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Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

Importance Current guidelines recommend consideration of surgical treatment for asymptomatic patients with severe mitral valve regurgitation, but the long-term prognosis and optimal treatment of those with moderate mitral regurgitation (MR) remain uncertain. Objective To evaluate the long-term prognostic value of baseline MR severity and the association of surgery with survival outcomes in asymptomatic patients. Design, Setting, and Participants This was a prospective cohort study with extended follow-up among 449 asymptomatic patients with holosystolic degenerative MR enrolled between January 1991 and November 2000 and followed up with through May 2025 (median [IQR] follow-up, 26 [14-29] years; maximum, 34 years) at a single tertiary referral center (the Mayo Clinic, Rochester, Minnesota). Exposure Baseline MR severity quantified by effective regurgitant orifice area (ERO). Patients were monitored under medical therapy and, when applicable, after subsequent surgical correction. Main Outcomes and Measures All-cause mortality, assessed according to baseline ERO using Cox proportional hazards models adjusted for cardiovascular risk factors. Results Among the 449 patients included, the mean (SD) age was 64 (14) years, and 278 participants (62%) were male. At baseline, 127 patients (28%) had ERO <20 mm 2 , 128 (28%) had ERO 20-39 mm 2 , and 194 (43%) had ERO ≥40 mm 2 . During follow-up, 254 patients (57%) underwent mitral valve surgery. Ten-year cumulative incidence of surgery was 18% for ERO <20 mm 2 , 64% for 20-39 mm 2 , and 88% for ≥40 mm 2 . Survival of medically treated patients declined progressively with increasing ERO category (5-year rate: 88% [<20 mm 2 ], 72% [20-39 mm 2 ], and 55% [≥40 mm 2 ]; P = .01), and multivariable analysis confirmed the incremental risk of higher ERO on a continuous scale (adjusted hazard ratio per IQR increase, 2.53; 95% CI, 1.30-4.93; P = .01). When extending the analysis through subsequent surgical follow-up, the survival difference between patients who did vs did not undergo operation widened with ERO (χ 2 3 = 8.4; interaction P = .04), starting at values above 30 mm 2 . Conclusions and Relevance In this 25-year follow-up cohort study, increasing ERO was associated with an increase in risk of mortality under medical therapy. Patients with upper-moderate MR (ERO 30-39 mm 2 ) at baseline demonstrated a significant association between surgical treatment and improved long-term survival.

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 < 0.03) were observed. Proportional hazards models confirmed EROA as the strongest TR severity marker (P < 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 The natural history of asymptomatic moderate or severe aortic regurgitation (AR) remains uncertain, with conflicting reports about its progression and surgical timing. We aimed to quantify adverse outcomes under conservative management and evaluate the association of aortic valve replacement/repair (AVR) with mortality. Methods Systematic searches (inception–July 2025) identified cohort studies of asymptomatic moderate/severe AR. Random-effects models estimated pooled incidence rates of adverse events; fixed-effects models were used for hazard ratios (HRs) of AVR vs conservative management. Results Twenty-seven studies (4720 patients; mean age 49 years; mean follow-up 3.9 years) were included. Pooled incidence rates per 100 person-years were 1.75 (95% CI 1.27 to 2.41) for all-cause mortality, 1.29 for cardiac death, 0.29 for sudden death, 4.30 for new symptoms and 7.01 for AVR. Asymptomatic low left ventricular ejection fraction occurred in only 0.9 per 100 person-years. Mortality rates were more than double those of the general population across age groups—2.45 (1.90 to 3.18) per 100 person-years for cohorts with mean age ≥50 years versus 0.59 (0.29 to 1.21) for younger cohorts. Early AVR was associated with lower mortality (pooled HR 0.33; 95% CI 0.30 to 0.37). Conclusion Asymptomatic moderate/severe AR carries significant excess mortality irrespective of age, contradicting its historically benign reputation. Given the rarity of asymptomatic LV dysfunction, earlier intervention guided by more sensitive markers of LV damage may improve outcomes, although heterogeneity and study quality warrant cautious interpretation. PROSPERO registration number CRD42024522683.

Background: Tricuspid regurgitation (TR) is associated with significant morbidity, but individualized treatment and timing of intervention are yet to be defined. We previously proposed and validated artificial intelligence electrocardiography (AI-ECG) scores for various diseases. Whether incorporating AI-ECG information adds to risk stratification above and beyond classical clinical parameters is unknown. Objectives: To identify AI-ECG scores independently associated with all-cause mortality in chronic TR and to create a composite AI-ECG risk score. Methods: Patients with a first echocardiographic diagnosis of ≥moderate TR between 2005-2016 and an ECG within ±15 days were included. The Tricuspid Regurgitation Impact on Outcome (TRIO) score was calculated as previously described based on 8 simple parameters (age, sex, severe TR, heart failure, lung disease, heart rate, creatinine and AST). AI-ECG probabilities of low ejection fraction, aortic stenosis, amyloid, cirrhosis, atrial fibrillation and hypertrophic cardiomyopathy were estimated by existing algorithms; the first 4 were associated with all-cause mortality and a composite AI-ECG risk score was calculated (0-4; 1 point for each AI-ECG above specific threshold). Results: A total of 12,377 pts were included (age 72±13 yrs, 55% women). Over a median follow-up of 1.3 yrs (IQR 0.1 to 4.4), 7,121 pts (58%) died. Mortality risk increased significantly with higher composite AI-ECG risk score (Fig. 1A). On multivariable analysis (proportional hazards method), the composite AI-ECG risk score remained independently associated with death, even after adjusting for age, sex, high NT-proBNP levels (>2,500 pg/mL), right ventricular systolic pressure (RVSP), diuretic use and TRIO risk score category (Fig. 1B). Conclusions: Patients with ≥moderate TR show progressively worse survival with increasing composite AI-ECG risk score. Incorporating AI-derived information from a simple and inexpensive ECG may identify patients with the highest risk of mortality beyond clinical risk scores as TRIO or classical clinical, laboratory and echocardiographic parameters. Prospective validation of this approach needs to be tested in future trials.