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Airway resistance variation correlates with prognosis of critically ill COVID-19 patients: A computational fluid dynamics study

OBJECTIVE: To evaluate the quantitative changes of respiratory functions for critically ill COVID-19 patients with mechanical ventilation, computational fluid dynamics (CFD) analysis was performed based on patient-specific three-dimensional airway geometry. METHODS: 37 cases of critically ill patien...

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Detalles Bibliográficos
Autores principales: Pan, Shi-yu, Ding, Ming, Huang, Jing, Cai, Yan, Huang, Ying-zi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231702/
https://www.ncbi.nlm.nih.gov/pubmed/34245951
http://dx.doi.org/10.1016/j.cmpb.2021.106257
Descripción
Sumario:OBJECTIVE: To evaluate the quantitative changes of respiratory functions for critically ill COVID-19 patients with mechanical ventilation, computational fluid dynamics (CFD) analysis was performed based on patient-specific three-dimensional airway geometry. METHODS: 37 cases of critically ill patients with COVID-19 admitted to the ICU of Huangshi Traditional Chinese Medicine Hospital from February 1st to March 20th, 2020 were retrospectively analyzed. 5 patients whose clinical data met the specific criteria were finally cataloged into death group (2 patients) and survival group (3 patients). The patient-specific three-dimensional airways were reconstructed from the central airways down to the 4th-5th bifurcation of the tracheobronchial tree. The volume changes of bronchi were calculated during the disease progression according to the comparison of two CT scans. Additionally, the changes of air flow resistance were analyzed using numerical simulation of CFD. RESULTS: Pearson correlation analysis demonstrated that there was negative correlation between the change of volume (ΔV) and the change of resistance (ΔR) for all COVID-19 patients (r=-0.7025). For total airway volume, an average decrease of -11.41±15.71% was observed in death group compared to an average increase of 1.86±10.80% in survival group (p=0.0232). For air flow through airways in lower lobe, the resistance increases for death group by 10.97±77.66% and decreases for survival group by -45.49±42.04% (p=0.0246). CONCLUSION: The variation of flow resistance in the airway could be used as a non-invasive functional evaluation for the prognosis and outcome of critically ill patients with COVID-19. The ‘virtual’ pulmonary function test by integrating follow-up CT scans with patient-derived CFD analysis could be a potentially powerful way in improving the efficiency of treatment for critically ill patients with COVID-19.