Evidence map›Paper›PMID 36167368›Full record

ArticleBMJ open2022

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort.

Casper G M J Eurlings, Sema Bektas, Sandra Sanders-van Wijk, Andrew Tsirkin, Vasily Vasilchenko, Steven J R Meex, Michael Failer, Caroline Oehri, Peter Ruff, Michael J Zellweger and 1 more

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

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  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

11 authors at 3 institutions in 2 countries.

Casper G M J EurlingsCardiology Department, Laurentius Hospital, Roermond, The Netherlands casper.eurlings@lzr.nl.ORCID 0000-0002-8086-8621
Sema BektasCardiology Department, Maastricht UMC+, Maastricht, The Netherlands.
Sandra Sanders-van WijkCardiology, Zuyderland Medical Centre Heerlen, Heerlen, The Netherlands.
Andrew TsirkinSoftware and Modeling, Exploris Health, Wallisellen/Zürich, Switzerland.
Vasily VasilchenkoSoftware and Modeling, Exploris Health, Wallisellen/Zürich, Switzerland.
Steven J R MeexCentral Diagnostic Laboratory, Clinical Chemistry, Maastricht UMC+, Maastricht, The Netherlands.
Michael FailerSoftware and Modeling, Exploris Health, Wallisellen/Zürich, Switzerland.
Caroline OehriSoftware and Modeling, Exploris Health, Wallisellen/Zürich, Switzerland.
Peter RuffSoftware and Modeling, Exploris Health, Wallisellen/Zürich, Switzerland.
Michael J ZellwegerCardiology Department, University of Basel, Basel, Switzerland.
Hans-Peter Brunner-La RoccaCardiology Department, Maastricht UMC+, Maastricht, The Netherlands.
Maastricht University Medical Centre · NLUniversity of Basel · CHZuyderland Medisch Centrum · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPredicting the presence or absence of coronary artery disease (CAD) is clinically important. Pretest probability (PTP) and CAD consortium clinical (CAD2) model and risk scores used in the guidelines are not sufficiently accurate as the only guidance for applying invasive testing or discharging a patient. Artificial intelligence without the need of additional non-invasive testing is not yet used in this context, as previous results of the model are promising, but available in high-risk population only. Still, validation in low-risk patients, which is clinically most relevant, is lacking.

designRetrospective cohort study.

settingSecondary outpatient clinic care in one Dutch academic hospital.

participantsWe included 696 patients referred from primary care for further testing regarding the presence or absence of CAD. The results were compared with PTP and CAD2 using receiver operating characteristic (ROC) curves (area under the curve (AUC)). CAD was defined by a coronary stenosis >50% in at least one coronary vessel in invasive coronary or CT angiography, or having a coronary event within 6 months. OUTCOME MEASURES: The first cohort validating the memetic pattern-based algorithm (MPA) model developed in two high-risk populations in a low-risk to intermediate-risk cohort to improve risk stratification for non-invasive diagnosis of the presence or absence of CAD.

resultsThe population contained 49% male, average age was 65.6±12.6 years. 16.2% had CAD. The AUCs of the MPA model, the PTP and the CAD2 were 0.87, 0.80, and 0.82, respectively. Applying the MPA model resulted in possible discharge of 67.7% of the patients with an acceptable CAD rate of 4.2%.

conclusionsIn this low-risk to intermediate-risk population, the MPA model provides a good risk stratification of presence or absence of CAD with a better ROC compared with traditional risk scores. The results are promising but need prospective confirmation.

Indexed as

Coronary Artery DiseaseAgedAmbulatory Care FacilitiesArtificial IntelligenceCohort StudiesCoronary AngiographyFemaleHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesRetrospective StudiesRisk AssessmentCARDIOLOGYCoronary heart diseaseInformation technology

Identifiers

PMID36167368
PMCPMC9516207
OpenAlexW4297394494

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.