Hu Anmin
Hu Anmin
Ventilator-induced lung injury is associated with increased morbidity and mortality. Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. However, an individualized mechanical ventilation approach remains a challenging task: A multitude of factors, e.g., lab values, vitals, comorbidities, disease progression, and other clinical data must be taken into consideration when choosing a patient's specific optimal ventilation regime. The aim of this work was to evaluate the machine learning ventilator decision system, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. Compare with standard controlled ventilation, to test whether the clinical application of the machine learning ventilator decision system reduces mechanical ventilation time and mortality.
Mechanical Ventilation
Critically Ill Patients
Machine Learning Ventilator Decision System
Not Applicable
Study Type : | Interventional |
Estimated Enrollment : | 300 participants |
Masking: | Triple |
Primary Purpose: | Treatment |
Official Title: | Effect of a Machine Learning Ventilator Decision System Versus Standard Controlled Ventilation on in Critical Care: a Randomized Trial |
Actual Study Start Date : | January 1, 2022 |
Estimated Primary Completion Date : | January 1, 2022 |
Estimated Study Completion Date : | December 1, 2024 |
Arm | Intervention/treatment |
---|---|
Experimental: Group A Machine Learning Ventilator Decision System Ventilation |
Device: Machine Learning Ventilator Decision System |
Active Comparator: Group B Standard Controlled Ventilation |
Device: Machine Learning Ventilator Decision System |
Ages Eligible for Study: | 18 Years |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
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