Longitudinal AI-ECG trajectory patterns independently predicted mortality in TAVR recipients (Persistently High pattern: HR 1.48, p=0.005) and improved risk reclassification beyond traditional scores.
Observational (n=5,672)
Does the AK-AVS AI-based ECG model accurately detect moderate/severe aortic stenosis in the community and predict long-term mortality in pre-TAVR patients?
An AI-enhanced ECG model can detect moderate/severe aortic stenosis in community screening and identify distinct longitudinal trajectories that independently predict mortality in TAVR recipients beyond traditional risk scores.
Hazard Ratio: 1.48
p-value: p=0.005
Abstract Background Early aortic stenosis (AS) detection remains challenging, with many patients presenting late when left ventricular dysfunction may be irreversible. We evaluated whether longitudinal AI-enhanced ECG patterns can predict outcomes years before intervention and assessed community screening potential of the AK-AVS model. Methods We conducted two complementary analyses: (1) community validation of the AK-AVS model in 3,632 cardiovascular disease-free ARIC participants, and (2) longitudinal trajectory analysis of 7,860 ECGs from 2,040 TAVR recipients collected up to 10 years pre-procedure. Unsupervised clustering identified distinct AK-AVS trajectories, with mortality associations assessed using Cox regression and net reclassification improvement. Results In community screening (n=16 moderate/severe AS), AK-AVS achieved an AUROC 0.79, sensitivity 75%, and specificity 75% for moderate/severe AS. At hypothetical screening prevalences of 1-5%, positive predictive values improved to 3.1-14.3%. False-positive predictions identified individuals at 4-fold increased risk for future AS hospitalization (HR 4.05, p0.001) and 52% increased risk for heart failure (HR 1.52, p=0.02). In the TAVR cohort, trajectory analysis revealed three distinct patterns: Stable Low (19.3%), Accelerated Progression (23.6%), and Persistently High (57.1%). Elevated trajectory groups were older (78.4 and 77.8 vs 72.6 years, p0.001) with higher pacemaker rates (16.4% and 17.3% vs 10.7%, p=0.008), despite similar hemodynamic severity. Both elevated patterns independently predicted mortality (Accelerated: HR 1.40, p=0.03; Persistently High: HR 1.48, p=0.005) and significantly improved risk reclassification beyond traditional risk scores (NRI 0.069-0.074). Conclusions Longitudinal AI-ECG trajectory patterns detect disease progression up to 4.5 years before TAVR and enhance mortality prediction beyond traditional risk scores. Community validation shows potential screening utility with “false-positives” identifying future risk.
Segar et al. (Sat,) conducted a observational in Aortic stenosis (n=5,672). AK-AVS AI-enhanced ECG model vs. Stable Low trajectory was evaluated on Mortality (HR 1.48, p=0.005). Longitudinal AI-ECG trajectory patterns independently predicted mortality in TAVR recipients (Persistently High pattern: HR 1.48, p=0.005) and improved risk reclassification beyond traditional scores.