Available online 24 April 2025

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AI can quantify abdominal aortic calcification on CTs performed for clinical care.

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Abdominal aortic calcification was predictive of coronary artery calcifications.

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Abdominal aortic calcification increased the risk of coronary events by 2-fold.

ABSTRACTBackground

Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events.

Methods

A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both non-contrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization).

Results

Our cohort included 3599 patients (median age 60 years, 62% male, 74% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r=0.56, P<0.001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683 to 0.718]; AUC for PREVENT plus AAC 0.782 [0.767 to 0.797]; P<0.001). There were 324 MACE, of which 246 were MCE. Following adjustment for the 10-year cardiovascular disease PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P<0.001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P<0.001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE.

Conclusions

Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.

Section snippetsINTRODUCTION

Cardiovascular morbidity and mortality are the number one causes of disability and death in the western world.1,2 It is estimated that up to 75% of cardiovascular disease (CVD) could be prevented by early detection and intervention.3 Therefore, strategies to detect CVD as early as possible have great potential to improve public health. In addition to traditional risk factors, such as blood pressure, cholesterol, weight, smoking status, and diabetes, the presence and severity of coronary artery

Patient Population

The study was conducted at NYU Langone Health, which includes more than 260 outpatient office sites and five acute care hospitals. We identified all patients in the NYU Langone Health electronic health record (EHR) who underwent ECG-triggered non-contrast CT CAC scoring and non-contrast abdominal CT imaging for routine clinical care between 3/18/2013 and 1/29/2024. No additional exclusion criteria were applied. The study was approved by the institutional review board and Health Insurance

RESULTS

We identified 3,599 participants with both non-contrast abdominal and coronary CT examinations with a mean interval of 11.4 [3.7, 42.5] months between imaging studies. In Baseline patient characteristics at the time of the first CT are described in Table 1. The median age was 60.2 years, 47.9% were women, and 73.6% were of White race.

AAC was present in 65.5% of participants, with 67% and 65% in women and men, respectively (Supplementary Figure 2). Prevalence of AAC increased with age and

DISCUSSION

We examined the predictive value of automated AI opportunistic CT measurements of AAC in a large health system. We found that AAC was predictive of having any CAC. Our findings were robust across strata of age, sex and race/ethnicity. We also found that having any AAC increased the risk of both MCE and MACE by approximately 2-fold. A doubling of the AAC increased the risk of MCE by 13% and MACE by 11% independently of other risk factors. Finally, the association between AAC and clinical events

Conclusions

In conclusion, assessment of AAC was predictive of CAC, and provided incremental value to the risk of major coronary and cardiovascular events over that of standard cardiovascular risk factors in both women and men.

Disclosures

MW: Dr. Westerhoff is an employee of Visage Imaging and shareholder of its parent company Promedicus Ltd.

BD: Dr. Dane received speaker honorarium, research support, and travel from Siemens Healthineers.

CRediT authorship contribution statement

Jeffrey S. Berger: Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Chen Lyu: Formal analysis, Methodology, Writing – review & editing. Eduardo Iturrate: Data curation, Formal analysis, Investigation, Writing – review & editing. Malte Westerhoff: Data curation, Investigation, Writing – review & editing. Soterios Gyftopoulos: Investigation, Writing – review & editing. Bari Dane: Investigation, Writing – review & editing. Judy

ACKNOWLEGEMENTS

Sources of Funding:

JSB: Dr Berger is supported by the National Institutes of Health (R35HL144993)

CL: Dr. Lyu is supported by the National Institutes of Health (R01AG065330 and R01LM013344)

JZ: Dr. Zhong is supported by the National Institutes of Health (R01AG065330 and R01LM013344)

MAB: Dr. Bredella is supported by the National Institutes of Health (UL1TR001445)

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