CONTROL ID: 4144067
PRESENTATION TYPE: Poster Only
CURRENT CATEGORY: 20.85 Biomarkers, Risk Assessment and Risk Prediction
AUTHORS (FIRST NAME, LAST NAME):
Morteza Naghavi, Anthony Reeves, Kyle Atlas, Chenyu Zhang, Thomas Atlas, Claudia Henschke, David Yankelevitz, Matthew J. Budoff, WENJUN FAN, Ruilin Yu, Andrea Branch, Ning Ma, Sion Roy, Khurram Nasir, Sabee Molloi, Zahi A. Fayad, Mike McConnell, Ioannis Kakadiaris, Javier Zulueta, David J. Maron, Jagat Narula, Prediman K. Shah, Kim A. Williams, Daniel Levy, and Nathan D. Wong.
INSTITUTIONS (ALL):
1. HeartLung.AI, Houston, TX, United States.
2. Cornell University, Ithaca , NY, United States.
3. Tustin Teleradiology, Tustin, CA, United States.
4. Mount Sinai Hospital, New York, NY, United States.
5. University of California Irvine, Irvine, CA, United States.
6. UCLA, Los Angeles, CA, United States.
7. Lundquist Institute at Harbor UCLA, Malibu, CA, United States.
8. Houston Methodist, Houston, TX, United States.
9. University of California, Irvine, Irvine, CA, United States.
10. Stanford University School of Medicine, Stanford, CA, United States.
11. University of Houston, Houston, TX, United States.
12. STANFORD UNIVERSITY, Palo Alto, CA, United States.
13. University of Louisville Medicine, Louisville, KY, United States.
14. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States.
15. Cedars-Sinai Medical Center, Los Angeles, CA, United States.
16. Icahn SCHOOL MEDICINE at Mount Sina, New York, NY, United States.
Abstract Body: Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston score which is used for coronary artery disease prediction only. We have previously reported that AI-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicts incident atrial fibrillation (AF), heart failure (HF), and stroke in the Multi-Ethnic Study of Atherosclerosis (MESA). Here we compare the distribution of cardiac chambers volumes vs. risk categories of ASCVD pooled cohorts’ equation (PCE) and PREVENT Risk scores.
Methods: We applied AI-CAC to 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of MESA. We calculated 10-year estimated risk from the PCE and PREVENT Risk Scores based on 4 categories of risk: <5%, 5-7.5%, 7.5-20%, >20% using baseline risk factors. The PREVENT total CVD base model was used in analysis, which excludes urinary albumin to creatinine ratio and social depravity index. We compared the distribution of the quartiles of left atrial (LA) and left ventricle (LV) volumes to categories of both risk scores. We defined enlarged cardiac chambers as the top quartile of LA (>82.7 cc) and LV (>136.5 cc) volume, which corresponded to 33% and 21.1% incidence of all-CVD events over 10 years (CHD, HF, AF, stroke, CVD deaths), respectively. LA and LV volumes were standardized by adjusting for body surface area (BSA).
Results: A substantial portion of cases categorized by PREVENT as low risk (10-year risk <5%) had enlarged cardiac chambers (Figure 1). In females the lowest category of PREVENT had 10.6% enlarged LA cases and 26.6% enlarged LV volume cases. In males, the lowest category of PREVENT had 13.7% enlarged LA cases and 29.4% enlarged LV volume cases. Similarly, in low risk PCE, females had 12.8% enlarged LA and 24.9%
enlarged LV, and males had 12.8% enlarged LA and 24.6% enlarged LV.
Conclusion: In this multi-ethnic longitudinal population study, a substantial portion of cases classified as lowrisk by PCE and PREVENT risk scores had enlarged LA and LV volumes detected by AI-CAC, putting them at risk for future HF, AF, and stroke.
KEYWORDS: Artificial Intelligence.
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