Precise identification of respiratory system air leakage via dynamic ventilator waveform monitoring: a controlled trial to improve mechanical ventilation care efficiency

BACKGROUND: Mechanical ventilation is a crucial intervention for respiratory support in intensive care unit (ICU) patients. However, air leakage in the ventilator system often compromises patient outcomes. Conventional leakage detection methods are limited by low sensitivity and delayed response, necessitating more precise approaches. This study focuses on the systematic implementation of a nurse-led waveform analysis protocol for respiratory air leakage detection, optimized for mechanically ventilated patients in cardiothoracic ICUs. METHODS: This comparative study was conducted in the cardiothoracic intensive care unit of a hospital in Nantong, China, from June 2019 to May 2020. Three hundred and two patients requiring mechanical ventilation were categorized into the control group or experimental group depending on the time of admission: empirical leakage detection methods were used in the control group and a ventilator waveform-based detection method in the experimental group. The primary outcome was time to detect leakage in the ventilator pipeline, while secondary outcomes included time to detect airway leakage and nurse satisfaction with the detection methods. RESULTS: Pipeline air leakage was detected within significantly shorter durations in the experimental group than in the control group (27.25 ± 18.42 vs. 7.21 ± 4.39 min; t = 13.86; P < 0.0001), even at a minimum cuff pressure of 30–50 cmH₂O (experimental group t = 17.48; P < 0.0001). Additionally, nurses in the experimental group expressed greater satisfaction with the leakage detection method (χ2 = 20.50, P < 0.001). CONCLUSION: The use of ventilator waveforms for detecting ventilator system leakage is more efficient and accurate than conventional empirical approaches. This innovative technique reduces the time to identify leaks while also enhancing nurse satisfaction, thereby potentially improving patient outcomes in ICU settings.