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.