Brijesh V Patel1,2, Shlomi Haar3,4,5,6, Rhodri Handslip7,8, John R Prowle9, Zudin Puthucheary9, Aldo A Faisal10,11,12,13, Chaiyawan Auepanwiriyakul3,4, Teresa Mei-Ling Lee7,8, Sunil Patel7,8, J Alex Harston3,4, Feargus Hosking-Jervis14, Donna Kelly15, Barnaby Sanderson16, Barbara Borgatta17, Kate Tatham7,18, Ingeborg Welters19, Luigi Camporota16, Anthony C Gordon7,20, Matthieu Komorowski7,20, David Antcliffe7,20. 1. Division of Anaesthetics, Pain Medicine & Intensive Care, Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London, UK. brijesh.patel@imperial.ac.uk. 2. Department of Adult Intensive Care, The Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London, UK. brijesh.patel@imperial.ac.uk. 3. Brain & Behaviour Lab, Dept. Of Computing, Imperial College London, London, UK. 4. Brain & Behaviour Lab, Dept. Of Bioengineering, Imperial College London, London, UK. 5. Dept. of Brain Sciences, Imperial College London, London, UK. 6. UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, UK. 7. Division of Anaesthetics, Pain Medicine & Intensive Care, Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London, UK. 8. Department of Adult Intensive Care, The Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London, UK. 9. Critical Care and Peri-Operative Medicine Research Group, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK. 10. Brain & Behaviour Lab, Dept. Of Computing, Imperial College London, London, UK. aldo.faisal@imperial.ac.uk. 11. Brain & Behaviour Lab, Dept. Of Bioengineering, Imperial College London, London, UK. aldo.faisal@imperial.ac.uk. 12. UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK. aldo.faisal@imperial.ac.uk. 13. MRC London Institute for Medical Sciences, London, UK. aldo.faisal@imperial.ac.uk. 14. Department of Surgery & Cancer, Faculty of Medicine, Imperial College London, London, UK. 15. Department of Critical Care, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. 16. Department of Critical Care, Guy's and St Thomas' NHS Foundation Trust, St Thomas' Hospital, London, UK. 17. Department of Critical Care, Aintree University Hospital Foundation Trust, Liverpool, UK. 18. Department of Anaesthetics and Critical Care, The Royal Marsden NHS Foundation Trust, London, UK. 19. Department of Critical Care, Liverpool University Hospitals NHS Foundation Trust and University of Liverpool, Liverpool, UK. 20. Department of Critical Care, Imperial College Healthcare NHS Trust, London, UK.
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.
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.
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