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1.
Intensive Care Med ; 48(8): 995-1008, 2022 08.
Article in English | MEDLINE | ID: covidwho-1995565

ABSTRACT

In patients with the acute respiratory distress syndrome (ARDS), lung imaging is a fundamental tool in the study of the morphological and mechanistic features of the lungs. Chest computed tomography studies led to major advances in the understanding of ARDS physiology. They allowed the in vivo study of the syndrome's lung features in relation with its impact on respiratory physiology and physiology, but also explored the lungs' response to mechanical ventilation, be it alveolar recruitment or ventilator-induced lung injuries. Coupled with positron emission tomography, morphological findings were put in relation with ventilation, perfusion or acute lung inflammation. Lung imaging has always been central in the care of patients with ARDS, with modern point-of-care tools such as electrical impedance tomography or lung ultrasounds guiding clinical reasoning beyond macro-respiratory mechanics. Finally, artificial intelligence and machine learning now assist imaging post-processing software, which allows real-time analysis of quantitative parameters that describe the syndrome's complexity. This narrative review aims to draw a didactic and comprehensive picture of how modern imaging techniques improved our understanding of the syndrome, and have the potential to help the clinician guide ventilatory treatment and refine patient prognostication.


Subject(s)
Respiratory Distress Syndrome , Ventilator-Induced Lung Injury , Artificial Intelligence , Humans , Lung , Respiration, Artificial/methods , Respiratory Distress Syndrome/diagnostic imaging , Respiratory Distress Syndrome/therapy , Tomography, X-Ray Computed , Ventilator-Induced Lung Injury/diagnostic imaging
2.
Crit Care ; 25(1): 431, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1854857

ABSTRACT

BACKGROUND: We hypothesized that as CARDS may present different pathophysiological features than classic ARDS, the application of high levels of end-expiratory pressure is questionable. Our first aim was to investigate the effects of 5-15 cmH2O of PEEP on partitioned respiratory mechanics, gas exchange and dead space; secondly, we investigated whether respiratory system compliance and severity of hypoxemia could affect the response to PEEP on partitioned respiratory mechanics, gas exchange and dead space, dividing the population according to the median value of respiratory system compliance and oxygenation. Thirdly, we explored the effects of an additional PEEP selected according to the Empirical PEEP-FiO2 table of the EPVent-2 study on partitioned respiratory mechanics and gas exchange in a subgroup of patients. METHODS: Sixty-one paralyzed mechanically ventilated patients with a confirmed diagnosis of SARS-CoV-2 were enrolled (age 60 [54-67] years, PaO2/FiO2 113 [79-158] mmHg and PEEP 10 [10-10] cmH2O). Keeping constant tidal volume, respiratory rate and oxygen fraction, two PEEP levels (5 and 15 cmH2O) were selected. In a subgroup of patients an additional PEEP level was applied according to an Empirical PEEP-FiO2 table (empirical PEEP). At each PEEP level gas exchange, partitioned lung mechanics and hemodynamic were collected. RESULTS: At 15 cmH2O of PEEP the lung elastance, lung stress and mechanical power were higher compared to 5 cmH2O. The PaO2/FiO2, arterial carbon dioxide and ventilatory ratio increased at 15 cmH2O of PEEP. The arterial-venous oxygen difference and central venous saturation were higher at 15 cmH2O of PEEP. Both the mechanics and gas exchange variables significantly increased although with high heterogeneity. By increasing the PEEP from 5 to 15 cmH2O, the changes in partitioned respiratory mechanics and mechanical power were not related to hypoxemia or respiratory compliance. The empirical PEEP was 18 ± 1 cmH2O. The empirical PEEP significantly increased the PaO2/FiO2 but also driving pressure, lung elastance, lung stress and mechanical power compared to 15 cmH2O of PEEP. CONCLUSIONS: In COVID-19 ARDS during the early phase the effects of raising PEEP are highly variable and cannot easily be predicted by respiratory system characteristics, because of the heterogeneity of the disease.


Subject(s)
COVID-19/therapy , Positive-Pressure Respiration , Respiratory Distress Syndrome/therapy , Ventilator-Induced Lung Injury , COVID-19/diagnosis , Critical Care , Humans , Hypoxia , Middle Aged , Oxygen/therapeutic use , SARS-CoV-2 , Ventilator-Induced Lung Injury/diagnostic imaging
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