Predicting Long-Term Outcome of Prolonged Disorder of Consciousness in Children Through Machine Learning Based on
Helin Zheng1, Shuang Ding1, Ningning Chen2
1Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China.
Insights
Machine learning models using conventional structural MRI can predict recovery of consciousness in children with prolonged disorders of consciousness (pDoC). The best model achieved high accuracy, aiding clinical decisions for pediatric brain injury prognosis.
Area of Science:
- Pediatric neurology
- Medical imaging analysis
- Machine learning in healthcare
Background:
- Prognosis for children with prolonged disorders of consciousness (pDoC) remains challenging for clinical decision-making.
- Accurate prediction of long-term outcomes is crucial for guiding patient care and management.
Purpose of the Study:
- To develop a machine learning (ML) model utilizing conventional structural magnetic resonance imaging (csMRI) for predicting outcomes in pediatric pDoC.
- To assess the efficacy of ML models in forecasting consciousness recovery one year post-brain injury.
Main Methods:
- 196 children with pDoC were analyzed, categorized by consciousness state one year after injury.
- csMRI scans underwent semi-quantitative visual assessment; Least Absolute Shrinkage and Selection Operator regression identified key predictors.
- Support Vector Machine (SVM), Random Forests (RF), and Logistic Regression (LR) models were trained and evaluated.
Main Results:
- Seven csMRI and four clinical features emerged as significant predictors of consciousness recovery.
- All developed models demonstrated satisfactory prognostic performance (AUCs > 0.70).
- The SVM-based csMRI model achieved the highest performance, with an AUC of 0.851, accuracy of 0.845, sensitivity of 0.844, and specificity of 0.846.
Conclusions:
- A novel csMRI-based ML model effectively predicts the prognosis of pediatric pDoC.
- The model shows significant potential for predicting consciousness recovery one year after brain injury.
- This predictive tool warrants wider adoption in clinical practice for improved patient outcomes.
Background:
The prognosis of prolonged disorders of consciousness (pDoC) in children has consistently posed a formidable challenge in clinical decision-making.
Objective:
This study aimed to develop a machine learning (ML) model based on conventional structural magnetic resonance imaging (csMRI) to predict outcomes in children with pDoC.
Methods:
A total of 196 children with pDoC were included in this study. Based on the consciousness states 1 year after brain injury, the children were categorized into either the favorable prognosis group or the poor prognosis group. They were then randomly assigned to the training set (n = 138) or the test set (n = 58). Semi-quantitative visual assessments of brain csMRI were conducted and Least Absolute Shrinkage and Selection Operator regression was used to identify significant features predicting outcomes. Based on the selected features, support vector machine (SVM), random forests (RF), and logistic regression (LR) were used to develop csMRI, clinical, and csMRI-clinical-merge models, respectively. Finally, the performances of all models were evaluated.
Results:
Seven csMRI features and 4 clinical features were identified as important predictors of consciousness recovery. All models achieved satisfactory prognostic performances (all areas under the curve [AUCs] >0.70). Notably, the csMRI model developed using the SVM exhibited the best performance, with an AUC, accuracy, sensitivity, and specificity of 0.851, 0.845, 0.844, and 0.846, respectively.
Conclusions:
A csMRI-based prediction model for the prognosis of children with pDoC was developed, showing potential to predict recovery of consciousness 1 year after brain injury and is worth popularizing in clinical practice.
