Tsaban, Gal

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

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.