Sabbah, Michael S.

Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

Background Transcatheter edge‐to‐edge repair (TEER) and replacement (TTVR) have emerged as new modalities to treat patients with symptomatic significant tricuspid regurgitation. This study aimed to compare invasive hemodynamic and right ventricular (RV) changes and symptom improvement over 1 year. Methods Patients who underwent TEER/TTVR at Mayo Clinic (Rochester, MN, 2017–2024) were included. Hemodynamic profiles were described before and immediately after intervention, in addition to RV enlargement/dysfunction ≥moderate at 30 days and change in symptoms over time. Results Of 100 patients included (50 per group, median age 81 versus 80 years and female 62% versus 42%, in TTVR and TEER, respectively), all had ≥ moderate–severe symptomatic tricuspid regurgitation. Post intervention, mean right atrial pressure decreased in TEER (16 to 14 mm Hg, P <0.001), V wave decreased in both groups (to 16 mm Hg, P <0.001). RV enlargement was less prevalent in both cohorts at 30 days (60%–40% in TEER, P =0.01, and 72 to 58% in TTVR, P =0.07), yet prevalence of RV dysfunction decreased only in TEER (26 to 15%, P =0.17) as opposed to TTVR (31 to 44%, P =0.13). TTVR resulted in a higher stroke volume index and more complete tricuspid regurgitation elimination at 30 days. Both groups resulted in similar improvements in New York Heart Association class, with overall similar survival. Conclusions TEER was associated with a greater reduction in right atrial pressure and more favorable RV remodeling at 30 days, as opposed to TTVR, which achieved more complete and sustained elimination of tricuspid regurgitation with higher forward stroke volume.

Abstract Aims Significant secondary tricuspid regurgitation (STR) is associated with worse morbidity and mortality. To-date, evidence on isolated tricuspid valve surgery in STR remains controversial with uncertainty regarding management strategies. We aimed to investigate sex differences in the association of isolated tricuspid valve surgical repair/replacement (TVR) with outcomes of mortality and heart failure (HF) hospitalizations in STR. Methods and results Patients with severe STR without significant aortic valve or organic mitral valve disease, congenital heart disease, pacemakers, previous valve intervention, malignancy, or end-stage kidney/liver disease were identified. Isolated TVR was studied as a time-dependent covariate in association with mortality and HF hospitalizations. Among 2,857 patients (median age 76 years; 62% women), 143 underwent isolated TVR (58% repair; 42% replacement; 64% with low TRI-SCORE risk category) at median 56 days. Over median 1.6 (IQR 0.2-5.2) years, 1,771 patients died and 1,259 had HF hospitalization. When adjusting to age, comorbidities, left/right ventricular function, pulmonary hypertension, hemoglobin level, and dyspnea, isolated TVR was associated with lower mortality in females (HR 0.54 95% CI 0.35-0.83) but not males (HR 1.29 95% CI 0.90-1.83), p-interaction&lt;0.01; this remained the case when restricting to groups with normal left/right ventricular function. Isolated TVR was associated with HF hospitalization without interaction with sex (HR 0.68 95% CI 0.46-0.998), p-interaction=0.51. Conclusion Isolated TVR for STR was associated with decreased HF hospitalizations in both females and males but improved survival only in females. Future studies are needed to understand differences in the natural history of TR between sexes and stratify outcomes after TR intervention by sex.

Abstract Aims Prognosis in tricuspid regurgitation (TR) is shaped by complex clinical and haemodynamic interactions, complicating risk stratification. We aimed to use explainable artificial intelligence (EAI) to model these relationships and identify key predictors of outcomes. Methods and results We analysed long-term all-cause mortality in a large prospective registry of patients with native TR across the full severity spectrum. Patients underwent detailed clinical evaluation and quantitative Doppler-echocardiography. Supervised EAI models were applied to identify and rank predictors of mortality under medical therapy, capturing non-linear associations and interactions among baseline characteristics. Among 9761 patients [mean age 73 ± 15 years; 56.9% women; effective regurgitant orifice area (EROA) 40 (30–56) mm2; regurgitant volume 39 (29–53) mL/beat; 42% severe TR], 2676 deaths occurred during 3.8 ± 4.6 years of follow-up. EAI identified and ranked 20 prognostic determinants of survival. Haemodynamic measures, led by elevated pulmonary-artery systolic and right-atrial pressures, were strongest, followed by age and comorbidity. TR-related variables ranked next, led by right ventricular dysfunction and EROA, which outperformed integrative TR grade and TRIScore. Left-ventricular parameters were less influential. Non-linear effects, including those involving ejection fraction, and interactions such as higher EROA-related risk with elevated pulmonary pressures or lower TRIScore (both P &lt; 0.03) were observed. Proportional hazards models confirmed EROA as the strongest TR severity marker (P &lt; 0.0001), with excess mortality beginning at 40 mm2 and increasing further at 60 mm2. Conclusion EAI ranked mortality predictors in TR, highlighting patient factors, haemodynamics, TR-specific measures, and left-ventricular function. Among TR-specific measures, EROA and TRIScore were most prognostic. Recognizing their impact can refine risk stratification, guide intervention timing, and inform long-term management.

