Dyspnoea is frequent and distressing in patients receiving mechanical ventilation, but it is often not properly evaluated by caregivers. Electroencephalographic signatures of dyspnoea have been identified experimentally in healthy subjects. We hypothesized that adjusting ventilator settings to relieve dyspnoea in MV patients would induce EEG changes. This was a first-of-its-kind observational study in a convenience population of 12 dyspnoeic, mechanically ventilated patients for whom a decision to adjust the ventilator settings was taken by the physician in charge (adjustments of pressure support, slope, or trigger). Pre- and post-ventilator adjustment electroencephalogram recordings were processed using covariance matrix statistical classifiers and pre-inspiratory potentials. The pre-ventilator adjustment median dyspnoea visual analogue scale was 3.0 (interquartile range: 2.5-4.0; minimum-maximum: 1-5) and decreased by (median) 3.0 post-ventilator adjustment. Statistical classifiers adequately detected electroencephalographic changes in 8 cases (area under the curve ≥0.7). Previously present pre-inspiratory potentials disappeared in 7 cases post-ventilator adjustment. Dyspnoea improvement was consistent with electroencephalographic changes in 9 cases. Adjusting ventilator settings to relieve dyspnoea produced detectable changes in brain activity. This paves the way for studies aimed at determining whether monitoring respiratory-related electroencephalographic activity can improve outcomes in critically ill patients under mechanical ventilation.
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http://dx.doi.org/10.1038/s41598-019-53152-y | DOI Listing |
BMC Anesthesiol
January 2025
Department of Anesthesiology and Reanimation, Faculty of Medicine, Van Yüzüncü Yıl University, Van, Turkey.
Background: Patient safety is important in daily anesthesia practices, and providing deep anesthesia is difficult. Current debates on the optimal anesthetic agents highlight the need for safer alternatives. This study was justified by the need for safer and more effective anesthetic protocols for outpatient hysteroscopic procedures, particularly those conducted outside the operating room.
View Article and Find Full Text PDFObjectives: This study aimed to develop a prediction model for the detection of early sepsis-associated acute kidney injury (SA-AKI), which is defined as AKI diagnosed within 48 hours of a sepsis diagnosis.
Design: A retrospective study design was employed. It is not linked to a clinical trial.
PLoS One
January 2025
Department of Pediatrics, University of Washington, Seattle, Washington, United States of America.
Aim: To evaluate the impact of heart rate-guided basic resuscitation compared to Helping Babies Breathe on neonatal outcomes and resuscitation practices in the Democratic Republic of the Congo.
Methods: We conducted a pre-post clinical trial comparing heart rate-guided basic resuscitation to Helping Babies Breathe in three facilities, enrolling in-born neonates ≥28 weeks gestation. We collected observational data during a convenience sample of resuscitations and extracted clinical data from the medical record for all participants.
<b>Background and Objective:</b> It is well documented that Whole Genome Sequencing (WGS) has recently used to explore new resistance patterns and track the dissemination of extensive and pan drug-resistant microbes in healthcare settings. This article explores the link between traumatic infections caused by road traffic accidents (RTAs) leading to coma and the development of chest infections caused by extensively drug-resistant (XDR) <i>Klebsiella pneumoniae</i> and <i>Pseudomonas aeruginosa</i>. <b>Materials and Methods:</b> The study was carried out from March to December 2022 which included a 45-year-old male patient admitted to the ICU of Al Ramadi Teaching Hospitals following a severe RTA that resulted in a TBI and subsequent coma.
View Article and Find Full Text PDFFront Nutr
January 2025
Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
Background: Although more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.
Objective: To thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.
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