目的 建立并验证针对脑瘫儿童独立行走年龄预后的列线图预测模型。 方法 采集脑瘫登记平台2016－2020年数据并建库，随机将70%脑瘫患儿纳入建模组，余30%患儿纳入验证组，对患儿性别、胆红素脑病、新生儿窒息、极低出生体重、早期早产、脑瘫分型、磁共振分类、2岁前粗大运动功能分级(GMFCS)、独坐年龄、2岁能否独坐、粗大运动功能评定-88(GMFM-88)A-E区评分、合并癫痫、智力障碍、视觉障碍及手术等因素进行COX单因素回归分析，将单因素回归分析筛选出的有效变量纳入COX多因素回归分析并建立针对脑瘫儿童独立行走的预测模型，通过绘制列线图展示结果。采用C统计量和校准曲线分别评估列线图的区分度及校准能力，采用净重新分类指数(NRI)评估列线图的净收益情况。 结果 共有807例脑瘫患儿纳入本研究，建模组共有565例，验证组共有242例。偏瘫型、双瘫型、不随意型及四肢瘫型患儿分别有93.96%、76.57%、25.49%和22.32%能实现独立行走。2岁前GMFCS评级、脑瘫分型、独坐年龄、智力障碍、早期早产等5个因素是影响脑瘫儿童独立行走预后的独立危险因素(P<0.05)，1~6岁患儿的C统计量均>0.8，提示预测模型具有较好的区分度。校准曲线显示预测1~4岁脑瘫患儿独立行走概率与观察概率相符程度较高，预测5~6岁患儿独立行走概率较观察概率偏高。NRI数据提示列线图预测模型净收益不低于全因素模型。 结论 本研究通过建立并验证脑瘫儿童独立行走年龄预后列线图预测模型，为预测脑瘫儿童独立行走概率提供参考依据。 Objective To establish and validate a model predicting the age at which a child with cerebral palsy will be able to walk independently. Methods Data spanning 2016 to 2020 were collected from the cerebral palsy registration platform to build a database. Then, 70% of the patients were randomly assigned to the modeling group, while the remaining 30% were reserved for validation. Factors such as gender, bilirubin encephalopathy, neonatal asphyxia, extremely low birth weight, early pre-term birth, cerebral palsy type, magnetic resonance classification, gross motor function classification (GMFCS) score before 2 years of age, independent sitting age, ability to sit independently at 2 years of age, sections A through E of the gross motor function measure (GMFM-88), epilepsy, intellectual disability, visual impairment and surgery were analyzed applying Cox univariate regression analysis. The variables highlighted by the univariate regression analysis were included in Cox multivariate regression analyses, and a prediction model for the independent walking of children with cerebral palsy was established. It is presented as a linear graph. The C-statistic and calibration curve were used to evaluate the graph′s discrimination ability and calibration. Net reclassification improvement (NRI) was used to evaluate the linear graph′s net benefit. Results A total of 807 cases were included in the study, with 565 and 242 in the model and validation groups, respectively. GMFCS score before 2 years of age, cerebral palsy type, independent sitting age, intellectual disability and early pre-term birth were found to be independent predictors of the age of independent walking. The C-statistics for 1-6 year-olds were all >0.8, indicating that the prediction model had good discrimination. The calibration curve showed that the predicted probability of independent walking at 1-4 years old was consistent with the observed probability, while the predicted probability of independent walking at 5-6 years old was higher than the observed probability. NRI suggested that the net benefit of the linear graph prediction model was not less than that of the full-factor model. Conclusion A linear model was developed which can usefully predict the age of independent walking for children with cerebral palsy.
Predicting the age of independent walking for children with cerebral palsy
Objective To establish and validate a model predicting the age at which a child with cerebral palsy will be able to walk independently. Methods Data spanning 2016 to 2020 were collected from the cerebral palsy registration platform to build a database. Then, 70% of the patients were randomly assigned to the modeling group, while the remaining 30% were reserved for validation. Factors such as gender, bilirubin encephalopathy, neonatal asphyxia, extremely low birth weight, early pre-term birth, cerebral palsy type, magnetic resonance classification, gross motor function classification (GMFCS) score before 2 years of age, independent sitting age, ability to sit independently at 2 years of age, sections A through E of the gross motor function measure (GMFM-88), epilepsy, intellectual disability, visual impairment and surgery were analyzed applying Cox univariate regression analysis. The variables highlighted by the univariate regression analysis were included in Cox multivariate regression analyses, and a prediction model for the independent walking of children with cerebral palsy was established. It is presented as a linear graph. The C-statistic and calibration curve were used to evaluate the graph′s discrimination ability and calibration. Net reclassification improvement (NRI) was used to evaluate the linear graph′s net benefit. Results A total of 807 cases were included in the study, with 565 and 242 in the model and validation groups, respectively. GMFCS score before 2 years of age, cerebral palsy type, independent sitting age, intellectual disability and early pre-term birth were found to be independent predictors of the age of independent walking. The C-statistics for 1-6 year-olds were all >0.8, indicating that the prediction model had good discrimination. The calibration curve showed that the predicted probability of independent walking at 1-4 years old was consistent with the observed probability, while the predicted probability of independent walking at 5-6 years old was higher than the observed probability. NRI suggested that the net benefit of the linear graph prediction model was not less than that of the full-factor model. Conclusion A linear model was developed which can usefully predict the age of independent walking for children with cerebral palsy.
万方数据
