Abstract 4359837: Clinical and Echocardiographic Predictors of Aortopathy in Patients with Bicuspid Aortic Valve: Insights from a Large Community Cohort

Background: The bicuspid aortic valve (BAV) is the most prevalent congenital heart defect and is frequently associated with aortopathy. The etiology of aortic dilation in BAV is multifactorial, involving both hemodynamic stress and intrinsic aortic wall abnormalities. While previous studies have proposed several mechanisms, the specific and valve-related predictors of aortopathy remain unclear. Research question: Which clinical and echocardiographic features independently predict the presence of aortopathy in patients with BAV? Goals: To identify the patient and value-related characteristics associated with aortopathy in a large, real-world BAV cohort using standardized imaging and statistical approaches. Approach: Between 2013 and 2017, across 33 echocardiography labs within a large U.S. health system, 1,227 patients diagnosed with BAV were retrospectively reviewed. BAV morphology, presence of raphe, calcification severity (grades 0-4), aortic regurgitation (AR), aortic stenosis (AS), age, sex, and body surface area (BSA) were collected. Aortopathy was defined based on measurements of the aorta at the sinus of Valsalva and mid-ascending portions, indexed to body surface area. Univariate analysis (Mann-Whitney U and Chi-square) identified candidate predictors. A multivariable logistic regression model was constructed to identify independent predictors of aortopathy. Multicollinearity was assessed using the variance inflation factor and tolerance. Results: Among 1,227 BAV patients, 755 (61.5%) had aortopathy. They were older (54.7 vs. 51.9 years, p=0.006), had higher BSA (2.09 vs. 2.00 m 2 , p<0.001), and were more often male (73.1% vs. 58.3%, p<0.001). Independent predictors of aortopathy included male sex, age, AR severity, and BAV Type 1 (vs. Types 2 and 3). Calcification was inversely associated. AS, BSA, and raphe were not significant predictors. The model demonstrated excellent calibration (Hosmer-Lemeshow p = 0.715). Conclusion: Clinical characteristics emerge to differentiate BAV patients with aortopathy. Although the relationships between aortopathy and BAV are complex, specific clinical and echocardiographic variables in this cohort, particularly BAV Type 1, age, Male sex, calcification, and AR severity, were independently associated with aortopathy. These findings support multifactorial pathogenesis of BAV-associated aortopathy and may inform future risk stratification and surveillance strategies.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-11-03
DOI
https://doi.org/10.1161/circ.152.suppl_3.4359837
Primary Topic
Aortic Disease and Treatment Approaches
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4359837: Clinical and Echocardiographic Predictors of Aortopathy in Patients with Bicuspid Aortic Valve: Insights from a Large Community Cohort

Renuka Jain, Muddasir Ashraf, M. Fuad Jan, Arshad Jahangir et al.
Circulation
Aortic Disease and Treatment Approaches
article

Abstract 4359837: Clinical and Echocardiographic Predictors of Aortopathy in Patients with Bicuspid Aortic Valve: Insights from a Large Community Cohort

Renuka Jain, Muddasir Ashraf, M. Fuad Jan, Arshad Jahangir, A. Jamil Tajik, Patrycja Galazka, Prabhjot Hundal
article en

Abstract

Background: The bicuspid aortic valve (BAV) is the most prevalent congenital heart defect and is frequently associated with aortopathy. The etiology of aortic dilation in BAV is multifactorial, involving both hemodynamic stress and intrinsic aortic wall abnormalities. While previous studies have proposed several mechanisms, the specific and valve-related predictors of aortopathy remain unclear. Research question: Which clinical and echocardiographic features independently predict the presence of aortopathy in patients with BAV? Goals: To identify the patient and value-related characteristics associated with aortopathy in a large, real-world BAV cohort using standardized imaging and statistical approaches. Approach: Between 2013 and 2017, across 33 echocardiography labs within a large U.S. health system, 1,227 patients diagnosed with BAV were retrospectively reviewed. BAV morphology, presence of raphe, calcification severity (grades 0-4), aortic regurgitation (AR), aortic stenosis (AS), age, sex, and body surface area (BSA) were collected. Aortopathy was defined based on measurements of the aorta at the sinus of Valsalva and mid-ascending portions, indexed to body surface area. Univariate analysis (Mann-Whitney U and Chi-square) identified candidate predictors. A multivariable logistic regression model was constructed to identify independent predictors of aortopathy. Multicollinearity was assessed using the variance inflation factor and tolerance. Results: Among 1,227 BAV patients, 755 (61.5%) had aortopathy. They were older (54.7 vs. 51.9 years, p=0.006), had higher BSA (2.09 vs. 2.00 m 2 , p<0.001), and were more often male (73.1% vs. 58.3%, p<0.001). Independent predictors of aortopathy included male sex, age, AR severity, and BAV Type 1 (vs. Types 2 and 3). Calcification was inversely associated. AS, BSA, and raphe were not significant predictors. The model demonstrated excellent calibration (Hosmer-Lemeshow p = 0.715). Conclusion: Clinical characteristics emerge to differentiate BAV patients with aortopathy. Although the relationships between aortopathy and BAV are complex, specific clinical and echocardiographic variables in this cohort, particularly BAV Type 1, age, Male sex, calcification, and AR severity, were independently associated with aortopathy. These findings support multifactorial pathogenesis of BAV-associated aortopathy and may inform future risk stratification and surveillance strategies.

CirculationVol. 152(Suppl_3)
Aurora Health Care (US), Aurora St. Luke's Medical Center (US)
Openalex Percentile: Top 54%
Aortic Disease and Treatment Approaches
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.