Extubation FailureNCT07918924
Predicting Extubation Failure Using Machine Learning
An observational study of Extubation Failure and Critical Illness, run by Universitas Indonesia at 2 sites in Indonesia.
Trial summary
The goal of this observational study is to predict who will have problems after the breathing tube is removed. Taking out the tube is called extubation, and having these problems is called extubation failure. Participants are adults in the intensive care unit (ICU) who use a breathing machine (ventilator). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Extubation Failure, Critical Illness
- How many people
- 1,065 participants
- Where
- 2 sitesIndonesia
- Study dates
- 4 Sep 2024to 31 Jan 2027
- How long will I take part?
- 1 Week
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults treated in the intensive care units (ICUs) of two tertiary referral hospitals in Indonesia: Dr. Cipto Mangunkusumo National General Hospital in Jakarta (development cohort) and Universitas Indonesia Hospital in Depok (external validation cohort).
Who may be able to join
- Aged 18 years or older
- Receiving invasive mechanical ventilation through a breathing tube in the windpipe (endotracheal tube) in the intensive care unit (ICU)
Who cannot join
- Used a breathing machine through a tracheostomy (a surgical opening in the neck) when admitted to the ICU
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Indonesia2 sites, 1 recruiting
- Completed
Universitas Indonesia Hospital (RS UI)
Depok, West Java, 16424
- Recruiting
Cipto Mangunkusumo National General Hospital (RSCM)
Jakarta, DKI Jakarta, 10430
Medical and Health Research Ethics Committee of RSCM, 021-315-7008, penelitian.anestesirscm2@gmail.com
Adhrie Sugiarto, MD, SpAn-KIC
About this study
Official title: Prediction of Extubation Failure in Mechanically Ventilated Adult Patients Using a Machine Learning-Based Approach
The goal of this observational study is to predict who will have problems after the breathing tube is removed. Taking out the tube is called extubation, and having these problems is called extubation failure. Participants are adults in the intensive care unit (ICU) who use a breathing machine (ventilator).
The main questions it aims to answer are:
Read more
- How well can computer tools predict problems in the 7 days after the tube is removed?
- Which information, such as age or blood tests, matters most for the prediction?
Extubation failure means the participant needs the tube put back in, needs a surgical opening in the neck to help breathing, or dies.
Researchers will use machine learning to build the computer tools. Machine learning is a type of artificial intelligence that finds patterns in data. Researchers will compare five types of machine learning to see which works best. They will build the tools with participants from Dr. Cipto Mangunkusumo National General Hospital in Jakarta. They will test the tools with participants from Universitas Indonesia Hospital in Depok.
Participants get their usual care. Doctors do not use the tools to make treatment decisions. Researchers will:
- Record health information about participants before the tube is removed
- Follow participants for 7 days after the tube is removed
Read the detailed description
This is a prospective, multicenter, observational cohort study. Extubation and reintubation decisions are made by the attending anesthesiologists as part of routine care and are not informed by the models. The models will be developed only after data collection is complete.
Data collection:
Candidate predictors are chosen from previous studies and clinical relevance. They fall into four groups: patient characteristics, clinical condition, mechanical ventilation parameters, and laboratory results. Trained ICU staff and research assistants record them from bedside monitors, ventilator displays, paper and electronic medical records, and family interviews. Patient characteristics and severity scores (APACHE II and SAPS II) are recorded at ICU admission. Before extubation, ventilator parameters are taken within 4 hours, clinical condition within 24 hours, and laboratory results within 48 hours, using the measurement closest to extubation. Patients are followed for 7 days after planned extubation through bedside monitoring and review of medical records after transfer to the ward.
Participants:
Participants are enrolled when they receive invasive mechanical ventilation in the ICU, before it is known whether they will have a planned extubation. After enrollment, participants are excluded from the analysis if they die or have a tracheostomy without the breathing tube being removed; stay on a breathing machine for more than 2 weeks without the tube being removed; have an unplanned extubation (the tube was pulled out by the participant or by accident); are transferred to another hospital before extubation or within 7 days after; or if they or their family decline further intensive care. The number of participants enrolled is therefore larger than the number analyzed. The number analyzed will be reported in the results article.
