Predicting significant coronary artery disease in patients with left ventricular dysfunction

Area of Science:

• Cardiology
• Cardiovascular Medicine
• Diagnostic Imaging

Background:

• Coronary artery disease (CAD) is common in heart failure patients with reduced ejection fraction (EF).
• Diagnostic cardiac catheterization is frequently performed in these patients.
• Accurate pre-procedural CAD risk assessment is needed.

Purpose of the Study:

• To develop a predictive model for significant CAD in patients with heart failure and low EF.
• To identify clinical factors associated with CAD in this population.
• To aid clinicians in determining the likelihood of CAD before invasive procedures.

Main Methods:

• Retrospective analysis of 2054 patients with EF < 45% undergoing cardiac catheterization.
• Development of a multivariable logistic regression model.
• Inclusion of demographic, clinical, ECG, and echocardiographic parameters, including segmental wall motion abnormality.

Main Results:

• 58% of patients had significant CAD (≥75% stenosis in a major epicardial vessel).
• Key predictors of CAD included: history of myocardial infarction, age, diabetes, Q wave, male sex, and wall motion abnormality.
• The model achieved an area under the receiver operating characteristic curve of 0.865.

Conclusions:

• A highly discriminatory diagnostic model for CAD in heart failure patients was developed using baseline characteristics.
• This model can assist in identifying patients with a high likelihood of CAD.
• Accurate pre-procedural assessment may reduce unnecessary invasive cardiac catheterizations.