Mohamad Alkhouli

BACKGROUND: The Michigan Structural Heart Consortium showed that transcatheter aortic valve replacement (TAVR) stroke rates were associated with hospital Comprehensive Stroke Center (CSC) designation, suggesting that hospital structural factors affect stroke reporting. We sought to determine whether CSC designation is nationally associated with reported stroke rates and the potential impact on hospital performance and Transcatheter Valve Therapy National Data Registry (TVTR) public reporting. METHODS: Consecutive TVTR cases from January 2016 through June 2023 were included, excluding sites reporting to the TVTR P =0.033 and 0.012) and disabling stroke ( P =0.033 and 0.007), but not falsification end points ( P =NS). Participation in TAVR trials and operator volume were independent predictors of 30-day stroke ( P =0.039 and P =0.041). The impact of CSC status on the 30-day composite model showed performance improvement for 8 of 153 CSC hospitals. CONCLUSIONS: CSC hospital status and participation in TAVR trials are significant predictors unique for stroke. Correcting for the association of CSC status on stroke rate resulted in an estimated 8 of 153 hospitals improving their TVTR 30-day composite model performance. Further work is needed to account for potential ascertainment bias for stroke in public reporting.

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 P 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.

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.