Abstract
BACKGROUND: Arterial pressure waveform shape conveys information regarding interactions between the left ventricle and aorta that could provide an estimate of biological heart age and cardiovascular disease (CVD) risk.
METHODS: Artificial intelligence heart age (AI-HA) was estimated by averaging results from 2 convolutional neural networks trained to predict mitral annulus tissue Doppler e’ and s’ peak velocities using an uncalibrated arterial tonometry or photoplethysmography waveform as input. Models were developed using FHS (Framingham Heart Study) participant pressure waveforms and echocardiographic measurements (N=6916 participants, 38 174 waveforms, 56% women, mean age 61±12). We validated AI-HA using Cox modeling in an FHS holdout set of baseline radial waveforms (N=7018, 54% women, age 50±16 years) and in UK Biobank participants (N=67 986, 53% women, age 57±8 years).
RESULTS: In FHS (up to 10 years of follow-up, 148 heart failure [HF] and 331 CVD events), using models that adjusted for PREVENT (AHA Predicting Risk of CVD Events) risk factors, AI-HA was associated with incident HF (hazard ratio, 2.09 [CI, 1.64-2.68]; continuous net reclassification, 0.22 [CI, 0.13-0.30]) and CVD (hazard ratio, 1.52 [CI, 1.28-1.81]; continuous net reclassification, 0.13 [CI, 0.07-0.20]). In UK Biobank (up to 10 years of follow-up, 1408 HF and 2709 CVD events), AI-HA was associated with incident HF (hazard ratio, 1.23 [CI, 1.13-1.33]; continuous net reclassification, 0.09 [CI, 0.06-0.12]) and CVD (hazard ratio, 1.22 [CI, 1.15-1.30]; continuous net reclassification, 0.08 [CI, 0.06-0.09]).
CONCLUSIONS: AI-HA is a novel and accessible measure of left ventricle function and HF risk in community-based samples.