痉挛型脑瘫患儿康复治疗预后不良的因素探讨及风险预测方案研究

doi:10.3760/cma.j.issn.1007-1245.2024.20.012

摘要/Abstract

摘要：

目的　探讨痉挛型脑瘫患儿康复治疗预后不良的风险因素，并建立风险预测模型。方法　回顾性纳入2018年1月至2023年3月在西安中医脑病医院接受康复治疗的168例痉挛型脑瘫患儿，其中男105例，女63例，年龄（6.75±2.04）岁。康复治疗包括运动疗法、作业疗法、引导式教育、按摩、针灸、理疗等，连续治疗8周。随访1年，根据预后情况将其分为预后不良组与预后良好组。对比两组患儿临床资料，采用多因素logistic回归分析探讨痉挛型脑瘫患儿预后不良的影响因素。构建患儿预后不良的风险预测Nomogram模型，并验证模型的预测价值。采用χ²检验。结果　预后良好组患儿病情重度、四肢瘫、振幅整合脑电图（aEEG）重度异常、营养不良占比均低于预后不良组［32.11%（35/109）比55.93%（33/59）、12.84%（14/109）比28.81%（17/59）、13.76%（15/109）比59.32%（35/59）、22.94%（25/109）比40.68%（24/59）］，差异均有统计学意义（均P<0.05）。多因素logistic回归分析显示，病情重度、痉挛型四肢瘫、aEEG重度异常、营养不良均是痉挛型脑瘫患儿康复治疗预后不良的危险因素（均P<0.05）。基于多因素logistic回归分析筛选的4个危险因素，构建痉挛型脑瘫患儿康复治疗预后不良的风险预测Nomogram模型。对模型进行验证，校准曲线显示校正曲线与理想曲线贴合较好，受试者操作特征曲线（ROC）显示曲线下面积、灵敏度、特异度分别为0.881、84.75%、83.05%，决策曲线显示当阈值概率在0.29～0.94时，该模型预测痉挛型脑瘫患儿康复治疗预后不良可获得净收益。结论　病情重度、痉挛型四肢瘫、aEEG重度异常、营养不良均是痉挛型脑瘫患儿康复治疗预后不良的危险因素。基于上述危险因素构建的患儿预后不良风险预测Nomogram模型具有良好的预测价值，可用于指导临床筛查高风险预后不良患儿，以便及时调整治疗方案。

关键词:

痉挛型脑瘫, 儿童, 康复治疗, 预后, 影响因素, Nomogram模型

Abstract:

Objective　To investigate the risk factors for poor prognosis in children with spastic cerebral palsy receiving rehabilitation treatment, and to establish a risk prediction model. Methods　A total of 168 children with spastic cerebral palsy, including 105 boys and 63 girls, aged (6.75±2.04) years, who received rehabilitation treatment in Xi'an Traditional Chinese Medicine Brain Disease Hospital from January 2018 to March 2023, were retrospectively included. They were divided into a poor prognosis group and a good prognosis group based on the prognosis after one year of following-up. The clinical data of the two groups were compared, and the influencing factors for poor prognosis in children with spastic cerebral palsy were analyzed by multivariate logistic regression analysis. A Nomogram model to predict the risk of poor prognosis in children was constructed, and the predictive value of the model was validated. χ² test was used. Results　The proportions of severe illness, quadriplegia, severe abnormalities of amplitude integrated electroencephalogram (aEEG), and malnutrition in the good prognosis group were lower than those in the poor prognosis group [32.11% (35/109) vs. 55.93% (33/59), 12.84% (14/109) vs. 28.81% (17/59), 13.76% (15/109) vs. 59.32% (35/59), 22.94% (25/109) vs. 40.68% (24/59)], with statistically significant differences (all P<0.05). Multivariate logistic regression analysis showed that severe illness, quadriplegia, severe abnormality of aEEG, and malnutrition were all risk factors for poor prognosis in children with spastic cerebral palsy after rehabilitation treatment (all P<0.05). A Nomogram model for predicting the risk of poor prognosis in children with spastic cerebral palsy after rehabilitation treatment was constructed based on the four risk factors screened through multiple logistic regression analysis. The model was validated, and the calibration curve showed that the calibration curve fitted well with the ideal curve, and the receiver operating characteristic curve (ROC) showed that the area under the curve, sensitivity, and specificity were 0.881, 84.75%, and 83.05%, respectively, and the decision curve showed that when the threshold probability was from 0.29 to 0.94, the model predicted that poor prognosis of rehabilitation treatment for children with spastic cerebral palsy could achieve net benefits. Conclusions　Severe illness, spastic quadriplegia, severe abnormalities of aEEG, and malnutrition are all risk factors for poor prognosis in children with spastic cerebral palsy after rehabilitation treatment, and the Nomogram model for predicting the risk of poor prognosis in children constructed based on the above factors has good predictive value, and it can be used to guide clinical screening of high-risk children with poor prognosis, so as to adjust treatment plans in a timely manner.

Key words:

Spastic cerebral palsy, Children, Rehabilitation therapy, Prognosis, Influencing factors, Nomogram model

白新朝贺维.

痉挛型脑瘫患儿康复治疗预后不良的因素探讨及风险预测方案研究 [J]. 国际医药卫生导报, 2024, 30(20): 3407-3412.

Bai Xinchao, He Wei.

