Evidence map›Paper›PMID 40939829›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2025

Integrating perfusion with AI-derived coronary calcium on CT attenuation scans to improve selection of low-risk studies for stress-only SPECT myocardial perfusion imaging.

Robert J H Miller, Orit Barrett, Aakash Shanbhag, Alan Rozanski, Damini Dey, Mark Lemley, Serge D Van Kriekinge, Paul B Kavanagh, Attila Feher, Edward J Miller and 7 more

Abstract read
In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2025. 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

17 authors.

Robert J H MillerDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada.
Orit BarrettDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Departments of Cardiology and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Aakash ShanbhagDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
Alan RozanskiDivision of Cardiology, Mount Sinai Morningside Hospital, New York, NY, USA.
Damini DeyDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Mark LemleyDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Serge D Van KriekingeDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Paul B KavanaghDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Attila FeherSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
Edward J MillerSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA.
Andrew J EinsteinDivision of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, N Y, USA.
Terrence D RuddyDivision of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Timothy BatemanCardiovascular Imaging Technologies LLC, Kansas City, MO, USA.
Philip A KaufmannDepartment of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland.
Joanna X LiangDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Daniel S BermanDepartments of Cardiology and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Piotr J SlomkaDepartments of Medicine (Division of Artificial Intelligence in Medicine) and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Piotr.Slomka@cshs.org.

Funding

Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
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 R01 HL089765NHLBI NIH HHS R35 HL161195
6 · The paper itself

Abstract

backgroundIn many contemporary laboratories, a completely normal stress perfusion single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is required for rest imaging cancelation. We hypothesized that an artificial intelligence (AI)-derived coronary artery calcium (CAC) score of 0 from computed tomography attenuation correction (CTAC) scans obtained during hybrid SPECT/CT may identify additional patients at a low risk of major adverse cardiovascular events (MACEs) who could be selected for stress-only imaging.

methodsPatients without known coronary artery disease who underwent SPECT/CT MPI and had stress total perfusion deficit (TPD) <5% were included. Stress TPD was categorized as no abnormality (stress TPD: 0%) or minimal abnormality (stress TPD: 1%-4%). CAC was automatically quantified from the CTAC scans. We evaluated associations with MACEs.

resultsIn total, 6884 patients (49.4% males and median age: 63 years) were included. Of these, 9.7% experienced MACE (15% non-fatal myocardial infarction, 2.7% unstable angina, 38.5% coronary revascularization and 43.8% deaths). Compared to patients with TPD 0%, those with TPD 1%-4% and CAC 0 had lower MACE risk (hazard ratio [HR]: 0.58; 95% confidence interval [CI]: 0.45-0.76), while those with TPD 1%%-4% and CAC score>0 had a higher MACE risk (HR: 1.90; 95% CI: 1.56-2.30). Compared to canceling rest scans only in patients with normal perfusion (TPD 0%), by canceling rest scans in patients with CAC 0, more than twice as many rest scans (55% vs 25%) could potentially be canceled.

conclusionUsing an AI-derived CAC of 0 on CT scans with hybrid SPECT/CT in patients with a stress TPD <5% can double the proportion of patients in whom stress-only procedures could be safely performed.

Indexed as

Artificial IntelligenceCalciumCoronary Artery DiseaseMyocardial Perfusion ImagingTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedVascular CalcificationAgedCoronary VesselsExercise TestFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentCalciumArtificial intelligenceAttenuation corrected coronary artery calciumCoronary calcium scoreSPECT

Identifiers

PMID40939829
PMCPMC13175775

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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.