Evidence map›Paper›PMID 42000549›Full record

ArticleJACC. Advances2026

Assessing Coronary Calcium Thresholds on Attenuation-Correction CT With Myocardial Perfusion Imaging Equating to Secondary Prevention.

Ioannis Kyriakoulis, Ibolya Csecs, Ahmed Ibrahim Ahmed, Catherine X Wright, Aakash Shanbhag, Mark Lemley, Piotr Slomka, Albert J Sinusas, Edward J Miller, Damianos G Kokkinidis and 1 more

Abstract read
In one paragraph

Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ioannis KyriakoulisFaculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece.
Ibolya CsecsSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Ahmed Ibrahim AhmedSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Catherine X WrightSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Aakash ShanbhagDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Mark LemleyDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Piotr SlomkaDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Albert J SinusasSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Edward J MillerSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Damianos G KokkinidisHeart and Vascular Institute, Yale New Haven Health, Northeast Medical Group, Waterford, Connecticut, USA.
Attila FeherSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA. Electronic address: attila.feher@yale.edu.

Funding

Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
NHLBI NIH HHS R35 HL161195
6 · The paper itself

Abstract

backgroundPatients with a coronary artery calcium (CAC) score >300 on dedicated CAC scoring computed tomography (CT) are at equivalent risk of major adverse cardiac events (MACE) as those with established atherosclerotic cardiovascular disease (ASCVD).

objectivesThe aim of the study was to identify the extent of CAC on CT performed for attenuation correction (CTAC) as part of nuclear myocardial perfusion imaging that equates to secondary prevention.

methodsWe retrospectively studied 17,901 patients (48% female, age 64 ± 12 years, body mass index 30 kg/m2 [26-36]) who underwent nuclear myocardial perfusion imaging with CTAC (single photon emission computed tomography/CT or positron emission tomography/CT) at a single center. Prior ASCVD was defined as myocardial infarction (MI), cerebrovascular accident, peripheral artery disease, or prior revascularization. A semiquantitative visually estimated CAC score was obtained by scoring CAC in each coronary artery from 0 (absent) to 3 (severe), yielding a total score of 0 to 12 (zero, mild 1-2, moderate 3-6, and severe ≥7). The primary outcome was the composite of death, MI, or late revascularization.

resultsAmong 13,852 patients without prior ASCVD (CAC zero 45%, mild 23%, moderate 21%, severe 11%) and 4,049 with ASCVD, 2,006 patients (11%) experienced MACE during a median follow-up of 25 (Q1-Q3: 10-43) months. In multivariable Cox regression, patients with severe calcification had no difference in risk for MACE, MI, or all-cause mortality vs ASCVD patients (P > 0.05).

conclusionsPatients without prior ASCVD but with severe CAC (score ≥7) on CTAC demonstrated a risk for cardiovascular events and mortality comparable to those with known ASCVD, highlighting the need for more aggressive management in this high-risk primary prevention group.

Indexed as

ASCVDattenuation correction CTcoronary artery calcificationmajor adverse cardiovascular eventsprimary preventionsecondary prevention

Identifiers

PMID42000549
PMCPMC13098618

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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.