Predicting Reintubation in Postoperative Pediatric Cardiac Surgery: A Machine Learning Approach

Abstract

Background: Accurate prediction of reintubation in pediatric patients following cardiac surgery is vital for enhancing postoperative care. This study aimed to identify key predictors of reintubation and train a multilayer perceptron (MLP) neural network model for prediction. Methods: This retrospective analysis included clinical data from 294 pediatric patients (1–24 months of age) who underwent cardiac surgery and postoperative mechanical ventilation between January and December 2024. Patients who were successfully extubated and monitored for reintubation were included. Significant predictors were identified using Pearson Chi-square (PC2) test and binomial logistic regression analysis (BLRA). An MLP neural network was trained using clinical covariates to predict reintubation. Results: Significant predictors of reintubation included low BMI (0.1–1 percentile, P < 0.01, PC2), emergency surgery (P < 0.01, PC2), previous infection (P < 0.01, PC2), pre-reintubation ABG levels (P < 0.001, PC2), and procedure type (aortoplasty, P = 0.05, PC2). Additionally, the duration of ventilation (P = 0.014, BLRA) and the RACHS2 score (P = 0.006, BLRA) were significant predictors. The MLP model achieved a sensitivity of 93.7% and a specificity of 90.5%, with an F1-score of 0.94. The sum of squared error was 0.152, the root mean squared error was 0.248, and the area under the receiver operating characteristic curve was 0.94 for both training and testing datasets. Conclusion: The MLP neural network exhibited excellent predictive accuracy for identifying risk factors associated with reintubation.