BLOod Test Trend for cancEr Detection (BLOTTED): protocol for an observational and prediction model development study using English primary care electronic health record data

Abstract

Background Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. Methods Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. Discussion These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.

Referral challenges for early-onset colorectal cancer: a qualitative study in UK primary care

“…The future for early diagnosis may be found in the implementation of risk stratification tools in primary care, as evaluated in a recent English cohort study incorporating blood test trends to identify patients at high risk of cancer. 45 Future research should evaluate whether the introduction of symptomatic qFIT use in NICaN primary care referral guidance 21 and the removal of age-based restrictions could have a potential impact on earlier diagnosis of patients with EOCRC. qFIT use in symptomatic patients has been found to have a high level of sensitivity for CRC, with the benefits of identifying a truly low-risk cohort of patients who can be managed in primary care, and the approval of its use in primary care.…”

“…By acknowledging the unique diagnostic challenges faced by EOCRC patients, and particularly women, we hope to improve their diagnostic journey. It is imperative that GPs have access to tools to aid decision making with regards to onward referrals, or improved diagnostic pathways.The future for early diagnosis may be found in the implementation of risk stratification tools in primary care, as evaluated in a recent English cohort study incorporating blood test trends to identify patients at high risk of cancer (45). Future research should evaluate the introduction of symptomatic qFIT use in NICAN primary care referral guidance (21) and the removal of age-based restrictions, and potential impact on earlier diagnosis of EOCRC patients.…”