Anup Das
In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications
Das, Anup; Saffaran, Sina; Chikhani, Marc; Scott, Timothy E; Laviola, Marianna; Yehya, Nadir; Laffey, John G; Hardman, Jonathan G; Bates, Declan G
Authors
Sina Saffaran
Marc Chikhani
Timothy E Scott
Dr MARIANNA LAVIOLA Marianna.Laviola@nottingham.ac.uk
ASSISTANT PROFESSOR
Nadir Yehya
John G Laffey
Professor JONATHAN HARDMAN J.HARDMAN@NOTTINGHAM.AC.UK
PROFESSOR OF ANAESTHESIA
Declan G Bates
Abstract
Objectives: Patients with COVID-19 Acute Respiratory Distress Syndrome (CARDS) appear to present with at least two distinct phenotypes: severe hypoxemia with relatively well-preserved lung compliance and lung gas volumes (Type 1) and a more conventional ARDS phenotype displaying the typical characteristics of the ‘baby lung’ (Type 2). We aimed to test plausible hypotheses regarding the pathophysiological mechanisms underlying CARDS, and to evaluate the resulting implications for ventilatory management.
Design: We adapted a high-fidelity computational simulator, previously validated in several studies of ARDS, to (a) develop quantitative insights into the key pathophysiologic differences between CARDS and conventional ARDS, and (b) assess the impact of different PEEP, FiO2 and tidal volume settings.
Setting: Interdisciplinary Collaboration in Systems Medicine Research Network.
Subjects: The simulator was calibrated to represent CARDS patients with both normal and elevated body mass indices undergoing invasive mechanical ventilation.
Measurements and Main Results: An ARDS model implementing disruption of hypoxic pulmonary vasoconstriction and vasodilation leading to hyperperfusion of collapsed lung regions failed to replicate clinical data on Type 1 CARDS patients. Adding mechanisms to reflect disruption of alveolar gas-exchange due to the effects of pneumonitis, and heightened vascular resistance due to the emergence of microthrombi, produced levels of V/Q mismatch and hypoxemia consistent with data from Type 1 CARDS patients, while preserving close to normal lung compliance and gas volumes. Atypical responses to PEEP increments between 5 and 15 cmH2O were observed for this Type 1 CARDS model across a range of measures: increasing PEEP resulted in reduced lung compliance and no improvement in oxygenation, while Mechanical Power, Driving Pressure and Plateau Pressure all increased. FiO2 settings based on ARDSnet protocols at different PEEP levels were insufficient to achieve adequate oxygenation. Incrementing tidal volumes from 5 to 10 ml/kg produced similar increases in multiple indicators of ventilator induced lung injury in the Type 1 CARDS model to those seen in a conventional ARDS model.
Conclusions: Our model suggests that use of standard PEEP/ FiO2 tables, higher PEEP strategies, and higher tidal volumes, may all be potentially deleterious in Type 1 CARDS patients, and that a highly personalized approach to treatment is advisable.
Citation
Das, A., Saffaran, S., Chikhani, M., Scott, T. E., Laviola, M., Yehya, N., Laffey, J. G., Hardman, J. G., & Bates, D. G. (2020). In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications. Critical Care Explorations, 2(9), Article e0202. https://doi.org/10.1097/CCE.0000000000000202
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 22, 2020 |
Online Publication Date | Sep 18, 2020 |
Publication Date | 2020-09 |
Deposit Date | Jul 24, 2020 |
Publicly Available Date | Oct 5, 2020 |
Journal | Critical Care Explorations |
Print ISSN | 2639-8028 |
Electronic ISSN | 2639-8028 |
Publisher | Lippincott, Williams & Wilkins |
Peer Reviewed | Peer Reviewed |
Volume | 2 |
Issue | 9 |
Article Number | e0202 |
DOI | https://doi.org/10.1097/CCE.0000000000000202 |
Keywords | COVID-19, ARDS, Mechanical Ventilation, Ventilator Induced Lung Injury |
Public URL | https://nottingham-repository.worktribe.com/output/4784844 |
Publisher URL | https://journals.lww.com/ccejournal/Fulltext/2020/09000/In_Silico_Modeling_of_Coronavirus_Disease_2019.23.aspx |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
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