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NCT Number: NCT07658131

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

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.

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Key information

Conditions

Respiratory Failure Critical Illness Disease Attributes Mechanical Ventilation Pathologic Processes Pathological Conditions, Signs and Symptoms Respiration Disorders Respiratory Insufficiency Respiratory Tract Diseases

Age range

20 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Taichung Veterans General Hospital

Taichung, Taiwan

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. Model Performance (AUROC)

    Time frame: 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.

Secondary outcomes

  1. Sensitivity

    Time frame: 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.

  2. Specificity

    Time frame: 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.

  3. Accuracy

    Time frame: 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.

  4. F1 Score

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Taichung Veterans General Hospital

Other

Collaborators

  • Kaohsiung Medical University Chung-Ho Memorial Hospital
  • Mackay Memorial Hospital
  • Tungs' Taichung Metroharbor Hospital

Registry information

Official study title

Using Multicenter Retrospective Data to Validate the Performance of AI-Aided Weaning Software

Important dates

Study start
2020
Primary completion
2024
Study completion
2024
First posted
Jun 18, 2026
Registry last updated
Jun 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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