BACKGROUND The Sport Concussion Assessment Tool (SCAT) could be improved by identifying critical subsets that maximize diagnostic accuracy and eliminate low information elements. OBJECTIVE To identify optimal SCAT subsets for acute concussion assessment. METHODS Using Concussion Assessment, Research, and Education (CARE) Consortium data, we compared student-athletes' and cadets' preinjury baselines (n = 2178) with postinjury assessments within 6 h (n = 1456) and 24 to 48 h (n = 2394) by considering demographics, symptoms, Standard Assessment of Concussion (SAC), and Balance Error Scoring System (BESS) scores. We divided data into training/testing (60%/40%) sets. Using training data, we integrated logistic regression with an engineering methodology-mixed integer programming-to optimize models with ≤4, 8, 12, and 16 variables (Opt-k). We also created models including only raw scores (Opt-RS-k) and symptom, SAC, and BESS composite scores (summary scores). We evaluated models using testing data. RESULTS At <6 h and 24 to 48 h, most Opt-k and Opt-RS-k models included the following symptoms: do not feel right, headache, dizziness, sensitivity to noise, and whether physical or mental activity worsens symptoms. Opt-k models included SAC concentration and delayed recall change scores. Opt-k models had lower Brier scores (BS) and greater area under the curve (AUC) (<6 h: BS = 0.072-0.089, AUC = 0.95-0.96; 24-48 h: BS = 0.085-0.093, AUC = 0.94-0.95) than Opt-RS-k (<6 h: BS = 0.082-0.087, AUC = 0.93-0.95; 24-48 h: BS = 0.095-0.099, AUC = 0.92-0.93) and summary score models (<6 h: BS = 0.14, AUC = 0.89; 24-48 h: BS = 0.15, AUC = 0.87). CONCLUSION We identified SCAT subsets that accurately assess acute concussion and improve administration time over the complete battery, highlighting the importance of eliminating "noisy" elements. These findings can direct clinicians to the SCAT components that are most sensitive to acute concussion.

中文翻译：

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优化用于急性脑震荡评估的运动脑震荡评估工具的组件

背景运动脑震荡评估工具（SCAT）可以通过识别最大化诊断准确性和消除低信息元素的关键子集来改进。目的 确定用于急性脑震荡评估的最佳 SCAT 子集。方法 使用脑震荡评估、研究和教育 (CARE) 联盟的数据，我们将学生运动员和学员的伤前基线 (n = 2178) 与 6 小时 (n = 1456) 和 24 至 48 小时 (n = 2394) 通过考虑人口统计学、症状、脑震荡标准评估 (SAC) 和平衡错误评分系统 (BESS) 分数。我们将数据分为训练/测试 (60%/40%) 集。使用训练数据，我们将逻辑回归与工程方法混合整数规划相结合，以优化具有≤4、8、12 和 16 个变量 (Opt-k) 的模型。我们还创建了仅包含原始分数 (Opt-RS-k) 和症状、SAC 和 BESS 综合分数（汇总分数）的模型。我们使用测试数据评估模型。结果 在 <6 小时和 24 至 48 小时，大多数 Opt-k 和 Opt-RS-k 模型包括以下症状：感觉不对、头痛、头晕、对噪音敏感，以及身体或精神活动是否使症状恶化。Opt-k 模型包括 SAC 浓度和延迟回忆变化分数。Opt-k 模型具有更低的 Brier 分数 (BS) 和更大的曲线下面积 (AUC)（<6 小时：BS = 0.072-0.089，AUC = 0.95-0.96；24-48 小时：BS = 0.085-0.093，AUC = 0.94-0.95）比 Opt-RS-k（<6 小时：BS = 0.082-0.087，AUC = 0.93-0.95；24-48 小时：BS = 0.095-0.099，AUC = 0.92-0.93）和总分模型（< 6 小时：BS = 0.14，AUC = 0.89；24-48 小时：BS = 0.15，AUC = 0.87）。结论我们确定了 SCAT 子集，可以准确评估急性脑震荡并改善整个电池组的给药时间，突出了消除“嘈杂”元素的重要性。这些发现可以将临床医生引导至对急性脑震荡最敏感的 SCAT 组件。