Citations

Abstract Background The tricuspid annular plane systolic excursion (TAPSE) to systolic pulmonary artery pressure (SPAP) ratio (TAPSE/SPAP) is touted as a measure of right ventricular (RV) to pulmonary artery (PA) coupling, but its intrinsic, independent and incremental impact on outcome remains uncertain, particularly across the quantified severity spectrum of tricuspid regurgitation (TR). Methods We enrolled a prospective repository of patients with native TR, in whom TR quantitation using proximal-isovelocity-surface-area (PISA), SPAP and TAPSE, were all prospectively measured between 09/2003 and 04/2023. We included only patients with functional TR, without congenital or organic heart disease and without indwelling catheter/lead. The primary outcome was mortality under medical management. Results A total of 3,436 patients (median age 77 years, 57.6% female, median effective-regurgitant-orifice-area 39 mm2) were included. The median TAPSE/SPAP ratio was 0.36 (interquartile-range 0.26-0.51). Over a mean follow-up period of 2.4±2.8 years, 854 (24.9%) patients died. Spline regression, adjusted for age, sex, left ventricular ejection fraction, EROA, comorbidity index, and heart failure severity (TriScore), demonstrated that a TAPSE/SPAP remain linked to mortality, with ratio below 0.4 significantly associated with excess mortality risk (Figure). Multivariable models confirmed that higher TAPSE/SPAP ratio remained independently inversely associated with mortality (adjusted-hazard-ratio 0.76 [0.72-0.80] per 0.1 increment, p<0.001). Furthermore TAPSE/SPAP ratio provided incremental and independent prognostic value above and beyond the individual values of TAPSE or SPAP (p<0.001) and threshold for excess mortality remained consistently at 0.4, irrespective of quantified TR severity. Conclusion The TAPSE/SPAP ratio measured prospectively in routine clinical practice, is a strong and independent prognostic indicator in patients with functional TR, that adds to the prognostic value of its components. A TAPSE/SPAP ratio below 0.4 consistently identifies a subgroup at high-risk of mortality under medical management. These data suggest that therapeutic interventions that may impact positively the TAPSE/SPAP ratio should be tested in patients with functional TR.Mortality risk across TAPSE/SPAP ratio

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend<0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p<0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF > 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Left ventricular ejection fraction (LVEF) and diastolic dysfunction (DD) are key prognostic factors in patients with ST-segment elevation myocardial infarction (STEMI). AI-enabled ECG (AI-ECG) has been shown to have excellent agreement with transthoracic echocardiography (TTE) assessment and incremental prognostic value to LVEF. Methods We retrospectively analyzed 1,005 adult acute STEMI patients referred for echocardiography (10/2007–01/2025) who had ECGs within seven days before and after the reference echocardiogram. The ECG closest to the TTE date was designated as the pre-STEMI and post-STEMI ECG, respectively. AI-ECG was used to classify diastolic function grades and grades 2 and 3 were considered as DD. Patients were stratified by LVEF (≤50% vs. >50%) at the time of TTE and the association between AI-ECG-determined DD and 10-year mortality was assessed using Cox regression and Kaplan-Meier curves. Results Among 1,005 patients (mean age 66.4 ± 13.4 years, 29.9% females), AI-ECG-determined DD prior to STEMI was present in 47.9% of patients with LVEF ≤50% and 30.3% of those with preserved LVEF (>50%). DD status remained unchanged post-STEMI in 79.3% of cases, while 10.2% showed worsening diastolic function. Regardless of LVEF, AI-ECG-determined DD prior to STEMI was associated with higher mortality risk (LVEF ≤50%: HR 3.03, 95% CI 2.16–4.24, p < 0.001; LVEF >50%: HR 2.32, 95% CI 1.58–3.41, p < 0.001). Post STEMI AI-ECG-determined DD remained a significant predictor of mortality (LVEF ≤50%: HR 2.54, 95% CI 1.82–3.53, p < 0.001; LVEF >50%: HR 2.43, 95% CI 1.66–3.58, p < 0.001). Echocardiographically determined DD also correlated with higher mortality (LVEF ≤50%: HR 4.91, 95% CI 2.95–8.15, p < 0.001; LVEF >50%: HR 2.72, 95% CI 1.68–4.41, p < 0.001). In patients with LVEF ≤50%, AI-ECG-determined DD remained associated with higher mortality even after adjusting for echocardiographically determined DD (pre-STEMI: HR 1.85, 95% CI 1.23–2.80, p < 0.001; post-STEMI: HR 1.90, 95% CI 1.23–2.91, p < 0.001). Conclusion AI-ECG-determined DD is a significant predictor of mortality in STEMI, independent of LVEF. The presence of normal filling pressure may indicate a better clinical outcome in patients with reduced LVEF patients. These findings underscore the clinical utility of AI-ECG in refining risk stratification beyond conventional LVEF assessment.

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.

