Alkhouli, Mohamad

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 Since its introduction in 2002, left atrial appendage closure (LAAC) has rapidly expanded as an alternative to anticoagulation in patients with atrial fibrillation at high risk of stroke. Harmonized processes for data collection, analysis, and reporting in LAAC trials are essential to enhance research quality and improve clinical practice. The Left Atrial Appendage Academic Research Consortium (LAARC) initiative is an independent collaboration of academic research organizations, cardiology and neurology experts, clinical trialists, and regulatory authorities from the USA, Europe, and Asia. The consortium engaged clinical experts, regulators—including the US Food and Drug Administration (FDA), European Notified Bodies, and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA)—and industry leaders to define standardized study elements and endpoints for LAAC trials. Key considerations included alignment with prior Academic Research Consortium initiatives, procedural and mechanistic insights, and clinical relevance. Consensus definitions were proposed for mortality, stroke, bleeding, and device performance, along with composite endpoints for safety and effectiveness. The proposed LAARC consensus definitions aim to standardize endpoint reporting, improve comparability across studies, and support regulatory and clinical trial applications for this evolving therapy through broad dissemination in the peer-reviewed literature.

Objective Data on cardiac index (CI) in patients undergoing coronary angiography (CAG) can enhance decision-making and improve clinical outcomes. However, this necessitates an additional invasive procedure, and therefore, it is frequently omitted. This study explores the novel application of convolutional neural networks (CNNs) to predict CI from routine CAGs. Methods and analysis Patients who underwent CAG and simultaneous right heart catheterisation at Mayo Clinic between 2002 and 2023 were included. A three-dimensional (3D)-CNN model (X3D architecture) was developed to predict binary CI categories (normal: CI≥2.2 vs abnormal: CI<2.2 L/min/m²) from CAG videos. CAGs were randomly split into training (70%), validation (15%) and test (15%) sets. All available videos from each study were entered into the model. The model’s performance was evaluated using the area under the curve (AUC), sensitivity, specificity and F1 score. Results A total of 15 297 CAG studies were considered. After applying a quality-check algorithm to exclude low-quality or irrelevant CAGs, 11 475 studies from 9103 patients were included. CI was abnormal in 25.2% of studies. The model achieved an AUC of 0.83 (95% CI 0.81 to 0.85), sensitivity of 0.72 (95% CI 0.68 to 0.77), specificity of 0.78 (95% CI 0.75 to 0.80) and F1 score of 0.62 (95% CI 0.58 to 0.65) on the test dataset. Limiting the input to 4, 3 or 2 prespecified CAG videos representing standard views did not significantly affect the model’s performance, supporting its applicability in varied clinical workflows. Conclusions This study demonstrates the feasibility of leveraging deep learning models to predict CI from CAG videos. The proposed 3D-CNN model shows promise as a tool for providing physicians with real-time data to aid decision-making during CAG procedures.

Abstract Background Women with severe aortic stenosis (AS) are diagnosed later and experience poorer outcomes than men, partly because clinical approaches rely on 2D, valve-centric thresholds derived from male-predominant cohorts that underutilize information from 3D left ventricular (LV) geometry. We hypothesize that a sex-specific computational framework integrating statistical shape analysis (SSA) of pre-TAVR CT with machine learning would improve prediction of 1-year LV mass regression (LVMR). Objective To develop a computational framework leveraging 3D LV geometry and evaluate whether it improves sex-specific prediction of 1-year LVMR after TAVR. Methods We studied 339 patients with severe AS who underwent TAVR from 2013 to 2020 and had pre-TAVR CT and 1-year post-TAVR echocardiography. LV geometries were segmented into digital twins, and shape modes predictive of LVMR were extracted using SSA and partial least squares. These modes were incorporated into support vector regression models and compared with conventional echocardiographic predictors, including pre-TAVR LVEF, LVMI, and E/A ratio. Performance was assessed using RMSE and R 2 . Results After one year, 65% of patients showed positive LVMR, with median regression of approximately 10%; regression was significant overall and within each sex ( p < 0.001) and similar between sexes ( p = 0.99). Predictive shape modes differed by sex ( p < 0.01), with women showing more localized variation and men broader geometric gradients. Sex-specific shape modes outperformed general modes and clinical metrics, particularly in women (R 2 = 0.80, RMSE = 0.09 vs. R 2 = 0.59, RMSE = 0.13; clinical-only baseline R 2 = 0.16, RMSE = 0.22). In men, sex-specific modes also performed strongly (R 2 = 0.89, RMSE = 0.08). Conclusion In severe AS, 3D LV geometry predicts post-TAVR reverse remodeling more accurately than conventional metrics and may improve risk stratification, particularly in women.

