Predicting Disease Progression in Patients with Bicuspid Aortic Stenosis Using Mathematical Modeling

Area of Science:

• Cardiovascular Medicine
• Biomathematics
• Medical Imaging Analysis

Background:

• Bicuspid aortic valve is a common congenital heart defect.
• Aortic stenosis and dilatation are progressive conditions requiring monitoring.
• Predictive models for bicuspid aortic valve disease progression are limited.

Purpose of the Study:

• To develop and validate a mathematical model for predicting aortic stenosis and dilatation progression.
• To assess the non-linearity of disease progression.
• To determine the predictive value of initial echocardiogram findings versus follow-up data.

Main Methods:

• Retrospective analysis of 126 bicuspid aortic valve patients with serial echocardiograms (2005-2017).
• Development of mathematical models describing aortic stenosis (logistic function) and dilatation (linear function).
• Model validation in an independent cohort of 43 patients.

Main Results:

• Aortic stenosis progression modeled by a logistic function; dilatation by a linear function (0.019 mm/month).
• Patients with rapid initial mean pressure gradient increase showed significantly faster disease progression (α=0.012/month vs. 0.0032/month).
• Models incorporating follow-up data demonstrated superior predictive power compared to baseline data alone.

Conclusions:

• A novel mathematical model accurately predicts bicuspid aortic valve disease progression.
• Including serial echocardiogram data enhances prognostic accuracy.
• This model aids in personalized risk stratification and management of bicuspid aortic valve disease.