Electrical Impedance Tomography to Monitor Hypoxemic Respiratory Failure.
Guillaume FranchineauAnnemijn H JonkmanLise PiquilloudTakeshi YoshidaEduardo Leite Vieira CostaHadrien RozéLuigi CamporotaThomas PirainoElena SpinelliAlain CombesGlasiele C AlcalaMarcelo AmatoTommaso MauriInéz FrerichsLaurent J BrochardMatthieu SchmidtPublished in: American journal of respiratory and critical care medicine (2024)
Hypoxemic respiratory failure is one of the leading causes of mortality in intensive care. Frequent assessment of individual physiological characteristics and delivery of personalized mechanical ventilation (MV) settings is a constant challenge for clinicians caring for these patients. Electrical impedance tomography (EIT) is a radiation-free bedside monitoring device that is able to assess regional lung ventilation and changes in aeration. With real-time tomographic functional images of the lungs obtained through a thoracic belt, clinicians can visualize and estimate the distribution of ventilation at different ventilation settings or following procedures such as prone positioning. Several studies have evaluated the performance of EIT to monitor the effects of different MV settings in patients with acute respiratory distress syndrome, allowing more personalized MV. For instance, EIT could help clinicians find the positive end-expiratory pressure that represents a compromise between recruitment and overdistension and assess the effect of prone positioning on ventilation distribution. The clinical impact of the personalization of MV remains to be explored. Despite inherent limitations such as limited spatial resolution, EIT also offers a unique noninvasive bedside assessment of regional ventilation changes in the ICU. This technology offers the possibility of a continuous, operator-free diagnosis and real-time detection of common problems during MV. This review provides an overview of the functioning of EIT, its main indices, and its performance in monitoring patients with acute respiratory failure. Future perspectives for use in intensive care are also addressed.
Keyphrases
- respiratory failure
- mechanical ventilation
- acute respiratory distress syndrome
- extracorporeal membrane oxygenation
- intensive care unit
- palliative care
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- deep learning
- prognostic factors
- risk factors
- cardiovascular disease
- type diabetes
- convolutional neural network
- optical coherence tomography
- magnetic resonance
- clinical evaluation