Lung density distribution in dynamic CT correlates with oxygenation in ventilated pigs with lavage ARDS † †This study contains parts of the doctoral thesis of Elena Ribel.

Markstaller, Klaus; Kauczor, Hans‐Ulrich; Weiler, Norbert; Karmrodt, J.; Doebrich, Marcus; Ferrante, Michael F.; Thelen, M.; Eberle, Balthasar

doi:10.1093/bja/aeg246

Cited by 43 publications

(17 citation statements)

References 24 publications

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“…With increasing PEEP, the V T redistributed from ventral to dorsal lung areas, as detected by both EIT and VRI. This effect occurred mainly in the middle (level 2) and lower dorsal (level 3) ROIs, reflecting recruitment of atelectasis in dependent lung areas, and consistent with other studies using the saline lavage model [22], [23]. The observed changes in regional distribution were quite small, most likely due to the less severe lung injury (P a O 2 /F I O 2 <300; mild ARDS) induced by the protocol used in our study.…”

Section: Discussionsupporting

confidence: 91%

Assessment of Regional Ventilation Distribution: Comparison of Vibration Response Imaging (VRI) with Electrical Impedance Tomography (EIT)

et al. 2014

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BackgroundVibration response imaging (VRI) is a bedside technology to monitor ventilation by detecting lung sound vibrations. It is currently unknown whether VRI is able to accurately monitor the local distribution of ventilation within the lungs. We therefore compared VRI to electrical impedance tomography (EIT), an established technique used for the assessment of regional ventilation.Methodology/Principal FindingsSimultaneous EIT and VRI measurements were performed in the healthy and injured lungs (ALI; induced by saline lavage) at different PEEP levels (0, 5, 10, 15 mbar) in nine piglets. Vibration energy amplitude (VEA) by VRI, and amplitudes of relative impedance changes (rel.ΔZ) by EIT, were evaluated in seven regions of interest (ROIs). To assess the distribution of tidal volume (VT) by VRI and EIT, absolute values were normalized to the VT obtained by simultaneous spirometry measurements. Redistribution of ventilation by ALI and PEEP was detected by VRI and EIT. The linear correlation between pooled VT by VEA and rel.ΔZ was R2 = 0.96. Bland-Altman analysis showed a bias of −1.07±24.71 ml and limits of agreement of −49.05 to +47.36 ml. Within the different ROIs, correlations of VT-distribution by EIT and VRI ranged between R2 values of 0.29 and 0.96. ALI and PEEP did not alter the agreement of VT between VRI and EIT.Conclusions/SignificanceMeasurements of regional ventilation distribution by VRI are comparable to those obtained by EIT.

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Section: Discussionsupporting

confidence: 91%

Assessment of Regional Ventilation Distribution: Comparison of Vibration Response Imaging (VRI) with Electrical Impedance Tomography (EIT)

et al. 2014

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“…Because these liquids are incompressible, their enclosure leads to an additional effective stiffness and thus to a decreased slope of P-V curves in the specific region. In our ARDS patient, the Hounsfield units of the lung range from -900 to -200, with an average value of -597 in good accordance with previously reported values for ARDS lungs (39,53). On the basis of these Hounsfield units, the local P-V curves are scaled on the volume axis while the qualitative shape of the curve is retained (see detailed explanation in Reduced-dimensional lung model).…”

Section: Changes In Local Acinar P-v Curves Based On Hounsfield Unitssupporting

confidence: 87%

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

Roth

Becher

Frerichs

et al. 2017

Journal of Applied Physiology

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Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future.

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“…Again ventral ROI was faster than before lung injury. Dorsal ROI was hit in late expiration, as expected, probably due to atelectasis [14,15]. …”

Section: Resultsmentioning

confidence: 66%

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

et al. 2014

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BackgroundA recent method determines regional gas flow of the lung by electrical impedance tomography (EIT). The aim of this study is to show the applicability of this method in a porcine model of mechanical ventilation in healthy and diseased lungs. Our primary hypothesis is that global gas flow measured by EIT can be correlated with spirometry. Our secondary hypothesis is that regional analysis of respiratory gas flow delivers physiologically meaningful results.MethodsIn two sets of experiments n = 7 healthy pigs and n = 6 pigs before and after induction of lavage lung injury were investigated. EIT of the lung and spirometry were registered synchronously during ongoing mechanical ventilation. In-vivo aeration of the lung was analysed in four regions-of-interest (ROI) by EIT: 1) global, 2) ventral (non-dependent), 3) middle and 4) dorsal (dependent) ROI. Respiratory gas flow was calculated by the first derivative of the regional aeration curve. Four phases of the respiratory cycle were discriminated. They delivered peak and late inspiratory and expiratory gas flow (PIF, LIF, PEF, LEF) characterizing early or late inspiration or expiration.ResultsLinear regression analysis of EIT and spirometry in healthy pigs revealed a very good correlation measuring peak flow and a good correlation detecting late flow. PIFEIT = 0.702 · PIFspiro + 117.4, r2 = 0.809; PEFEIT = 0.690 · PEFspiro-124.2, r2 = 0.760; LIFEIT = 0.909 · LIFspiro + 27.32, r2 = 0.572 and LEFEIT = 0.858 · LEFspiro-10.94, r2 = 0.647. EIT derived absolute gas flow was generally smaller than data from spirometry. Regional gas flow was distributed heterogeneously during different phases of the respiratory cycle. But, the regional distribution of gas flow stayed stable during different ventilator settings. Moderate lung injury changed the regional pattern of gas flow.ConclusionsWe conclude that the presented method is able to determine global respiratory gas flow of the lung in different phases of the respiratory cycle. Additionally, it delivers meaningful insight into regional pulmonary characteristics, i.e. the regional ability of the lung to take up and to release air.

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Lung density distribution in dynamic CT correlates with oxygenation in ventilated pigs with lavage ARDS † †This study contains parts of the doctoral thesis of Elena Ribel.

Cited by 43 publications

References 24 publications

Assessment of Regional Ventilation Distribution: Comparison of Vibration Response Imaging (VRI) with Electrical Impedance Tomography (EIT)

Assessment of Regional Ventilation Distribution: Comparison of Vibration Response Imaging (VRI) with Electrical Impedance Tomography (EIT)

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

Determination of respiratory gas flow by electrical impedance tomography in an animal model of mechanical ventilation

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