Data management:
Data are entered into REDCap, a secure web-based data management application, using study codes without names, medical record numbers, or dates of birth. Data are stored at the Big Data Center, Indonesia Medical Education and Research Institute (IMERI), Universitas Indonesia. Research assistants periodically check the data for completeness and implausible values. Values that are not measured are left blank during data collection and will be handled in the analysis.
Sample size:
The development cohort target was calculated to estimate sensitivity (80%, precision of 10 percentage points, 95% confidence), assuming a 10% extubation failure rate and 10% dropout. The external validation target was calculated to estimate the extubation failure rate with a 5 percentage point margin of error. The development sample is smaller than formal prediction model sample size criteria would require. The study is therefore exploratory, and the external validation is preliminary.
Planned analysis:
Five supervised machine learning algorithms will be developed: LightGBM, CatBoost, XGBoost, random forest, and support vector machine. The development cohort will be randomly split, stratified by outcome, into training, validation, and holdout test sets. Missing values will be imputed, and class imbalance will be handled during model training. The classification threshold will be chosen on the validation set. Performance will be assessed on the holdout test set and on the external validation cohort, using the final models without modification. Predictor importance will be assessed with SHAP values for tree-based models and permutation importance for the support vector machine.
Reporting:
This study follows the TRIPOD+AI reporting guideline (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, updated for artificial intelligence).
- Conditions
- Extubation Failure, Critical Illness
- Keywords
- Machine Learning, Airway Extubation, Ventilator Weaning, Mechanical Ventilation, Intensive Care Units, Indonesia, Clinical Prediction Model, Extubation Failure, External Validation, Artificial Intelligence, Prospective Cohort
- Medical subject headings
- 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 |
|---|---|---|
| Development Cohort (RSCM) | Adult ICU patients (≥18 years) on invasive mechanical ventilation via endotracheal tube at Dr. Cipto Mangunkusumo National General Hospital (RSCM), Jakarta, who underwent planned extubation. Enrolled between Sept 2024-Sept 2026. Pre-extubation predictors (patient characteristics, clinical condition, ventilator parameters, laboratory results) are recorded prospectively. Outcome: extubation failure, defined as reintubation, tracheostomy, or death from any cause within 7 days after planned extubation. Data are used to develop and internally validate machine learning models (LightGBM, CatBoost, XGBoost, random forest, SVM). No intervention is assigned; extubation follows routine care. | |
| External Validation Cohort (RS UI) | Adult ICU patients (≥18 years) on invasive mechanical ventilation via endotracheal tube at Universitas Indonesia Hospital (RS UI) who underwent planned extubation, using the same eligibility criteria, predictors, and 7-day outcome definition as the development cohort. Recruited during a period overlapping with the development cohort (Feb 2025-Sept 2026). Data are used only to externally validate the final models developed in the RSCM cohort, without modification. No intervention is assigned; extubation follows routine care. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC) of machine learning models for predicting extubation failure
Discrimination of five supervised machine learning models (LightGBM, CatBoost, XGBoost, random forest, SVM) for predicting extubation failure in adult ICU patients on mechanical ventilation, including the difference in AUROC among the five algorithms. Extubation failure is defined as reintubation, tracheostomy, or death from any cause within 7 days after planned extubation. AUROC (unitless, range 0-1) is reported with 95% CI in the internal testing set (RSCM) and the external validation cohort (RS UI).
Measured: up to 7 days after planned extubation
Secondary outcomes (5)
Accuracy of machine learning models for predicting extubation failure
Proportion of patients correctly classified (true positives plus true negatives, divided by all patients) by each of the five machine learning models for predicting extubation failure, at a probability threshold determined in the validation set by maximizing the F1-score. Reported as a percentage with 95% CI in the internal testing set and the external validation cohort.
Measured: up to 7 days after planned extubation
Sensitivity (recall) of machine learning models for predicting extubation failure
Proportion of patients with extubation failure who are correctly identified (true positives divided by true positives plus false negatives) by each of the five machine learning models, at a probability threshold determined in the validation set by maximizing the F1-score. Reported as a percentage with 95% CI in the internal testing set and the external validation cohort.
