Mechanical VentilationNCT05132751
Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation
A study testing Machine Learning Ventilator Decision System in people with Mechanical Ventilation and Critically Ill Patients, run by Hu Anmin.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Mechanical Ventilation, Critically Ill Patients
- What is being tested?
- Machine Learning Ventilator Decision System
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2022to 1 Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- only the first ICU stay was eligible;
- adults ≥ 18 years of age on ICU admission;
- estimate mechanical ventilation time ≥24 hours;
Who cannot join
None listed.
About this study
Official title: 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.
- Conditions
- Mechanical Ventilation, Critically Ill Patients
- Being tested
- Machine Learning Ventilator Decision System
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning Ventilator Decision SystemDevice
Artificial intelligence ventilator system for personalized mechanical ventilation
| Study group | Role | What they receive |
|---|---|---|
| Group A | Experimental | Machine Learning Ventilator Decision System Ventilation Receives: Device: Machine Learning Ventilator Decision System |
| Group B | Active comparator | Standard Controlled Ventilation Receives: Device: Machine Learning Ventilator Decision System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mechanical ventilation time
Measured: through study completion, an average of 5 days
Secondary outcomes (3)
Length of ICU stay time
Measured: through study completion, an average of 1 week
Length of hospital stay
Measured: through study completion, an average of 2 weeks
In-hospital mortality
Measured: through study completion, an average of 2 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Nov 2021
- Study start1 Jan 2022 (estimated)
- Primary completion1 Jan 2022 (estimated)
- Study completion1 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mechanical VentilationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- ventilator decision system
- Masking
- Triple blind
- Who is masked
- Participant, Investigator, Outcomes assessor
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hu Anmin
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hu Anmin, The Second Clinical Medical College of Jinan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05132751
- Sponsor's protocol ID
- LL-KY-2021396
- Status last verified
- Nov 2021
- First submitted
- 7 Oct 2021
- Last updated
- 24 Nov 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 24 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of a Machine Learning Ventilator Decision System Versus Standard Controlled Ventilation on in Critical Care: a Randomized Trial
- ClinicalTrials.gov ID
- NCT05132751
- Acronym / sponsor ID
- LL-KY-2021396
- Conditions
- Mechanical Ventilation, Critically Ill Patients
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Hu AnminUniversity, hospital or other
- Study start
- 1 Jan 2022 (estimated)
- Primary completion
- 1 Jan 2022 (estimated)
- Study completion
- 1 Dec 2024 (estimated)
- First posted
- 24 Nov 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- ventilator decision system
| Arm | Type | Receives |
|---|---|---|
| Group A | Experimental | Device: Machine Learning Ventilator Decision SystemMachine Learning Ventilator Decision System Ventilation |
| Group B | Active comparator | Device: Machine Learning Ventilator Decision SystemStandard Controlled Ventilation |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- only the first ICU stay was eligible;
- adults ≥ 18 years of age on ICU admission;
- estimate mechanical ventilation time ≥24 hours;
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine Learning Ventilator Decision SystemArtificial intelligence ventilator system for personalized mechanical ventilation | Device | Group A, Group B |
Primary outcome
Mechanical ventilation time
Time frame: through study completion, an average of 5 days
3 secondary outcomes
Length of ICU stay time
Time frame: through study completion, an average of 1 week
Length of hospital stay
Time frame: through study completion, an average of 2 weeks
In-hospital mortality
Time frame: through study completion, an average of 2 weeks
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Nov 2021.
- Individual participant dataSharing planned: Undecided
Related trials for Mechanical Ventilation
RecruitingPhase 3
Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community- Acquired Pneumonia
Respiratory Tract Diseases, Influenza (Pandemic)
RecruitingObservational study
Role of End-Tidal CO2 During Passive Leg Raising to Predict Fluid Responsiveness in ICU
Circulatory Failure, Critical Illness
RecruitingObservational study
Comparison of Scores for Early Brain Damage Assessment at Intensive Care Unit Admission After Cardiac Arrest
Cardiac Arrest
Recruiting
A Randomized Clinical Trial for Predictors of Successful Extubation of Pediatric Patients With or Without High Velocity Nasal Insufflation
High Velocity Nasal Insufflation, Simple Oxygen
Not yet recruitingPhase 1/2
Little Lungs Study
Invasive Mechanical Ventilation, Severe Respiratory Distress Syndrome
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Machine Learning Ventilator Decision System in people with Mechanical Ventilation and Critically Ill Patients, run by Hu Anmin.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 24 Nov 2021. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mechanical ventilation time (time frame: through study completion, an average of 5 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Nov 2021.