Citations

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.

Abstract Background Right ventricular (RV) systolic function is an independent predictor of outcome, but a robust automated method of quantitation is lacking. Purpose To develop and validate a deep learning model for quantifying RV systolic function from 2D echo clips and assess its prognostic value in various loading states and left-heart disease. Methods A deep learning convolutional neural network (CNN) was developed to quantitate RV systolic function using all available apical 4 chamber (A4C) views from 13,039 patients (61,187 A4C video clips) in a US institution. Videos were converted to frames of 256x256 grayscale pixels, with graphics, including the ECG, removed. The model used the S3D architecture, a 3D-CNN with many of the spatial and temporal convolutions separated. RV free wall longitudinal strain (RVFWLS) was used to define RV function: normal (≤-25%); mild (-20% to –24%); moderate (-15% to –19%) and severe RV dysfunction (0% to –14%). The AI-estimate was the average of multiple 32-frame segments for each clip identified as an A4C, including both standard and RV-focused variations. The model was validated in four distinct clinical cohorts from independent data sources: 1) 1365 patients referred to an external diagnostic echocardiography lab in a different US state; 2) 569 patients recruited prospectively with known or suspected pulmonary hypertension (PH); 3) 416 patients with moderate or greater tricuspid valve regurgitation (TR); 4) 872 patients imaged at an Eastern European University with left-sided cardiac diseases (valvular disease, cardiomyopathies, etc.), athletes, or healthy volunteers. Results Model output was strongly correlated with the presence of RV dysfunction by manual measurement [AUC 0.907 (95% CI: 0.888-0.926)] in the internal validation set. Results were similar in an external validation US cohort [N=1365; AUC 0.919 (95% CI: 0.899-0.939)]. When the model was applied to 569 patients with known or suspected PH, presence and severity of the AI measures of RV dysfunction were strongly associated with functional class, 6 min walk distance, NT-proBNP levels and right heart failure (p<0.001). The presence and severity of RV dysfunction (as categorized by the AI model) was strongly associated with all-cause mortality in patients with PH (Figure 1A) and in patients with significant TR (Figure 1B). The European cohort was a geographically and institutionally distinct cohort with a low prevalence of >mild TR (10.8%) or PH (6.4%). In this cohort the AI model predicted a reduced RVEF (< 45%) with an AUC of 0.864 (95% CI: 0.831-0.898) and associated with both all-cause mortality (Figure 2A) and a composite endpoint of heart failure hospitalization or death (Figure 2B). Conclusions A fully automated, AI-based assessment of RV systolic function, using 2D apical 4 chamber echo views alone, is highly feasible and can accurately categorize disease severity and predict outcome across a wide spectrum of cardiovascular conditions.Figure 1 Figure 2

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.

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.

Abstract Aims The pathophysiology of tricuspid regurgitation (TR) during exercise in heart failure with preserved ejection fraction (HFpEF) is not well understood. Methods and results We investigated dynamic changes in TR severity during exercise in patients with HFpEF and associated relationships with haemodynamics, cardiopulmonary reserve, and prognosis using invasive haemodynamic exercise testing with simultaneous echocardiography and expired gas analysis. Among 169 patients with HFpEF (91 no TR, 44 mild TR, 34 ≥ moderate TR), older age and atrial fibrillation were more common in patients with greater TR severity. Right heart remodelling and dysfunction were more frequently observed with greater TR severity, particularly in the right atrium, along with stepwise increases in right atrial (RA) pressure and interventricular septal flattening, and decreases in cardiac output (CO). During exercise, TR severity frequently worsened, and over half of the patients with moderate TR at rest displayed severe TR during exercise, accompanied by profound RA dilatation, reduced RA reservoir strain, increased septal flattening, and more severely impaired CO reserve. Abnormalities in tricuspid annular motion and ventricular interaction were amplified by exercise with greater TR, but exercise pulmonary vascular pressures did not differ across groups. Patients with ≥ moderate TR during exercise displayed higher event rates (hazard ratio 2.85, 95% confidence interval 1.18–6.85), but patients with mild TR had similar prognosis as those without TR in adjusted models. Conclusions Tricuspid regurgitation in HFpEF is related to advanced RA myopathy and often worsens during exercise leading to dynamic right-sided heart failure, exaggerated ventricular interaction, impaired CO reserve, and increased risk for adverse outcomes. These data emphasize the clinical importance of exercise evaluation and call for further study of novel treatments in those with significant resting and provocable TR.