Measured: up to 7 days after planned extubation
Positive predictive value (precision) of machine learning models for predicting extubation failure
Proportion of patients predicted to have extubation failure who actually have extubation failure (true positives divided by true positives plus false positives) for each of the five machine learning models, at a probability threshold determined in the validation set by maximizing the F1-score. Reported as a percentage with 95% CI in the internal testing set and the external validation cohort.
Measured: up to 7 days after planned extubation
Mean absolute SHAP value of candidate predictors for extubation failure (tree-based models)
Contribution of candidate predictors measured before extubation (patient characteristics, clinical condition, mechanical ventilation parameters, and laboratory results) to the predictions of each tree-based model (LightGBM, CatBoost, XGBoost, random forest), expressed as mean absolute SHAP value on the predicted probability scale (unitless, range 0-1).
Measured: up to 7 days after planned extubation
Permutation importance of candidate predictors for extubation failure (SVM model)
Contribution of candidate predictors measured before extubation to the predictions of the support vector machine model, expressed as the mean decrease in AUROC (unitless) when the values of each predictor are randomly permuted in the internal testing set.
Measured: up to 7 days after planned extubation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Nov 2026
- Study start4 Sep 2024
- Primary completion23 Jan 2027 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Extubation FailureStudies of the same condition, open and under way.
- More trials run by Universitas IndonesiaThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 1,065 participants (estimated)
- Target duration
- 1 Week
Sponsor and investigators
- Lead sponsor
- Universitas Indonesia
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Adhrie Sugiarto, Universitas Indonesia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07918924
- Sponsor's protocol ID
- IndonesiaUAnes0897
- Status last verified
- Oct 2026
- First submitted
- 18 Oct 2026
- Last updated
- 5 Nov 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Epstein SK, Ciubotaru RL, Wong JB. Effect of failed extubation on the outcome of mechanical ventilation. Chest. 1997 Jul;112(1):186-92. doi: 10.1378/chest.112.1.186.
PubMed 9228375Epstein SK. Extubation failure: an outcome to be avoided. Crit Care. 2004 Oct;8(5):310-2. doi: 10.1186/cc2927. Epub 2004 Aug 10.
PubMed 15469587Epstein SK. Decision to extubate. Intensive Care Med. 2002 May;28(5):535-46. doi: 10.1007/s00134-002-1268-8. Epub 2002 Apr 12.
PubMed 12029399Kulkarni AP, Agarwal V. Extubation failure in intensive care unit: predictors and management. Indian J Crit Care Med. 2008 Jan;12(1):1-9. doi: 10.4103/0972-5229.40942.
PubMed 19826583Saeed F, Lasrado S. Extubation. 2023 Feb 9. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK539804/
PubMed 30969626
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Extubation Failure in Mechanically Ventilated Adult Patients Using a Machine Learning-Based Approach
- ClinicalTrials.gov ID
- NCT07918924
- Acronym / sponsor ID
- IndonesiaUAnes0897
- Conditions
- Extubation Failure, Critical Illness
- Keywords
- Machine Learning, Airway Extubation, Ventilator Weaning, Mechanical Ventilation, Intensive Care Units, Indonesia, Clinical Prediction Model, Extubation Failure
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,065 participants (estimated)
- Lead sponsor
- Universitas IndonesiaUniversity, hospital or other
- Study start
- 4 Sep 2024 (actual)
- Primary completion
- 23 Jan 2027 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 5 Nov 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.
The goal of this observational study is to predict who will have problems after the breathing tube is removed. Taking out the tube is called extubation, and having these problems is called extubation failure. Participants are adults in the intensive care unit (ICU) who use a breathing machine (ventilator).
The main questions it aims to answer are:
- How well can computer tools predict problems in the 7 days after the tube is removed?
- Which information, such as age or blood tests, matters most for the prediction?
Extubation failure means the participant needs the tube put back in, needs a surgical opening in the neck to help breathing, or dies.
Researchers will use machine learning to build the computer tools. Machine learning is a type of artificial intelligence that finds patterns in data. Researchers will compare five types of machine learning to see which works best. They will build the tools with participants from Dr. Cipto Mangunkusumo National General Hospital in Jakarta. They will test the tools with participants from Universitas Indonesia Hospital in Depok.
