ArticleJournal of perinatology : official journal of the California Perinatal Association2023
Prediction of extubation failure among low birthweight neonates using machine learning.
Article in Journal of perinatology : official journal of the California Perinatal Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Nasal continuous positive airway pressure immediately after extubation for preventing morbidity in preterm infants.The Cochrane database of systematic reviews · 2024Pooled it
- Physiologic signatures during extubation readiness trials in neonates >35 weeks' postmenstrual age.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Association between the respiratory severity score and extubation success in severe bronchopulmonary dysplasia.Journal of perinatology : official journal of the California Perinatal Association · 2026Observational
- Validation of a novel Bayesian predictive algorithm for detection of carbon dioxide retention using retrospective neonatal ICU data.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.Pediatric research · 2025Review
- Use of Prophylactic Methylxanthines to Prevent Extubation Failure in Preterm Neonates with a Birth Weight of 1250-2499 g: A Propensity Score-Matched Analysis.Journal of clinical medicine · 2025Article
- Neonatal Tracheal Intubation in the NICU: A Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Dual-phase machine learning framework for paediatric ventilator liberation: forecasting extubation readiness and nowcasting extubation outcomes.BMJ digital health & AI · 2025Article
- Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.The Journal of pediatrics · 2024Observational
- A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities.Frontiers in pediatrics · 2024Review
- Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study.International journal of reproductive biomedicine · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data. STUDY
designRetrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days.
results1348 low birthweight (≤2500 g) neonates who received mechanical ventilation within the first 7 days were included, of which 350 (26%) failed a trial of extubation. The best-performing model was a boosted-tree model incorporating demographics, vital signs, ventilator parameters, and medications (AUROC 0.82). The most important features were birthweight, last FiO
conclusionsMachine learning models identified low birthweight ventilated neonates at risk for extubation failure. These models will need to be validated across multiple centers to determine generalizability of this tool.
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Registered trials
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