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP&gt;15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Abstract Aims We explored the manifestations of individual weight loss (WL) response to long-term lifestyle interventions on cardiometabolic risk. Methods and results We pooled data from three large long-term lifestyle WL-intervention trials: 24-month DIRECT (ClinicalTrials.gov: NCT00160108; n = 322; 87% adherence), 18-month CENTRAL (ClinicalTrials.gov: NCT01530724; n = 278; 86% adherence), and 18-month DIRECT PLUS (ClinicalTrials.gov: NCT03020186; n = 294; 89% adherence). We analyzed longitudinal changes in cardiometabolic risk markers, including anthropometrics, blood biomarkers, and magnetic-resonance-imaging-assessed fat depots, and measured DNA-methylation, proteomics, and metabolomics. Among trial completers (n = 761, mean age = 50.4 years; 89% men, baseline body-mass-index = 30.1 kg/m2), mean WL was −3.3 kg (−3.5%). We classified participants as Successful-WL (36%) with relative-WL &gt; 5%, WL-Resistant (28%) who did not lose or gained weight, and Moderate-WL (36%) with WL between 0% and 5%. Successful-WL achieved the greatest improvements in multiple health indicators. However, the WL-Resistant also showed some significant improvements, with increased high-density-lipoprotein-cholesterol (HDLc) and decreased leptin and visceral fat (P &lt; 0.05 vs. baseline). Overall, each 1 kg sustained lifestyle-induced WL was associated with improvements in lipid markers and insulin resistance [HDLc (+1.44%), triglycerides (−1.37%), insulin (−2.46%), HOMA-IR (−2.71%), leptin (−2.79%)] and intrahepatic-fat regression (−0.49 absolute-units)] and modest but significant change in systolic and diastolic blood pressures (−0.26% and −0.36%). We identified 12 significant methylation sites that are associated with Successful-WL (FDR &lt; 0.05; AUC = 0.73). Conclusion While only ∼one-third of individuals achieved long-term WL, the Moderate-WL and WL-Resistant individuals could benefit improvements in visceral adiposity and cardiometabolic risk by shifting towards a healthy lifestyle pattern, beyond WL. Site-specific DNA methylation may predict an individual's likelihood of successful WL. Registration NCT00160108, NCT01530724, NCT03020186.

Abstract Aims To develop a deep learning model that: (i) utilizes transthoracic echocardiography (TTE) clips to detect left ventricular (LV) enlargement without being provided information regarding a patient’s sex and body size; and (ii) can be accurately applied to clips acquired using either standard comprehensive TTE or handheld cardiac ultrasound (HCU). Methods and results Using retrospective TTE data (training: 8722 patients; internal validation: 468 patients), we developed a deep learning model that estimates a patient’s end-diastolic LV volume (indexed to body surface area and normalized across the sexes), and then thresholds this estimate to perform the following classifications: (1) normally sized LV vs. ≥ mild LV enlargement; (2) normal/mildly enlarged LV vs. ≥ moderate LV enlargement. For retrospective datasets, the model showed strong performance in TTE across three geographically distinct locations (Minnesota and Wisconsin: 1082 patients, AUC = 0.925 and 0.953 for classifications 1 and 2, respectively; Arizona: 1475 patients, AUC = 0.935 and 0.969; and Florida: 1481 patients, AUC = 0.934 and 0.970). Additionally, performance was strong for both TTE and HCU clips collected from a prospective cohort of 410 patients who underwent HCU immediately following TTE (TTE: AUC = 0.925 and 0.971; HCU: AUC = 0.874 and 0.902, for classifications 1 and 2, respectively). Conclusion An automated deep learning model applied to TTE or HCU images accurately categorizes LV volumes. These results lay a foundation for future work aimed at optimizing clinical outcomes for heart failure patients by enabling early detection of LV enlargement across various point-of-care settings.

Abstract Acute Myocarditis (AM) encompasses a broad spectrum of clinical presentations and causes. Despite the recent advances in cardiovascular imaging, pathology, virology, and genetics, specific therapies are still lacking. This collaborative review aims to analyse the current evidence to answer practical questions that physicians may face during the early management of patients presenting with an acute form of the disease, complicated or not. This review analyses current evidence to address practical questions posed by acute cardiovascular physicians during the early management of acute, or potentially, myocarditis. Based on the current literature, this review provides a step-by-step approach to treat AM patients from their admission in the cardiac intensive care unit (CICU) to discharge, by answering 10 clinical questions: Might this patient be suffering from an AM? Should I hospitalize this patient and, if so, where? Which cardiac imaging exam should I perform and what can I learn from it? Is this patient requiring an EMB? What should the non-invasive aetiological work-up be? Is her/his episode of AM of viral, toxic, or other origin? Does this patient need specific treatments or mechanical circulatory support? Is there an indication for guideline-directed medical heart failure treatment? When can the patient be discharged and resume physical activity? Notably, this review highlights the need to build a multidisciplinary response team to address the many diagnostic and therapeutic challenges of AM patients. It also points out the lack of evidence to guide the treatment of these patients.