Participants get their usual care. Doctors do not use the tools to make treatment decisions. Researchers will:
- Record health information about participants before the tube is removed
- Follow participants for 7 days after the tube is removed
Detailed description
This is a prospective, multicenter, observational cohort study. Extubation and reintubation decisions are made by the attending anesthesiologists as part of routine care and are not informed by the models. The models will be developed only after data collection is complete.
Data collection:
Candidate predictors are chosen from previous studies and clinical relevance. They fall into four groups: patient characteristics, clinical condition, mechanical ventilation parameters, and laboratory results. Trained ICU staff and research assistants record them from bedside monitors, ventilator displays, paper and electronic medical records, and family interviews. Patient characteristics and severity scores (APACHE II and SAPS II) are recorded at ICU admission. Before extubation, ventilator parameters are taken within 4 hours, clinical condition within 24 hours, and laboratory results within 48 hours, using the measurement closest to extubation. Patients are followed for 7 days after planned extubation through bedside monitoring and review of medical records after transfer to the ward.
Participants:
Participants are enrolled when they receive invasive mechanical ventilation in the ICU, before it is known whether they will have a planned extubation. After enrollment, participants are excluded from the analysis if they die or have a tracheostomy without the breathing tube being removed; stay on a breathing machine for more than 2 weeks without the tube being removed; have an unplanned extubation (the tube was pulled out by the participant or by accident); are transferred to another hospital before extubation or within 7 days after; or if they or their family decline further intensive care. The number of participants enrolled is therefore larger than the number analyzed. The number analyzed will be reported in the results article.
Data management:
Data are entered into REDCap, a secure web-based data management application, using study codes without names, medical record numbers, or dates of birth. Data are stored at the Big Data Center, Indonesia Medical Education and Research Institute (IMERI), Universitas Indonesia. Research assistants periodically check the data for completeness and implausible values. Values that are not measured are left blank during data collection and will be handled in the analysis.
Sample size:
The development cohort target was calculated to estimate sensitivity (80%, precision of 10 percentage points, 95% confidence), assuming a 10% extubation failure rate and 10% dropout. The external validation target was calculated to estimate the extubation failure rate with a 5 percentage point margin of error. The development sample is smaller than formal prediction model sample size criteria would require. The study is therefore exploratory, and the external validation is preliminary.
Planned analysis:
Five supervised machine learning algorithms will be developed: LightGBM, CatBoost, XGBoost, random forest, and support vector machine. The development cohort will be randomly split, stratified by outcome, into training, validation, and holdout test sets. Missing values will be imputed, and class imbalance will be handled during model training. The classification threshold will be chosen on the validation set. Performance will be assessed on the holdout test set and on the external validation cohort, using the final models without modification. Predictor importance will be assessed with SHAP values for tree-based models and permutation importance for the support vector machine.
Reporting:
This study follows the TRIPOD+AI reporting guideline (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, updated for artificial intelligence).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Week
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Development Cohort (RSCM) | – | –Adult ICU patients (≥18 years) on invasive mechanical ventilation via endotracheal tube at Dr. Cipto Mangunkusumo National General Hospital (RSCM), Jakarta, who underwent planned extubation. Enrolled between Sept 2024-Sept 2026. Pre-extubation predictors (patient characteristics, clinical condition, ventilator parameters, laboratory results) are recorded prospectively. Outcome: extubation failure, defined as reintubation, tracheostomy, or death from any cause within 7 days after planned extubation. Data are used to develop and internally validate machine learning models (LightGBM, CatBoost, XGBoost, random forest, SVM). No intervention is assigned; extubation follows routine care. |
| External Validation Cohort (RS UI) | – | –Adult ICU patients (≥18 years) on invasive mechanical ventilation via endotracheal tube at Universitas Indonesia Hospital (RS UI) who underwent planned extubation, using the same eligibility criteria, predictors, and 7-day outcome definition as the development cohort. Recruited during a period overlapping with the development cohort (Feb 2025-Sept 2026). Data are used only to externally validate the final models developed in the RSCM cohort, without modification. No intervention is assigned; extubation follows routine care. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults treated in the intensive care units (ICUs) of two tertiary referral hospitals in Indonesia: Dr. Cipto Mangunkusumo National General Hospital in Jakarta (development cohort) and Universitas Indonesia Hospital in Depok (external validation cohort).
