Respiratory FailureNCT07658131
External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data
An observational study of Respiratory Failure and Mechanical Ventilation, run by Taichung Veterans General Hospital at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Respiratory Failure, Mechanical Ventilation, Critical Illness
- How many people
- 1,500 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Jan 2020to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Patients who did not receive invasive mechanical ventilation.
- Duration of mechanical ventilation less than 72 hours.
- Missing key clinical variables required for model validation.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the detailed description
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.
- Keywords
- Artificial Intelligence, Machine Learning, Mechanical Ventilation, Extubation, Intensive Care Unit, Weaning, Critical Care, External Validation
- Medical subject headings
- Respiratory Insufficiency, Critical Illness
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Using data collected during ICU admission
Secondary outcomes (4)
Sensitivity
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.
Measured: ICU admission
Specificity
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.
Measured: ICU admission
Accuracy
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.
Measured: ICU admission
F1 Score
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.
Measured: ICU admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2026
- Study start1 Jan 2020
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Respiratory FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Taichung Veterans General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Mackay Memorial Hospital, Kaohsiung Medical University Chung-Ho Memorial Hospital, Tungs' Taichung Metroharbour Hospital
- Responsible party
- Principal investigator, Chieh-Liang Wu, Taichung Veterans General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07658131
- Sponsor's protocol ID
- TCVGH-AI-WEAN-2026
- Other IDs
- TCVGH-AI-Weaning-2026
- Status last verified
- Jun 2026
- First submitted
- 14 Jun 2026
- Last updated
- 22 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 36465940
Study at a glance
From the registry record, last updated by the sponsor on 22 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Multicenter Retrospective Data to Validate the Performance of AI-Aided Weaning Software
- ClinicalTrials.gov ID
- NCT07658131
- Acronym / sponsor ID
- TCVGH-AI-WEAN-2026
- Keywords
- Artificial Intelligence, Machine Learning, Mechanical Ventilation, Extubation, Intensive Care Unit, Weaning, Critical Care, External Validation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,500 participants (actual)
- Lead sponsor
- Taichung Veterans General HospitalUniversity, hospital or other
- Collaborators
- Mackay Memorial Hospital, Kaohsiung Medical University Chung-Ho Memorial Hospital, Tungs' Taichung Metroharbour Hospital
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 18 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- Patients who did not receive invasive mechanical ventilation.
- Duration of mechanical ventilation less than 72 hours.
- Missing key clinical variables required for model validation.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
4 secondary outcomes
Sensitivity
Time frame: ICU admission
Specificity
Time frame: ICU admission
Accuracy
Time frame: ICU admission
F1 Score
Time frame: ICU admission
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Jun 2026.
- Results publication: PubMed 36465940Liu 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Respiratory Failure and Mechanical Ventilation, run by Taichung Veterans General Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Model Performance (AUROC) (time frame: Using data collected during ICU admission). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07658131, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Jun 2026.