Post-COVID syndrome screening through breath analysis using electronic nose technology.

Area of Science:

• Biomedical Engineering
• Respiratory Medicine
• Analytical Chemistry

Background:

• Post-Coronavirus Disease Syndrome (PCS) presents diagnostic challenges, often mimicking symptoms of asthma and COPD.
• Undetected COVID-19 cases complicate accurate diagnosis and management of long-term respiratory conditions.
• Non-invasive breath analysis offers a promising avenue for early disease detection and screening.

Purpose of the Study:

• To evaluate the efficacy of electronic (E-) nose technology for diagnosing Post-Coronavirus Disease Syndrome (PCS).
• To assess the E-nose's ability to differentiate PCS from asthma and control subjects using breath volatile organic compounds (VOCs).
• To establish a reliable, point-of-care diagnostic tool for PCS.

Main Methods:

• A pilot study utilized an E-nose device to analyze breath samples from patients with PCS, asthma, and healthy controls.
• Volatile Organic Compounds (VOCs) in exhaled breath were analyzed to identify disease-specific signatures.
• Machine learning algorithms, including k-nearest neighbors (k-NN), were employed for diagnostic classification.

Main Results:

• The E-nose prediction model achieved 100% sensitivity and 100% specificity for PCS diagnosis.
• Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 1, indicating perfect discrimination.
• The E-nose successfully differentiated between PCS, asthma, and control cohorts based on breath VOC profiles.

Conclusions:

• Electronic nose technology demonstrates high accuracy and reliability for diagnosing Post-Coronavirus Disease Syndrome (PCS).
• Breath analysis using E-nose can serve as an effective non-invasive, point-of-care diagnostic method for PCS.
• This technology holds potential for early screening and management of PCS, especially in primary healthcare settings.