Using Artificial Intelligence To Improve Ventilator Settings For Intensive Care Patients
Research on Intelligent Optimization of Ventilator Parameters for Intensive Care Patients Based on Multimodal Large Models
Studieoversigt
Status
Status
Betingelser
Betingelser
Undersøgelsestype
Undersøgelsestype
Tilmelding (Faktiske)
Tilmelding
Kontakter og lokationer
Studiesteder
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Zhejiang
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Wenzhou, Zhejiang, Kina, 325000
- The Second Affiliated Hospital of Wenzhou Medical University and Yuying Children's Hospital
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Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
- Voksen
Tager imod sunde frivillige
Prøveudtagningsmetode
Studiebefolkning
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
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
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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.
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Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Number of ventilator-free days within 28 days
Tidsramme: From the start of tracheal intubation until 28 days after tracheal intubation.
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Days survived and free from invasive ventilation
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From the start of tracheal intubation until 28 days after tracheal intubation.
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Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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mortality rate
Tidsramme: 28 and 90 days after the initiation of tracheal intubation
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All-cause mortality at 28 and 90 days following tracheal intubation
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28 and 90 days after the initiation of tracheal intubation
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Mechanical Ventilation-Related Complications
Tidsramme: From the start of tracheal intubation to Day 28
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Cumulative duration of mechanical ventilation, reintubation rate (within 48 hours of extubation), ventilator-associated pneumonia (VAP), barotrauma.
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From the start of tracheal intubation to Day 28
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Length of Hospital Stay
Tidsramme: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
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PICU Length of Stay, Total Hospital Length of Stay
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The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
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Artificial Intelligence System Evaluation
Tidsramme: From the start of tracheal intubation to Day 28
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Physician Adoption Rates and Outcomes of Cases Involving Discrepancies Between AI Recommendations and Physician Decisions
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From the start of tracheal intubation to Day 28
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Health Economics
Tidsramme: The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
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PICU Hospitalization Costs
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The duration from the time of admission to discharge for pediatric patients-up to a maximum of three months.
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Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Publikationer og nyttige links
Generelle publikationer
- 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.
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Studiestart
Primær færdiggørelse (Faktiske)
Primær færdiggørelse
Studieafslutning (Faktiske)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- 2026-K-78-01
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Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
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