. 2023 Sep 19;82(12):1192-1202.

doi: 10.1016/j.jacc.2023.06.040.

Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes

Affiliations

¹ Department of Medicine, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA. Electronic address: https://twitter.com/AllisonWPeng.
² Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
³ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California, USA.
⁴ Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.
⁵ Bunkerhill Health, Palo Alto, California, USA.
⁶ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
⁷ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Veteran's Affairs Palo Alto Healthcare System, Palo Alto, California, USA. Electronic address: https://twitter.com/ATSandhu.
⁸ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA. Electronic address: frodrigu@stanford.edu.

PMID: 37704309
PMCID: PMC11009374
DOI: 10.1016/j.jacc.2023.06.040

Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes

Allison W Peng et al. J Am Coll Cardiol. 2023.

. 2023 Sep 19;82(12):1192-1202.

doi: 10.1016/j.jacc.2023.06.040.

Authors

Affiliations

¹ Department of Medicine, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA. Electronic address: https://twitter.com/AllisonWPeng.
² Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
³ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California, USA.
⁴ Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA.
⁵ Bunkerhill Health, Palo Alto, California, USA.
⁶ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
⁷ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Veteran's Affairs Palo Alto Healthcare System, Palo Alto, California, USA. Electronic address: https://twitter.com/ATSandhu.
⁸ Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA. Electronic address: frodrigu@stanford.edu.

PMID: 37704309
PMCID: PMC11009374
DOI: 10.1016/j.jacc.2023.06.040

Abstract

Background: Coronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis.

Objectives: The authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods.

Methods: Incidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations.

Results: Of 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0.

Conclusions: Incidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.

Keywords: cardiovascular outcomes; coronary artery calcium; nongated computed tomography; primary prevention; risk prediction; screening.

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Conflict of interest statement

Funding Support and Author Disclosures This work was supported by the Stanford University Human-Centered Artificial Intelligence Seed Grant. Mr Khandwala and Mr Eng are employees and shareholders of Bunkerhill Health. Dr Chaudhari has received research support from the Stanford University Precision Health and Integrated Diagnostics Seed Grant and the Stanford University Human-Centered Artificial Intelligence–Artificial Intelligence in Medicine and Imaging Seed Grant; has provided consulting services to Subtle Medical, Chondrometrics GmbH, Image Analysis Group, Edge Analytics, ICM, and Culvert Engineering; is a shareholder of Subtle Medical, LVIS Corporation, and Brain Key; and receives research support from GE Healthcare and Philips, all outside of the submitted work. Dr Sandhu has received research support from the National Heart, Lung, and Blood Institute (1K23HL151672-01). Dr Rodriguez was funded by grants from the National Institutes of Health National Heart, Lung, and Blood Institute (1K01HL144607), the American Heart Association/Harold Amos Faculty Development program, and the Doris Duke Foundation (Grant #2022051); and has consulting relationships with Healthpals, Novartis, Novo Nordisk, Esperion, and AstraZeneca outside of the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

**Figure 1 –. Study cohort flow diagram.**
This figure details the initial population considered for the study, exclusion criteria used to determine the final study cohort, and details regarding the follow-up period. The arrows in the figure point to exclusion criteria at each step or additional data during the follow-up period. The median time between censoring and end-of-study was 235 days (IQR 20–985), and median follow-up time was 5.9 years (IQR 4.1–7.3) for the 4,622 patients who were alive by the end-of-study date. The median follow-up time for the study cohort overall was 5.6 years (IQR 2.6–7.0). ECG = electrocardiogram; CT = computed tomography; ASCVD = atherosclerotic cardiovascular disease; IQR = interquartile range.

**Figure 2A/B/C –. Cumulative risk of death, death/MI/stroke, and death/MI/stroke/revascularization by DL-CAC group.**
(A) Risk of death by DL-CAC group and according numbers of patients at risk. Log-rank p<0.01. (B) Risk of death/MI/stroke by DL-CAC group and according numbers of patients at risk. Log-rank p<0.01. (C) Risk of death/MI/stroke/revascularization by DL-CAC group and according numbers of patients at risk. Log-rank p<0.01. MI = myocardial infarction.

**Central Illustration –. Incidental CAC on Non-ECG-Gated CTs and Cardiovascular Events and Mortality.**
^aAdjusted on age, sex, race, ethnicity, comorbidities (measured by Elixhauser Comorbidity Index), and pooled cohort equations variables (systolic blood pressure, total cholesterol level, HDL cholesterol level, diabetes status, smoking status, and anti-hypertensive use). ASCVD = atherosclerotic cardiovascular disease; CI = confidence interval; CVD = cardiovascular disease; HR = hazard ratio; MI = myocardial infarction; PCE = pooled cohort equations.

See this image and copyright information in PMC

Comment in

Incidental Coronary Artery Calcium: Nothing Is More Expensive Than a Missed Opportunity.
Blumenthal RS, Grant J, Whelton SP. Blumenthal RS, et al. J Am Coll Cardiol. 2023 Sep 19;82(12):1203-1205. doi: 10.1016/j.jacc.2023.06.039. J Am Coll Cardiol. 2023. PMID: 37704310 No abstract available.

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Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes

Affiliations

Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Comment in

References

Publication types

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Grants and funding

LinkOut - more resources

Full Text Sources

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Medical

Research Materials