METHODS
Database and patient population . Yale-New Haven Hospital is a
tertiary care center in the United States. Institutional electronic
medical record system was queried to identify patients with age ≥ 18
years old who had an ECHO for any indication from calendar year 2013 to
2018, either as an in inpatient, outpatient, or during an emergency
department visit. ECHO reports were searched for a diagnosis of BAV.
Patients were categorized according to their age at time of
presentation: Young age group (18 to <40 years old), Middle
age group (40 to 65 years old), and Old age group (>66
years old). Medical records of identified BAV patients were further
reviewed for associated valve disease, aortic aneurysm disease and any
surgical intervention offered for these problems. The Institutional
Review Board at Yale University approved this study.
Patient characteristics . Age, height, and weight indicated
values were recorded at the time of the ECHO. Race was categorized into
Caucasian, African American, Asian, and other. Smoking was defined by
more than 5 years of smoking. Comorbidities (hypertension, diabetes,
dyslipidemia, congestive heart failure, chronic kidney disease,
myocardial infarction, chronic obstructive pulmonary disease) were
chosen as commonly evaluated cardiovascular comorbidities and were
defined using ICD-10 codes. To define aortic aneurysm, a 4 cm cut-off
value was used for aortic root, ascending aorta and aortic arch.
Statistical analysis . Differences in the patient
characteristics were compared with one-way ANOVA followed by Tukey test
for continuous variables and Chi-square for trend test followed by
pair-wise comparison when there was a difference for categorical
variables. Multivariable logistic regression model was fitted to
identify risk factors for aortic aneurysm or dilatation and the model
included the following variables: age, sex, BSA, history of
hypertension, diabetes, dyslipidemia, years of smoking, moderate or
severe aortic valve stenosis and moderate or severe aortic
insufficiency. P value of <0.05 and 95% confidence interval
(CI)were used to define statistically significant difference. Analysis
was conducted using Microsoft excel 2019 and Prism 8.2 (GraphPad
Software, San Diego, CA) for simple analysis and SAS 9.4 (SAS Institute
Inc, Cary, NC) for modelling.