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In the realm of innovative medical procedures, TEER (transcatheter edge-to-edge repair) has emerged as a promising field, showcasing significant growth and advancements. Mitral TEER has been performed for the last two decades; in contrast, tricuspid TEER is newer, with long-term outcomes pending. This article aims to provide a comprehensive review of the current literature, with a primary focus on outcomes and potential complications associated with both procedures. Both procedures carry a low risk of complications when done by experienced providers. A team approach involving specialists in cardiology, cardiothoracic surgery, cardiac imaging and heart failure ensures comprehensive care. A unified approach encompassing preprocedural workup, risk assessment, and standardised care throughout the procedure and recovery contributes to successful outcomes.

BACKGROUND: Percutaneous microaxial ventricular assist devices (pVADs) have the potential to reduce mortality of patients with cardiogenic shock (CS). However, the association between the distribution of pVAD-performing centers and outcomes of CS has not been explored. METHODS: This observational study included Medicare fee-for-service beneficiaries aged 65 to 99 years treated with pVAD for CS from 2016 to 2020. It examined the associations between patient outcomes and 2 exposure variables: hospitals’ procedure volumes of pVAD and patient-hospital distances (in quintiles [Qn]). We developed Cox proportional hazards regression for 180-day mortality and heart failure readmission rates, and multivariable logistic regression for in-hospital outcomes, adjusting for patient demographics, comorbidities, concomitant treatments, and hospital characteristics, including CS volume, teaching status, and the ability to perform extracorporeal membrane oxygenation. RESULTS: A total of 6637 patients with CS underwent pVAD at 1041 hospitals, with the annualized hospital volume ranging widely from 0.3 to 55.6 cases/year. Patients treated at higher-volume centers experienced lower 180-day mortality compared with those treated at lower-volume centers (Qn1=reference; Qn2: adjusted hazard ratio [aHR], 0.88 [95% CI, 0.79–0.97]; Qn3: aHR, 0.88 [95% CI, 0.79–0.98]; Qn4: aHR, 0.88 [95% CI, 0.78–0.99]; Qn5: aHR, 0.84 [95% CI, 0.74–0.95]; P for trend, 0.026), while we found no evidence that patient-hospital distances were associated with mortality (Qn1=reference; Qn2: aHR, 0.99 [95% CI, 0.89–1.09]; Qn3: aHR, 0.94 [95% CI, 0.85–1.04]; Qn4: aHR, 1.01 [95% CI, 0.92–1.11]; Qn5: aHR, 0.91 [95% CI, 0.82–1.01]; P for trend, 0.160). We found no evidence that the hospital volume and patient-hospital distances were associated with in-hospital bleeding, intracranial hemorrhage, or renal replacement therapy initiation. CONCLUSIONS: Hospital volume was more strongly associated with mortality than patient-hospital distances, suggesting that rational distribution of pVAD-performing centers while ensuring adequate procedure volumes may optimize patient mortality.

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

Abstract Background Pulmonary Hypertension (PH) is a diverse condition associated with elevated mortality. We sought to determine the association of Transthoracic Echocardiography (TTE) parameters with in-hospital mortality in patients with PH admitted to Cardiac Intensive Care Unit (CICU). Methods We included 1 Clinic CICU admissions from 2007 to 2018, with available TTE parameters within the first day of CICU admission who had PH defined as an estimated right ventricular systolic pressure (RVSP) ≥36 mmHg. The primary outcome was in-hospital mortality, evaluated using logistic regression. Results We included 3085 unique CICU patients with PH, predominantly WHO group 2 and 3. Median age was 73.7 (63.8, 82.4) years, and 1343 (43.5%) were females. Heart failure (65.6%) and respiratory failure (34.0%) were the most common admission diagnoses. The median RVSP was 47 (41, 56) mmHg, and 1314 (42.6%) had RVSP ≥50 mmHg. A total of 337 (10.9%) patients died during hospitalization. In-hospital deaths had higher RVSP (51 versus 47 mmHg, p <0.001), reflecting higher right atrial (RA) pressure (14 versus 10 mmHg, p <0.001) (Figure). In-hospital mortality increased with worse TTE parameters reflecting Right Ventricle-Pulmonary Artery (RV-PA) coupling, such as Tricuspid Annular Plane Systolic Excursion/ Right Ventricular Systolic Pressure (RVSP), Tricuspid Annular Systolic Velocity (TASV)/RVSP and TASV/ Tricuspid Regurgitation velocity ratios. Conclusion In CICU patients with PH, we have identified the specific Doppler TTE parameters with strong associations with outcomes. Specifically, RA pressure and parameters of RV-PA coupling had the highest discrimination for in-hospital mortality. Comprehensive Doppler TTE is valuable for identification of high-risk PH patients to facilitate further investigation plans, management, and prognostication. Figure: In-hospital mortality in association with right ventricular (RV) function and right atrial pressure (RAP).In-hospital mortality in association wit