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Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation

NCT ID: NCT05132751

Last Updated: 2021-11-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

300 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-01-01

Study Completion Date

2024-12-01

Brief Summary

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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.

Detailed Description

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Conditions

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Mechanical Ventilation Critically Ill Patients

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

ventilator decision system
Primary Study Purpose

TREATMENT

Blinding Strategy

TRIPLE

Participants Investigators Outcome Assessors

Study Groups

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Group A

Machine Learning Ventilator Decision System Ventilation

Group Type EXPERIMENTAL

Machine Learning Ventilator Decision System

Intervention Type DEVICE

Artificial intelligence ventilator system for personalized mechanical ventilation

Group B

Standard Controlled Ventilation

Group Type ACTIVE_COMPARATOR

Machine Learning Ventilator Decision System

Intervention Type DEVICE

Artificial intelligence ventilator system for personalized mechanical ventilation

Interventions

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Machine Learning Ventilator Decision System

Artificial intelligence ventilator system for personalized mechanical ventilation

Intervention Type DEVICE

Eligibility Criteria

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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;
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hu Anmin

OTHER

Sponsor Role lead

Responsible Party

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Hu Anmin

The Second Clinical Medical College of Jinan University

Responsibility Role SPONSOR_INVESTIGATOR

Other Identifiers

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LL-KY-2021396

Identifier Type: -

Identifier Source: org_study_id