Monitoring Airway Resistance Using Machine Learning to Guide Sputum Suction and Assess Lung Inflammation in Ventilated Patients

What this Trial Is About

Researchers are studying the use of dynamic airway viscous resistance monitoring combined with machine learning to guide sputum suction timing in patients who require mechanical ventilation. The study aims to validate this monitoring method, explore its relationship with inflammatory biomarkers, and provide evidence for selecting the best time to perform suctioning, which may impact the progression of lung inflammation. This research is important because current suctioning decisions mainly rely on physician experience and lack scientific data. Participants are randomly assigned to one of two groups. One group will have their airway viscous resistance continuously monitored, and suctioning will be performed when resistance increases by 6 cmH2O or more. The control group will receive suctioning based on usual clinical signs such as rhonchi and increased peak airway pressure. This comparison aims to assess the reliability and clinical value of airway resistance monitoring for guiding suctioning. During the study, researchers will monitor changes in airway viscous resistance before and after suctioning within 30 minutes. They will also evaluate laboratory test abnormalities and perform sputum bacterial cultures within three days. Participants will be assessed regularly to measure the impact on pulmonary inflammation and clinical outcomes. The study lasts until December 2029 and includes adult patients aged 18 to 90 years diagnosed with conditions like ARDS, severe pneumonia, or acute exacerbations of COPD.

Dynamic Airway Resistance & ML: Guide Sputum Suction in Ventilated Patients