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

NCT07658131 | Completed

• 2 other identifiers: TCVGH-AI-WEAN-2026TCVGH-AI-Weaning-2026
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Respiratory Failure; Mechanical Ventilation; Critical Illness

Keywords

Artificial Intelligence; Machine Learning; Mechanical Ventilation; Extubation; Intensive Care Unit; Weaning; Critical Care; External Validation

Outcome Measures

Primary Outcomes (1)

• Model Performance (AUROC)

  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

Secondary Outcomes (4)

• Sensitivity

  ICU admission

• Specificity

  ICU admission

• Accuracy

  ICU admission

• F1 Score

  ICU admission

Study Arms (1)

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.

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• Patients who did not receive invasive mechanical ventilation.
• Duration of mechanical ventilation less than 72 hours.
• Missing key clinical variables required for model validation.

Sponsors & Collaborators

• Taichung Veterans General Hospital: lead
• Mackay Memorial Hospital: collaborator
• Kaohsiung Medical University Chung-Ho Memorial Hospital: collaborator
• Tungs' Taichung Metroharbor Hospital: collaborator

Study Sites (1)

Taichung Veterans General Hospital

Taichung, Taiwan

Location

Related Publications (1)

• 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.

  PMID: 36465940 RESULT

MeSH Terms

Conditions

Respiratory Insufficiency; Critical Illness

Condition Hierarchy (Ancestors)

Respiration Disorders; Respiratory Tract Diseases; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice Superintendent

Study Record Dates

• First Submitted: June 14, 2026
• First Posted: June 18, 2026
• Study Start: January 1, 2020
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: June 22, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)