Evidence map›Paper›PMID 38084894›Full record

ArticleEuropean heart journal. Cardiovascular Imaging2024

Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischaemia algorithm for patients with suspected coronary artery disease.

Sarah Bär, Takeru Nabeta, Teemu Maaniitty, Antti Saraste, Jeroen J Bax, James P Earls, James K Min, Juhani Knuuti

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In one paragraph

Article in European heart journal. Cardiovascular Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Sarah BärTurku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland.ORCID 0000-0003-0045-6596
Takeru NabetaDepartment of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
Teemu MaaniittyTurku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland.ORCID 0000-0001-5819-4635
Antti SarasteTurku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland.
Jeroen J BaxDepartment of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
James P EarlsCleerly Inc., NewYork, NY, USA.
James K MinCleerly Inc., NewYork, NY, USA.
Juhani KnuutiTurku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland.ORCID 0000-0003-3156-9593

Funding

Cleerly IncFinnish Foundation for Cardiovascular ResearchFinnish State Research Funding
6 · The paper itself

Abstract

aimsCoronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial intelligence-guided quantitative computed tomography ischaemia algorithm (AI-QCTischaemia) aims to identify myocardial ischaemia directly from CTA images and may be helpful to improve risk stratification. The aims were to investigate (i) the prognostic value of AI-QCTischaemia amongst symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA and (ii) the prognostic value of AI-QCTischaemia separately amongst patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis). METHODS AND

resultsFor this cohort study, AI-QCTischaemia was calculated by blinded analysts amongst patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). A total of 1880/2271 (83%) patients had conclusive AI-QCTischaemia result. Patients with an abnormal AI-QCTischaemia result (n = 509/1880) vs. patients with a normal AI-QCTischaemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint [adjusted hazard ratio (HRadj) 1.96, 95% confidence interval (CI) 1.46-2.63, P < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina]. An abnormal AI-QCTischaemia result was associated with significantly higher crude and adjusted rates of the primary endpoint amongst patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81, 95% CI 1.09-3.00, P = 0.022), but not amongst those with obstructive CAD (n = 474/1847) (HRadj 1.26, 95% CI 0.75-2.12, P = 0.386) (P-interaction = 0.032).

conclusionAmongst patients with suspected CAD, an abnormal AI-QCTischaemia result was associated with a two-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischaemia may be useful to improve risk stratification, especially amongst patients with no/non-obstructive CAD on coronary CTA.

Indexed as

AlgorithmsArtificial IntelligenceComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseAgedCohort StudiesFemaleHumansMaleMiddle AgedMyocardial IschemiaPrognosisRisk Assessmentartificial intelligencecoronary computed tomography angiographyischaemianon-invasive imagingprognosis

Identifiers

PMID38084894
PMCPMC11057943

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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.