Management of Respiratory Distress Syndrome due to COVID-19 infection

Navas-Blanco, Jose R; Dudaryk, Roman

doi:10.1186/s12871-020-01095-7

Cited by 71 publications

(57 citation statements)

References 44 publications

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“…Early reports suggested the likelihood of an atypical pathophysiology to explain the pulmonary and systemic manifestations such as the presence of severe hypoxemia with preserved pulmonary mechanics. 3 Some patients with COVID-19 ARDS present with low PaO 2 :FiO 2 ratios despite preserved compliance, which differs from classic ARDS. 4,5 However, emerging evidence indicate that the respiratory system mechanics of patients with ARDS, with or without COVID-19, are broadly similar, advocating standard evidence-based management for ARDS.…”

mentioning

confidence: 99%

Prone positioning in mechanically ventilated patients with severe acute respiratory distress syndrome and coronavirus disease 2019

Gleissman

Forsgren

Andersson

et al. 2020

Acta Anaesthesiol Scand

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mentioning

confidence: 99%

Prone positioning in mechanically ventilated patients with severe acute respiratory distress syndrome and coronavirus disease 2019

Gleissman

Forsgren

Andersson

et al. 2020

Acta Anaesthesiol Scand

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“…An AI model to predict a 'CORISK' score There is wide variation in the clinical course of patients who present to the hospital with symptoms of COVID-19, with some experiencing rapid deterioration in respiratory function requiring different interventions in order to prevent or mitigate hypoxemia 41,42 . A critical decision made during the evaluation of a patient at the initial point of care or the ED, is whether the patient is likely to require more invasive or resource-limited counter-measures or interventions (such as mechanical ventilation or monoclonal antibodies), and should therefore receive a scarce but effective therapy, a therapy with a narrow risk-bene t ratio due to side effects, or a higher level of care, such as admittance to the ICU 43,44 .…”

Section: A Global Dataset For Covid-19 Image Analysismentioning

confidence: 99%

Federated Learning used for predicting outcomes in SARS-COV-2 patients

Flores¹,

Dayan

Roth³

et al. 2021

Preprint

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‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

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“…13,14,31 The early lack of understanding of the pathophysiology and the overtime evolution of COVID-19 pneumonia. 32,33 Also, the conflicting evidence regards the therapeutic interventions for COVID-19 pneumonia. [34][35][36] We recognize that there are several limitations to the current study.…”

Section: Ecmo Patientsmentioning

confidence: 99%

Extracorporeal membrane oxygenation for COVID-19 induced hypoxia: Single-center study

et al. 2020

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Introduction: The pandemic of the coronavirus disease 2019 (COVID-19) and associated pneumonia represent a clinical and scientific challenge. The role of Extracorporeal Membrane Oxygenation (ECMO) in such a crisis remains unclear. Methods: We examined COVID-19 patients who were supported for acute respiratory failure by both conventional mechanical ventilation (MV) and ECMO at a tertiary care institution in Washington DC. The study period extended from March 23 to April 29. We identified 59 patients who required invasive mechanical ventilation. Of those, 13 patients required ECMO. Results: Nine out of 13 ECMO (69.2%) patients were decannulated from ECMO. All-cause ICU mortality was comparable between both ECMO and MV groups (6 patients [46.15%] vs. 22 patients [47.82 %], p = 0.92). ECMO non-survivors vs survivors had elevated D-dimer (9.740 mcg/ml [4.84-20.00] vs. 3.800 mcg/ml [2.19-9.11], p = 0.05), LDH (1158 ± 344.5 units/L vs. 575.9 ± 124.0 units/L, p = 0.001), and troponin (0.4315 ± 0.465 ng/ml vs. 0.034 ± 0.043 ng/ml, p = 0.04). Time on MV as expected was significantly longer in ECMO groups (563.3 hours [422.1-613.9] vs. 247.9 hours [101.8-479] in MV group, p = 0.0009) as well as ICU length of stay 576.2 hours [457.5-652.8] in ECMO group vs. 322.2 hours [120.6-569.3] in MV group, p = 0.012). Conclusion: ECMO is a supportive intervention for COVID-19 associated pneumonia that could be considered if the optimum mechanical ventilation is deemed ineffective. Biomarkers such as D-dimer, LDH, and troponin could help with discerning the clinical prognosis in patients with COVID-19 pneumonia.

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Management of Respiratory Distress Syndrome due to COVID-19 infection

Cited by 71 publications

References 44 publications

Prone positioning in mechanically ventilated patients with severe acute respiratory distress syndrome and coronavirus disease 2019

Prone positioning in mechanically ventilated patients with severe acute respiratory distress syndrome and coronavirus disease 2019

Federated Learning used for predicting outcomes in SARS-COV-2 patients

Extracorporeal membrane oxygenation for COVID-19 induced hypoxia: Single-center study

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