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S100B 在轻度外伤性脑损伤后头部 CT 扫描识别颅内损伤方面优于临床决策规则。

S100B outperforms clinical decision rules for the identification of intracranial injury on head CT scan after mild traumatic brain injury.

机构信息

Departments of Emergency Medicine, Orthopaedics, and Public Health Sciences, University of Rochester, School of Medicine and Dentistry, Rochester, New York, USA.

Department of Emergency Medicine, University of Rochester, School of Medicine and Dentistry, Rochester, New York, USA.

出版信息

Brain Inj. 2020 Feb 23;34(3):407-414. doi: 10.1080/02699052.2020.1725123. Epub 2020 Feb 17.

Abstract

: To compare the classification accuracy of S100B to two clinical decision rules- Canadian CT Head Rule (CCHR) and New Orleans Criteria (NOC)-for predicting traumatic intracranial injuries (ICI) after mild traumatic brain injury (mild TBI).: A secondary analysis of a prospective observational study of mild TBI patients was performed. The diagnostic performance of S100B for predicting ICI on head CT was compared to both the CHRR and NOC. Area under receiver operator characteristic (AUC) curves were used and multivariable analysis was used to create a new decision rule based on a combination of S100B and decision rule-related variables.: S100B had the highest negative predictive value (97.3%), positive predictive value (7.21%), specificity (33.6%) and positive likelihood ratio (1.3), and the lowest negative likelihood ratio (0.5). The proportion of mild TBI subjects with potentially avoidable head CT scans was highest using S100B (37.7%). The addition of S100B to both clinical decision rules significantly increased AUC. A novel decision rule adding S100B to three decision rule-related variables significantly improved prediction ( < 0.05).: Serum S100B outperformed clinical decision rules for identifying mild TBI patients with ICI. Incorporating clinical variables with S100B maximized ICI prediction, but requires validation in an independent cohort.

摘要

: 比较 S100B 与两种临床决策规则(加拿大 CT 头规则 [CCHR] 和新奥尔良标准 [NOC])对预测轻度创伤性脑损伤(mild TBI)后创伤性颅内损伤(ICI)的分类准确性。

: 对轻度 TBI 患者的前瞻性观察性研究进行了二次分析。将 S100B 对头 CT 预测 ICI 的诊断性能与 CCHR 和 NOC 进行了比较。使用接收器操作特性(ROC)曲线下面积(AUC),并使用多变量分析基于 S100B 和决策规则相关变量创建新的决策规则。

: S100B 具有最高的阴性预测值(97.3%)、阳性预测值(7.21%)、特异性(33.6%)和阳性似然比(1.3),以及最低的阴性似然比(0.5)。使用 S100B 时,具有潜在可避免头 CT 扫描的轻度 TBI 受试者比例最高(37.7%)。将 S100B 添加到两个临床决策规则中显著增加了 AUC。添加 S100B 到三个决策规则相关变量的新决策规则显著改善了预测(<0.05)。

: 血清 S100B 在识别具有 ICI 的轻度 TBI 患者方面优于临床决策规则。将临床变量与 S100B 结合使用可以最大限度地提高 ICI 预测,但需要在独立队列中进行验证。

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