Research on Intelligent Optimization of Ventilator Parameters for Intensive Care Patients Based on Multimodal Large Models
Sponsor study identifier
observational
2,000
1 country
1
Study Start
January 1, 2021
Actual (reported)Primary Completion
December 31, 2025
Actual (reported)Study Completion
December 31, 2025
Actual (reported)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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 31, 2025
Actual (reported)Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 1, 2021
Actual (reported)December 31, 2025
Actual (reported)December 31, 2025
Actual (reported)May 10, 2026
Actual (reported)May 18, 2026
Actual (reported)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.
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
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.
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.
The Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital
Wenzhou, Zhejiang, 325000, China
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.
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.
2,000 participants (actual enrollment reported by the registry).
First Submitted
May 10, 2026
First Posted
May 18, 2026
Study Start
January 1, 2021
Actual (reported)Plasma Biomarkers of Cardiopulmonary Bypass Induced Acute Respiratory Distress Syndrome (CPB-ARDS)
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.
Contact the study team to confirm eligibility.
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.
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.
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.
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.
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.
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.
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.
Primary Completion
December 31, 2025
Actual (reported)Study Completion
December 31, 2025
Actual (reported)Last Updated
May 18, 2026
Record last verified: 2026-05
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.
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Source: ClinicalTrials.gov.
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