四种评分系统评价婴幼儿颅脑创伤预后的比较研究

doi:10.3969/j.issn.1672-6731.2020.07.010

摘要/Abstract

摘要：

目的比较婴幼儿神经创伤评分（TINS）、Marshall CT分级、Rotterdam CT评分和HelsinkiCT评分对婴幼儿（≤ 3岁）颅脑创伤患者预后的预测价值。方法选择2012年1月至2017年12月诊治的婴幼儿颅脑创伤患者共37例，创伤后6个月采用Glasgow预后分级（GOS）评价预后，TINS评分、Marshall CT分级、Rotterdam CT评分和Helsinki CT评分预测预后，绘制受试者工作特征曲线（ROC）并计算曲线下面积，并比较4种评分系统对婴幼儿颅脑创伤患者预后的预测价值。结果创伤后6个月恢复良好（GOS分级5级）者19例（51.35%）、轻残（GOS分级4级）11例（29.73%）、重残（GOS分级3级）6例（16.22%）、死亡（GOS分级1级）1例（2.70%）。ROC曲线显示，创伤后6个月TINS评分、Marshall CT分级、Rotterdam CT评分和Helsinki CT评分预测预后不良（GOS评分1~3分）的曲线下面积分别为0.774（95% CI：0.539~1.000，P=0.026）、0.807（95% CI：0.668~0.946，P=0.012）、0.748（95% CI：0.535~0.960，P=0.044）和0.740（95% CI：0.533~0.948，P=0.050）。结论 TINS评分、Marshall CT分级、Rotterdam CT评分和Helsinki CT分级系统对婴幼儿颅脑创伤远期预后均有较好的预测价值，尤以Marshall CT分级对创伤后6个月预后的预测更具优势。

关键词: 脑损伤, 创伤性, 预后, 体层摄影术, X线计算机, 婴儿

Abstract:

Objective To compare the predictive efficiency of different scoring systems including Trauma Infant Neurologic Score (TINS), Marshall CT score, Rotterdam CT score and Helsinki CT score for the long-term prognosis of infant (≤ 3 years old) traumatic brain injury (TBI). Methods Clinical records and imaging data of 37 cases with infant TBI (admitted to Department of Neurosurgery, Xiangya Hospital, Central South University from January 2012 to December 2017) were analyzed retrospectively. Glasgow Outcome Scale (GOS) was used to evaluate the prognosis 6 months after TBI. TINS score, Marshall CT score, Rotterdam CT score and Helsinki CT score were used to predict the prognosis. The Receiver Operating Characteristics Curve (ROC) comparing among TINS, Marshall CT, Rotterdam CT and Helsinki CT scores was performed to predict the prognosis of infant TBI. Results Among 37 cases, 19 cases (51.35%) had good prognosis (GOS grade 5), 11 cases (29.73%) had slight disability (GOS grade 4), 6 cases (16.22%) had severe disability (GOS grade 3), and one case (2.70%) died (GOS grade 1). The areas under the curve (AUC) of the TINS, Marshall CT, Rotterdam CT and Helsinki CT scores at 6-month after injury in GOS grade 1-3 group were 0.774 (95% CI:0.539-1.000, P=0.026), 0.807 (95% CI:0.668-0.946, P=0.012), 0.748 (95% CI:0.535-0.960, P=0.044) and 0.740 (95% CI:0.533-0.948, P=0.050), respectively. Conclusions TINS, Marshall CT, Rotterdam CT and Helsinki CT scores have good evaluation efficiency on the prognosis of infant TBI in long-term. In comparison, Marshall CT score is more advantageous in evaluating the prognosis of infant TBI 6 months after discharge.

Key words: Brain injuries, traumatic, Prognosis, Tomography, X-ray computed, Infant

向忠豪, 陈鑫, 陈益磊, 吴昀, 兰松, 罗湘颖, 毕长龙, 刘劲芳. 四种评分系统评价婴幼儿颅脑创伤预后的比较研究[J]. 中国现代神经疾病杂志, 2020, 20(7): 614-619.

