Center for Injury Research and Prevention, Children's Hospital of Philadelphia, PA.
Division of Emergency Medicine, Children's Hospital of Philadelphia, PA.
J Athl Train. 2023 Nov 1;58(11-12):962-973. doi: 10.4085/1062-6050-0020.22.
Multiple clinical evaluation tools exist for adolescent concussion with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility.
To determine the combination of elements from 4 commonly used clinical concussion batteries that maximize discrimination of adolescents with concussion from those without concussion.
Cross-sectional study.
Suburban school and concussion program of a tertiary care academic center.
A total of 231 participants with concussion (from a suburban school and a concussion program) and 166 participants without concussion (from a suburban school) between the ages of 13 and 19 years.
MAIN OUTCOME MEASURE(S): Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, fifth edition (SCAT5; including the modified Balance Error Scoring System), King-Devick test (K-D), and Postconcussion Symptom Inventory (PCSI) were evaluated. The 24 subcomponents of these tests were grouped into interpretable factors using sparse principal component analysis. The 13 resultant factors were combined with demographic and clinical covariates into a logistic regression model and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared with each of the clinical batteries using the area under the receiver operating characteristic curve (AUC).
A cluster of 4 factors (factor 1 [VVE saccades and vestibulo-ocular reflex], factor 2 [modified Balance Error Scoring System double-legged stance], factor 3 [SCAT5/PCSI symptom scores], and factor 4 [K-D completion time]) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC = 0.816 [95% CI = 0.731, 0.889]) compared with the SCAT5 (AUC = 0.784 [95% CI = 0.692, 0.866]), PCSI (AUC = 0.776 [95% CI = 0.674, 0.863]), VVE (AUC = 0.711 [95% CI = 0.602, 0.814]), and K-D (AUC = 0.708 [95% CI = 0.590, 0.819]).
A multifaceted assessment for adolescents with concussion, comprising symptoms, attention, balance, and the visio-vestibular system, is critical. Current diagnostic batteries likely measure overlapping domains, and the sparse principal component analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.
有多种用于青少年脑震荡的临床评估工具,它们之间的相关性各不相同,这给临床医生确定这些工具中的哪些元素具有最大的诊断效用带来了挑战。
确定 4 种常用临床脑震荡电池中元素的组合,最大限度地提高对患有脑震荡和未患有脑震荡的青少年的区分能力。
横断面研究。
郊区学校和三级护理学术中心的脑震荡计划。
年龄在 13 至 19 岁之间的 231 名脑震荡患者(来自郊区学校和脑震荡计划)和 166 名无脑震荡患者(来自郊区学校)。
评估视-前庭检查(VVE)、运动性脑震荡评估工具第五版(SCAT5,包括改良平衡错误评分系统)、King-Devick 测试(K-D)和脑震荡后症状量表(PCSI)的各个元素。使用稀疏主成分分析将这些测试的 24 个子成分组合成可解释的因子。将这 13 个因子与人口统计学和临床协变量相结合,纳入逻辑回归模型,并根据其纳入理想模型的频率进行排序,然后使用受试者工作特征曲线下面积(AUC)比较理想模型与每个临床电池的预测性能。
出现了 4 个因子簇(因子 1 [VVE 眼球运动和前庭眼反射]、因子 2 [改良平衡错误评分系统双腿站立]、因子 3 [SCAT5/PCSI 症状评分]和因子 4 [K-D 完成时间])。具有最佳因子的模型在预测脑震荡状态方面的表现与每个电池一样好(AUC = 0.816[95%CI=0.731,0.889]),与 SCAT5(AUC = 0.784[95%CI=0.692,0.866])、PCSI(AUC = 0.776[95%CI=0.674,0.863])、VVE(AUC = 0.711[95%CI=0.602,0.814])和 K-D(AUC = 0.708[95%CI=0.590,0.819])相比。
对患有脑震荡的青少年进行多方面评估,包括症状、注意力、平衡和视-前庭系统,至关重要。目前的诊断电池可能测量重叠的领域,稀疏主成分分析显示了在各种环境中简化全面脑震荡评估的策略。