Importance Left atrial appendage occlusion (LAAO) is used in patients with contraindications to oral anticoagulation, who are at increased risk for major bleeding (MB). Objective To evaluate the incidence, predictors, management, and clinical outcomes of MB after LAAO in the Amulet IDE trial. Design, Setting, and Participants The randomized clinical Amulet IDE trial enrolled patients from 2016 through 2019 with 5-year follow-up. Procedures were performed at specialized LAAO centers. Of 1878 randomized patients (1:1 Amulet vs Watchman), 1833 underwent attempted LAAO implantation. These data were analyzed from May 2025 through November 2025. Main Outcomes and Measures Pooled (Amulet and Watchman) patients with vs without MB (Bleeding Academic Research Consortium [BARC] score ≥3: overt bleeding with transfusion and ≥3 g/dL hemoglobin drop) were compared. Results This study included a total of 1833 patients (57.7% male and 42.3% female; mean [SD] age, 75.8 [7.5] years). MB occurred in 331 patients (18.1%) over 5 years (annualized rate 5.9% per year). Patients with MB were older (76.8 vs 74.7 years) and had higher CHA 2 DS 2 -VASc (4.8 vs 4.6) and HAS-BLED (3.4 vs 3.2) scores, as well as greater prevalence of diabetes (38.7% vs 34.1%), prior MB (45.3% vs 28.0%), and kidney disease (9.4% vs 4.5%). MB risk was highest in the first 6 months after LAAO (20.5% per year), then decreased to 3.9% per year through 5 years. Most MB events were nonprocedural (88.8%) with gastrointestinal bleeding accounting for 252 of 438 events. Independent predictors of MB included increasing age (hazard ratio [HR], 1.04; 95% CI, 1.02-1.06), female sex (HR, 1.25; 95% CI, 1.01-1.56), diabetes (HR, 1.26; 95% CI, 1.01-1.58), prior MB (HR, 1.93; 95% CI, 1.55-2.40), and kidney disease (HR, 2.15; 95% CI, 1.48, 3.12). At first MB event, 47.1% of patients were not receiving antithrombotic therapy and 92 patients had recurrent events. Patients with MB had significantly higher rates of the composite of stroke, systemic embolism, or cardiovascular death (32.5% vs 18.0%; P &amp;lt; .001), driven by higher stroke (14.7% vs 6.6%) and cardiovascular death (24.4% vs 12.9%) rates. All-cause mortality was also higher (49.2% vs 25.4%; P &amp;lt; .001). MB was fatal (BARC score of 5) in 25 patients (1.4%). Conclusions and Relevance In this study, MB was common in this high-risk population and largely related to underlying patient factors. Early MB risk was highest 6 months post-LAAO, then dropped considerably. Most events were gastrointestinal and occurred even in patients on minimal or no antithrombotic therapy. MB after LAAO was strongly associated with increased stroke and mortality. Trial Registration ClinicalTrials.gov Identifier: NCT02879448

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

Abstract Aims We evaluated clinical outcomes with GLP-1 RA use post-TAVI in patients with T2D. GLP-1 receptor agonists (GLP1-RAs) lower cardiovascular risk in type 2 diabetes (T2D), but their impact in patients with valvular heart disease, including those undergoing transcatheter aortic-valve implantation (TAVI), remained unexplored. Methods and results We conducted a retrospective cohort study using the TriNetX network, including 25 184 adults (≥18 years) with T2D who underwent TAVI (2015–2023). Patients were stratified based on GLP1-RA initiation within 14 days post-TAVI. Baseline characteristics, comorbidities, medications, and laboratory data were balanced using 1:1 propensity score matching (PSM). Cox models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for events from one-month post-TAVI until the most recent follow-up. PSM yielded 1101 patients per group, with a mean age of 73 years and 39% female. Mean follow-up duration was 2.2 ± 1.1 and 2.6 ± 1.8 years for GLP1-RA users and non-users, respectively. GLP1-RA use was also associated higher survival probability (HR: 0.708 [95% CI: 0.583–0.859]), and lower rates of all-cause hospitalization (HR: 0.790 [95% CI, 0.706–0.885]), major cardiovascular events (HR: 0.849 [95% CI: 0.749–0.963]), and acute heart failure exacerbation (HR: 0.785 [95% CI: 0.677–0.909]), acute kidney injury (HR: 0.843 [95% CI: 0.719–0.988]), and major kidney events (HR: 0.665 [95% CI: 0.510–0.866]). No differences were observed in acute myocardial infarction, cerebral infarction, or cardiac arrest. Conclusion In patients with T2D undergoing TAVI, GLP1-RA use is associated with significant survival benefits and improved cardiorenal outcomes. However, these findings are limited by the observational design, high comorbidity burden, lack of data on medication compliance, and specific cause of death. Prospective trials are warranted to confirm these benefits.

