Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support

Condition(s) studied

Investigational drug(s) / intervention(s)

Artificial Intelligence-based Decision support: Decision support to optimise invasive mechanical ventilation settings

Study summary

Invasive mechanical ventilation is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, extracorporeal lung support is initiated when mechanical ventilation is insufficient. However, mechanical ventilation is recognised as potentially harmful, because inappropriate mechanical ventilation settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that mechanical ventilation is often not provided adequately despite clear evidence and guidelines. Variables at the ventilator and extracorporeal lung support device can be set automatically using optimization functions and clinical recommendations, but the handling of experts may still deviate from those settings depending upon the clinical characteristics of individual patients. Artificial intelligence can be used to learn from those deviations as well as the patient's condition in an attempt to improve the combination of settings and accomplish lung support with reduced risk of damage.

Eligibility

Primary outcome measure(s)

• Relative time of same device settings of the health care provider and the IntelliLung algorithm — From date of intubation to date of extubation or date of discharge, which ever came first, assessed up to 12 month

Trial sites (8)

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