (RAT) models (9). These were tested as decision support tools based solely on a generated risk score, for multiple cancers (including PDAC and ovarian adenocarcinoma), and while reporting fair performance, their adoption by primary care physicians has been slow (9). These models are likely limited by their "provider dependency," or varied general practitioner interpretation of patient symptoms leading to inconsistent estimates by the model, as well as by design and integration of the tool (10).…”

“…These models are likely limited by their "provider dependency," or varied general practitioner interpretation of patient symptoms leading to inconsistent estimates by the model, as well as by design and integration of the tool (10). There are also no reports of these models continuously generating an updated risk score based on new data as it becomes available (9).…”

Recognising Colorectal Cancer in Primary Care

“…Clinical decision support tools (CDSTs) have been developed to allow GPs to make the most of data available in patient medical records (such as symptoms, patient characteristics such as age and sex and test results) in order to calculate patients’ risk of cancer [ 95 – 97 ] (Box 1).…”