ABSTRACT Background Valve‐in‐valve transcatheter aortic valve replacement (ViV‐TAVR) is increasingly used to treat structural valve degeneration. Permanent pacemaker implantation (PPI) following TAVR is associated with increased morbidity and mortality, yet predictors specific to ViV‐TAVR remain poorly defined. Objective To identify clinical and procedural predictors of PPI in patients undergoing ViV‐TAVR. Methods We conducted a retrospective observational study of consecutive ViV‐TAVR cases at the Mayo Clinic from 2012 to 2022. Clinical, ECG, procedural data and fluoroscopic measurements were analyzed, and propensity score matching was used to adjust for confounding. Predictors of PPI were identified using multivariable logistic regression. Results Among 261 patients, 24 (9.2%) underwent PPI. Balloon‐expandable valves were used in 66.7% and self‐expanding valves in 33.3%. Predictors of PPI included Preprocedural bifascicular block (OR 12.15, 95% CI: 2.52–68.89, p = 0.003), greater septal implantation depth (OR 1.29, 95% CI: 1.04–1.65, p = 0.019), and post‐procedural new left (OR 23.56, 95% CI: 4.91–48.52, p = 0.0004) and right bundle branch blocks (OR 24.4, 95% CI: 3.82–30.51, p = 0.001). Aortic regurgitation as the mechanism of prosthetic valve failure was associated with reduced PPI odds (OR 0.28, 95% CI: 0.10–0.76, p = 0.014). Pacing requirement—defined as > 90% ventricular pacing—increased from 25% at 3 months to 56.25% at 12 months. At a median follow‐up of 962.5 days (IQR 481.5–1555.3), adjusted mortality was significantly higher in the PPI group (aOR 9.4, 95% CI: 1.9–60.4, p = 0.028). Conclusion Bifascicular block, greater septal depth, and new bundle branch blocks predict PPI post‐ViV‐TAVR, while aortic regurgitation appears protective. Risk stratification and procedural planning are critical.

Background/Objectives: Transcatheter aortic valve replacement (TAVR) has been introduced as an optimal treatment for patients with severe aortic stenosis, offering a minimally invasive alternative to surgical aortic valve replacement. Predicting these outcomes following TAVR is crucial. Artificial intelligence (AI) has emerged as a promising tool for improving post-TAVR outcome prediction. In this systematic review and meta-analysis, we aim to summarize the current evidence on utilizing AI in predicting post-TAVR outcomes. Methods: A comprehensive search was conducted to evaluate the studies focused on TAVR that applied AI methods for risk stratification. We assessed various ML algorithms, including random forests, neural networks, extreme gradient boosting, and support vector machines. Model performance metrics—recall, area under the curve (AUC), and accuracy—were collected with 95% confidence intervals (CIs). A random-effects meta-analysis was conducted to pool effect estimates. Results: We included 43 studies evaluating 366,269 patients (mean age 80 ± 8.25; 52.9% men) following TAVR. Meta-analyses for AI model performances demonstrated the following results: all-cause mortality (AUC = 0.78 (0.74–0.82), accuracy = 0.81 (0.69–0.89), and recall = 0.90 (0.70–0.97); permanent pacemaker implantation or new left bundle branch block (AUC = 0.75 (0.68–0.82), accuracy = 0.73 (0.59–0.84), and recall = 0.87 (0.50–0.98)); valve-related dysfunction (AUC = 0.73 (0.62–0.84), accuracy = 0.79 (0.57–0.91), and recall = 0.54 (0.26–0.80)); and major adverse cardiovascular events (AUC = 0.79 (0.67–0.92)). Subgroup analyses based on the model development approaches indicated that models incorporating baseline clinical data, imaging, and biomarker information enhanced predictive performance. Conclusions: AI-based risk prediction for TAVR complications has demonstrated promising performance. However, it is necessary to evaluate the efficiency of the aforementioned models in external validation datasets.