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Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation

NCT05132751 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 300

Last updated 2021-11-24

No results posted yet for this study

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

DEVICE

Machine Learning Ventilator Decision System

Artificial intelligence ventilator system for personalized mechanical ventilation

Sponsors & Collaborators

  • Hu Anmin

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-01-01
Primary Completion
2022-01-01
Completion
2024-12-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05132751 on ClinicalTrials.gov