Using Artificial Intelligence To Improve Ventilator Settings For Intensive Care Patients
15. mai 2026 oppdatert av: Wu Rongzhou
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.
Studieoversikt
Status
Fullført
Studietype
Observasjonsmessig
Registrering (Faktiske)
2000
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiesteder
-
-
Zhejiang
-
Wenzhou, Zhejiang, Kina, 325000
- The Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital
-
-
Deltakelseskriterier
Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Barn
- Voksen
Tar imot friske frivillige
Nei
Prøvetakingsmetode
Ikke-sannsynlighetsprøve
Studiepopulasjon
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.
Beskrivelse
Inclusion Criteria:
- PICU patients aged 1 month to 18 years.
- Receiving invasive mechanical ventilation, expected to last ≥ 48 hours.
- Informed consent signed prior to enrollment.
Exclusion Criteria:
- 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
Studieplan
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
Kohorter og intervensjoner
Gruppe / Kohort |
|---|
|
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.
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Number of ventilator-free days within 28 days
Tidsramme: From the start of tracheal intubation until 28 days after tracheal intubation.
|
Days survived and free from invasive ventilation
|
From the start of tracheal intubation until 28 days after tracheal intubation.
|
Sekundære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
mortality rate
Tidsramme: 28 and 90 days after the initiation of tracheal intubation
|
All-cause mortality at 28 and 90 days following tracheal intubation
|
28 and 90 days after the initiation of tracheal intubation
|
|
Mechanical Ventilation-Related Complications
Tidsramme: From the start of tracheal intubation to Day 28
|
Cumulative duration of mechanical ventilation, reintubation rate (within 48 hours of extubation), ventilator-associated pneumonia (VAP), barotrauma.
|
From the start of tracheal intubation to Day 28
|
|
Length of Hospital Stay
Tidsramme: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
|
PICU Length of Stay, Total Hospital Length of Stay
|
The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
|
|
Artificial Intelligence System Evaluation
Tidsramme: From the start of tracheal intubation to Day 28
|
Physician Adoption Rates and Outcomes of Cases Involving Discrepancies Between AI Recommendations and Physician Decisions
|
From the start of tracheal intubation to Day 28
|
|
Health Economics
Tidsramme: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
|
PICU Hospitalization Costs
|
The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
|
Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Sponsor
Publikasjoner og nyttige lenker
Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.
Generelle publikasjoner
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Studierekorddatoer
Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.
Studer hoveddatoer
Studiestart (Faktiske)
1. januar 2021
Primær fullføring (Faktiske)
31. desember 2025
Studiet fullført (Faktiske)
31. desember 2025
Datoer for studieregistrering
Først innsendt
10. mai 2026
Først innsendt som oppfylte QC-kriteriene
15. mai 2026
Først lagt ut (Faktiske)
18. mai 2026
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
18. mai 2026
Siste oppdatering sendt inn som oppfylte QC-kriteriene
15. mai 2026
Sist bekreftet
1. mai 2026
Mer informasjon
Begreper knyttet til denne studien
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- 2026-K-78-01
Plan for individuelle deltakerdata (IPD)
Planlegger du å dele individuelle deltakerdata (IPD)?
NEI
IPD-planbeskrivelse
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.
Legemiddel- og utstyrsinformasjon, studiedokumenter
Studerer et amerikansk FDA-regulert medikamentprodukt
Nei
Studerer et amerikansk FDA-regulert enhetsprodukt
Nei
Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .