Anand, Vidhu

Introduction: Severe tricuspid regurgitation (TR) is associated with considerable morbidity/mortality in an elderly population with multiple comorbidities. There is interest in transcatheter interventions to manage severe TR in this population. Understanding right heart (RH) anatomy has implications for patient/device selection for transcatheter intervention. Hypothesis: We analyzed RH anatomy by CT of patients referred for TR therapy to determine transcatheter tricuspid intervention device sizing needs. Methods: Retrospective Mayo Clinic study of 29 patients considered for intervention of severe TR from 3/1/2016-12/15/2020; echocardiogram and CT angiogram obtained in all patients. Results: Mean age was 83±8 (83% female), 100% had atrial fibrillation, and 62% had chronic kidney disease ≥3a. 97% were symptomatic, 93% were prescribed loop diuretics, and 24% had device leads. Mean tricuspid annular plane systolic excursion 16.8±4.5 mm, effective regurgitant orifice area (EROA) 81±33 mm 2 , and cardiac index 2.6±0.6 L/min/m 2 . 41% had at least moderate right ventricular (RV) dysfunction with a mean RV systolic pressure of 46±16 mmHg. Patients receiving intervention had significantly larger EROA (101±33 v. 63±22 mm 2 , P=0.033), shorter tricuspid leaflet tenting length (6.5±3.0 v. 8.9±2.7 mm, P=0.042), and smaller annuloplasty diagnostic perimeter during diastole (120.1±16.6 v. 131.1±7.4 mm, P=0.041). Intervention patients tended to have better right ventricular function, smaller RV and IVC size, and more severe symptoms. Max tricuspid annulus diameter in systole and diastole was 51±5 and 53±7 mm, respectively. Conclusions: Severe TR patients referred for transcatheter intervention present with severe RH enlargement with a large proportion having tricuspid annulus dimensions outside the range for current devices available in clinical trials. These data have implications for device development/selection and procedural feasibility.

Abstract Aims The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines. Methods and results The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.

Abstract Aims Cardiac power is a measure of cardiac performance that incorporates both pressure and flow components. Prior studies have shown that cardiac power predicts outcomes in patients with reduced left ventricular (LV) ejection fraction (EF). We sought to evaluate the prognostic significance of peak exercise cardiac power and power reserve in patients with normal EF. Methods and results We performed a retrospective analysis in 24 885 patients (age 59 ± 13 years, 45% females) with EF ≥50% and no significant valve disease or right ventricular dysfunction, undergoing exercise stress echocardiography between 2004 and 2018. Cardiac power and power reserve (developed power with stress) were normalized to LV mass and expressed in W/100 g of LV myocardium. Endpoints at follow-up were all-cause mortality and diagnosis of heart failure (HF). Patients in the higher quartiles of power/mass (rest, peak stress, and power reserve) were younger and had higher peak blood pressure and heart rate, lower LV mass, and lower prevalence of comorbidities. During follow-up [median 3.9 (0.6–8.3) years], 929 patients died. After adjusting for age, sex, metabolic equivalents (METs) achieved, ischaemia/infarction on stress test results, medication, and comorbidities, peak stress power/mass was independently associated with mortality [adjusted hazard ratio (HR), highest vs. lowest quartile, 0.5, 95% confidence interval (CI) 0.4–0.6, P < 0.001] and HF at follow-up [adjusted HR, highest vs. lowest quartile, 0.4, 95% CI (0.3, 0.5), P < 0.001]. Power reserve showed similar results. Conclusion The assessment of cardiac power during exercise stress echocardiography in patients with normal EF provides valuable prognostic information, in addition to stress test findings on inducible myocardial ischaemia and exercise capacity.