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

Pradeep S. Virdee; Clare Bankhead; Constantinos Koshiaris; Cynthia Wright Drakesmith; Jason Oke; Diana Withrow; Subhashisa Swain; Kiana Collins; Lara Chammas; Andres Tamm; Tingting Zhu; Eva Morris; Tim Holt; Jacqueline Birks; Rafael Perera; F. D. Richard Hobbs; Brian D. Nicholson

doi:10.1186/s41512-022-00138-6

Diagnostic and Prognostic Research (Jan 2023)

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

Pradeep S. Virdee,
Clare Bankhead,
Constantinos Koshiaris,
Cynthia Wright Drakesmith,
Jason Oke,
Diana Withrow,
Subhashisa Swain,
Kiana Collins,
Lara Chammas,
Andres Tamm,
Tingting Zhu,
Eva Morris,
Tim Holt,
Jacqueline Birks,
Rafael Perera,
F. D. Richard Hobbs,
Brian D. Nicholson

Affiliations

Pradeep S. Virdee: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Clare Bankhead: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Constantinos Koshiaris: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Cynthia Wright Drakesmith: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Jason Oke: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Diana Withrow: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Subhashisa Swain: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Kiana Collins: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Lara Chammas: Big Data Institute, University of Oxford
Andres Tamm: Big Data Institute, University of Oxford
Tingting Zhu: Department of Engineering Science, University of Oxford
Eva Morris: Big Data Institute, University of Oxford
Tim Holt: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Jacqueline Birks: Centre for Statistics in Medicine, NDORMS, University of Oxford
Rafael Perera: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
F. D. Richard Hobbs: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter
Brian D. Nicholson: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter

DOI: https://doi.org/10.1186/s41512-022-00138-6
Journal volume & issue: Vol. 7, no. 1
pp. 1 – 8

Abstract

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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.

Published in Diagnostic and Prognostic Research

ISSN: 2397-7523 (Online)
Publisher: BMC
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General)
Website: https://diagnprognres.biomedcentral.com/

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