切换至 "中华医学电子期刊资源库"

中华神经创伤外科电子杂志 ›› 2020, Vol. 06 ›› Issue (02) : 81 -85. doi: 10.3877/cma.j.issn.2095-9141.2020.02.004

所属专题: 专题评论 文献

临床研究

急性神经重症患者7天内进展为脑死亡的预测评分
徐桂兴1, 刘华2, 于萍1, 郑东华3,()   
  1. 1. 510080 广州,中山大学附属第一医院神经外科
    2. 510080 广州,中山大学附属第一医院儿科
    3. 510080 广州,中山大学附属第一医院重症医学科
  • 收稿日期:2020-01-16 出版日期:2020-04-15
  • 通信作者: 郑东华
  • 基金资助:
    广东省医学科研基金(A2019118)

Predictive score for progression to brain death within 7 d in acute neurocritical patients

Guixing Xu1, Hua Liu2, Ping Yu1, Donghua Zheng3,()   

  1. 1. Department of Neurosurgery, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
    2. Department of Pediatric, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
    3. Department of Critical Medicine, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
  • Received:2020-01-16 Published:2020-04-15
  • Corresponding author: Donghua Zheng
  • About author:
    Corresponding author: Zheng Donghua, Email:
引用本文:

徐桂兴, 刘华, 于萍, 郑东华. 急性神经重症患者7天内进展为脑死亡的预测评分[J/OL]. 中华神经创伤外科电子杂志, 2020, 06(02): 81-85.

Guixing Xu, Hua Liu, Ping Yu, Donghua Zheng. Predictive score for progression to brain death within 7 d in acute neurocritical patients[J/OL]. Chinese Journal of Neurotraumatic Surgery(Electronic Edition), 2020, 06(02): 81-85.

目的

构建急性神经重症患者在7 d内进展为脑死亡的预测评分,为此类患者的临床决策(姑息治疗或器官捐献)提供指导。

方法

前瞻性纳入中山大学附属第一医院神经外科和重症医学科自2017年5月至2019年4月收治的急性神经重症患者,动态记录评估患者脑损伤的相关参数,依据入院时间将患者分为2组,训练集(2017年5月至2018年4月)和验证集(2018年5月至2019年4月)。在训练集中,以严重脑损伤(GCS<8分)发生后7 d,作为分组因素;通过单因素和多因素分析组间差异,筛选与急性神经重症患者在7 d内进展为脑死亡的相关因素,并进行Cox回归统计分析,构建曲线下面积(AUC);构建神经学预测评分,并在验证集中验证评分效能。

结果

纳入研究的患者231例,其中139例进入训练集,92例进入验证集。在训练集中,经单因素和多因素分析显示:气管插管行机械通气(OR=4.87,95%CI:1.36~17.35)、对光反射消失(OR=4.86,95%CI:1.75~33.92)、咳嗽反射消失(OR=4.43,95%CI:1.97~20.21)和中线结构移位≥5 mm(OR=3.82,95%CI:1.05~12.32)是患者7 d内进展为脑死亡独立危险因素。在验证集中,结果一致。基于相关因素构建的预测评分,其对于7 d内进展为脑死亡预测的AUC值为0.84。进一步对评分进行分层,其中4~6分对7 d内进展为脑死亡的阳性预测率为85.7%,而0~3分对7 d内不进展为脑死亡的阴性预测率为74.3%。

结论

基于脑损伤评估参数构建的神经学评分,可预测急性神经重症患者进展为脑死亡的时间,结果有待外部数据验证。

Objective

To build a predictive score of progression to brain death within 7 d in acute neurocritical patients, which was used to guide clinical decision-making (palliative treatment or organ donation) of such patients.

Methods

From May 2017 to April 2019, acute neurocritical patient admitted in the Department of Neurosurgery and Critical Medicine of the First Affiliated Hospital of Sun Yat-Sen University were prospectively enrolled, the related factor of brain injury in those patients were dynamically record. According to the time of admission, the patients were divided into two parts: training set (from May 2017 to April 2018) and validation set (from May 2018 to April 2019). In the training set, 7 d after the occurrence of severe brain injury (GCS<8) was taken as the grouping factor; the univariate and multivariate analysis were used to screen the factors related to the progression to brain death within 7 d, and the Cox regression was used to build area under curve (AUC) curve; a neurology predictive score was built, and its performance was tested in the validation set.

