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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Critically Ill Patients; Mechanical Ventilation
• Intervention / Treatment: Device: Machine Learning Ventilator Decision System

• Study Type: Interventional
• Enrollment (Anticipated): 300
• Phase: Not Applicable

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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;

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Group A; Machine Learning Ventilator Decision System Ventilation	Device: Machine Learning Ventilator Decision System; Artificial intelligence ventilator system for personalized mechanical ventilation
Active Comparator: Group B; Standard Controlled Ventilation	Device: Machine Learning Ventilator Decision System; Artificial intelligence ventilator system for personalized mechanical ventilation

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Mechanical ventilation time	through study completion, an average of 5 days

Secondary Outcome Measures

Outcome 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

Collaborators and Investigators

• Sponsor: Hu Anmin

Study record dates

Study Major Dates

• Study Start (Anticipated): January 1, 2022
• Primary Completion (Anticipated): January 1, 2022
• Study Completion (Anticipated): December 1, 2024

Study Registration Dates

• First Submitted: October 7, 2021
• First Submitted That Met QC Criteria: November 12, 2021
• First Posted (Actual): November 24, 2021

Study Record Updates

• Last Update Posted (Actual): November 24, 2021
• Last Update Submitted That Met QC Criteria: November 12, 2021
• Last Verified: November 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Critical Illness
• Other Study ID Numbers: LL-KY-2021396

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No