AI-derived left-to-right cardiac chamber volume ratios in coronary artery calcium scans strongly predict heart

Seyed Reza Mirjalili1, Kyle Atlas1, Anthony P Reeves1

  • 1Artificial Intelligence Research Unit, HeartLung.AI, Houston, TX, USA.

Insights

Left-to-right cardiac chamber volume ratios derived from coronary artery calcium scans effectively predict heart failure (HF). The left-to-right ventricular (LV/RV) ratio showed the strongest predictive power for HF incidence.

Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Predictive Analytics

Background:

  • Coronary artery calcium (CAC) scans traditionally provide a CAC score.
  • AI-CVD initiative extracts deeper insights from CAC scans.
  • Previous work showed AI-derived cardiac chamber volumes predict heart failure (HF).

Purpose of the Study:

  • To evaluate if left-to-right cardiac chamber volume ratios predict HF better than chamber volumes alone.
  • To assess the predictive performance of LV/RV, LA/RA, and LA/RV ratios for incident HF.

Main Methods:

  • Utilized AI-CVD cardiac volumetry data from 5,732 asymptomatic Multi-Ethnic Study of Atherosclerosis (MESA) participants.
  • Analyzed left-to-right ventricular (LV/RV), atrial (LA/RA), and left atrial-to-right ventricular (LA/RV) volume ratios.
  • Employed multivariable Cox models and feature selection; validated in the Framingham Heart Study Offspring (FHS-O) cohort.

Main Results:

  • Elevated LV/RV, LA/RA, and LA/RV ratios were strongly associated with incident HF in MESA.
  • The LV/RV ratio (≥95th percentile) showed a hazard ratio (HR) of 4.04 for HF.
  • LV/RV ratio remained a significant HF predictor even in participants with normal LV size and was the strongest predictor in FHS-O.

Conclusions:

  • AI-derived LV/RV ratio from CAC scans is a strong predictor of incident HF in two prospective cohorts.
  • These imaging biomarkers hold potential for clinical utility in HF risk stratification.
  • Further clinical trials are warranted to establish the utility of these AI-derived imaging biomarkers.
Abstract

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