Development of A Model to Predict Mixed Cerebral Palsy in Infants with Periventricular White Matter Injury
10.3969/j.issn.1005-5185.2024.07.004
- VernacularTitle:脑室周围白质损伤患儿发生混合型脑性瘫痪预测模型的建立
- Author:
Tingting HUANG
1
;
Lan ZHANG
;
Wei XING
;
Zhen LI
;
Fei WANG
;
Gang ZHANG
Author Information
1. 河南中医药大学第一附属医院放射科,河南 郑州 450000
- Keywords:
Periventricular white matter injury;
Mixed cerebral palsy;
Spastic cerebral palsy;
Magnetic resonance imaging;
Prediction model
- From:
Chinese Journal of Medical Imaging
2024;32(7):659-666
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To development a model to predict mixed cerebral palsy(CP)in infants with periventricular white matter injury(PWMI).Materials and Methods This study retrospectively included infants with PWMI on MRI aged 6 to 24 months and were diagnosed as CP after age of 2 years in the First Affiliated Hospital of Henan University of Chinese Medicine,from September 2015 to October 2022.The eligible infants were divided into mixed group and spastic CP group.Multivariable Logistic regression model was used to select the MRI features associated with PWMI and mixed CP,and was internal validated by using the five-fold cross and repeated cross validation.Model performance was evaluated by the discrimination,calibration and decision curve.The correlation between independent MRI features and the gross motor function classification system levels was evaluated.Results A total of 135 infants with PWMI and CP were included in this study,100 with spastic CP,and 35 with mixed CP.Multivariable Logistic regression analysis found that the involvement of ventralateral of thalamus(OR=27.500,95%CI 8.293-90.942),posterior putamen(OR=13.700,95%CI 4.489-41.549),hippocampus(OR=7.200,95%CI 1.702-30.813)and caudate nucleus(OR 5.800,95%CI 1.973-16.950)were associated with PWMI and mixed CP.A prediction model was constructed using the above four MRI features.The model yielded an area under the receiver operating characteristic curve of 0.960(95%CI 0.934-0.988)and 0.95,0.96 in the five-fold cross and repeated cross validation,respectively.The model had good calibration(χ2=3.712,P=0.529)and clinical application.Furthermore,the four MRI variables were associated with gross motor function classification system levels(r=0.559,0.581,0.171,0.409,all P<0.05).Conclusion The model can early and accurately predict infants with PWMI at high risk of mixed CP.
