The aim of this study was to analyze the best combination of clinical variables associated with concussion subtypes using a multidomain assessment comprising medical history; symptoms; and cognitive, ocular, and vestibular impairment in a cohort of patients presenting to a concussion specialty clinic.

Adolescent patients (n = 293) completed demographics and medical history, Concussion Clinical Profiles Screening, Immediate Post-Concussion Assessment and Cognitive Testing, and vestibular ocular motor screening at their first visit (mean 7.6 ± 7.8 days postinjury) to a concussion specialty clinic. Each participant was adjudicated to have one or more subtype (anxiety/mood, cognitive, migraine, ocular, and vestibular) by a healthcare professional based on previously published criteria. A series of backward, stepwise logistic regressions were used to identify significant predictors of concussion subtypes, and predictive probabilities from the logistic regression models were entered into area under the receiver operating characteristic curve (AUC) models.

Each of 5 logistic regression models predicting primary subtypes accounted for 28%–50% of the variance (R² = 0.28–0.50, p < 0.001) and included 2–8 significant predictors per model. Each of the models significantly differentiated the primary subtype from all other subtypes (AUC = 0.76–0.94, p < 0.001).

These findings suggest that each concussion subtype can be identified using specific outcomes from a multidomain assessment. Clinicians can employ such an approach to better identify and monitor recovery from subtypes as well as guide interventions.

本研究的目的是使用包括病史的多领域评估来分析与脑震荡亚型相关的临床变量的最佳组合；症状; 和脑震荡专科诊所就诊的一组患者的认知、眼部和前庭功能障碍。

青少年患者 (n = 293) 在第一次到脑震荡专科诊所就诊时（平均 7.6 ± 7.8 天）完成了人口统计学和病史、脑震荡临床概况筛查、脑震荡后评估和认知测试以及前庭眼球运动筛查。根据先前公布的标准，医疗保健专业人员将每位参与者判定为具有一种或多种亚型（焦虑/情绪、认知、偏头痛、眼部和前庭）。一系列向后的逐步逻辑回归用于识别脑震荡亚型的重要预测因子，并将逻辑回归模型的预测概率输入接受者操作特征曲线 (AUC) 模型下的区域。

预测主要亚型的 5 个逻辑回归模型中的每一个都占方差的 28%–50%（R ² = 0.28–0.50，p < 0.001），每个模型包括 2–8 个显着预测因子。每个模型都将主要亚型与所有其他亚型显着区分开来（AUC = 0.76-0.94，p < 0.001）。

这些发现表明，可以使用多领域评估的特定结果来识别每种脑震荡亚型。临床医生可以采用这种方法来更好地识别和监测亚型的恢复情况以及指导干预措施。