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.
Mohamad S. Alabdaljabar, Mohamed Elhadi, J. Thaden et al.· Journal of the American Hear...· 0 citations
Importance
Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel.
Objective
To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)-guided focused cardiac ultrasound (FoCUS) acquired by novice operators.
Design, Setting, and Participants
This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort.
Exposure
AI-guided FoCUS acquisition and automated deep learning-based assessment for detection of moderate or greater AS.
Main Outcomes and Measures
Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value.
Results
The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%.
Conclusions and Relevance
A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.
Eunjung Lee, J. Naser, Conor J. Kane et al.· JAMA cardiology· 1 citation
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