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Published on: May 21, 2017
Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and
Chieh-Mei Tsai1, Jwan A Naser1, Gal Tsaban1
1Department of Cardiovascular Medicine Mayo Clinic Rochester Minnesota.
Background:
Artificial intelligence (AI)-enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance of AI-enabled electrocardiography-derived diastolic dysfunction (DD) in patients undergoing transcatheter aortic valve replacement had not been assessed.
Methods:
We analyzed 3197 patients undergoing transcatheter aortic valve replacement for severe aortic stenosis between 2010 and 2023 with baseline 12-lead ECGs processed by a validated AI model to classify diastolic function (grades 0-3). Multivariable Cox models and nested model comparisons assessed associations with all-cause mortality, including a prespecified analysis in patients with indeterminate echocardiographic grading. Trajectories were defined by change in AI-enabled electrocardiography DD grade (<2 versus ≥2) from baseline to 30-day or 1-year follow-up.
Results:
At baseline, 21% had grade 3, 57% had grade 2, 8% had grade 1, and 14% had grade 0 AI-enabled electrocardiography DD. Higher grades were associated with adverse cardiac remodeling and comorbidities. Over a median follow-up of 3.4 years, grade 3 AI-enabled electrocardiography DD independently predicted mortality (hazard ratio [HR], 1.80 [95% CI, 1.47-2.20]; P<0.001). AI-enabled electrocardiography DD improved prognostic discrimination beyond clinical and echocardiographic measures (ΔHarrell concordance statistic, 0.016; Δχ2=57; P<0.001). Among 1259 patients with indeterminate echocardiographic grading, AI-enabled electrocardiography added prognostic value (ΔHarrell concordance statistic, 0.02; Δχ2=13; P=0.006). Worsening or persistently high-risk trajectories were associated with increased mortality (HRs, 1.45-1.80; all P<0.05).
Conclusions:
AI-enabled electrocardiography-derived DD independently predicts mortality after transcatheter aortic valve replacement, adds value beyond echocardiographic grading, and enables dynamic risk stratification through longitudinal tracking.
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