Severe COVID-19 illness is characterised by the development of Acute Respiratory Distress Syndrome (ARDS), for which the mainstay of treatment is represented by mechanical ventilation. Mortality associated with ARDS due to other causes is in the range of 40-60%, but currently available data are not yet sufficient to draw safe conclusions on the prognosis of COVID-19 patients who require mechanical ventilation. Based on data from cohorts of the related coronavirus-associated illnesses, that is to say Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), prognosis would seem to be worse than ARDS due to other causes such as trauma and other infections. Discussion of prognosis is central to obtaining informed consent for intubation, but in the absence of definitive data it is not clear exactly what this discussion should entail.
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http://dx.doi.org/10.4081/monaldi.2020.1296 | DOI Listing |
J Paediatr Child Health
January 2025
WHO Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Victoria, Australia.
Aims: Primary aim was to review severe acute respiratory infections (SARI) hospitalisations caused by respiratory syncytial virus (RSV) in children aged < 2 years in paediatric hospitals in Australia. Secondary aims included RSV subtyping, assessing RSV seasonality and contributing to the World Health Organisation's RSV surveillance programme.
Methods: We prospectively reviewed the medical records of children (< 2 years of age) with a confirmed SARI who were admitted to one of four major Australian paediatric hospitals and had a respiratory sample analysed by Polymerase Chain Reaction (PCR).
J Coll Physicians Surg Pak
January 2025
Department of Emergency Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
The Valsalva manoeuvre is widely recognised for its effectiveness in reverting supra-ventricular tachycardia (SVT) in patients with good coordination. However, this is not applicable in sedated ventilated patients and there is a dearth of literature regarding the application of Valsalva in unconscious patients on mechanical ventilation. The authors, for the first time, present a novel non-pharmacological method to treat SVT in critically ill patients on mechanical ventilation, employing the high positive end-expiratory pressure (PEEP) technique.
View Article and Find Full Text PDFJ Coll Physicians Surg Pak
January 2025
Department of Pathology, National Institute of Cardiovascular Diseases, Karachi, Pakistan.
Objective: To determine the frequency of multidrug-resistant (MDR) bacterial isolates in respiratory specimens obtained from ventilated patients admitted to critical care units at the National Institute of Cardiovascular Diseases (NICVD), along with COVID-19-positive cases.
Study Design: An observational study. Place and Duration of the Study: National Institute of Cardiovascular Diseases, between November 2021 and March 2022.
Crit Care
January 2025
Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Background: Despite the physiological advantages of positive end-expiratory pressure (PEEP), its optimal utilization during one-lung ventilation (OLV) remains uncertain. We aimed to investigate whether individualized PEEP titration by lung compliance is associated with a reduced risk of postoperative pulmonary complications during OLV.
Methods: We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials until April 1, 2024, to identify published randomized controlled trials that compared individualized PEEP titration by lung compliance with fixed PEEP during OLV.
BMC Med Inform Decis Mak
January 2025
Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.
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