Identifying Patients with Bicuspid Aortic Valve Disease in UK Primary Care: A Case-Control Study and Prediction Model

Area of Science:

• Cardiology
• Congenital Heart Disease
• Epidemiology

Background:

• Bicuspid aortic valve disease (BAV) is the most prevalent congenital heart anomaly.
• Early identification of BAV is critical for enhancing patient prognosis and management.
• Current diagnostic pathways may benefit from improved strategies for timely BAV detection.

Purpose of the Study:

• To identify clinical features associated with BAV diagnosis in primary care.
• To assess the prevalence of BAV within a large UK primary care database.
• To develop a predictive model for BAV to aid in patient stratification for echocardiography screening.

Main Methods:

• A case-control study utilizing the Clinical Practice Research Datalink (CPRD) electronic health records.
• Propensity-score matching was employed to select up to five controls for each BAV case.
• Multivariable regression analysis was used to identify significant clinical predictors of BAV.

Main Results:

• The study identified 2,898 cases of BAV, with a prevalence of 1 in 5,181 in the CPRD, lower than anticipated.
• Key clinical features associated with BAV included palpitations (OR: 2.86), atrial fibrillation (AF) (OR: 2.25), and hypertension (OR: 1.72).
• The developed prediction model achieved an AUC of 0.669, with a PPV of 5.9% and NPV of 99% at 1% population prevalence.

Conclusions:

• Palpitations, hypertension, and AF should raise clinical suspicion for BAV, warranting echocardiographic evaluation.
• The findings support the development of a predictive model to identify patients who may benefit from echocardiography screening for BAV.
• Improved diagnostic and recording practices are suggested to enhance BAV detection rates.