Using airway resistance changes to guide suctioning for ventilated patients

Mechanisms of Dynamic Airway Resistance Monitoring and Machine Learning for Assessing Pulmonary Inflammation and Guiding Sputum Suction in Mechanically Ventilated Patients

This study will test whether monitoring airway viscous resistance from ventilator waveforms can help time sputum suctioning for patients on mechanical ventilation with ARDS, AECOPD, or severe pneumonia.

Quick facts

• Phase: Not applicable
• Study type: Interventional
• Enrollment: 258 (estimated)
• Ages: 18 Years to 90 Years
• Sex: All
• Sponsor: Affiliated Hospital of Nantong University (Academic / other)
• Locations: 3 sites (Bengbu, Anhui and 2 other locations)
• Trial ID: NCT07375667 on ClinicalTrials.gov

What this trial studies

The trial continuously derives airway viscous resistance metrics from ventilator waveforms and compares dynamic patterns to conventional suctioning schedules. It correlates changes in viscous resistance with inflammatory biomarkers over time to explore temporal relationships. The team aims to establish reference thresholds for special populations and to validate the feasibility and reliability of resistance-based monitoring. Data will be collected at multiple tertiary hospitals in China and used to inform optimal suctioning timing.

Who should consider this trial

Good fit: Patients on invasive mechanical ventilation for ARDS, acute exacerbation of COPD, or severe pneumonia who meet ICU clinical criteria are the intended participants.

Not a fit: Patients with multiple organ failure or active multi-site bleeding (who are excluded) or those not on mechanical ventilation are unlikely to benefit from the intervention.

Why it matters

Potential benefit: If successful, this approach could reduce pulmonary inflammation and complications by prompting suctioning at more precise times, potentially lowering ventilator-associated pneumonia rates and improving recovery.

How similar studies have performed: Previous work supports that timely suctioning improves outcomes, but using ventilator-derived viscous resistance and machine-learning guidance is a novel approach with limited prior clinical validation.

Eligibility criteria

Inclusion Criteria:

* Clinical diagnosis of Acute Respiratory Distress Syndrome (ARDS)
* Clinical diagnosis of Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD)
* Clinical diagnosis of Severe pneumonia

Exclusion Criteria:

* Clinical diagnosis of multiple organ failure;
* Clinical diagnosis of multiple organ bleeding;

Where this trial is running

Bengbu, Anhui and 2 other locations

• The First Affiliated Hospital of Bengbu Medical University — Bengbu, Anhui, China (Recruiting)
• Peking Union Medical College Hospital — Beijing, Beijing Municipality, China (Recruiting)
• the Second Affiliated Hospital of Zhejiang University School of Medicine — Hangzhou, Zhejiang, China (Recruiting)

Study contacts

• Study coordinator: Hong-lei Wu Nursing Department, MS

Conditions Mechanical Ventilation Pressure High; Ventilator-Associated Pneumonia; Respiratory Depression Neonatal; Airway resistance