Mechanisms of Dynamic Airway Resistance Monitoring and Machine Learning for Assessing Pulmonary Inflammation and Guiding Sputum Suction in Mechanically Ventilated Patients
Research has shown that timely suctioning not only improves survival rates but also enhances the quality of life in ventilator-dependent patients. However, clinical judgment on the optimal timing for suctioning currently relies primarily on physician experience, lacking scientific evidence \[10\]. Airway viscous resistance reflects the frictional resistance encountered by gas flow within the airways and is closely associated with airway patency. When airway secretions increase, viscous resistance undergoes dynamic changes. Therefore, analyzing these dynamic variations in viscous resistance derived from ventilator waveforms to determine the optimal suctioning timing and assess its clinical impact on the progression of pulmonary inflammation holds significant scientific value and offers new insights and methodologies for clinical practice.
• Clinical diagnosis of Acute Respiratory Distress Syndrome (ARDS)
• Clinical diagnosis of Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD)
• Clinical diagnosis of Severe pneumonia