Echocardiographic evaluation of the right atrial size and function: Relevance for clinical practice

The Prognostic Value of Tricuspid Annular Dimensions in TAVI Patients: A CT-Based Retrospective Analysis of Risk Stratification and Long-Term Outcomes

Background: Transcatheter aortic valve implantation (TAVI) has transformed the treatment of severe aortic stenosis (AS), particularly in high-risk patients. However, comorbidities such as pulmonary hypertension (PH) and secondary tricuspid regurgitation (TR) contribute to adverse outcomes. Tricuspid annulus (TA) dilatation (TAD), a key marker of right ventricular dysfunction, has been associated with PH and TR progression. While echocardiographic assessment of TA has limitations, cardiac computed tomography (CT), routinely performed before TAVI, enables precise TA measurement. This study aimed to determine clinically relevant TA and TA indexed to body surface area (TA/BSA) cut-offs and assess their prognostic significance for long-term mortality. Methods: This retrospective, single-center study included 522 patients who underwent transfemoral TAVI between 2016 and 2022. Pre-procedural CT-derived TA measurements were analyzed to establish cut-off values predictive of right ventricular dysfunction in TAVI. Receiver operating characteristic (ROC) analysis was performed, and Kaplan-Meier survival curves, log-rank tests, and Cox regression were used to assess the impact of TA dimensions on long-term survival. Results: TAD correlated moderately with right ventricular dysfunction, with optimal cut-offs identified as TA ≥ 44.50 mm and TA/BSA ≥ 23.00 mm/m2. However, Kaplan-Meier and Cox regression analyses demonstrated no significant association between TA or TA/BSA and long-term survival, with area under the curve (AUC) values close to 0.50, indicating poor prognostic value. Conclusions: Despite its relevance regarding right ventricular dysfunction in TAVI patients, TAD does not independently predict long-term mortality following TAVI. These findings challenge prior assumptions and suggest that TA dimensions alone should not guide risk stratification in TAVI patients. Further research is needed to refine prognostic models integrating multiple clinical and imaging parameters.

PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning

Importance

Echocardiography is a cornerstone of cardiovascular care but relies on expert interpretation and manual reporting from a series of videos. We propose an artificial intelligence (AI) system, PanEcho, to automate echocardiogram interpretation with multi-task deep learning.

Objective

To develop and evaluate the accuracy of PanEcho on a comprehensive set of 39 echocardiographic labels and measurements on transthoracic echocardiography (TTE).

Design Setting and Participants

This study represents the development and retrospective, multi-site validation of an AI system. PanEcho was developed using a sample of TTE studies conducted at Yale-New Haven Health System (YNHHS) hospitals and clinics from January 2016-June 2022 during routine care. The trained model was internally validated in a temporally distinct YNHHS cohort from July-December 2022, externally validated across four diverse external cohorts, and made publicly available.

Main Outcomes and Measures

The primary outcome was the area under the receiver operating characteristic curve (AUC) for diagnostic classification tasks and mean absolute error (MAE) for parameter estimation tasks, comparing AI predictions with the assessment of the interpreting cardiologist.

Results

This study included 1.2 million echocardiographic videos from 32,265 TTE studies of 24,405 patients across YNHHS hospitals and clinics. PanEcho performed 18 diagnostic classification tasks with a median AUC of 0.91 (IQR: 0.88-0.93) and estimated 21 echocardiographic parameters with a median normalized MAE of 0.13 (0.10-0.18) in internal validation. For instance, the model accurately estimated left ventricular (LV) ejection fraction (MAE: 4.2% internal; 4.5% external) and detected moderate or higher LV systolic dysfunction (AUC: 0.98 internal; 0.99 external), RV systolic dysfunction (0.93 internal; 0.94 external), and severe aortic stenosis (0.98 internal; 1.00 external). PanEcho maintained excellent performance in limited imaging protocols, performing 15 diagnosis tasks with 0.91 median AUC (IQR: 0.87-0.94) in an abbreviated TTE cohort and 14 tasks with 0.85 median AUC (0.77-0.87) on real-world point-of-care ultrasound acquisitions by non-experts from YNHHS emergency departments.

Conclusions and Relevance

We report an AI system that automatically interprets echocardiograms, maintaining high accuracy across geography and time from complete and limited studies. PanEcho may be used as an adjunct reader in echocardiography labs or rapid AI-enabled screening tool in point-of-care settings.

Simple electrocardiographic index for A4-wave amplitude of the VDD leadless pacemaker

Background

A4-wave amplitude (A4-amplitude) is a crucial factor determining the percentage of atrioventricular synchrony (%AVS) in a mechanical sensing-based VDD leadless pacemaker (VDD-LP). We hypothesized that 12-lead electrocardiographic (ECG) parameters related to right atrial (RA) excitation could predict A4-amplitude.

