Heart failure and atrial fibrillation prediction from non-gated chest CT scans using AI-based cardiac chamber volumetry: an AI-CVD study within the multi-ethnic study of atherosclerosis (MESA).

BACKGROUND

We previously demonstrated that AI-derived cardiac chamber volumetry in ECG-gated CT scans predicts atrial fibrillation (AF) and heart failure (HF). Since most chest CTs are non-gated, and the American Heart Association recently highlighted their value for opportunistic cardiovascular risk assessment using AI-based coronary artery calcium scoring, we extended this concept to evaluate whether AI-derived chamber volumetry from non-gated CTs can reliably predict HF and AF.s.

METHODS

We compared AI-derived chamber volumetry from non-gated and ECG-gated chest CTs in 2053 Multi-Ethnic Study of Atherosclerosis (MESA) Exam 5 participants using mean and standard-deviation of absolute differences, intraclass correlation coefficients (ICC), and Cohen's κ. Variability between paired ECG-gated scans was determined by analyzing scans acquired 2 min apart in 5749 MESA Exam 1 participants. Predictive performance for incident HF and AF was compared using cumulative incidence curves, age- and sex-adjusted C-indices, and non-inferiority testing (δ=0.05).

RESULTS

Non-gated scans demonstrated excellent overall agreement with ECG-gated scans, with comparable performance for detecting cardiac chamber enlargement (98.8-99.2%, κ=0.76-0.84), relative to agreement observed between paired ECG-gated scans (98.6-98.9%, κ=0.72-0.77). Cumulative incidence curves for HF and AF stratified by quartiles showed substantial overlap. Age- and sex-adjusted C-indices were comparable for left atrial volume (0.75 vs 0.74, p = 0.001, non-inferiority p < 0.001), ventricular volume (both 0.77, p = 0.97, non-inferiority p < 0.001), and ventricular ratio (both 0.74, p = 0.57, non-inferiority p < 0.001) in predicting AF and HF.

CONCLUSION

AI-based evaluation of non-gated chest CT may provide reliable cardiac chamber volumetry and cardiovascular outcome prediction, enabling opportunistic screening for HF and AF risk.