Background Atrial fibrillation (AF) impacts management decisions in valvular heart disease but its significance in patients with chronic aortic regurgitation (AR) is not well established. Objectives To evaluate the prevalence and prognostic role of AF in patients with chronic, haemodynamically significant AR. Methods Adults with chronic ≥moderate-to-severe AR, from March 2004 to April 2019, were retrospectively identified. Those with acute AR, moderate or worse other valve disease or prior valve surgery were excluded. Patients with AF were identified based on 12-lead ECGs, 1-lead ECGs (showing AF for ≥30 s), or episodes recorded by implanted cardiac devices. Primary outcome was all-cause mortality. Cox regression models were used to assess associations with mortality: model 1 adjusted for age, symptoms (New York Heart Association class II–IV), Charlson Comorbidity Index, left ventricle (LV) end-systolic volume index and AF; model 2 added left atrial volume index and model 3 further added systolic pulmonary artery pressure. Results Of the 1006 included patients (age 59±18 years; 822 (82%) men), 446 (46%) had hypertension and 364 (36%) had a bicuspid aortic valve. AF was present in 161 (16%) patients at the time of ≥moderate-to-severe AR diagnosis. During a median follow-up of 1.6 years (IQR 0.2–6.2), AF was associated with an increased mortality risk in univariate and in all three multivariable models (HR 1.61 (95% CI 1.07 to 2.43), HR 2.21 (95% CI 1.31 to 3.72) and HR 2.06 (95% CI 1.21 to 3.52), respectively), including after propensity matching (HR 1.58 (95% CI 1.01 to 2.48)). Conclusions AF is present in one in six patients with chronic≥moderate-to-severe AR and is associated with an increased risk of mortality even after adjusting for measures of LV systolic and diastolic function. There is a need for future prospective studies to validate these findings and evaluate AF as a potential trigger for early surgery.

Background Tricuspid valve surgery is recommended for severe isolated tricuspid regurgitation (TR) if symptoms persist despite optimal medical therapy. The definition of optimal therapy for TR is unclear beyond managing underlying conditions. Diuretics are the primary treatment for volume retention in TR, but it is unknown whether a “safe diuretic dose” exists for conservative management. We sought to evaluate the association between diuretic dose and outcomes. Methods Adult patients with ≥moderate–severe isolated TR were retrospectively identified from echocardiographic reports. Clinical and echocardiographic data were extracted from electronic medical records and manually reviewed. The primary end point was all‐cause mortality. Loop diuretic doses were converted to furosemide equivalent and categorized into 4 groups: <40 mg, 40 to 79 mg, 80 to 159 mg, and ≥160 mg daily. Results Of 1302 patients (age 72±13 years, 60% female), 565 (43%) had moderate–severe TR and 737 (57%) had severe TR. Median follow‐up was 8.7 (1.4, 14.0) years. Comorbidities increased with higher diuretic doses. A linear relationship between diuretic dose and mortality was observed in spline‐curve analysis. Hazard ratios (and CIs) for mortality adjusted for age, sex, coronary disease, chronic kidney disease, mild pulmonary hypertension, and New York Heart Association III/IV symptoms were 1.21 (0.96–1.53) ( P =0.12) for <40 mg, 1.50 (1.23–1.82) ( P <0.001) for 40 to 79 mg, 1.86 (1.46–2.37) ( P <0.001) for 80 to 159 mg, and 2.90 (2.18–3.88) ( P <0.001) for ≥160 mg furosemide equivalent. Any diuretic dose was associated with significant symptoms (New York Heart Association III–IV), with more patients experiencing symptoms at higher doses. Conclusion A daily loop diuretic dose ≥40 mg furosemide equivalent is associated with increased mortality and greater symptom severity in patients with TR.