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

Diagnostic and Prognostic Research

Protocol

Eva Morris¹  Lara Chammas¹  Andres Tamm¹  Jacqueline Birks²  Tingting Zhu³  Constantinos Koshiaris⁴  Jason Oke⁴  Tim Holt⁴  F. D. Richard Hobbs⁴  Brian D. Nicholson⁴  Pradeep S. Virdee⁴  Clare Bankhead⁴  Kiana Collins⁴  Diana Withrow⁴  Cynthia Wright Drakesmith⁴  Subhashisa Swain⁴  Rafael Perera⁴ 

[1] Big Data Institute, University of Oxford, Oxford, UK;Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK;Department of Engineering Science, University of Oxford, Oxford, UK;Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, OX2 6GG, Oxford, UK;

关键词: Cancer;    Early detection;    Blood test;    Trend;    Primary care;    CPRD;   

DOI  :  10.1186/s41512-022-00138-6

 received in 2022-09-15, accepted in 2022-12-15,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundSimple 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.MethodsPrimary 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.DiscussionThese 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.

【 授权许可】

CC BY   
© The Author(s) 2022