What Has Computed Tomography Taught Us about the Acute Respiratory Distress Syndrome?

Gattinoni, Luciano; Caironi, Pietro; Pelosi, Paolo; Goodman, Lawrence R.

doi:10.1164/ajrccm.164.9.2103121

Cited by 657 publications

(568 citation statements)

References 82 publications

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“…The lung CT images were analyzed following the method of Gattinoni et al, 14 which classifies lung regions according to the degree of aeration as measured by the Hounsfield Unit (HU) values in the image. Regions with CT numbers -1000 to -900 HU were overdistended, -900 to -500 HU were normally aerated, -500 to -100 were poorly aerated, and [-100 were non-aerated.…”

Section: Computed Tomography Imagingmentioning

confidence: 99%

Resolution of pulmonary edema with variable mechanical ventilation in a porcine model of acute lung injury

Graham

Gulati

Kha

et al. 2011

Can J Anesth/J Can Anesth

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Objective Resolution of the acute respiratory distress syndrome (ARDS) requires clearance of pulmonary edema. Biologically variable ventilation (BVV) strategies that improve gas exchange, lung mechanics, and inflammatory mediators in ARDS may be beneficial in this regard. We used quantitative computed tomography (CT), a single indicator thermodilution system (PiCCOÒ) to determine extravascular lung water (EVLW), and the change in edema protein concentration over time to compare edema clearance with BVV vs conventional mechanical ventilation (CMV) in a porcine ARDS model. Methods Sixteen pigs with oleic acid lung injury were randomized to four hours of ventilation with either CMV (n = 8) or BVV (n = 8) at identical low tidal volume and minute ventilation over time. Hemodynamic variables, gas exchange, lung mechanics, and PiCCO derived EVLW were determined hourly. Computed tomography images and edema fluid samples were obtained at baseline lung injury and after four hours of ventilation. Wet and dry lung weights were determined postmortem.Results At four hours with BVV, peak airway pressure was decreased significantly and lung compliance improved compared with CMV (P = 0.003; P \ 0.001, respectively). Hemodynamic variables and gas exchange were not different between groups. Also at four hours, computed tomography revealed an increase in total gas volume (P = 0.001) and a decrease in total lung weight and global lung density (P = 0.005; P = 0.04 respectively) with BVV. These findings were associated with a significant increase in the gas volume of normally aerated lung regions (P \ 0.001) and a decrease in the poorly and non-aerated lung regions (P = 0.001). No change in any CT parameter occurred with CMV. The lung weights derived from computed tomography correlated well with postmortem wet weights (R 2 = 0.79; P \ 0.01). The decrease in PiCCO derived EVLW from injury to four hours did not differ significantly between BVV and CMV. Extravascular lung water showed no correlation with postmortem wet weights and significantly underestimated lung water. Average alveolar fluid clearance rates were positive (1.4%Áhr -1 (3%)) with BVV and negative with CMV (-2.0%Áhr -1 (4%)). Conclusions In a comparison between BVV and CMV, computed tomography evidence suggests that BVV facilitates enhanced clearance and/or redistribution of edema fluid with improved recruitment of atelectatic and poorly aerated lung regions; no such evidence was seen with either single thermodilution measurement of EVLW or edema clearance rates. The results of computed tomography provide further evidence of the benefit of BVV over conventional ventilation in ARDS. RésuméObjectif La re´solution d'un syndrome de de´tresse respiratoire aigue¨(SDRA) ne´cessite l'e´limination de

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Section: Computed Tomography Imagingmentioning

confidence: 99%

Resolution of pulmonary edema with variable mechanical ventilation in a porcine model of acute lung injury

Graham

Gulati

Kha

et al. 2011

Can J Anesth/J Can Anesth

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“…Although lung CT scan requires the transportation of patients and exposure to radiation, it remains the reference method for assessing lung recruitability [11]. Lung recruitability is computed using dedicated software [10,11,33]. However, contrary to healthy or emphysematous lung, in which an automatic segmentation of lung CT images is possible [34][35][36], in an ARDS lung a manual delineation separating the lung parenchyma from the pleural effusion, rib cage, or soft tissue is required because the software is not able to differentiate the not inflated tissue and pleural effusion because they present similar HU values.…”

Section: Discussionmentioning

confidence: 99%

“…Thus at the present time, quantitative lung CT scan analysis remains the reference method for computing PEEP-induced lung recruitment [10,11]. However, this analysis requires dedicated software and a manual delineation of the perimeter of the lungs in each CT image [10].…”

Section: Introductionmentioning

confidence: 99%

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Visual anatomical lung CT scan assessment of lung recruitability

et al. 2012

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Purpose: The computation of lung recruitability in acute respiratory distress syndrome (ARDS) is advocated to set positive end-expiratory pressure (PEEP) for preventing lung collapse. The quantitative lung CT scan, obtained by manual image processing, is the reference method but it is time consuming. The aim of this study was to evaluate the accuracy of a visual anatomical analysis compared with a quantitative lung CT scan analysis in assessing lung recruitability. Methods: Fifty sets of two complete lung CT scans of ALI/ARDS patients computing lung recruitment were analyzed. Lung recruitability computed at an airway pressure of 5 and 45 cmH 2 O was defined as the percentage decrease in the collapsed/ consolidated lung parenchyma assessed by two expert radiologists using a visual anatomical analysis and as the decrease in not aerated lung regions using a quantitative analysis computed by dedicated software.Results: Lung recruitability was 11.3 % (interquartile range 7.39-16.41) and 15.5 % (interquartile range 8.18-21.43) with the visual anatomical and quantitative analysis, respectively. In the Bland-Altman analysis, the bias and agreement bands between the visual anatomical and quantitative analysis were -2.9 % (-11.8 to ?5.9 %). The ROC curve showed that the optimal cutoff values for the visual anatomical analysis in predicting high versus low lung recruitability was 8.9 % (area under the ROC curve 0.9248, 95 % CI 0.8550-0.9946). Considering this cutoff, the sensitivity, specificity, and diagnostic accuracy were 0.96, 0.76, and 0.86, respectively. Conclusions: Visual anatomical analysis can classify patients into those with high and low lung recruitability allowing more intensivists to get access to lung recruitability assessment.

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“…La presencia de colapso alveolar en el SDRA está extensamente documentada 8,9 . Estudios recientes han mostrado que el colapso puede afectar de un 20 a un 40% del volumen pulmonar total en pacientes con SDRA de distinto origen [10][11][12][13] .…”

Section: Fundamento De Las Maniobras De Reclutamiento En El Sdraunclassified

Utilidad de las maniobras de reclutamiento (PRO)

Sipmann

2009

Medicina Intensiva

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What Has Computed Tomography Taught Us about the Acute Respiratory Distress Syndrome?

Cited by 657 publications

References 82 publications

Resolution of pulmonary edema with variable mechanical ventilation in a porcine model of acute lung injury

Resolution of pulmonary edema with variable mechanical ventilation in a porcine model of acute lung injury

Visual anatomical lung CT scan assessment of lung recruitability

Utilidad de las maniobras de reclutamiento (PRO)

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