Using Artificial Intelligence To Improve Ventilator Settings For Intensive Care Patients

NCT07592039 | Completed

• 1 other identifier: 2026-K-78-01
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Acute Respiratory Distress Syndrome; Pneumonia in Children; Respiratory Failure (Pediatric Patients)

Keywords

Multimodal Large Language Model; Pediatric Intensive Care Unit; Ventilator

Outcome Measures

Primary Outcomes (1)

• Number of ventilator-free days within 28 days

  Days survived and free from invasive ventilation

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

Secondary Outcomes (5)

• mortality rate

  28 and 90 days after the initiation of tracheal intubation

• Mechanical Ventilation-Related Complications

  From the start of tracheal intubation to Day 28

• Length of Hospital Stay

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

• Artificial Intelligence System Evaluation

  From the start of tracheal intubation to Day 28

• Health Economics

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

Study Arms (1)

AI-Assisted Ventilator Parameter Optimization in Pediatric ICU

Clinical 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.

Eligibility Criteria

• Age: 1 Month - 18 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Wu Rongzhou: lead

Study Sites (1)

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

Wenzhou, Zhejiang, 325000, China

Location

Related Publications (9)

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  PMID: 29539284 RESULT

• 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.

  PMID: 31965266 RESULT

• 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.

  PMID: 27620287 RESULT

• 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.

  PMID: 25466337 RESULT

• 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.

  PMID: 28657867 RESULT

• 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.

  PMID: 30349085 RESULT

• 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.

  PMID: 10793162 RESULT

• 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.

  PMID: 30617335 RESULT

• 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.

  PMID: 30617339 RESULT

MeSH Terms

Conditions

Respiratory Distress Syndrome; Respiratory Insufficiency

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Respiration Disorders

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: PhD

Study Record Dates

• First Submitted: May 10, 2026
• First Posted: May 18, 2026
• Study Start: January 1, 2021
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: May 18, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)