天津医药 ›› 2021, Vol. 49 ›› Issue (12): 1328-1332.doi: 10.11958/20210135

• 应用研究 • 上一篇    下一篇

创伤后脑积水合并严重意识障碍患者脑室-腹腔分流术预后预测模型的建立与评价

卞琴,杨鹏,张秋芳△   

  1. 苏州大学附属第一医院创伤中心(邮编215006)
  • 收稿日期:2021-01-19 修回日期:2021-10-20 出版日期:2021-12-15 发布日期:2021-12-27
  • 通讯作者: 张秋芳 E-mail:zhangqiufang@suda.edu.cn
  • 作者简介:卞琴(1982),女,本科,主管护师,主要从事创伤性脑损伤方面研究。E-mail:71561876@qq.com
  • 基金资助:
    2019年苏州市卫生人才培养项目(GSWS2019037

Establishment and evaluation of prognostic prediction model for ventriculo-peritoneal shunt in#br# post-traumatic hydrocephalus patients with severe disturbance of consciousness #br#

BIAN Qin, YANG Peng, ZHANG Qiu-fang△   

  1. Department of Trauma Center, the First Affiliated Hospital of Soochow University, Suzhou 215006, China △Corresponding Author E-mail: zhangqiufang@suda.edu.cn
  • Received:2021-01-19 Revised:2021-10-20 Published:2021-12-15 Online:2021-12-27
  • Contact: △通信作者 E-mail:zhangqiufang@suda.edu.cn E-mail:zhangqiufang@suda.edu.cn

摘要: 目的 探讨创伤后脑积水合并严重意识障碍患者脑室-腹腔分流术预后的危险因素,并构建列线图预测 模型,指导早期识别高危患者。方法 以确诊创伤后脑积水合并严重意识障碍患者为研究对象,所有患者均接受脑 室-腹腔分流术治疗。治疗后3个月时根据格拉斯哥预后评分(GOS)判断患者预后,分为预后良好组(GOS评分>3 分)和预后不良组(GOS评分≤3分)。采用多因素Logistic回归筛选危险因素,并在该基础上构建列线图预测模型,采 用受试者工作特征(ROC)曲线及曲线下面积(AUC)和Hosmer-Lemeshow检验对模型进行评价。结果 共218例患 者纳入分析,其中模型组 153 例,验证组 65 例;模型组预后良好 90 例(58.8%),预后不良 63 例(41.2%)。多因素 Logistic回归分析显示,年龄≥50岁(OR=1.356,95%CI:1.101~1.639)、中度脑积水(OR=2.859,95%CI:2.325~3.212)、发 病时格拉斯哥昏迷评分为 9~12 分(OR=2.421,95%CI:2.056~2.857)和发病至分流术的间隔≥3 个月(OR=1.639, 95%CI:1.325~2.124)是影响预后的独立危险因素(均P<0.05)。根据上述因素绘制的列线图预测模型组和验证组临 床预后的AUC分别为0.896(95%CI:0.842~0.933)和0.875(95%CI:0.825~0.916)。Hosmer-Lemeshow检验显示列线图 模型有较好的拟合度(模型组χ2=0.896,验证组χ2=0.567,均P>0.05)。根据列线图模型进行风险分层后,模型组和验 证组高、中风险患者预后不良比例明显高于低风险患者。结论 本研究构建的列线图模型对评估创伤后脑积水合 并严重意识障碍患者脑室-腹腔分流术后的预后有较好的价值,可指导临床早期识别高危患者。

关键词: 颅脑损伤, 脑积水, 意识障碍, 危险因素, 列线图, 脑室腹膜分流术

Abstract: Objective To investigate the risk factors for the prognosis of ventriculoperitoneal shunt in patients with post-traumatic hydrocephalus complicated with severe disturbance of consciousness, and to construct a linear prediction model to guide the early identification of high-risk patients. Methods Patients diagnosed with post-traumatic hydrocephalus complicated with severe disturbance of consciousness in our hospital were selected as research objects. All patients were treated with ventriculoperitoneal shunt. Three months after treatment, patients were divided into the good prognosis group (GOS score > 3 points) and the poor prognosis group (GOS score ≤3 points). Multivariate Logistic regression was used to screen risk factors, and a nomogram prediction model was built on this basis. The receiver operating characteristic (ROC) curve, area under curve (AUC) and Hosmer-Lemeshow test were used to evaluate the model. Results A total of 218 patients were included in the analysis, including 153 in the model group and 65 in the validation group. In the model group, 90 cases (58.8%) had a good prognosis, and 63 cases (41.2%) had a poor prognosis. Multivariate Logistic regression analysis showed that age ≥50 years (OR=1.356, 95%CI: 1.101-1.639), moderate hydrocephalus (OR=2.859, 95%CI: 2.325-3.212), Glasgow coma score 9-12 points at onset (OR=2.421, 95%CI: 2.056-2.857) and interval from onset to shunt≥3 months (OR=1.639, 95%CI: 1.325-2.124) were independent risk factors affecting prognosis (all P<0.05). According to the above factors, the AUC of clinical prognosis were 0.896 (95%CI: 0.842-0.933) and 0.875 (95%CI: 0.825- 0.916) in the model group and the validation group respectively (P<0.05). Hosmer-Lemeshow test showed that there was a good fitting degree for the line graph model (χ2=0.896 in the model group, χ2=0.567 in the verification group, both P< 0.05). After risk stratification based on the line graph model, the proportion of poor prognosis was significantly higher in high and medium risk patients than that of the low risk patients in the model group and the validation group. Conclusion The model constructed in this study has a high value in evaluating patients with post-traumatic hydrocephalus and severe disturbance of consciousness undergoing ventriculoperitoneal shunt, and which can guide the early clinical identification of high-risk patients.

Key words: craniocerebral trauma, hydrocephalus, consciousness disorders, risk factors, nomograms, ventriculoperitoneal shunt

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