Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study

Weaver, Liam; Das, Anup; Saffaran, Sina; Yehya, Nadir; Chikhani, Marc; Scott, Timothy; Laffey, John G.; Hardman, Jonathan G.; Camporota, Luigi; Bates, Declan G.

doi:10.1016/j.bja.2022.02.037

Cited by 6 publications

(6 citation statements)

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“…Static stress represents the static transpulmonary pressure associated with CPAP. It has been previously estimated from passive properties of the respiratory system (normal lung to chest wall elastance ratio: E R = 0.7) [ 6 , 33 ]. However, in spontaneously breathing patients with possible expiratory muscles activation [ 28 – 31 , 34 ], direct assessment of changes in end-expiratory transpulmonary pressure (P L(exp) ), a proxy of changes in EELV [ 20 , 21 ], might be a better option.…”

Section: Discussionmentioning

confidence: 99%

Effects of CPAP and FiO2 on respiratory effort and lung stress in early COVID-19 pneumonia: a randomized, crossover study

Giosa,

Collins,

Sciolla

et al. 2023

Ann. Intensive Care

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Background in COVID-19 acute respiratory failure, the effects of CPAP and FiO2 on respiratory effort and lung stress are unclear. We hypothesize that, in the compliant lungs of early Sars-CoV-2 pneumonia, the application of positive pressure through Helmet-CPAP may not decrease respiratory effort, and rather worsen lung stress and oxygenation when compared to higher FiO2 delivered via oxygen masks. Methods In this single-center (S.Luigi Gonzaga University-Hospital, Turin, Italy), randomized, crossover study, we included patients receiving Helmet-CPAP for early (< 48 h) COVID-19 pneumonia without additional cardiac or respiratory disease. Healthy subjects were included as controls. Participants were equipped with an esophageal catheter, a non-invasive cardiac output monitor, and an arterial catheter. The protocol consisted of a random sequence of non-rebreather mask (NRB), Helmet-CPAP (with variable positive pressure and FiO2) and Venturi mask (FiO2 0.5), each delivered for 20 min. Study outcomes were changes in respiratory effort (esophageal swing), total lung stress (dynamic + static transpulmonary pressure), gas-exchange and hemodynamics. Results We enrolled 28 COVID-19 patients and 7 healthy controls. In all patients, respiratory effort increased from NRB to Helmet-CPAP (5.0 ± 3.7 vs 8.3 ± 3.9 cmH2O, p < 0.01). However, Helmet’s pressure decreased by a comparable amount during inspiration (− 3.1 ± 1.0 cmH2O, p = 0.16), therefore dynamic stress remained stable (p = 0.97). Changes in static and total lung stress from NRB to Helmet-CPAP were overall not significant (p = 0.07 and p = 0.09, respectively), but showed high interpatient variability, ranging from − 4.5 to + 6.1 cmH2O, and from − 5.8 to + 5.7 cmH2O, respectively. All findings were confirmed in healthy subjects, except for an increase in dynamic stress (p < 0.01). PaO2 decreased from NRB to Helmet-CPAP with FiO2 0.5 (107 ± 55 vs 86 ± 30 mmHg, p < 0.01), irrespective of positive pressure levels (p = 0.64). Conversely, with Helmet’s FiO2 0.9, PaO2 increased (p < 0.01), but oxygen delivery remained stable (p = 0.48) as cardiac output decreased (p = 0.02). When PaO2 fell below 60 mmHg with VM, respiratory effort increased proportionally (p < 0.01, r = 0.81). Conclusions In early COVID-19 pneumonia, Helmet-CPAP increases respiratory effort without altering dynamic stress, while the effects upon static and total stress are variable, requiring individual assessment. Oxygen masks with higher FiO2 provide better oxygenation with lower respiratory effort. Trial registration Retrospectively registered (13-May-2021): clinicaltrials.gov (NCT04885517), https://clinicaltrials.gov/ct2/show/NCT04885517.

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Section: Discussionmentioning

confidence: 99%

Effects of CPAP and FiO2 on respiratory effort and lung stress in early COVID-19 pneumonia: a randomized, crossover study

Giosa,

Collins,

Sciolla

et al. 2023

Ann. Intensive Care

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“…The prominent advantage of both oxygen therapies is their effect on avoiding invasive ventilation-related complications associated with unnecessary endotracheal intubation and sedation. However, recent research has demonstrated that excess spontaneous inspiratory effort could result in high transpulmonary pressure fluctuation (11) and large total lung strain (12,13) and finally lead to additional lung injury associated with treatment failure (14), especially when NIV therapy is coupled with high tidal volume (15) and rapid respiratory rate (16). Therefore, identifying predictive risk factors and modeling treatment failure may facilitate the early identification of high-risk patients and improve clinical decision-making and outcomes.…”

Section: Introductionmentioning

confidence: 99%

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction—a retrospective analysis of the MIMIC database

Fu,

Liu,

Guan

et al. 2024

Front. Med.

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BackgroundThis study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation.MethodsThe study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm.ResultA total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors.ConclusionNIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients’ SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

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“…The authors, who are experienced in simulation, 2 , 3 , 4 , 5 , 6 used data concerning COVID-19 acute respiratory distress syndrome (ARDS) pathophysiology to configure a virtual cohort of 120 patients with various combinations of poorly aerated lung tissue, and lung tissue affected by microthrombi. 1 Simulation has been defined as a ‘technique to replace or amplify real experiences with guided experiences that evoke or replicate substantial aspects of the real world in a fully interactive manner’. 7 It is rather a technique, not technology, referring to a device, the ‘simulator’, that represents a simulated patient (or a specific body part or structure) that interacts appropriately with the actions taken by the simulation participant.…”

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confidence: 99%

“…Simulation is invaluable when addressing issues relating to management of mass casualties. With this perspective, Weaver and colleagues 1 evaluated the underlying pathophysiology in COVID-19 ARDS and proposed optimal respiratory management to minimise secondary lung injury. Their study broadens the areas of computational modelling focus to include the concept of P-SILI.…”

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confidence: 99%

“…To quantify respiratory effort, they applied four combinations of ventilatory frequency (30, 27, 24, 21 bpm) and negative respiratory muscle pressure (26, 23, 20, 17 cm H 2 O). 1 One of the strengths of the study is that the simulation experiments were based on all the currently available knowledge regarding COVID-19 ARDS pathophysiological mechanisms. COVID-19 ARDS combines severe hypoxaemia with preserved lung mechanics, at least in the early stages of the disease.…”

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Simulation to minimise patient self-inflicted lung injury: are we almost there?

Tsolaki

Zakynthinos

2022

British Journal of Anaesthesia

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Optimising respiratory support for early COVID-19 pneumonia: a computational modelling study

Cited by 6 publications

References 14 publications

Effects of CPAP and FiO2 on respiratory effort and lung stress in early COVID-19 pneumonia: a randomized, crossover study

Effects of CPAP and FiO2 on respiratory effort and lung stress in early COVID-19 pneumonia: a randomized, crossover study

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction—a retrospective analysis of the MIMIC database

Simulation to minimise patient self-inflicted lung injury: are we almost there?

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