NCT05132751

Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation

Effect of a Machine Learning Ventilator Decision System Versus Standard Controlled Ventilation on in Critical Care: a Randomized Trial

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 300 (estimated)
• Sponsor: Hu Anmin · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Conditions

• Mechanical Ventilation
• Critically Ill Patients

Interventions

Type	Name	Description
DEVICE	Machine Learning Ventilator Decision System	Artificial intelligence ventilator system for personalized mechanical ventilation

Timeline

• Start date: 2022-01-01
• Primary completion: 2022-01-01
• Completion: 2024-12-01
• First posted: 2021-11-24
• Last updated: 2021-11-24