Zhao Weidong, Bai Yuxin, Wang Bo, Fan Yanping, Yu Wenbi, Song Ping
Department of Children's Rehabilitation, Dapeng New District Maternal and Child Health Hospital, Shenzhen, Guangdong, China.
Department of Pediatrics, Shenzhen People's Hospital, Shenzhen, Guandong, China.
Front Med (Lausanne). 2025 Jun 25;12:1599712. doi: 10.3389/fmed.2025.1599712. eCollection 2025.
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.
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 ( = 181) and a validation set ( = 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.
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 ( < 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 = 11.944, = 0.154 and χ = 8.087, = 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.
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.
基于脑血流速度和脑代谢指标构建并验证神经易化技术联合康复训练对脑瘫患儿临床疗效的预测模型。
选取2020年1月至2023年12月在我院接受治疗的259例脑瘫患儿作为研究对象。这些患儿按7∶3的比例分为训练集（n = 181）和验证集（n = 78）。采用Logistic回归分析确定影响临床疗效的独立因素。基于这些因素构建列线图预测模型。采用受试者操作特征（ROC）曲线和校准曲线评估该模型的预测效能和临床价值。
Logistic回归分析显示，大脑中动脉平均血流速度（MCA-MFV）、N-乙酰天门冬氨酸/肌酸（NAA/Cr）比值和胆碱/肌酸（Cho/Cr）比值是影响神经易化技术联合康复训练对脑瘫患儿临床疗效的独立因素（P < 0.05）。ROC曲线分析显示，大脑中动脉舒张末期血流速度（MCA-EDV）、大脑前动脉舒张末期血流速度（ACA-EDV）、大脑后动脉舒张末期血流速度（PCA-EDV）、大脑后动脉平均血流速度（PCA-MFV）、NAA/Cr比值和Cho/Cr比值的AUC值均>0.600，表明它们对临床疗效具有预测价值。在训练集和验证集中，列线图模型的C指数分别为0.892和
