Predicting Extubation Failure Using Machine Learning
NCT07918924, Recruiting, Universitas Indonesia
Trial 8
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
| Field | Value | Source |
|---|---|---|
| Title | Predicting Extubation Failure Using Machine Learning | ClinicalTrials.gov |
| NCT number | NCT07918924 | ClinicalTrials.gov |
| Status | Recruiting | ClinicalTrials.gov |
| Enrollment | 1,065 participants, estimated | ClinicalTrials.gov |
| Lead sponsor | Universitas Indonesia | ClinicalTrials.gov |
| Conditions | Extubation Failure, Critical Illness | ClinicalTrials.gov |
| First posted | 2026-11-05 | ClinicalTrials.gov |
| Last updated | 2026-11-05 | ClinicalTrials.gov |
Registered outcomes 6
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description | Source |
|---|---|---|---|---|
| Primary | Area under the receiver operating characteristic curve (AUROC) of machine learning models for predicting extubation failure | 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). | ClinicalTrials.gov |
| Secondary | Accuracy of machine learning models for predicting extubation failure | up to 7 days after planned extubation | 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. | ClinicalTrials.gov |
| Secondary | Sensitivity (recall) of machine learning models for predicting extubation failure | up to 7 days after planned extubation | 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. | ClinicalTrials.gov |
| Secondary | Positive predictive value (precision) of machine learning models for predicting extubation failure | up to 7 days after planned extubation | 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. | ClinicalTrials.gov |
| Secondary | Mean absolute SHAP value of candidate predictors for extubation failure (tree-based models) | up to 7 days after planned extubation | 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). | ClinicalTrials.gov |
| Secondary | Permutation importance of candidate predictors for extubation failure (SVM model) | up to 7 days after planned extubation | 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. | ClinicalTrials.gov |
Outcomes 0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
| Type | Outcome | Time frame | Population | Units | Measured as | Source |
|---|
Results by group 0
Each measurement the trial posted, group by group, in the words and numbers it posted.
| Outcome | Group | Category | Value | Spread | Units | Source |
|---|
Participant flow 0
How many started, completed or left each group, period by period.
| Period | Milestone | Group | Participants | Units counted | Source |
|---|
Adverse events 0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
| Type | Event | Organ system | Group | Affected | At risk | Events | Source |
|---|
Adverse event totals 0
The totals the trial posted for each group.
| Total | Group | Affected | At risk | Source |
|---|
Papers 5
The papers the trial lists on ClinicalTrials.gov.
Grants 0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
| Project | Title | Principal investigators | Organization | Funder | Fiscal years | Source |
|---|
Trial comparisons 0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
| Comparison | Source |
|---|
Tests 0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
| Test | Named as | Source |
|---|
Source 5
| Field | Value | Source |
|---|---|---|
| Release | trials-2026-11-12-0.4.0 | ClinicalTrials.gov |
| Release manifest SHA-256 | 0f4a8d8c48aee2a7573ae0fa94ecbeab70c1adbe449bbc95c424bc7d3e97b9d4 | ClinicalTrials.gov |
| Registry capture | 2026-11-12 | ClinicalTrials.gov |
| Results copy, AACT | 2026-03-23 | ClinicalTrials.gov |
| NIH clinical studies file | 2026-11-13 | ClinicalTrials.gov |
Release trials-2026-11-12-0.4.0
ClinicalTrials.gov's posted results, through CTTI's AACT: ClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299 ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
ClinicalTrials.gov: none stated ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
NIH RePORTER's clinical studies and project files: Public domain (NIH RePORTER)