Looking at the ICU ventilator with AEGLE’s eyes – using Big Data Analytics to improve mechanical ventilation in ICU
Author: Katerina Vaporidi (Physician at University Hospital of Heraklio)
Problem: Providing optimal mechanical ventilation in critically ill patients is a highly desired but difficult task to achieve in everyday clinical practice within the intensive care unit. One major challenge is the highly variable breathing patterns that critically ill patient’s exhibit. To identify and treat any instances of poor mechanical ventilation the physician would be required to continuously look at the ventilator screen. As this is not feasible, there is a clear need for a computer-aided solution.
How AEGLE helps the physician ‘look’ at the ventilator:
Two important problems that occur during mechanical ventilation have been addressed by AEGLE. The first is the problem of ineffective efforts, which is when a patient is trying to breathe but the ventilator does not respond appropriately by supporting the patient’s effort. This is stressful for the patient, and research has shown that not only ineffective efforts are the most common form of ventilator-asynchrony, but also, more importantly, when patients have too many ineffective efforts, it is associated with longer ICU stay, and more chance of death. The second critical condition addressed by AEGLE is the presence of lung overstretching during assisted ventilation. It is well known that overstretching the lung is the major mechanism by which mechanical ventilation can worsen lung injury in patients. Until now, for patients breathing with a partial assist from the ventilator, there is no way to monitor for the presence of lung over-stretch.
The user-interface developed by AEGLE receives the signals from the ventilator and patient-ventilator interaction monitoring devices. The platform algorithms identify when a patient is having a clinically significant large amount of clustered ineffective efforts. Additionally, an algorithm analysing all the signals from the ventilator and the monitor provides information about potential causes to facilitate the physician to provide targeted interventions. The AEGLE analytics also compute the pressures measuring the extent of lung stretch, and identify, not only when this pressure is sustained at injurious high levels, but also predicts when a patient is at risk of developing this phenomenon in the next minutes, so the physician can act to prevent it.
This way the AEGLE solution provides the physician with a new way to approach the continuously changing parameters of the ventilator for patients in a partial ventilatory assist. The AEGLE solution captures the variability of assisted breathing and provides the physician with targeted information for clinically significant events, such as clusters of ineffective efforts and periods of sustained, or at risk for sustained lung overstretch. This information is provided along with all other important clinical information available in patients’ electronic medical record. Using the AEGLE tools, the physician in the ICU is able to provide better ventilation for the patients, and the researcher can explore in depth the complex pathophysiology of patient-ventilator interaction.
If you are in any way involved in mechanical ventilation in ICU, you may be interested in learning more about AEGLE.