Detecting Coronary Artery Disease Using Exhaled Breath Analysis.

Area of Science:

• Cardiology
• Biomedical Engineering
• Analytical Chemistry

Background:

• Coronary artery disease (CAD) is a major global health concern, necessitating improved diagnostic methods.
• Current CAD detection tools often require invasive procedures or are costly.
• There is a critical need for simple, noninvasive, and affordable CAD detection tools.

Purpose of the Study:

• To evaluate the potential of breath analysis as a noninvasive method for detecting coronary artery disease (CAD).
• To assess the diagnostic performance of an electronic nose (eNose) system in identifying CAD in patients presenting with acute chest pain.

Main Methods:

• A prospective study involving breath sampling from patients with acute chest pain using a portable electronic nose (eNose).
• Breath samples were analyzed and correlated with cardiac imaging results (CT angiography or coronary angiography) to categorize patients as obstructive CAD, nonobstructive CAD, or no-CAD.
• Classification models were developed and their diagnostic performance (sensitivity, specificity, accuracy, NPV, PPV) was evaluated on a separate patient cohort.

Main Results:

• One model distinguished obstructive CAD from nonobstructive/no-CAD with 89% sensitivity, 31% specificity, and 52% accuracy.
• Another model differentiated any extent of CAD from no-CAD with 69% sensitivity, 67% specificity, and 68% accuracy.
• The study demonstrated the feasibility of using breath analysis for CAD detection.

Conclusions:

• Breath analysis presents a potential novel, rapid, and noninvasive diagnostic tool for CAD.
• This technology could assist in the identification of CAD in emergency settings for patients with acute chest pain.
• Further validation is warranted to establish breath analysis as a reliable CAD diagnostic method.