Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
J R Soc Interface. 2021 Jun;18(179):20210260. doi: 10.1098/rsif.2021.0260. Epub 2021 Jun 2.
Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.
多重脑损伤标准（BIC）是为了快速量化头部撞击后脑损伤的风险而制定的。这些源于不同头部撞击类型（如运动和车祸）的 BIC 广泛应用于风险评估中。然而，在跨头部撞击类型的脑损伤风险评估中使用 BIC 的准确性尚未得到评估。从生理学角度来看，脑应变通常被认为是脑损伤的关键参数。为了评估 BIC 在五个数据集（包括不同的头部撞击类型）上的风险估计准确性，使用线性回归模型来模拟 95%最大主应变、胼胝体处的 95%最大主应变和 18 个 BIC 的累积应变损伤（15%）。结果表明，BIC 和脑应变之间的关系在不同数据集中存在显著差异，这表明相同的 BIC 值在不同的头部撞击类型中可能表示不同的脑应变。如果将 BIC 回归模型拟合到与 BIC 开发的头部撞击类型不同的数据集上，而不是在相同类型的数据集上，脑应变回归的准确性通常会降低。有鉴于此，本研究对将 BIC 应用于估计与 BIC 开发的头部撞击不同的头部撞击的脑损伤风险提出了关注。
