standardized-lung-recruitment-positive-end-expiratory-pressure

Standardized Lung Recruitment and Positive End-expiratory Pressure Titration in Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) is characterized by diffuse alveolar injury, refractory hypoxemia, and high mortality. Lung recruitment ...

Zhejiang University

Positive end-expiratory airway pressure (PEEP) is a potent component of management for patients receiving mechanical ventilation (MV). However, PEEP may cause the development of diaphragm remodeling, making it difficult for patients to be weaned from MV. The current study aimed to explore the role of PEEP in VIDD.

PEEP application during mechanical ventilation contributes to fibrosis in the diaphragm.

Mechanical ventilation can cause acute atrophy and injury in the diaphragm, which are related to adverse clinical results. However, the underlying mechanisms of ventilation-induced diaphragm dysfunction (VIDD) have not been well elucidated. The current study aimed to explore the role of cellular senescence in VIDD.

Cellular senescence contributes to mechanical ventilation-induced diaphragm dysfunction by upregulating p53 signalling pathways.

. Air trapping is a major symptom of respiratory diseases like chronic obstructive pulmonary disease and asthma, and has always been a significant problem in treating patients using mechanical ventilation. If not handled timely, it can pose risk of severe respiratory dysfunction and potential life-threatening complications. Currently, the assessment of air trapping for ventilated patients largely relies on clinical experience of medical staffs.. We introduced an interpretable dual-channel one-dimensional convolutional neural network (DC-1DCNN) with a simple structure, which enables fast inference. This model is designed to classify whether a respiratory waveform is indicative of air trapping. A global average pooling layer was integrated into the DC-1DCNN model to highlight the segments of the respiratory waveform that the model focused on when making a classification. An air trapping index (ATI) was introduced to quantify the condition of air trapping in the ventilated patients and to evaluate the effectiveness of bronchodilator nebulized treatments.. The results demonstrate a satisfactory accuracy of 96.4% in identifying air trapping breath cycles, with highlighted critical sections in breath cycles that match the understanding of clinical experts for air trapping. The efficacy of bronchodilators can be well assessed by the ATI.. The findings suggest that the proposed DC-1DCNN can help detect air trapping in real-time, and help the clinicians better monitor the airway condition of the ventilated patients.

Automated detection of air trapping from mechanical ventilation waveform through interpretable dual-channel 1D convolutional neural network.

Heterogeneity is a critical characteristic of severe coronavirus disease 2019 (COVID-19) pneumonia. Integrating chest computed tomography (CT) imaging and plasma proteomics holds the potential to elucidate Image-Expression Axes (IEAs) that can effectively address this disease heterogeneity.

Article	Year
PEEP application during mechanical ventilation contributes to fibrosis in the diaphragm. Respiratory research\| PubMed ID: 36782202	2023
Cellular senescence contributes to mechanical ventilation-induced diaphragm dysfunction by upregulating p53 signalling pathways. BMC pulmonary medicine\| PubMed ID: 38097957	2023
Automated detection of air trapping from mechanical ventilation waveform through interpretable dual-channel 1D convolutional neural network. Physiological measurement\| PubMed ID: 40588005	2025
Deep learning integration of chest computed tomography and plasma proteomics to identify novel aspects of severe COVID-19 pneumonia. Journal of intensive medicine\| PubMed ID: 40933743	2025

Xiaoli Qian

Biography

JoVE Journal Publications

Other Publications