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 ...

the-application-point-care-ultrasonography-pocus-management-acute

The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that necessitates dynamic, non-invasive monitoring for optimal management. ...

Zhejiang University

Zhejiang University School of Medicine

Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated.

Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network.

 Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics.  The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs).  A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90;  < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13;  < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates.  Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).

ArticleTotal : 2	Year
Publication title	2025
Publication title	2025

Article	Year
Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network. Computers in biology and medicine\| PubMed ID: 32250853	2020
Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm. Frontiers in medicine\| PubMed ID: 33324663	2020

Liuqing Jiang

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