Evidence map›Paper›PMID 32213167›Full record

ArticleBMC gastroenterology2020

The use of electronic healthcare records for colorectal cancer screening referral decisions and risk prediction model development.

Jennifer Anne Cooper, Ronan Ryan, Nick Parsons, Chris Stinton, Tom Marshall, Sian Taylor-Phillips

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 16% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. A diagnostic prediction model for colorectal cancer in elderlies via internet of medical things.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2021
    Article
  6. Correlation between immunity from BCG and the morbidity and mortality of COVID-19.Tropical diseases, travel medicine and vaccines · 2020
    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

6 authors at 2 institutions in 1 country.

Jennifer Anne CooperDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK. jennifer.a.cooper@warwick.ac.uk.
Ronan RyanInstitute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK.
Nick ParsonsDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Chris StintonDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Tom MarshallInstitute of Applied Health Research, University of Birmingham, Birmingham, B15 2TT, UK.
Sian Taylor-PhillipsDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
University of Warwick · GBUniversity of Birmingham · GB

Funding

NIHR Career Development Fellowship CDF-2016-09-018NIHR CLAHRC West Midlands N/ANIHR Infrastructure Doctoral Training Exchange Award (IDTE) IDTE-03016-0#
6 · The paper itself

Abstract

backgroundThe database used for the NHS Bowel Cancer Screening Programme (BCSP) derives participant information from primary care records. Combining predictors with FOBTs has shown to improve referral decisions and accuracy. The richer data available from GP databases could be used to complement screening referral decisions by identifying those at greatest risk of colorectal cancer. We determined the availability of data for key predictors and whether this information could be used to inform more accurate screening referral decisions.

methodsAn English BCSP cohort was derived using the electronic notifications received from the BCSP database to GP records. The cohort covered a period between 13th May 2009 to 17th January 2017. Completeness of variables and univariable associations were assessed. Risk prediction models were developed using Cox regression and multivariable fractional polynomials with backwards elimination. Optimism adjusted performance metrics were reported. The sensitivity and specificity of a combined approach using the negative FOBT model plus FOBT positive patients was determined using a probability equivalent to a 3% PPV NICE guidelines level.

results292,059 participants aged 60-74 were derived for the BCSP screening cohort. A model including the screening test result had a C-statistic of 0.860, c-slope of 0.997, and R

conclusionsThis research has identified several potential predictors for CRC in a BCSP population. A risk prediction model developed for BCSP FOBT negative patients was not clinically useful due to a low sensitivity and increased referral rate. The predictors identified in this study should be investigated in a refined algorithm combining the quantitative FIT result. Combining data from multiple sources enables fuller patient profiles using the primary care and screening database interface.

Indexed as

Early Detection of CancerElectronic Health RecordsModels, StatisticalReferral and ConsultationAgedAge FactorsAlcohol DrinkingCohort StudiesColorectal NeoplasmsFemaleHumansMaleMiddle AgedPrimary Health CareRisk AssessmentRisk FactorsColorectal neoplasmsEarly detection of cancerElectronic health recordsMass screeningOccult bloodPrediction model

Identifiers

PMID32213167
PMCPMC7093989
OpenAlexW3013072565

What OpenQuestion 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 OpenQuestion graph.