Critically Ill Patients, Mechanical Ventilation
Conditions
Brief 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.
Interventions
DEVICEMachine Learning Ventilator Decision System
Artificial intelligence ventilator system for personalized mechanical ventilation
Sponsors
Hu Anmin
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Intervention model description
ventilator decision system
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. only the first ICU stay was eligible; 2. adults ≥ 18 years of age on ICU admission; 3. estimate mechanical ventilation time ≥24 hours;
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Mechanical ventilation time | through study completion, an average of 5 days |
Secondary
| Measure | Time frame |
|---|---|
| Length of ICU stay time | through study completion, an average of 1 week |
| Length of hospital stay | through study completion, an average of 2 weeks |
| In-hospital mortality | through study completion, an average of 2 weeks |
Outcome results
None listed