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

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.

Keywords: ARDS; Artificial intelligence; COVID-19; Mechanical ventilation; Mortality risk; Prone position.

Fig. 1

Study population flowchart

Fig. 2

ICU survival curves based on admission severities of hypoxaemia as defined by the Berlin definition of ARDS

Fig. 3

a Week-long day-by-day performances of our logistic regression (LR) and three-layer multilayer perceptron (3-MLP) model. As shown in the figures, our models' performances rise daily from admission to the end of the first week. Daily accuracy for mortality prediction are: [Log-Reg, 3MLP]: Day 0—[58.9%, 61.9%], Day 1—[63.2%, 63.3%], Day 2—[65.3%, 66.7%], Day 3—[70%, 69.7%], Day 4—[70.3%, 71.9%], Day 5—[71.7%, 72.8%], Day 6—[74.1%, 73.6%]. b The importance of clinical parameters for the week-long mortality prediction model changes day-by-day. We are showing here the evolution of the SHAP (SHapley Additive exPlanations) importance of the clinical variables from admission (Day 1–2) to end of the first week, particularly PaO₂/FiO₂, higher peak pressure, higher ventilatory ratio, lower pH, higher lactate, lower platelet count, higher CRP, lower oxygen saturations, higher PEEP and higher PaCO₂. Note, how some of the variables are systematically high importance or low importance while others are systematically increasing from day 1 and vice versa, see Fig. S4 for details

Fig. 4

Alluvial diagram of patient movements between ARDS severity groups: Mild hypoxaemia (PaO₂/FiO₂ > 26.7), Moderate hypoxaemia (PaO₂/FiO₂: 26.6–13.3), and severe hypoxaemia (PaO₂/FiO₂ < 13.3) and patient outcome (discharged—green, deceased—red). Each solid bar represents an ARDS severity group at a given number of days since initiation of IMV. Shaded coloured streams between bars represent transitions of patients between the severity groups from one-time point to the next, which is either their new severity or their outcome. The height of the bars represents the proportion of patients at that time point (i.e., they stack up to 100%) and the height of a stream field represents the size of the components contained in both bars connected by the stream

Fig. 5

ICU survival curves for the prone responder (blue) versus non-responder (red) versus patients who received no prone position (grey). Responsiveness to prone position with responders defined as maintenance of a mean PaO₂/FiO₂ > 20 kPa over 7 days after the first prone episode