Objective: To determine the prevalence of elevated mechanical power (MP) values (>17J/min) used in routine clinical practice.
Design: Observational, descriptive, cross-sectional, analytical, multicenter, international study conducted on November 21, 2019, from 8:00 AM to 3:00 PM. NCT03936231.
Setting: One hundred thirty-three Critical Care Units.
Patients: Patients receiving invasive mechanical ventilation for any cause.
Interventions: None.
Main Variables Of Interest: Mechanical power.
Results: A population of 372 patients was analyzed. PM was significantly higher in patients under pressure-controlled ventilation (PC) compared to volume-controlled ventilation (VC) (19.20±8.44J/min vs. 16.01±6.88J/min; p<0.001), but the percentage of patients with PM>17J/min was not different (41% vs. 35%, respectively; p=0.382). The best models according to AICcw expressing PM for patients in VC are described as follows: Surrogate Strain (Driving Pressure) + PEEP+Surrogate Strain Rate (PEEP/Flow Ratio) + Respiratory Rate. For patients in PC, it is defined as: Surrogate Strain (Expiratory Tidal Volume/PEEP) + PEEP+Surrogate Strain Rate (Surrogate Strain/Ti) + Respiratory Rate+Expiratory Tidal Volume+Ti.
Conclusions: A substantial proportion of mechanically ventilated patients may be at risk of experiencing elevated levels of mechanical power. Despite observed differences in mechanical power values between VC and PC ventilation, they did not result in a significant disparity in the prevalence of high mechanical power values.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.medine.2023.11.004 | DOI Listing |
Sensors (Basel)
January 2025
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.
The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT).
View Article and Find Full Text PDFSensors (Basel)
December 2024
China Institute of Atomic Energy, P.O. Box 275 (26), Beijing 102413, China.
Fast-neutron reactors are an important representative of Generation IV nuclear reactors, and due to the unique structure and material properties of fast reactor fuel, traditional mechanical cutting methods are not applicable. In contrast, laser cutting has emerged as an ideal alternative. However, ensuring the stability of optical fibers and laser cutting heads under high radiation doses, as well as maintaining cutting quality after irradiation, remains a significant technical challenge.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Biofluids Laboratory, Perm National Research Polytechnic University, 614990 Perm, Russia.
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid-structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!