Non-invasive diagnosis of coronary artery disease by quantitative stress echocardiography: optimal diagnostic models...

Area of Science:

• Cardiology
• Echocardiography
• Diagnostic Imaging

Background:

• Coronary artery disease (CAD) diagnosis often requires invasive procedures.
• Non-invasive methods are crucial for early and accurate CAD detection.
• Dobutamine stress echocardiography (DSE) assesses myocardial function under stress.

Purpose of the Study:

• To develop and optimize objective, non-invasive diagnostic methods for CAD.
• To utilize myocardial Doppler velocities during DSE for improved diagnostic accuracy.
• To establish quantitative criteria for CAD detection using stress echocardiography.

Main Methods:

• Acquired tissue Doppler data during DSE in 289 subjects.
• Analyzed myocardial velocities in 11 left ventricular segments.
• Developed logistic regression models using systolic velocities, heart rate, age, and gender.

Main Results:

• Logistic regression models achieved optimal diagnostic accuracy for CAD.
• Models using 7 myocardial segments demonstrated high sensitivity and specificity.
• Diagnostic performance for LAD, Cx, and RCA disease ranged from 80-93% sensitivity and 80-82% specificity.
• Models outperformed simple velocity cut-offs (p<0.001).

Conclusions:

• Quantitative stress echocardiography with diagnostic models offers effective non-invasive CAD diagnosis.
• Models incorporating segmental velocities at peak stress are superior.
• Adjusting for heart rate, age, and gender further enhances diagnostic precision.