Study on the factors affecting poor prognosis and risk prediction in children with spastic cerebral palsy receiving rehabilitation treatment [J]. International Medicine and Health Guidance News, 2024, 30(20): 3407-3412.

参考文献

[1] Yang S, Xia J, Gao J, Wang L. Increasing prevalence of cerebral palsy among children and adolescents in China 1988-2020: a systematic review and meta-analysis[J]. J Rehabil Med, 2021, 53(5): jrm00195. DOI: 10.2340/16501977-2841.
[2] Ueda K, Aravamuthan BR, Pearson TS. Dystonia in individuals with spastic cerebral palsy and isolated periventricular leukomalacia[J]. Dev Med Child Neurol, 2023, 65(1):94-99. DOI: 10.1111/dmcn.15300.
[3] 张红敏,高晶,王丽娜. 虚拟现实技术结合常规康复治疗对痉挛型脑性瘫痪患儿下肢运动功能的影响[J]. 中国现代医学杂志,2023,33(23):42-46. DOI:10.3969/j.issn.1005- 8982.2023.23.008.
[4] Kong WQ, Shao C, Du YK, et al. Nomogram for predicting venous thromboembolism after spinal surgery[J]. Eur Spine J, 2024, 33(3): 1098-1108. DOI: 10.1007/s00586- 023-08043-2.
[5] 中国康复医学会儿童康复专业委员会,中国残疾人康复协会小儿脑性瘫痪康复专业委员会,«中国脑性瘫痪康复指南»编委会. 中国脑性瘫痪康复指南(2015)[J]. 中国实用乡村医生杂志,2015,22(22):12-19. DOI:10.3969/j.issn.1672-7185.2015.22.005.
[6] Palisano R, Rosenbaum P, Walter S, et al. Development and reliability of a system to classify gross motor function in children with cerebral palsy[J]. Dev Med Child Neurol, 1997, 39(4):214-223. DOI: 10.1111/j.1469-8749.1997.tb07414.x.
[7] Mohanty T, Joseph SD, Gunasekaran PK, et al. Predictors of risk for cerebral palsy: a review[J]. pediatr phys ther, 2023, 35(3):347-357. DOI: 10.1097/PEP.0000000000001020.
[8] Tapia C, Constanzo J, González V, et al. The effectiveness of aquatic therapy based on the halliwick concept in children with cerebral palsy: a systematic review[J]. Dev Neurorehabil, 2023, 26(6-7): 371-376. DOI: 10.1080/17518423.2023.2259986.
[9] 李玲玲,黄海量.物理因子疗法对痉挛型脑瘫患儿下肢运动功能影响的网状Meta分析[J].哈尔滨医药,2023,43(3):55-58.DOI:10.3969/j.issn.1001-8131.2023.03.020.
[10] 艾克拜尔·哈里克,逯霞,木塔力甫·努热合买提,等. 血清泛素C末端水解酶L1动态变化与痉挛性脑瘫患儿的预后相关性分析[J]. 标记免疫分析与临床,2017,24(10):1171-1174. DOI:10.11748/bjmy.issn.1006-1703.2017. 10.024.
[11] 魏民,蒋文彬,詹琪佳,等. 选择性脊神经后根切断术治疗痉挛型脑瘫患儿疗效与影响因素分析[J]. 中国现代神经疾病杂志,2023,23(5):405-411. DOI:10.3969/j.issn.1672- 6731.2023.05.005.
[12] 早木来提古丽·吐尔逊. 不同类型痉挛型脑瘫患儿生存质量影响因素分析[D]. 乌鲁木齐:新疆医科大学,2021.
[13] 张九菊,梁丽霞,袁志忠,等. 振幅整合脑电图联合全身运动和婴儿运动量表在脑瘫高危早产儿运动发育异常中的预测效果[J]. 中华生物医学工程杂志,2020,26(2):170-175. DOI:10.3760/cma.j.cn115668-20200115-00006.
[14] 逯霞,艾克拜尔·哈里克,闫宝锋,等. 振幅整合脑电图联合头颅MRI对痉挛型脑瘫患儿病情及远期预后评估[J]. 检验医学与临床,2017,14(16):2364-2365,2368. DOI:10.3969/j.issn.1672-9455.2017.16.012.
[15] Jahan I, Muhit M, Hardianto D, et al. Epidemiology of malnutrition among children with cerebral palsy in low- and middle-income countries: findings from the Global LMIC CP register[J]. Nutrients, 2021, 13(11): 3676. DOI: 10.3390/nu13113676.
[16] Foster BA, Lane JE, Massey E, et al. The impact of malnutrition on hospitalized children with cerebral palsy[J]. Hosp Pediatr, 2020, 10(12): 1087-1095. DOI: 10.1542/hpeds.2020-0177.
[17] Nakamoto T, Yoshida T, Katayama S, et al. Development and validation of a preoperative nomogram for endoscopic management decision making in upper urinary tract urothelial carcinoma[J]. Ann Surg Oncol, 2024, 31(2): 1393-1401. DOI: 10.1245/s10434-023- 14514-z.
[18] Liao T, Lu Y, Su T, et al. Development and validation of prognostic nomogram for cirrhotic patients with acute kidney injury upon ICU admission[J]. Intern Emerg Med, 2024, 19(1): 49-58. DOI: 10.1007/s11739-023-03436-z.