Key inclusion criteria (2 listed)
- Aged 18 years or older
- Receiving invasive mechanical ventilation through a breathing tube in the windpipe (endotracheal tube) in the intensive care unit (ICU)
Key exclusion criteria (1 listed)
- Used a breathing machine through a tracheostomy (a surgical opening in the neck) when admitted to the ICU
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
Area under the receiver operating characteristic curve (AUROC) of machine learning models for predicting extubation failure
Time frame: up to 7 days after planned extubation
Discrimination of five supervised machine learning models (LightGBM, CatBoost, XGBoost, random forest, SVM) for predicting extubation failure in adult ICU patients on mechanical ventilation, including the difference in AUROC among the five algorithms. Extubation failure is defined as reintubation, tracheostomy, or death from any cause within 7 days after planned extubation. AUROC (unitless, range 0-1) is reported with 95% CI in the internal testing set (RSCM) and the external validation cohort (RS UI).
5 secondary outcomes
Accuracy of machine learning models for predicting extubation failure
Time frame: up to 7 days after planned extubation
Sensitivity (recall) of machine learning models for predicting extubation failure
Time frame: up to 7 days after planned extubation
Positive predictive value (precision) of machine learning models for predicting extubation failure
Time frame: up to 7 days after planned extubation
Mean absolute SHAP value of candidate predictors for extubation failure (tree-based models)
Time frame: up to 7 days after planned extubation
Permutation importance of candidate predictors for extubation failure (SVM model)
Time frame: up to 7 days after planned extubation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Nov 2026.
- Background: PubMed 9228375Epstein SK, Ciubotaru RL, Wong JB. Effect of failed extubation on the outcome of mechanical ventilation. Chest. 1997 Jul;112(1):186-92. doi: 10.1378/chest.112.1.186.
- Background: PubMed 15469587Epstein SK. Extubation failure: an outcome to be avoided. Crit Care. 2004 Oct;8(5):310-2. doi: 10.1186/cc2927. Epub 2004 Aug 10.
- Background: PubMed 12029399Epstein SK. Decision to extubate. Intensive Care Med. 2002 May;28(5):535-46. doi: 10.1007/s00134-002-1268-8. Epub 2002 Apr 12.
- Background: PubMed 19826583Kulkarni AP, Agarwal V. Extubation failure in intensive care unit: predictors and management. Indian J Crit Care Med. 2008 Jan;12(1):1-9. doi: 10.4103/0972-5229.40942.
- Background: PubMed 30969626Saeed F, Lasrado S. Extubation. 2023 Feb 9. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK539804/
- Individual participant dataSharing planned: No
Related trials for Extubation Failure
Not yet recruitingObservational study
Combined RSBI, TOF, and BIS for Prediction of Extubation Success
Extubation Success, Extubation Failure
RecruitingObservational study
Characterization of Weaning Practices in ICUs of Global South Countries
Weaning From Mechanical Ventilation, Weaning Failure
Not yet recruiting
Noninvasive High Frequency Oscillatory Ventilation Versus Nasal Intermittent Mandatory Ventilation After Extubation in Neonates
Extubation Failure in Neonates
Not yet recruiting
One-Hour Positive Pressure Ventilation After a T-Piece Spontaneous Breathing Trial
Airway Extubation, Extubation Failure
Not yet recruiting
One-Hour Positive Pressure Ventilation After a Pressure Support Spontaneous Breathing Trial
Airway Extubation, Weaning Mechanical Ventilation
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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Extubation Failure and Critical Illness, run by Universitas Indonesia at 2 sites in Indonesia.
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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 5 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Indonesia. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Adhrie Sugiarto (+62213157008, adhrie@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve (AUROC) of machine learning models for predicting extubation failure (time frame: up to 7 days after planned extubation). 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 NCT07918924, which also lists 5 linked publications.
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 5 Nov 2026.