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence–enabled electrocardiogram (AI-ECG) alone vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (i) AI-ECG alone and (ii) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼55.9% for AS, 28.9% for ILVWT, 20.7% for LVSD, and 40.8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.

Background Tricuspid regurgitation (TR) is a common valvular disorder that can affect patients’ quality of life and survival. The impact of TR etiology on overall survival and the associated risk factors in each subgroup are not well studied. Methods Multisite, retrospective study evaluating survival based on TR etiology using Kaplan–Meier and Cox regression models. Stepwise approach was used to define TR etiology (≥moderate): primary (PTR; presence of a primary valvular pathology), lead associated (LTR; lead interfering with closure), and secondary TR (STR) in the setting of left‐sided valvular disease, left ventricular disease, other causes of pulmonary hypertension, right ventricular disease, and atrial secondary TR. Results A total of 12 899 patients were included (3% PTR, 90% STR, 7% LTR). The mean age was 68±14, 73±13, and 71±13 years, respectively (44%–55% female patients). Adjusting for age, sex, and TR severity, patients with PTR had better overall survival (PTR: reference; STR hazard ratio [HR], 1.4 [1.2–1.6], LTR HR, 1.5 [1.1–1.7]). On multivariable Cox regression, the Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across all groups ( P <0.05 for all). In addition, other risk factors associated with worse outcomes included pulmonary hypertension and ≥moderate right ventricular enlargement in PTR; left‐sided valvular disease, pulmonary hypertension, and ≥moderate right ventricular dysfunction in STR; and left ventricular disease and ≥moderate right ventricular dysfunction in LTR. Conclusions Patients with PTR have better survival than STR and LTR. Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across etiologies, whereas other risk factors are etiology specific. Improved risk profiling may enhance patient selection, treatment decisions, and outcomes.

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 &lt;1 year diagnosis of HFrEF with mildly (40% ≤ EF &lt; 50%) or moderately (&lt;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.

BACKGROUND: Chronic aortic regurgitation (AR) is associated with significant cardiac remodeling, but the prevalence and prognostic impact of extravalvular cardiac damage remain unexplored. METHODS: Adults with moderate or greater chronic AR identified on echocardiogram between January 2008 and July 2024 were included. Exclusion criteria were acute AR, hypertrophic and infiltrative cardiomyopathies, prior cardiac surgery, and valve stenosis. Cardiac damage was classified into hierarchical stages: no cardiac damage (stage 0), left ventricular damage (stage 1), moderate or greater mitral regurgitation or left atrial enlargement or atrial fibrillation (stage 2), pulmonary hypertension or moderate or greater tricuspid regurgitation (stage 3), and significant right ventricular dysfunction (stage 4). The primary outcome was the association between cardiac damage stages and all-cause mortality under medical surveillance. RESULTS: Of 4026 patients (median age, 72 [61–80] years), 78% had moderate AR, 11% had moderate-severe, and 11% had severe AR. Cardiac damage was present in 87% of patients: 14% in stage 1, 53% in stage 2, 18% in stage 3, and 2% in stage 4. In a multivariable model, including age, sex, AR severity, and Charlson Comorbidity Index, cardiac damage stages were associated with mortality. Adjusted hazard ratios were 1.42 (95% CI, 1.04–1.96) for stage 1, 1.58 (95% CI, 1.21–2.06) for stage 2, 2.78 (95% CI, 2.10–3.67) for stage 3, and 5.34 (95% CI, 3.67–7.76) for stage 4. Adding cardiac damage staging to multivariable models improved predictive accuracy for mortality, increasing the concordance statistics from 0.73 (95% CI, 0.71–0.75) to 0.76 (95% CI, 0.74–0.77). CONCLUSIONS: Cardiac damage is present in nearly 90% of patients with moderate or greater AR and is associated with increased mortality, highlighting the need for a more comprehensive evaluation of cardiac structure and function beyond the aortic valve and left ventricle.