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Related Concept Videos

Factors Affecting Pulmonary Ventilation01:19

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Mechanical ventilation is a life-saving technique for managing acute respiratory failure and other respiratory complications. The process involves using a machine known as a ventilator to supply oxygen to the lungs and assist in removing carbon dioxide. It serves as a bridge to long-term mechanical ventilation or a temporary measure until ventilatory support is discontinued. The ventilator can maintain this function for a prolonged period, providing critical support for patients until they can...
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Noninvasive positive-pressure ventilation (NIPPV), continuous positive airway pressure (CPAP), and bilevel positive airway pressure (BiPAP) are essential methods in respiratory care. These ventilation techniques offer unique benefits for patients with various respiratory conditions, providing adequate support without requiring intubation. Let's explore how each method is crucial in improving patient outcomes and enhancing respiratory therapy.
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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

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Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning

Huiqing Ge1,2, Kailiang Duan1, Jimei Wang1

  • 1Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

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|December 16, 2020
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Patient-ventilator asynchronies (PVAs) are common but not linked to mortality or VAEs when protective ventilation is used. Factors like time of day and ventilation mode influence PVA occurrence.

Keywords:
critical caredeep learningmechanical ventilalionmortalitypatient ventilator asynchrony

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Area of Science:

  • Critical Care Medicine
  • Respiratory Physiology
  • Health Informatics

Background:

  • Patient-ventilator asynchronies (PVAs) are frequent in mechanically ventilated patients, yet their clinical impact remains debated.
  • Understanding the epidemiology and risk factors of PVAs is crucial for optimizing mechanical ventilation strategies.

Purpose of the Study:

  • To investigate the epidemiology and risk factors of PVAs in critically ill patients.
  • To determine the impact of PVAs on mortality and ventilator-associated events (VAEs) using big data analytics.

Main Methods:

  • A retrospective analysis of 146 patients undergoing mechanical ventilation over 50,124 hours.
  • Utilized negative binomial regression and distributed lag non-linear models (DLNM) to identify risk factors for PVAs.
  • Employed time-varying covariate Cox regression to assess the association between PVAs, mortality, and VAEs.

Main Results:

  • PVAs were influenced by time of day, ventilation mode (PCV, PSV), and sedation/analgesia.
  • Double triggering was less common during daytime, while ineffective effort was more frequent.
  • No significant association was found between PVAs and mortality or VAEs after adjusting for protective ventilation parameters.

Conclusions:

  • PVAs are significantly influenced by various clinical factors, including ventilation settings and patient management.
  • Despite their prevalence, PVAs did not independently predict mortality or VAEs when protective ventilation strategies were employed.