EIT Study With Healthy Patients

Chronic lung disease can sometimes progress to the extent that patients can no longer clear the waste gas from their blood. Treatment can be offered with a mask and machine (ventilator) that helps people breathe and aims to improve their lung condition. It is common for people's lungs to be affected variably, i.e. left more than right or top of lung more than bases of lungs. The way in which the ventilator is set may affect how well the machine deals with these differences. If the lung is better ventilator patients may find the machine more comfortable and it may be more effective.

Electrical impedance tomography (EIT) is a new technology that involves wearing a belt of sensors around the chest that provides information on how well the lungs are being filled with air by the ventilator. It allows the assessment of these differences, which previously required the use of invasive equipment to obtain.

Optimising ventilator settings in the administration of non-invasive ventilation (NIV) can be improved with the addition of individual physiological data. This approach is limited due to the invasive techniques required to obtain this information, often leading to less ideal NIV settings promoting patient-ventilator asynchrony. It has been recently demonstrated by our group that all patients established on domiciliary NIV have a degree of patient-ventilator asynchrony and that the commonest type of asynchrony are triggering issues. Triggering asynchrony is promoted by mismatch between a patient's intrinsic positive end-expiratory pressure (iPEEP) and applied expiratory positive airway pressure (EPAP) with these ineffective efforts contributing to an increased work of breathing and patient discomfort. Previous strategies used to optimise patient triggering have involved the placement of oesophageal catheters in order to measure neural respiratory drive (NRD) to the diaphragm by electromyography (EMG) but again this process is invasive and often poorly tolerated. Electrical Impedance Tomography (EIT) is a non-invasive, bedside monitoring technique that provides semi-continuous, real-time information about the regional distribution of the changes in electrical resistivity of the lung tissue due to variations in ventilation in relation to a reference state.

Information is gained by repeatedly injecting small alternating electric currents (usually 5 mA) at high frequency of 50 - 80 kHz through a system of skin electrodes (usually 16) applied circumferentially around the thorax in a single plane between the 4th and 6th intercostal space. While an adjacent pair of electrodes 'injects' the current ('adjacent drive configuration'), all the remaining adjacent passive electrode pairs measure the differences in electric potential. A resistivity (impedance) image is reconstructed from this data by a mathematical algorithm using a two dimensional model and a simplified shape to represent the thoracic cross-section.

The resulting image possesses a high temporal and functional resolution making it possible to monitor dynamic physiological phenomena (e.g. delay in regional inflation or recruitment) on a breath by breath basis. It is important to realize that the EIT images are based on image reconstruction techniques that require at least one measurement on a well-defined reference state. All quantitative data are related to this reference and can only indirectly quantify (relative) changes in local lung impedance (but not absolute).

EIT can be used in mechanically ventilated patients to assess recruitment and to optimise ventilator settings to reduce risk of iatrogenic ventilator associated lung injury.

To date, EIT has only been validated in patient invasively ventilated or during self-ventilation.

The investigators hope to use the EIT technology to assess patients breathing using different ventilator modes in order to see if one is more effective and or more comfortable than the others. The investigators can use this information to help them set ventilators for future patients.

Inclusion criteria

• Absence of any underlying lung disease
• FEV1/FVC > 70%

Exclusion criteria

• Pregnancy
• Aged <18, >80
• Significant physical or psychiatric comorbidity
• that would prevent compliance with trial protocol