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Updated: Jun 9, 2025
Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
Published on: February 8, 2022
Echocardiographic Screening Model for Improved Assessment of Atrial Septal Defect Closure: A Multicenter
Hezhi Li1, Zehan Huang1, Gangcheng Zhang2
1Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Insights
A new echocardiography model accurately screens adults with atrial septal defects (ASD) for suitability for closure. This noninvasive tool aids in identifying patients needing intervention, potentially preventing pulmonary hypertension and right heart failure.
Area of Science:
- Cardiology
- Medical Imaging
- Congenital Heart Disease
Background:
- Atrial septal defect (ASD) is a common congenital heart condition in adults.
- Untreated ASD can lead to severe complications like pulmonary hypertension and right heart failure.
- Current diagnostic methods, such as right heart catheterization (RHC), are invasive, necessitating noninvasive screening tools.
Purpose of the Study:
- To develop and validate a novel, noninvasive echocardiography-based model for screening adult ASD patients.
- To identify predictors of a correctable shunt using LASSO regression.
- To compare the predictive performance of the new ASD model against existing models for congenital heart disease (CHD).
Main Methods:
- A multicenter, retrospective study involving 924 adult ASD patients (2012-2022).
- LASSO regression was employed to identify key echocardiographic predictors for correctable shunts.
- The developed ASD model incorporates estimated pulmonary artery systolic pressure (ePASP), pulmonary valve (PV) peak velocity, tricuspid valve (TVE) peak E-wave velocity, and right atrial (RA) longitudinal dimension.
Main Results:
- The ASD model demonstrated strong discriminative capability with an AUC of 0.941 in the derivation group and showed good performance in the validation cohort.
- The model showed superior predictive capabilities for identifying correctable shunts compared to the established CHD model.
- Statistical significance was supported by Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) analyses.
Conclusions:
- The developed ASD model is a highly effective and superior tool for prescreening adult patients with atrial septal defects.
- This noninvasive model can aid clinicians in identifying suitable candidates for ASD closure.
- The findings advocate for the clinical adoption of this echocardiographic model to improve patient management and outcomes.
Background:
Atrial septal defect (ASD) is a prevalent congenital heart condition in adults, which finally leads to pulmonary hypertension and right heart failure if left untreated. Right heart catheterization (RHC), the current gold standard for determining ASD closure feasibility, is invasive. Thus, a noninvasive prescreening tool is urgently needed.
Methods And Results:
In a multicenter, retrospective study, we assessed 924 ASD patients (2012-2022) to determine their suitability for ASD closure. Using LASSO regression, we identified predictors for a correctable shunt, enabling us to create the ASD model. The ASD model, comprising of estimated pulmonary artery systolic pressure (ePASP), peak velocity through the pulmonary valve (PV), peak E-wave velocity through the tricuspid valve (TVE), and right atrial longitudinal dimension (RA) by echocardiography, was constructed and exhibited favorable discriminative capability with an area under the curve (AUC) of 0.941 (95% CI: 0.920-0.961) in the derivation group. The model also demonstrated good calibration and discriminative abilities in the validation cohort. When juxtaposed with the earlier congenital heart disease (CHD) model, the newly developed ASD model demonstrated superior predictive capabilities for correctable shunt, supported by the net reclassification index (NRI) [0.063 (95% CI: 0.001-0.127, p = 0.047)] and integrated discrimination improvement (IDI) [0.023 (95% CI: 0.011-0.036, p < 0.001)].
Conclusion:
In summary, our research advocates the ASD model as a superior tool for screening suitable ASD defect closure candidates.