Results

Two hundred and thirty-one patients were included in the study, 139 patients entered the training set and 92 entered the validation set. In the training set, the univariate and multivariate analysis showed: mechanical ventilation with endotracheal intubation (OR=4.87, 95%CI: 1.36-17.35), light reflex absence (OR=4.86, 95%CI: 1.75-33.92), cough reflex absence (OR=4.43, 95%CI: 1.97-20.21) and midline shift ≥5 mm (OR=3.82, 95%CI: 1.05-12.32) were related with progression to brain death within 7 d. In the validation set, the results are consistent with the training set. A score was built based on those related factors, and the AUC of score was 0.84. Further grading of score, the positive predictive rate (progressing to brain death within 7 d) of 4-6 points was 85.7%, while the negative predictive rate (not progressing to brain death) of 0-3 points was 74.3%.

Conclusion

The neurological score based on brain injury assessment can predict the progression to brain death in acute neurocritical patients, but the results need further external validation.

表1 纳入研究患者的一般资料
表2 急性神经重症患者7 d内进展为脑死亡影响因素的单因素分析[例(%)]
表3 急性神经重症患者7 d内进展为脑死亡影响因素的多因素分析[例(%)]
表4 神经学预测评分
图1 基于脑损伤评估参数构建的神经学评分的曲线下面积
[1]
国家卫生健康委员会脑损伤质控评价中心.中国儿童脑死亡判定标准与操作规范[J].中华儿科杂志, 2019, 57(5): 331-335.
[2]
国家卫生健康委员会脑损伤质控评价中心,中华医学会神经病学分会神经重症协作组,中国医师协会神经内科医师分会神经重症专业委员会.中国成人脑死亡判定标准与操作规范(第二版)[J].中华医学杂志, 2019, 99(17): 1288-1292.
[3]
Firsching R. Coma after acute head injury[J]. Dtsch Arztebl Int, 2017, 114(18): 313-320.
[4]
Nyam TE, Ao KH, Hung SY, et al. Four score predicts early outcome in patients after traumatic brain injury[J]. Neurocrit Care, 2017, 26(2): 225-231.
[5]
Xu G, Guo Z, Liang W, et al. Prediction of potential for organ donation after circulatory death in neurocritical patients[J]. J Heart Lung Transplant, 2018, 37(3): 358-364.
[6]
He X, Xu G, Liang W, et al. Nomogram for predicting time to death after withdrawal of life-sustaining treatment in patients with devastating neurological injury[J]. Am J Transplant, 2015, 15(8): 2136-2142.
[7]
Roth C, Ferbert A. subarachnoid hemorrhage and isolated brainstem death[J]. Fortschr Neurol Psychiatr, 2016, 84(6): 377-384.
[8]
Peacock SH, Tomlinson AD. Multimodal neuromonitoring in neurocritical care[J]. AACN Adv Crit Care, 2018, 29(2): 183-194.
[9]
Honeybul S, Ho KM, Lind CR, et al. Validation of the crash model in the prediction of 18-month mortality and unfavorable outcome in severe traumatic brain injury requiring decompressive craniectomy[J]. J Neurosurg, 2014, 120(5): 1131-1137.
[10]
Talari HR, Fakharian E, Mousavi N, et al. The rotterdam scoring system can be used as an independent factor for predicting traumatic brain injury outcomes[J]. World Neurosurg, 2016, 87: 195-199.
[11]
Prasad GL. Intracranial pressure monitoring in traumatic brain injury[J]. World Neurosurg, 2017, 100: 702-703.
[12]
Haji A, Kimura S, Ohi Y. A model of the central regulatory system for cough reflex[J]. Biol Pharm Bull, 2013, 36(4): 501-508.
