Bär, Sarah; Nabeta, Takeru; Maaniitty, Teemu; Saraste, Antti; Bax, Jeroen J; Earls, James P; Min, James K; Knuuti, Juhani (2024). Prognostic Value of a Novel Artificial Intelligence-Based Coronary Computed Tomography Angiography-Derived Ischemia Algorithm for Patients with Suspected Coronary Artery Disease. European heart journal - cardiovascular imaging, 25(5), pp. 657-667. Oxford University Press 10.1093/ehjci/jead339
Full text not available from this repository.AIMS
Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to identify myocardial ischemia directly from CTA images and may be helpful to improve risk stratification. The aims were 1) the prognostic value of AI-QCTischemia among symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA, and 2) the prognostic value of AI-QCTischemia separately among patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis).
METHODS AND RESULTS
For this cohort study, AI-QCTischemia was calculated by blinded analysts among 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). 1880/2271 (83%) patients were analyzable by AI-QCTischemia. Patients with an abnormal AI-QCTischemia result (n = 509/1880) vs. patients with a normal AI-QCTischemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint (HRadj 1.96,95% CI 1.46-2.63, p < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina). An abnormal AI-QCTischemia result was associated with significantly higher crude and adjusted rates of the primary endpoint among patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81,95% CI 1.09-3.00, p = 0.022), but not among those with obstructive CAD (n = 474/1847) (HRadj 1.26,95% CI 0.75-2.12, p = 0.386) (p-interaction = 0.032).
CONCLUSION
Among patients with suspected CAD, an abnormal AI-QCTischemia result was associated with a 2-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischemia may be useful to improve risk stratification, especially among patients with no/non-obstructive CAD on coronary CTA.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
?? 3206CF1930A5491AE053980C5C820121 ?? |
UniBE Contributor: |
Bär, Sarah |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2047-2412 |
Publisher: |
Oxford University Press |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
13 Dec 2023 08:27 |
Last Modified: |
01 May 2024 00:13 |
Publisher DOI: |
10.1093/ehjci/jead339 |
PubMed ID: |
38084894 |
Uncontrolled Keywords: |
artificial intelligence coronary computed tomography angiography ischemia non-invasive imaging prognosis |
URI: |
https://boris.unibe.ch/id/eprint/190242 |
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