Validation and Longitudinal Trajectory Analysis of an AI-Based Ecg Model for Aortic Stenosis: From Community Screening to Pre-TA\VR Risk Stratification

Authors

• Matthew W Segar
• Kaleb D Lambeth
• Alexander Postalian
• Stephanie Coulter
• Jasen Gilge
• Naveed Razvi
• Mohammad Saeed
• Robert D Paisley
• Ambarish Pandey
• Mehdi Razavi

Language

English

Publication Date

3-1-2026

Journal

European Heart Journal - Digital Health

DOI

10.1093/ehjdh/ztag018

PMID

41716932

PMCID

PMC12912914

PubMedCentral® Posted Date

2-3-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Aims: 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 the community screening potential of the AK-AVS model.

Methods and results: We conducted two complementary analyses: (1) community validation of the AK-AVS model in 3632 cardiovascular disease-free ARIC participants, and (2) longitudinal trajectory analysis of 7860 ECGs from 2040 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. In community screening (n = 16 moderate/severe AS), AK-AVS achieved an AUROC of 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 hospitalisation (HR 4.05, P < 0.001) 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, P < 0.001) 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).

Conclusion: 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.

Keywords

Artificial intelligence, Electrocardiography, Aortic stenosis, Community screening, Risk predictionArtificial intelligence, Electrocardiography, Aortic stenosis, Community screening, Risk predictionvvv

Published Open-Access

yes

Recommended Citation

Segar, Matthew W; Lambeth, Kaleb D; Postalian, Alexander; et al., "Validation and Longitudinal Trajectory Analysis of an AI-Based Ecg Model for Aortic Stenosis: From Community Screening to Pre-TA\VR Risk Stratification" (2026). Faculty, Staff and Students Publications. 6807.
https://digitalcommons.library.tmc.edu/baylor_docs/6807