18
IRUS Total
Downloads

Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom

File Description SizeFormat 
Patel2021_Article_NaturalHistoryTrajectoryAndMan (1).pdfPublished version1.56 MBAdobe PDFView/Open
Title: Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom
Authors: Patel, BV
Haar, S
Handslip, R
Auepanwiriyakul, C
Lee, TM-L
Patel, S
Harston, JA
Hosking-Jervis, F
Kelly, D
Sanderson, B
Borgatta, B
Tatham, K
Welters, I
Camporota, L
Gordon, AC
Komorowski, M
Antcliffe, D
Prowle, JR
Puthucheary, Z
Faisal, AA
Item Type: Journal Article
Abstract: Purpose The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions. Methods We included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU). Results Data for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P < 0.001; median PaO2/FiO2 on the day of death was 12.3(8.9–18.4) kPa] and non-response to proning (69.5% vs.31.1%; P < 0.001). Two ML models using weeklong data demonstrated an increased predictive accuracy for mortality compared to admission data (74.5% and 76.3% vs 60%, respectively). XAI models highlighted the increasing importance, over the first week, of PaO2/FiO2 in predicting mortality. Prone positioning improved oxygenation only in 45% of patients. A higher peak pressure (OR 1.42[1.06–1.91]; P < 0.05), raised respiratory component (OR 1.71[ 1.17–2.5]; P < 0.01) and cardiovascular component (OR 1.36 [1.04–1.75]; P < 0.05) of the sequential organ failure assessment (SOFA) score and raised lactate (OR 1.33 [0.99–1.79]; P = 0.057) immediately prior to application of prone positioning were associated with lack of oxygenation response. Prone positioning was not applied to 76% of patients with moderate hypoxemia and 45% of those with severe hypoxemia and patients who died without receiving proning interventions had more missed opportunities for prone intervention [7 (3–15.5) versus 2 (0–6); P < 0.001]. Despite the severity of gas exchange deficit, most patients received lung-protective ventilation with tidal volumes less than 8 mL/kg and plateau pressures less than 30cmH2O. This was despite systematic errors in measurement of height and derived ideal body weight. Conclusions Refractory hypoxaemia remains a major association with mortality, yet evidence based ARDS interventions, in particular prone positioning, were not implemented and had delayed application with an associated reduced responsiveness. Real-time service evaluation techniques offer opportunities to assess the delivery of care and improve protocolised implementation of evidence-based ARDS interventions, which might be associated with improvements in survival.
Issue Date: 11-May-2021
Date of Acceptance: 18-Mar-2021
URI: http://hdl.handle.net/10044/1/88460
DOI: 10.1007/s00134-021-06389-z
ISSN: 0342-4642
Publisher: Springer Science and Business Media LLC
Start Page: 549
End Page: 565
Journal / Book Title: Intensive Care Medicine
Volume: 47
Copyright Statement: © 2021 The Author(s). This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
Sponsor/Funder: NIHR
Keywords: Science & Technology
Life Sciences & Biomedicine
Critical Care Medicine
General & Internal Medicine
COVID-19
ARDS
Mechanical ventilation
Prone position
Mortality risk
Artificial intelligence
GAS-EXCHANGE
EPIDEMIOLOGY
MORTALITY
PATTERNS
ARDS
Artificial intelligence
COVID-19
Mechanical ventilation
Mortality risk
Prone position
Adult
Artificial Intelligence
COVID-19
Humans
Prone Position
Respiration, Artificial
SARS-CoV-2
United Kingdom
United Kingdom COVID-ICU National Service Evaluation
Humans
Respiration, Artificial
Prone Position
Artificial Intelligence
Adult
United Kingdom
COVID-19
SARS-CoV-2
1103 Clinical Sciences
1117 Public Health and Health Services
Emergency & Critical Care Medicine
Publication Status: Published
Open Access location: https://link.springer.com/article/10.1007/s00134-021-06389-z
Online Publication Date: 2021-05-11
Appears in Collections:Bioengineering
Department of Surgery and Cancer
Computing
Faculty of Medicine
Imperial College London COVID-19
Department of Brain Sciences
Faculty of Engineering



This item is licensed under a Creative Commons License Creative Commons