XIANG Zhong-hao, CHEN Xin, CHEN Yi-lei, WU Yun, LAN Song, LUO Xiang-ying, BI Chang-long, LIU Jin-fang. Comparative study of different scoring system on the prognosis of infant traumatic brain injury[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2020, 20(7): 614-619.

http://journal11.magtechjournal.com/cjcnn/CN/Y2020/V20/I7/614

参考文献

[1] Jiang JY, Gao GY, Feng JF, Mao Q, Chen LG, Yang XF, Liu JF, Wang YH, Qiu BH, Huang XJ. Traumatic brain injury in China[J]. Lancet Neurol, 2019, 18:286-295.
[2] Zhong W, Sima X, Huang S, Chen H, Cai B, Sun H, Hu Y, Liu Y, You C. Traumatic extradural hematoma in childhood[J].Childs Nerv Syst, 2013, 29:635-641.
[3] Beni-Adani L, Flores I, Spektor S, Umansky F, Constantini S.Epidural hematoma in infants:a different entity[J]? J Trauma, 1999, 46:306-311.
[4] Marshall LF, Marshall SB, Klauber MR, van Berkum Clark M, Eisenberg HM, Jane JA, Luerssen TG, Marmarou A, Foulkes MA. A new classification of head injury based on computerized tomography. J Neurosurg, 1991, 75:S14-20.
[5] Raj R, Siironen J, Skrifvars MB, Hernesniemi J, Kivisaari R.Predicting outcome in traumatic brain injury:development of a novel computerized tomography classiffication system (Helsinki computerized tomography score)[J]. Neurosurgery, 2014, 75:632-646.
[6] Ongun EA, Dursun O. Prediction of mortality in pediatric traumatic brain injury:implementations from a tertiary pediatric intensive care facility[J]. Ulus Travma Acil Cerrahi Derg, 2018, 24:199-206.
[7] Deepika A, Prabhuraj AR, Saikia A, Shukla D. Comparison of predictability of Marshall and Rotterdam CT scan scoring system in determining early mortality after traumatic brain injury[J]. Acta Neurochir (Wien), 2015, 157:2033-2038.
[8] Araki T, Yokota H, Morita A. Pediatric traumatic brain injury:characteristic features, diagnosis, and management[J]. Neurol Med Chir (Tokyo), 2017, 57:82-93.
[9] Leetch AN, Wilson B. Pediatric major head injury:not a minor problem[J]. Emerg Med Clin North Am, 2018, 36:459-472.
[10] Liu QB, Liang P. Review of craniocerebral truama in children[J]. Yi Yao Qian Yan, 2016, 6:22-23.[刘棋斌, 梁平. 儿童颅脑外伤[J]. 医药前沿, 2016, 6:22-23.]
[11] Deng G, Chen QH. Epidemiological analysis of traumatic brain injury in children. Zhongguo Fu You Jian Kang Yan Jiu, 2015, 26:581-583.[邓刚, 陈奇翰. 儿童外伤性颅脑损伤流行病学分析[J]. 中国妇幼健康研究, 2015, 26:581-583.]
[12] Chaitanya K, Addanki A, Karambelkar R, Ranjan R. Traumatic brain injury in Indian children[J]. Childs Nerv Syst, 2018, 34:1119-1123.
[13] Dewan MC, Mummareddy N, Wellons JC 3rd, Bonfield CM.Epidemiology of global pediatric traumatic brain injury:qualitative review[J]. World Neurosurg, 2016, 91:497-509.
[14] Case ME. Accidental traumatic head injury in infants and young children[J]. Brain Pathol, 2008, 18:583-589.
