As part of an admission to an Intensive Care Unit (ICU) many patients have breathing tube inserted into their windpipe, also referred to as intubation . This is done for a variety of reasons which can include the need for invasive breathing support and to place the patient into a medically induced coma to allow their body to rest and be treated for the underlying cause of their deterioration. One the patient has started to recover from their illness, the process of removing the breathing tube (extubation) and stopping the breathing machine begins. Optimising this process and ensuring successful removal of the breathing tube is extremely important to optimise outcomes. Therefore, identifying new methods to determine the likelihood for successful extubation is of clinical significance. There are several markers of extubation readiness that clinicians can use to support their decision making discussed in the literature. A novel method is the use of respiratory muscle activity monitoring via sensory electromyogram (sEMG). sEMG has previously been shown to be useful to measure the strength and activity of muscles. However, until recently measuring diaphragm activity has been a challenge due to noise from the chest movements, cardiac artefacts. Recent advancements in signal processing and growing interest in better diagnostic methods has led to an improvement in metrics. Using wavelet transforms and machine learning, new techniques have been developed to measure diaphragm muscle activity using sEMG. sEMG has been shown to be effective in detecting patient-ventilator asynchrony as well as respiratory strength in those not requiring mechanical ventilation. However, its use in assessing patients nearing readiness for extubation has not been explored. Therefore, the purpose of this feasibility trial is to explore the use of sEMG in patients undergoing trials of extubation. The results of this study will be used to design a multi-site trial.
Age range
18 Years
Sex
ALL
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Feasibility of recruitment and retention of participants
Timeframe: 1 year