[13]
Bolser DC, Poliacek I, Jakus J, et al. Neurogenesis of cough, other airway defensive behaviors and breathing: a holarchical system?[J]. Respir Physiol Neurobiol, 2006, 152(3): 255-265.
[14]
Sharshar T, Porcher R, Siami S, et al. Brainstem responses can predict death and delirium in sedated patients in intensive care unit[J]. Crit Care Med, 2011, 39(8): 1960-1967.
[15]
Maas AI, Hukkelhoven CW, Marshall LF, et al. 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(6): 1173-1182; discussion 1173-1182.
[16]
Huang YH, Deng YH, Lee TC, et al. Rotterdam computed tomography score as a prognosticator in head-injured patients undergoing decompressive craniectomy[J]. Neurosurgery, 2012, 71(1): 80-85.
[17]
Servadei F, Nasi MT, Giuliani G, et al. Ct prognostic factors in acute subdural haematomas: the value of the 'worst’ CT scan[J]. Br J Neurosurg, 2000, 14(2): 110-116.
[18]
Quattrocchi KB, Prasad P, Willits NH, et al. Quantification of midline shift as a predictor of poor outcome following head injury[J]. Surg Neurol, 1991, 35(3): 183-188.
[19]
Yang WS, Li Q, Li R, et al. Defining the optimal midline shift threshold to predict poor outcome in patients with supratentorial spontaneous intracerebral hemorrhage[J]. Neurocrit Care, 2018, 28(3): 314-321.
[1] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[2] 陈晓玲, 钟永洌, 刘巧梨, 李娜, 张志奇, 廖威明, 黄桂武. 超高龄髋膝关节术后谵妄及心血管并发症风险预测[J/OL]. 中华关节外科杂志(电子版), 2024, 18(05): 575-584.
[3] 奚玲, 仝瀚文, 缪骥, 毛永欢, 沈晓菲, 杜峻峰, 刘晔. 基于肌少症构建的造口旁疝危险因素预测模型[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 48-51.
[4] 屈勤芳, 束方莲. 盆腔器官脱垂患者盆底重建手术后压力性尿失禁发生的影响因素及列线图预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 606-612.
[5] 公宇, 廖媛, 尚梅. 肝细胞癌TACE术后复发影响因素及预测模型建立[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 818-824.
[6] 中华医学会器官移植学分会. 肝移植术后缺血性胆道病变诊断与治疗中国实践指南[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 739-748.
[7] 王守森, 傅世龙, 鲜亮, 林珑. 深入理解控制性减压技术对创伤性颅脑损伤术中脑膨出的预防机制与效果[J/OL]. 中华神经创伤外科电子杂志, 2024, 10(05): 257-262.
[8] 王贝贝, 崔振义, 王静, 王晗妍, 吕红芝, 李秀婷. 老年股骨粗隆间骨折患者术后贫血预测模型的构建与验证[J/OL]. 中华老年骨科与康复电子杂志, 2024, 10(06): 355-362.
[9] 孙晗, 于冰, 武侠, 周熙朗. 基于循环肿瘤DNA 甲基化的结直肠癌筛查预测模型的构建与验证[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 500-506.
[10] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[11] 蔡晓雯, 李慧景, 丘婕, 杨翼帆, 吴素贤, 林玉彤, 何秋娜. 肝癌患者肝动脉化疗栓塞术后疼痛风险预测模型的构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 722-728.
[12] 王誉英, 刘世伟, 王睿, 曾娅玲, 涂禧慧, 张蒲蓉. 老年乳腺癌新辅助治疗病理完全缓解的预测因素分析[J/OL]. 中华临床医师杂志(电子版), 2024, 18(07): 641-646.
[13] 董晟, 郎胜坤, 葛新, 孙少君, 薛明宇. 反向休克指数乘以格拉斯哥昏迷评分对老年严重创伤患者发生急性创伤性凝血功能障碍的预测价值[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 541-547.
[14] 黄圣楷, 许斌, 苏健, 孙龙. 海南省2010~2020年乙型肝炎流行趋势的时间序列分析及预测[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 555-561.
[15] 孙志军, 梁立丰, 柳晓娜, 杨汪洋, 邸北冰, 张妮潇, 彭晖. 接受ICM 的不明原因晕厥患者需行起搏治疗的临床预测因素分析[J/OL]. 中华脑血管病杂志(电子版), 2024, 18(05): 446-453.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?