Respiratory Monitoring in Supraglottic Airway Anesthesia

Plain-language summary

This prospective observational study evaluates the feasibility and clinical utility of AI-enhanced continuous respiratory sound monitoring during intravenous anesthesia with supraglottic airway placement. With the increasing volume of surgical procedures requiring anesthesia, continuous respiratory monitoring has become essential. While standard monitors track anesthetic depth, end-tidal CO₂, oxygen saturation, and respiratory rate, real-time respiratory sound analysis offers additional clinical value. This study aims to verify whether continuous respiratory sound monitoring using the Airmod electronic stethoscope can detect respiratory depression and airway obstruction before hypoxemia develops, thereby improving the safety of supraglottic airway anesthesia. The protocol involves collecting 60 patients undergoing elective breast surgery with supraglottic airway anesthesia (inclusion criteria: age ≥18 years, BMI \<35; exclusion criteria: emergency cases, anticipated difficult airways, age \<18, BMI \>35). During surgery, an electronic stethoscope patch provides continuous respiratory sound recording, converted to spectral data and analyzed by artificial intelligence, while standard anesthetic monitoring includes blood pressure, heart rate, bispectral index (BIS), SpO₂, and EtCO₂. Researchers document specific intraoperative events including airway positioning, oxygen flow adjustments, ventilation parameter changes, oxygen desaturation episodes, and abnormalities detected via auscultation. Anesthetic records, surgical notes, and recovery records are compiled in Excel format integrated with electronic medical records, with statistical analysis performed using SigmaPlot software. This research builds upon the Airmod electronic stethoscope approved for marketing in February 2025, aiming to establish device-specific respiratory monitoring protocols while enhancing patient safety during non-intubated anesthesia procedures.

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