[15] Liu W, Yi W, Gong W, Zhang L. Application of trauma neurologic score combined with CT examination in children with traumatic brain edema[J]. Lin Chuang Wai Ke Za Zhi, 2015, 23:63-64.[刘伟, 易伟, 龚威, 张亮. 神经创伤评分结合CT检查在小儿外伤性脑水肿中的应用研究[J]. 临床外科杂志, 2015, 23:63-64.]
[16] Pinto FC, Oliveira MF, Prist R, Silva MR, da Silva LF, Neto AC. Effect of volume replacement during combined experimental hemorrhagic shock and traumatic brain injury in prostanoids, brain pathology and pupil status[J]. Arq Neuropsiquiatr, 2015, 73:499-505.
[17] Brown AW, Pretz CR, Bell KR, Hammond FM, Arciniegas DB, Bodien YG, Dams-O'Connor K, Giacino GT, Hart T, Johnson-Greene D, Kowalski RG, Walker WC, Weintraub A, Zafonte R. Predictive utility of an adapted Marshall head CT classification scheme after traumatic brain injury[J]. Brain Inj, 2019, 33:610-617.
[18] MRC CRASH Trial Collaborators; Perel P, Arango M, Clayton T, Edwards P, Komolafe E, Poccock S, Roberts I, Shakur H, Steyerberg E, Yutthakasemsunt S. Predicting outcome after traumatic brain injury:practical prognostic models based on large cohort of international patients[J]. BMJ, 2008, 336:425-429.
[19] Meshcheryakov SV, Semenova ZB, Lukianov VI, Sorokina EG, Karaseva OV. Prognosis of severe traumatic brain injury outcomes in children[J]. Acta Neurochir Suppl, 2018, 126:11-16.
[20] Hale AT, Stonko DP, Brown A, Lim J, Voce DJ, Gannon SR, Le TM, Shannon CN. Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury[J]. Neurosurg Focus, 2018, 45:E2.
[21] Maas AI, Hukkelhoven CW, Marshall LF, Steyerberg EW.Prediction of outcome in traumatic brain injury with computed tomographic characteristics:a comparison between the computed tomographic classification and combinations of computed tomographic predictors[J]. Neurosurgery, 2005, 57:1173-1182.
[22] Cao CL, Li YL, Song J, Yao S, Fang L, Yan Y, Xu GZ.Prediction of early death in patients with traumatic brain injury:a comparison between Marshall and Rotterdam CT scoring systems[J]. Zhongguo Lin Chuang Shen Jing Wai Ke Za Zhi, 2017, 22:676-679.[曹成龙, 李艳玲, 宋健, 姚顺, 房莉, 闫研, 徐国政. 急性中、重型颅脑损伤早期病死率的预测:Marshall CT分级和Rotterdam CT评分的比较[J]. 中国临床神经外科杂志, 2017, 22:676-679.]
[23] Zhang B, Yang MF. Clinical research for the Rotterdam CT scores assessing the prognosis of TBI after standard decompressive craniectomy[J]. Shi Yong Yi Xue Za Zhi, 2018, 34:1368-1375.[张博, 杨明飞. 鹿特丹CT评分评估行去骨瓣减压术颅脑外伤患者的预后[J]. 实用医学杂志, 2018, 34:1368-1375.]
[24] Yao S, Song J, Li S, Cao C, Fang L, Wang C, Xu G. Helsinki computed tomography scoring system can independently predict long-term outcome in traumatic brain injury[J]. World Neurosurg, 2017, 101:528-533.
[25] Currie S, Saleem N, Straiton JA, Macmullen-Price J, Warren DJ, Craven IJ. Imaging assessment of traumatic brain injury[J]. Postgrad Med J, 2016, 92:41-50.

