Clinical efficacy of neurofacilitation technology combined with rehabilitation training based on cerebral blood flow velocity and cerebral metabolism in children with cerebral palsy

Weidong Zhao^1,2Yuxin Bai^1*Bo Wang¹Yanping Fan²Wenbi Yu²Ping Song²

• ¹Shenzhen Second People's Hospital, Shenzhen, China
• ²Dapeng New District Maternal and Child Health Hospital, Shengzhou, China

Objective: To construct and validate a predictive model for the clinical efficacy of neurofacilitation technology combined with rehabilitation training in children with cerebral palsy based on cerebral blood flow velocity and cerebral metabolism indicators. Methods: A total of 259 children with cerebral palsy who were treated in our hospital from January 2020 to December 2023 were selected as the study subjects. These children were divided into a training set (n=181) and a validation set (n=78) at a 7:3 ratio. Logistic regression analysis was used to identify independent factors influencing clinical efficacy. A nomogram prediction model was constructed based on these factors. The predictive efficiency and clinical value of the model were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Results: Logistic regression analysis revealed that the MCA-MFV, NAA/Cr ratio, and Cho/Cr ratio were independent factors affecting the clinical efficacy of neurofacilitation technology combined with rehabilitation training in children with cerebral palsy (P<0.05). ROC curve analysis revealed that the AUC values of the MCA-EDV, ACA-EDV, PCA-EDV, PCA-MFV, NAA/Cr ratio, and Cho/Cr ratio were all >0.600, thereby indicating their predictive value for clinical efficacy. In the training and validation sets, the C-indices of the nomogram model were 0.892 and 0.853, respectively. The calibration curves revealed mean absolute errors of 0.127 and 0.161 between the predicted and true values, with Hosmer-Lemeshow test results of χ ² =11.944, P=0.154 and χ²=8.087, P=0.425, respectively. The ROC curve demonstrated that the AUC value of the nomogram model for predicting clinical efficacy was 0.894 (95% CI: 0.838-0.950) in the effective group and 0.849 (95% CI: 0.746-0.952) in the ineffective group, with sensitivity and specificity values of 0.756 and 0.913, respectively, for the effective group, as well as values of 0.690 and 0.750, respectively, for the ineffective group. Conclusion: Cerebral blood flow velocity and cerebral metabolism indicators can serve as key factors in the construction of a predictive model. The developed nomogram model exhibits high predictive value for the clinical efficacy of neurofacilitation technology combined with rehabilitation training in children with cerebral palsy and can provide valuable guidance for clinical decision-making.