Objectives

We aimed to investigate the relationship between A4-amplitude and 12-lead ECG parameters reflecting RA excitation and assess its predictive power for achieving an appropriate A4-amplitude associated with high %AVS.

Methods

This single-center, retrospective, observational study enrolled consecutive patients undergoing VDD-LP implantation. The relationship between A4-amplitude and the positive peak amplitude of the P wave in lead II (P2), the positive peak amplitude of the P wave in lead V1 (V1P), and the sum of P2 and V1P (V1PP2) were assessed.

Results

Of the 67 patients undergoing VDD-LP implantation, 46 without atrial fibrillation bradycardia were enrolled. They had a data set of manual atrial mechanical sensing tests and 12-lead ECG. Among P2, V1P, and V1PP2, only V1PP2 was correlated with A4-amplitude (R 2=0.10; P=.029). In 30 patients in VDD pacing mode, the median %AVS was 67.8%. The A4-amplitude cutoff for %AVS ≥ 67.8% was 3.2 m/s2 (area under the curve [AUC] 0.81; P=.002). For A4-amplitude ≥ 3.2 m/s2, V1PP2 had moderate predictive power (AUC 0.72; P=.007). In 30 patients without sick sinus syndrome, the predictive power of V1PP2 for A4-amplitude ≥ 3.2 m/s2 was increased (AUC 0.80; cutoff value 110 μV; sensitivity 83%; specificity 71%; P=.011).

Conclusion

V1PP2, reflecting RA excitation, was related to A4-amplitude and had moderate predictive power. Notably, its predictive power increased when limited to patients without sick sinus syndrome. V1PP2 is a simple ECG predictor of A4-amplitude.

Cardiac Magnetic Resonance Imaging in the Evaluation of Functional Impairments in the Right Heart

Cardiac magnetic resonance (cMRI) imaging has recently become essential in cardiology. cMRI is widely recognized as the most reliable imaging technique for assessing the size and performance of the right ventricle. It allows for objective and functional cardiac tissue evaluations. Early in disease progression, cardiac structure and activity decrease subclinically. Late-phase clinically visible signs have been associated with less favourable outcomes. Subclinical alterations ought to be recognized for rapid evaluations and accurate treatment. An increasing amount of evidence supports cMRI deformation parameter quantification. Strain imaging enables cardiologists to assess heart function beyond traditional measurements. Prognostic information for cardiovascular disease patients is obtained through the right ventricle (RV) strain, including information primarily about the left ventricle (LV). Right atrial (RA) function evaluations using RA strain have been promising in recent studies. Therefore, this narrative review aims to present an overview of the data that are currently available for assessing right myocardial strain and biomechanics using cMRI.

Improvement in right heart function following kidney transplantation in esrd patients: insights from speckle tracking echocardiography analysis

Chronic kidney disease (CKD) is commonly associated with unfavorable cardiovascular outcomes and remains the leading cause of mortality in individuals with end-stage renal disease (ESRD). Despite substantial knowledge about the impact of CKD on the left heart, the right heart, which holds significant clinical relevance, has often been overlooked and inadequately assessed in ESRD patients who have undergone kidney transplant (KTx). This study aimed to evaluate the effects of KTx on the right heart chambers in ESRD patients. 57 adult KTx candidates were enrolled in this prospective longitudinal study, while 49 of them were included in the final assessment. Patients underwent a comprehensive cardiac assessment, including conventional echocardiography, speckle tracking echocardiography, and three-dimensional heart modeling both before and after surgery. Echocardiographic assessments showed significant increases in right ventricular (RV) ejection fraction, RV fractional area change (RVFAC), tricuspid annular plain systolic excursion, RV fractional shortening, right atrial (RA) reservoir, conduit, and booster strains, and RV global longitudinal strain (RVGLS). Moreover, significant reductions in RV end-diastolic volume (RVEDV), RV end-systolic volume (RVESV), RV stroke volume, RV end-diastolic diameter (RVEDD) in mid-cavity view, systolic pulmonary artery pressure was observed (all P values < 0.05). However, no significant difference was found in S velocity, as well as RVEDD in basal and apex-to-annulus view. Moreover, pre-KTx measurements of RVGLS, RVEDD (apex-to-annulus diameter), RV fractional shortening, and S velocity were predictors of RVGLS after KTx. RA conduit strain was also identified as a predictor of RA conduit strain after KTx. Additionally, age, RVEDV, RVESV, RVFAC, and RA reservoir strain before KTx were identified as independent predictors of RA reservoir strain after KTx. The findings of this study demonstrate a significant improvement in right heart function following KTx. Furthermore, strain analysis can provide valuable insights for predicting right heart function after KTx.