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Acute Respiratory Distress SyndromeNCT07592039

Using Artificial Intelligence To Improve Ventilator Settings For Intensive Care Patients

An observational study of Acute Respiratory Distress Syndrome and Pneumonia in Children, run by Wu Rongzhou at 1 site in China.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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Trial summary

This observational study aims to determine whether an AI-assisted decision support system can improve clinical outcomes for mechanically ventilated pediatric patients (aged 1 month to 18 years) in the PICU, compared to standard care provided by medical staff. Read more about this study

Who is it for?
Ages 1 month to 18 years
How many people
2,000 participants
Where
1 siteChina
Study dates
1 Jan 2021to 31 Dec 2025
Phase
Observational
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 1 month to 18 years
  • All sexes
  • No healthy volunteers
  • Child, Adult

Study population: Pediatric patients aged 1 month to 18 years admitted to the Pediatric Intensive Care Unit (PICU) of the Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital, who are receiving mechanical ventilation.

Who may be able to join

  • PICU patients aged 1 month to 18 years.
  • Receiving invasive mechanical ventilation, expected to last ≥ 48 hours.
  • Informed consent signed prior to enrollment.

Who cannot join

  • Expected survival < 24 hours
  • Irreversible brain injury (GCS = 3 + absence of brainstem reflexes)
  • Severe congenital cardiopulmonary malformations affecting ventilation assessment
  • Pregnancy (must be ruled out in adolescent girls)
  • Currently participating in other ventilation intervention trials
  • Guardian refusal to participate

Trial locations and contacts

1 site in 1 country.

China1 site

  • The Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital

    Wenzhou, Zhejiang, 325000

Other trials in China

About this study

Official title: Research on Intelligent Optimization of Ventilator Parameters for Intensive Care Patients Based on Multimodal Large Models

This observational study aims to determine whether an AI-assisted decision support system can improve clinical outcomes for mechanically ventilated pediatric patients (aged 1 month to 18 years) in the PICU, compared to standard care provided by medical staff. The primary question addressed is: Do patients whose ventilator parameter optimization decisions are guided by AI assistance achieve a greater number of ventilator-free days within 28 days than those managed by medical staff? By utilizing clinical data collected following tracheal intubation to generate AI-driven recommendations-and comparing these against the actual adjustments made by physicians-this study seeks to assess whether the AI-assisted decision support system can effectively improve clinical outcomes for mechanically ventilated patients in the PICU.

Keywords
Multimodal Large Language Model, Pediatric Intensive Care Unit, Ventilator
Medical subject headings
Respiratory Distress Syndrome, Respiratory Insufficiency

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT07592039
Study groupRoleWhat they receive
AI-Assisted Ventilator Parameter Optimization in Pediatric ICUClinical data from key time points following tracheal intubation in each pediatric patient were input into an AI system to generate recommendations. These recommendations were then compared against the actual adjustments made by physicians, enabling a counterfactual assessment to determine whether-had the AI's suggestions been adopted-the number of ventilator-free days within a 28-day period would have been superior.

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Number of ventilator-free days within 28 days

    Days survived and free from invasive ventilation

    Measured: From the start of tracheal intubation until 28 days after tracheal intubation.

Secondary outcomes (5)

  1. mortality rate

    All-cause mortality at 28 and 90 days following tracheal intubation

    Measured: 28 and 90 days after the initiation of tracheal intubation

  2. Mechanical Ventilation-Related Complications

    Cumulative duration of mechanical ventilation, reintubation rate (within 48 hours of extubation), ventilator-associated pneumonia (VAP), barotrauma.

    Measured: From the start of tracheal intubation to Day 28

  3. Length of Hospital Stay

    PICU Length of Stay, Total Hospital Length of Stay

    Measured: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.

  4. Artificial Intelligence System Evaluation

    Physician Adoption Rates and Outcomes of Cases Involving Discrepancies Between AI Recommendations and Physician Decisions

    Measured: From the start of tracheal intubation to Day 28

  5. Health Economics

    PICU Hospitalization Costs

    Measured: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted18 May 2026
  2. Study start1 Jan 2021
  3. Primary completion31 Dec 2025
  4. Study completion31 Dec 2025

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Retrospective
Enrolment
2,000 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07592039
Sponsor's protocol ID
2026-K-78-01
Status last verified
May 2026
First submitted
10 May 2026
Last updated
18 May 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No: Individual participant data will not be shared, the study's ethical approvals and consent agreements do not permit public data sharing. Access may be considered upon reasonable request to the corresponding author, subject to institutional review and data use agreements to ensure patient privacy and compliance with regulations.

Publications

  • Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care - Addressing Ethical Challenges. N Engl J Med. 2018 Mar 15;378(11):981-983. doi: 10.1056/NEJMp1714229. No abstract available.

    PubMed 29539284

  • Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, Swart EL, Girbes ARJ, Thoral P, Ercole A, Hoogendoorn M, Elbers PWG. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020 Mar;46(3):383-400. doi: 10.1007/s00134-019-05872-y. Epub 2020 Jan 21.

    PubMed 31965266

  • Gattinoni L, Tonetti T, Cressoni M, Cadringher P, Herrmann P, Moerer O, Protti A, Gotti M, Chiurazzi C, Carlesso E, Chiumello D, Quintel M. Ventilator-related causes of lung injury: the mechanical power. Intensive Care Med. 2016 Oct;42(10):1567-1575. doi: 10.1007/s00134-016-4505-2. Epub 2016 Sep 12.

    PubMed 27620287

  • Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ. Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med. 2015 Jan;3(1):42-52. doi: 10.1016/S2213-2600(14)70239-5. Epub 2014 Nov 24.

    PubMed 25466337

  • Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. N Engl J Med. 2017 Jun 29;376(26):2507-2509. doi: 10.1056/NEJMp1702071. No abstract available.

    PubMed 28657867

  • Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med. 2018 Nov;24(11):1716-1720. doi: 10.1038/s41591-018-0213-5. Epub 2018 Oct 22.

    PubMed 30349085

  • Acute Respiratory Distress Syndrome Network; Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000 May 4;342(18):1301-8. doi: 10.1056/NEJM200005043421801.

    PubMed 10793162

  • Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019 Jan;25(1):24-29. doi: 10.1038/s41591-018-0316-z. Epub 2019 Jan 7.

    PubMed 30617335

  • Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.

    PubMed 30617339

Questions about this trial

What is this clinical trial studying?

An observational study of Acute Respiratory Distress Syndrome and Pneumonia in Children, run by Wu Rongzhou at 1 site in China.

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 6 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: finished, as of the sponsor's last update on 18 May 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.

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?

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

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?

Number of ventilator-free days within 28 days (time frame: From the start of tracheal intubation until 28 days after tracheal intubation.). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07592039, which also lists 9 linked publications.

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 18 May 2026.

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