scholarly journals Electrical impedance tomography-guided prone positioning in a patient with acute cor pulmonale associated with severe acute respiratory distress syndrome

2015 ◽  
Vol 30 (1) ◽  
pp. 161-165 ◽  
Author(s):  
Toru Kotani ◽  
Hitoshi Tanabe ◽  
Hiroaki Yusa ◽  
Satoshi Saito ◽  
Kenji Yamazaki ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Cor Pulmonale ◽  
Prone Positioning ◽  
Impedance Tomography ◽  
Acute Cor Pulmonale

scholarly journals Electrical Impedance Tomography in Acute Respiratory Distress Syndrome

2010 ◽  
Vol 2 (1) ◽  
pp. 110-118 ◽  
Author(s):  
Inéz Frerichs ◽  
Sven Pulletz ◽  
Gunnar Elke ◽  
Günther Zick ◽  
Norbert Weiler
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Impedance Tomography

scholarly journals Positive End-Expiratory Pressure Setting in COVID-19-Related Acute Respiratory Distress Syndrome: Comparison Between Electrical Impedance Tomography, PEEP/FiO2 Tables, and Transpulmonary Pressure

2021 ◽  
Vol 8 ◽  
Author(s):  
Sébastien Gibot ◽  
Marie Conrad ◽  
Guilhem Courte ◽  
Aurélie Cravoisy
Keyword(s):  
Mechanical Ventilation ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Invasive Technique ◽  
Positive End Expiratory Pressure ◽  
Impedance Tomography

Introduction: The best way to titrate the positive end-expiratory pressure (PEEP) in patients suffering from acute respiratory distress syndrome is still matter of debate. Electrical impedance tomography (EIT) is a non-invasive technique that could guide PEEP setting based on an optimized ventilation homogeneity.Methods: For this study, we enrolled the patients with 2019 coronavirus disease (COVID-19)-related acute respiratory distress syndrome (ARDS), who required mechanical ventilation and were admitted to the ICU in March 2021. Patients were monitored by an esophageal catheter and a 32-electrode EIT device. Within 48 h after the start of mechanical ventilation, different levels of PEEP were applied based upon PEEP/FiO2 tables, positive end-expiratory transpulmonary (PL)/ FiO2 table, and EIT. Respiratory mechanics variables were recorded.Results: Seventeen patients were enrolled. PEEP values derived from EIT (PEEPEIT) were different from those based upon other techniques and has poor in-between agreement. The PEEPEIT was associated with lower plateau pressure, mechanical power, transpulmonary pressures, and with a higher static compliance (Crs) and homogeneity of ventilation.Conclusion: Personalized PEEP setting derived from EIT may help to achieve a more homogenous distribution of ventilation. Whether this approach may translate in outcome improvement remains to be investigated.

scholarly journals Electrical impedance tomography in acute respiratory distress syndrome

Critical Care ◽  
2018 ◽  
Vol 22 (1) ◽  
Author(s):  
M Consuelo Bachmann ◽  
Caio Morais ◽  
Guillermo Bugedo ◽  
Alejandro Bruhn ◽  
Arturo Morales ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Impedance Tomography

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

2017 ◽  
Vol 122 (4) ◽  
pp. 855-867 ◽  
Author(s):  
Christian J. Roth ◽  
Tobias Becher ◽  
Inéz Frerichs ◽  
Norbert Weiler ◽  
Wolfgang A. Wall
Keyword(s):  
Mechanical Ventilation ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Distress Syndrome ◽  
Lung Model ◽  
Patient Specific ◽  
Impedance Tomography

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. NEW & NOTEWORTHY 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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