Machine Learning-Based Prediction of 48-Hour Extubation Success in Mechanically Ventilated Children: A Single-Center Retrospective Cohort Study.

Background

Accurate assessment of extubation readiness in mechanically ventilated children remains difficult because successful sustained breathing depends on the interaction of respiratory, metabolic, inflammatory, neurological, and cardiovascular factors. This study developed and internally validated machine-learning models for predicting 48 h extubation success using routinely available pre-extubation data.

Methods

Of 1097 total PICU admissions, 341 mechanically ventilated children treated between 2021 and 2026 constituted the final analytic cohort. The primary outcome was survival without reintubation during the first 48 h after planned extubation. Demographic, clinical, laboratory, blood gas, illness severity, and ventilator variables were evaluated. Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine models were assessed using stratified 5-fold cross-validation. Unsupervised k-means clustering was performed to identify physiological phenotypes.

Results

Extubation was successful in 298 children (87.4%) and failed in 43 (12.6%). Failure was associated with higher oxygenation index, lactate, procalcitonin, C-reactive protein, PaCO2, pSOFA, PEEP, and rapid shallow breathing index, together with lower arterial pH, bicarbonate, ionized calcium, hemoglobin, albumin, sodium, and Glasgow Coma Scale scores. Random Forest yielded the numerically highest discrimination, with an AUC of 0.983 (95% CI, 0.971-0.993), a sensitivity of 0.980, a specificity of 0.814, an accuracy of 0.959, and a Brier score of 0.032. Arterial pH, the oxygenation index, bicarbonate, ionized calcium, procalcitonin, lactate, and PaCO2 showed the highest Random Forest Gini importance scores. Clustering identified a low-severity phenotype (n = 302; 97% success; 0% mortality) and a high-severity phenotype (n = 39; 10% success; 100% mortality).

Conclusions

Multidimensional machine-learning models predicted 48 h pediatric extubation success with strong internal discrimination. Prospective multicenter external validation is required before clinical implementation.