External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data
17. juni 2026 opdateret af: Chieh-Liang Wu, Taichung Veterans General Hospital
Using Multicenter Retrospective Data to Validate the Performance of AI-Aided Weaning Software
This multicenter retrospective study aims to externally validate an artificial intelligence-aided weaning software developed using intensive care unit data from Taichung Veterans General Hospital between 2015 and 2019.
The model predicts the optimal timing for extubation using routinely collected clinical variables including ventilator parameters, physiologic measurements, and fluid and nutrition information.
De-identified data from four hospitals collected between 2020 and 2024 will be used to evaluate model performance.
Performance metrics include sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUROC), and F1 score.
Studieoversigt
Status
Afsluttet
Betingelser
Detaljeret beskrivelse
Critical care generates a large amount of digitized clinical data that may benefit from artificial intelligence-assisted decision support. The AI-Aided Weaning Software was previously developed using ICU data from Taichung Veterans General Hospital collected between 2015 and 2019.
This retrospective multicenter validation study will evaluate the external performance of the established model using independent datasets from four hospitals in Taiwan, including Taichung Veterans General Hospital, Mackay Memorial Hospital, Kaohsiung Medical University Chung-Ho Memorial Hospital, and Tungs' Taichung MetroHarbor Hospital.
The study population includes adult ICU patients with respiratory failure who received mechanical ventilation for at least 72 hours between January 2020 and December 2024. De-identified routine clinical records will be collected according to a predefined case report form and analyzed centrally.
The primary objective is to assess the external validity of the AI-Aided Weaning Software across different hospitals. Model performance will be evaluated using sensitivity, specificity, accuracy, AUROC, and F1 score.
Undersøgelsestype
Observationel
Tilmelding (Faktiske)
1500
Kontakter og lokationer
Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.
Studiesteder
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Taichung, Taiwan
- Taichung Veterans General Hospital
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Deltagelseskriterier
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Ingen
Prøveudtagningsmetode
Ikke-sandsynlighedsprøve
Studiebefolkning
Adult patients with acute respiratory failure who were admitted to participating hospitals between January 2022 and December 2024 and required invasive mechanical ventilation for at least 24 hours.
This is a retrospective study using existing clinical and imaging data for model validation.
Beskrivelse
Inclusion Criteria:
- Adult patients aged 20 years or older.
- Admitted to the intensive care unit (ICU) at one of the participating hospitals between January 1, 2020 and December 31, 2024.
- Received invasive mechanical ventilation for at least 72 hours.
- Availability of de-identified clinical data required for model validation.
Exclusion Criteria:
- Patients who did not receive invasive mechanical ventilation.
- Duration of mechanical ventilation less than 72 hours.
- Missing key clinical variables required for model validation.
Studieplan
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
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Mechanically Ventilated ICU Patients
Adult intensive care unit patients aged 20 years or older who received invasive mechanical ventilation for at least 72 hours between January 2020 and December 2024 at four participating hospitals.
Retrospective de-identified clinical data were used to validate the performance of AI-Aided Weaning Software.
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Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Model Performance (AUROC)
Tidsramme: Using data collected during ICU admission
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Area under the receiver operating characteristic curve (AUROC) for predicting successful extubation.
AUROC ranges from 0.5 to 1.0, with higher values indicating better discriminative performance of the prediction model.
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Using data collected during ICU admission
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Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
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Sensitivity
Tidsramme: ICU admission
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SensitivitySensitivity of the prediction model for successful extubation.
Sensitivity ranges from 0 to 1 (or 0% to 100%), with higher values indicating better identification of patients who achieve successful extubation.
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ICU admission
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Specificity
Tidsramme: ICU admission
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Specificity of the prediction model for successful extubation.
Specificity ranges from 0 to 1 (or 0% to 100%), with higher values indicating better identification of patients who do not achieve successful extubation.
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ICU admission
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Accuracy
Tidsramme: ICU admission
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Accuracy of the prediction model for successful extubation.
Accuracy ranges from 0 to 1 (or 0% to 100%), with higher values indicating better overall prediction performance.
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ICU admission
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F1 Score
Tidsramme: ICU admission
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F1 score of the prediction model for successful extubation.
F1 score ranges from 0 to 1, with higher values indicating better balance between precision and recall.
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ICU admission
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Samarbejdspartnere og efterforskere
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Publikationer og nyttige links
Den person, der er ansvarlig for at indtaste oplysninger om undersøgelsen, leverer frivilligt disse publikationer. Disse kan handle om alt relateret til undersøgelsen.
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Faktiske)
1. januar 2020
Primær færdiggørelse (Faktiske)
31. december 2024
Studieafslutning (Faktiske)
31. december 2024
Datoer for studieregistrering
Først indsendt
14. juni 2026
Først indsendt, der opfyldte QC-kriterier
14. juni 2026
Først opslået (Faktiske)
18. juni 2026
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
22. juni 2026
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
17. juni 2026
Sidst verificeret
1. juni 2026
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- TCVGH-AI-WEAN-2026
- TCVGH-AI-Weaning-2026 (Anden identifikator: Taichung Veterans General Hospital)
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