选择文件类型/文献管理软件名称

选择包含的内容

2 四种评分系统评价婴幼儿颅脑创伤预后的比较研究

Comparative study of different scoring system on the prognosis of infant traumatic brain injury

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘俊其, 王蠡, 王振霖, 张秋航, 齐岩, 危维. 内镜下经口鼻联合入路手术切除颅底脊索瘤[J]. 中国现代神经疾病杂志, 2023, 23(7): 580-585.
[2]	魏民, 詹琪佳, 蒋文彬, 李森, 刘晨, 肖波. 内镜下颅缝条状切除术治疗矢状缝早闭疗效分析[J]. 中国现代神经疾病杂志, 2023, 23(7): 621-626.
[3]	邹婷婷, 马莉, 潘文静, 李长秀, 胡乃霞, 王擂. 重型颅脑创伤并发颅内感染危险因素分析及列线图预测模型构建[J]. 中国现代神经疾病杂志, 2023, 23(6): 496-502.
[4]	张磊, 张涛. 脑电双频指数与自发性脑出血患者预后相关性分析[J]. 中国现代神经疾病杂志, 2023, 23(6): 515-521.
[5]	刘臣超, 刘慧权, 袁小勇, 王健, 陈绅, 王义义, 柳兴军. 重症结核性脑膜炎预后不良影响因素分析[J]. 中国现代神经疾病杂志, 2023, 23(6): 522-529.
[6]	徐蓉, 宁泽淑, 康庆云, 陈波, 廖红梅, 杨理明, 吴丽文. 钾离子通道基因变异相关婴儿癫痫性脑病临床表型与基因变异特点分析[J]. 中国现代神经疾病杂志, 2023, 23(3): 196-204.
[7]	刘雪菲, 王佳伟. 视神经炎合并慢性乙型肝炎病毒感染临床分析[J]. 中国现代神经疾病杂志, 2022, 22(9): 770-776.
[8]	王旭, 李琼阁, 赵澄, 单艺, 卢洁. 直立位X线对腰椎滑脱诊断效能分析[J]. 中国现代神经疾病杂志, 2022, 22(8): 675-679.
[9]	蒋丽丹, 杨俊, 张洪兵, 王宁. 基于电子病历首页的颅脑创伤出院时结局影响因素：附4058例分析[J]. 中国现代神经疾病杂志, 2022, 22(7): 608-614.
[10]	袁莉, 张建兴, 王素洁, 陈菲菲, 何小红, 陈丽丽. 入院时美国国立卫生研究院卒中量表联合血清超敏C-反应蛋白对缺血性卒中预后的预测价值[J]. 中国现代神经疾病杂志, 2022, 22(7): 615-620.
[11]	王志平, 张义森, 曲丽莹. 蛛网膜下腔出血CT分布对颅内动脉瘤检出的价值[J]. 中国现代神经疾病杂志, 2022, 22(7): 621-627.
[12]	何世豪, 刘子琪, 王嵘. 成人烟雾病脑血管搭桥术后短期神经功能和临床症状改善单中心研究[J]. 中国现代神经疾病杂志, 2022, 22(5): 348-352.
[13]	王德胜, 刘士敏, 魏铭, 李宏. 外周血游离CD147及其诱导产物基质金属蛋白酶-9对颅脑创伤患者短期预后的评估意义[J]. 中国现代神经疾病杂志, 2022, 22(3): 195-199.
[14]	徐伟, 谢之易, 王泽旭, 马潞, 游潮, 胡鑫. 核因子E2相关因子2对脑出血继发性损伤调控机制研究进展[J]. 中国现代神经疾病杂志, 2022, 22(10): 836-840.
[15]	杨翀, 李旭东, 吕良福, 袁恒杰, 张毅. 大数据模型预测动脉瘤夹闭术后动脉瘤性蛛网膜下腔出血患者预后临床研究：模型建立与评价[J]. 中国现代神经疾病杂志, 2022, 22(10): 841-849.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

2 四种评分系统评价婴幼儿颅脑创伤预后的比较研究

Comparative study of different scoring system on the prognosis of infant traumatic brain injury

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价