Trial reportBMC pulmonary medicine2022
Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.
Trial report in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03704324 (Incidence and Risk Factors of PostopeRativE Delirium in ICU in China), which is not on this map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Incidence and Risk Factors of PostopeRativE Delirium in ICU in China
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Machine learning-based prediction models for noninvasive respiratory support failure in acute respiratory failure: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.Frontiers in medical technology · 2025Pooled it
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Feature selection methodology based on explainable AI: application in predicting extubation success in the intensive care unit.Scientific reports · 2026Article
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Factors Associated with Outcomes of Non-Invasive Ventilation in Acute Hypercapnic Respiratory Failure with Acidosis: A Study from a Tertiary Care Hospital in Pakistan.Journal of clinical medicine · 2026Article
- Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.Intensive care medicine experimental · 2026Article
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Bridging the Gap: The Role of Non-Invasive Respiratory Supports in Weaning from Invasive Mechanical Ventilation.Journal of clinical medicine · 2025Review
- Using machine learning techniques for early prediction of tracheal intubation in patients with septic shock: a multi-center study in South Korea.Acute and critical care · 2025Article
- The ethical considerations of integrating artificial intelligence into surgery: a review.Interdisciplinary cardiovascular and thoracic surgery · 2025Review
- Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- AI-Driven Decision Support Framework for Preventing Medical Equipment Failure and Enhancing Patient Safety: A New Perspective.Journal of multidisciplinary healthcare · 2025Article
- Personalized Noninvasive Respiratory Support in the Perioperative Setting: State of the Art and Future Perspectives.Journal of personalized medicine · 2023Review
- Development and validation of a prediction model for in-hospital death in patients with heart failure and atrial fibrillation.BMC cardiovascular disorders · 2023Article
- Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.Frontiers in medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundNoninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).
methodsPatients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University.
resultsOf 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO
conclusionsThis study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring.
trial registrationNCT03704324. Registered 1 September 2018, https://register. CLINICALTRIALS: gov .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.