Evidence map›Paper›PMID 36624489›Full record

ArticleDiagnostic and prognostic research2023

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 and 7 more

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In one paragraph

Article in Diagnostic and prognostic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Anaemia, ethnicity, and cancer incidence: a retrospective cohort study in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Article
  6. Article
  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Pradeep S VirdeeNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK. pradeep.virdee@phc.ox.ac.uk.ORCID http://orcid.org/0000-0002-3006-8730
Clare BankheadNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Constantinos KoshiarisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Cynthia Wright DrakesmithNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Jason OkeNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Diana WithrowNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Subhashisa SwainNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Kiana CollinsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Lara ChammasBig Data Institute, University of Oxford, Oxford, UK.
Andres TammBig Data Institute, University of Oxford, Oxford, UK.
Tingting ZhuDepartment of Engineering Science, University of Oxford, Oxford, UK.
Eva MorrisBig Data Institute, University of Oxford, Oxford, UK.
Tim HoltNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Jacqueline BirksCentre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.
Rafael PereraNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
F D Richard HobbsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.
Brian D NicholsonNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, Radcliffe Primary Care Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.

Funding

Cancer Research UK Population Research Committee Postdoctoral Fellowship RCCPDF\100005
6 · The paper itself

Abstract

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. 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.

Indexed as

Blood testCancerCPRDEarly detectionPrimary careTrend

Identifiers

PMID36624489
PMCPMC9830700

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.