Evidence map›Paper›PMID 39514142›Full record

ArticleThe international journal of cardiovascular imaging2025

Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography.

Aurelien Cagnina, Adil Salihu, David Meier, Wongsakorn Luangphiphat, Benjamin Faltin, Ioannis Skalidis, Aurelia Zimmerli, David Rotzinger, Salah Dine Qanadli, Olivier Muller and 2 more

Abstract readMulticenter StudyComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Aurelien Cagnina *Cardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
Adil Salihu *Cardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
David MeierCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
Wongsakorn LuangphiphatCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
Benjamin FaltinInstitute of Mathematics, School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
Ioannis SkalidisCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
Aurelia ZimmerliCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
David RotzingerRadiology Department, University Hospital of Lausanne, Lausanne, Switzerland.
Salah Dine QanadliRadiology Department, University Hospital of Lausanne, Lausanne, Switzerland.
Olivier MullerCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland.
Emmanuel AbbeInstitute of Mathematics, School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland.
Stephane FournierCardiology Department, University Hospital of Lausanne and University of Lausanne, Rue de Bugnon 46, Lausanne, 1011, Switzerland. stephane.fournier@chuv.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to evaluate the efficacy of the Chat Generative Pre-trained Transformer (ChatGPT) in guiding the need for invasive coronary angiography (ICA) in high-risk non-ST-elevation (NSTE) acute coronary syndrome (ACS) patients based on both standard clinical data and coronary computed tomography angiography (CCTA) findings.

methodsThis investigation is a sub-study of a larger prospective multicentric double blinded project where high-risk NSTE-ACS patients underwent CCTA prior to ICA to compare coronary lesion by both modalities. ChatGPT analyzed clinical vignettes containing patient data, electrocardiograms, troponin levels, and CCTA results to determine the necessity of ICA. The AI's recommendations were then compared to actual ICA findings to assess its decision-making accuracy.

resultsIn total, 86 patients (age: 62 ± 13 years old, female 27%) were included. ChatGPT recommended against ICA for 19 patients, 16 of whom indeed had no significant findings. For 67 patients, ChatGPT advised proceeding with ICA, and a significant lesion was confirmed in 58 of them. Consequently, ChatGPT's overall accuracy stood at 86%, with a sensitivity of 95% (95% confidence interval (CI) 0.76-0.92) and a specificity of 64% (95% CI 0.62-0.94). The model's negative predictive value was 84% (95% CI 0.44-0.79), and its positive predictive value was 87% 95% CI 0.86-0.97).

conclusionPreliminary evidence suggests that ChatGPT can effectively assist in making ICA decisions for high-risk NSTE-ACS patients, potentially reducing unnecessary procedures. However, the study underscores the importance of data accuracy and calls for larger, more diverse investigations to refine artificial intelligence's role in clinical decision-making.

Indexed as

Acute Coronary SyndromeArtificial IntelligenceComputed Tomography AngiographyCoronary AngiographyDecision Support TechniquesNon-ST Elevated Myocardial InfarctionRadiographic Image Interpretation, Computer-AssistedAgedClinical Decision-MakingDouble-Blind MethodFemaleHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesArtificial intelligenceChatGPTlarge language modelsmachine learning

Identifiers

PMID39514142
PMCPMC11742328

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Registered trials

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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.