Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation

Mechanical Ventilation Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05132751
• Other study ID #: LL-KY-2021396
• Status: Not yet recruiting
• Phase: N/A
• Start date: January 1, 2022
• Est. completion date: December 1, 2024

Study information

• Verified date: November 2021
• Source: The Second Clinical Medical College of Jinan University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 300
• Est. completion date: December 1, 2024
• Est. primary completion date: January 1, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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;

Related Conditions & MeSH terms

• Critical Illness
• Critically Ill Patients
• Mechanical Ventilation

Intervention

Device:
• Machine Learning Ventilator Decision System
Artificial intelligence ventilator system for personalized mechanical ventilation

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Hu Anmin

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Mechanical ventilation time		through study completion, an average of 5 days
Secondary	Length of ICU stay time		through study completion, an average of 1 week
Secondary	Length of hospital stay		through study completion, an average of 2 weeks
Secondary	In-hospital mortality		through study completion, an average of 2 weeks