Eleid, Mackram F.

Background Bileaflet aortic valve prevalence in transcatheter aortic valve replacement (TAVR) patients is poorly defined. We evaluated a TAVR cohort to determine the bileaflet aortic valve prevalence and understand features which may improve detection. In addition, we related valvar morphology to the occurrence of permanent pacemaker implantation (PPI) following TAVR. Methods Aortic valvar morphology diagnosis was recorded from the pre-procedural cardiac CTA reports prior to TAVR. Commissural angles, comparison of commissural heights, and dynamic visual inspection of the aortic valve were subsequently evaluated on pre-procedural cardiac CTA by an expert cardiac anatomist and imager, methods previously validated in a surgical cohort, to determine aortic valvar morphology and compared to the historical diagnosis. Relationships between valvar morphological characteristics with the need for PPM within 30-days post-TAVR were determined. Results Four-hundred and thirty-three (mean age 81.3 ± 6.6 years, 53.8% female) underwent TAVR [corrected diagnosis: 393 (90.8%) trileaflet vs. 40 (9.2%) bileaflet valves]. Bileaflet valves were historically misdiagnosed in 80% of pre-procedural cardiac CTA reports. Thirty-four (85.0%) had intercoronary leaflet fusion [mean commissural angle = 148.1 (18.3) degrees]. A commissural angle threshold of 141.1 degrees had a sensitivity of 0.73 and specificity of 0.86 for identifying a bileaflet valve. PPI post-TAVR occurred in 38% bileaflet vs. 19% trileaflet patients ( p = 0.0114 ) [unadjusted OR for bileaflet valve requiring PPI = 2.54, 95% CI (1.25–5.01)]. Conclusions Bileaflet aortic valves are commonly misdiagnosed. Assessment of the commissural angle and comparison of commissural heights may improve CTA-based diagnostic accuracy prior to TAVR. Improved detection may guide improved outcomes in this higher risk population.

Abstract Aims We aimed to assess regression and development rates of ≥moderate secondary mitral regurgitation (MR) and tricuspid regurgitation (TR) in patients with heart failure and reduced ejection fraction (HFrEF) receiving contemporary guideline-directed medical therapy (GDMT). Methods Patients with a <1 year diagnosis of HFrEF with mildly (40% ≤ EF < 50%) or moderately (<40%) reduced EF enrolled in GDMT titration programme 2019–2024 were identified. Regression rates of ≥moderate MR/TR to ≤mild and development rates of incident ≥moderate MR/TR were calculated. Results Among 844 patients [median age 64; 68% men; 26% mildly reduced EF; 24% New York Heart Association (NYHA) Class III/IV], 28% had ≥moderate MR and 14% had ≥moderate TR. Over median 7.1 months, 171 (75%) patients with ≥moderate MR and 87 (75%) of patients with ≥moderate TR experienced regression to no/mild MR and TR, respectively. Conversely, over median 13 months, incident ≥moderate MR and TR developed in 5% and 6%, respectively, of patients with no/mild baseline MR and TR. Number of GDMT medications at follow-up was less in patients who did not have MR/TR regression (vs who did) and in patients who developed MR/TR (vs who did not). Patients with NYHA Class I/II received a similar number of GDMT and had a similar rate of MR and TR regression and development vs patients with NYHA Class III/IV. However, patients with mildly reduced EF received less GDMT medications vs patients with moderately reduced EF and experienced less MR regression. Conclusion In patients with a recent diagnosis of HFrEF receiving contemporary GDMT, the majority with ≥moderate secondary MR and TR achieved regression of MR/TR, and the minority without significant baseline MR/TR developed ≥moderate regurgitation at follow-up.

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; Δ χ 2 =57; P <0.001). Among 1259 patients with indeterminate echocardiographic grading, AI‐enabled electrocardiography added prognostic value (ΔHarrell concordance statistic, 0.02; Δ χ 2 =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.