Critical Care 29, Article number: 17 (2025)
Published: 09 January 2025
Background
Patients supported by extracorporeal membrane oxygenation
(ECMO) are at a high risk of brain injury, contributing to significant
morbidity and mortality. This study aimed to employ machine learning (ML)
techniques to predict brain injury in pediatric patients ECMO and identify key
variables for future research.
Methods
Data from pediatric patients undergoing ECMO were collected
from the Chinese Society of Extracorporeal Life Support (CSECLS) registry
database and local hospitals. Ten ML methods, including random forest, support
vector machine, decision tree classifier, gradient boosting machine, extreme
gradient boosting, light gradient boosting machine, Naive Bayes, neural
networks, a generalized linear model, and AdaBoost, were employed to develop
and validate the optimal predictive model based on accuracy and area under the
curve (AUC). Patients were divided into retrospective cohort for model
development and internal validation, and one cohort for external validation.
Results
A total of 1,633 patients supported by ECMO were included in
the model development, of whom 181 experienced brain injury. In the external
validation cohort, 30 of the 154 patients experienced brain injury. Fifteen
features were selected for the model construction. Among the ML models tested,
the random forest model achieved the best performance, with an AUC of 0.912 for
internal validation and 0.807 for external validation.
Conclusion
The Random Forest model based on machine learning
demonstrates high accuracy and robustness in predicting brain injury in
pediatric patients supported by ECMO, with strong generalization capabilities
and promising clinical applicability.
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