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External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data

17 juni 2026 bijgewerkt door: 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.

Studie Overzicht

Toestand

Voltooid

Conditie

Gedetailleerde beschrijving

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.

Studietype

Observationeel

Inschrijving (Werkelijk)

1500

Contacten en locaties

In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.

Studie Locaties

Taiwan
- - Taichung, Taiwan
    - Taichung Veterans General Hospital

Deelname Criteria

Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.

Geschiktheidscriteria

Leeftijden die in aanmerking komen voor studie

Volwassen
Oudere volwassene

Accepteert gezonde vrijwilligers

Nee

Bemonsteringsmethode

Niet-waarschijnlijkheidssteekproef

Studie Bevolking

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.

Beschrijving

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.

Studie plan

Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.

Hoe is de studie opgezet?

Ontwerpdetails

Aantal groepen / cohorten

Cohorten en interventies

Groep / Cohort
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.

Wat meet het onderzoek?

Primaire uitkomstmaten

Uitkomstmaat	Maatregel Beschrijving	Tijdsspanne
Model Performance (AUROC) Tijdsspanne: Using data collected during ICU admission	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.	Using data collected during ICU admission

Secundaire uitkomstmaten

Uitkomstmaat	Maatregel Beschrijving	Tijdsspanne
Sensitivity Tijdsspanne: ICU admission	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.	ICU admission
Specificity Tijdsspanne: ICU admission	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.	ICU admission
Accuracy Tijdsspanne: ICU admission	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.	ICU admission
F1 Score Tijdsspanne: ICU admission	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.	ICU admission

Medewerkers en onderzoekers

Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.

Sponsor

Taichung Veterans General Hospital

Medewerkers

Mackay Memorial Hospital

Kaohsiung Medical University Chung-Ho Memorial Hospital

Tungs' Taichung Metroharbor Hospital

Publicaties en nuttige links

De persoon die verantwoordelijk is voor het invoeren van informatie over het onderzoek stelt deze publicaties vrijwillig ter beschikking. Dit kan gaan over alles wat met het onderzoek te maken heeft.

Algemene publicaties

Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne). 2022 Nov 18;9:935366. doi: 10.3389/fmed.2022.935366. eCollection 2022.

Studie record data

Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.

Bestudeer belangrijke data

Studie start (Werkelijk)

1 januari 2020

Primaire voltooiing (Werkelijk)

31 december 2024

Studie voltooiing (Werkelijk)

31 december 2024

Studieregistratiedata

Eerst ingediend

14 juni 2026

Eerst ingediend dat voldeed aan de QC-criteria