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.
Study Type
OBSERVATIONAL
Enrollment
318,542
Decision support to optimise invasive mechanical ventilation settings
Cleveland Clinic Foundation, Cleveland, USA
Cleveland, Ohio, United States
RECRUITINGUniversity Hospital Carl Gustav Carus Dresden
Dresden, Germany
RECRUITINGInstitut Fur Angewandte Informatik (Infai) Ev
Leipzig, Germany
ACTIVE_NOT_RECRUITINGInstitut Mihajlo Pupin
Belgrade, Serbia
ACTIVE_NOT_RECRUITINGBetter Care Sl
Sabadell, Spain
ACTIVE_NOT_RECRUITINGFundacio Parc Tauli
Sabadell, Spain
COMPLETEDFundacion Publica Andaluza Progreso Y Salud
Seville, Spain
ACTIVE_NOT_RECRUITINGInselspital, Universitätsspital Bern
Bern, Switzerland
COMPLETEDRelative time of same device settings of the health care provider and the IntelliLung algorithm
Time frame: From date of intubation to date of extubation or date of discharge, which ever came first, assessed up to 12 month
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