Imperial College London
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Dr Shlomi Haar

Faculty of Medicine, Department of Brain Sciences

Edmond and Lily Safra Research Fellow and UK DRI Fellow
 
 
 
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Contact

 

s.haar Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Patel:2021:10.1007/s00134-021-06389-z,
author = {Patel, BV and Haar, S and Handslip, R and Auepanwiriyakul, C and Lee, TM-L and Patel, S and Harston, JA and Hosking-Jervis, F and Kelly, D and Sanderson, B and Borgatta, B and Tatham, K and Welters, I and Camporota, L and Gordon, AC and Komorowski, M and Antcliffe, D and Prowle, JR and Puthucheary, Z and Faisal, AA},
doi = {10.1007/s00134-021-06389-z},
journal = {Intensive Care Medicine},
pages = {549--565},
title = {Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom},
url = {http://dx.doi.org/10.1007/s00134-021-06389-z},
volume = {47},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeThe 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.MethodsWe 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).ResultsData 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
AU  - Patel,BV
AU  - Haar,S
AU  - Handslip,R
AU  - Auepanwiriyakul,C
AU  - Lee,TM-L
AU  - Patel,S
AU  - Harston,JA
AU  - Hosking-Jervis,F
AU  - Kelly,D
AU  - Sanderson,B
AU  - Borgatta,B
AU  - Tatham,K
AU  - Welters,I
AU  - Camporota,L
AU  - Gordon,AC
AU  - Komorowski,M
AU  - Antcliffe,D
AU  - Prowle,JR
AU  - Puthucheary,Z
AU  - Faisal,AA
DO  - 10.1007/s00134-021-06389-z
EP  - 565
PY  - 2021///
SN  - 0342-4642
SP  - 549
TI  - Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom
T2  - Intensive Care Medicine
UR  - http://dx.doi.org/10.1007/s00134-021-06389-z
UR  - https://link.springer.com/article/10.1007%2Fs00134-021-06389-z
UR  - http://hdl.handle.net/10044/1/88460
VL  - 47
ER  -
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