IEEE/ACM Trans Comput Biol Bioinform. 2022 May-Jun;19(3):1365-1378. doi: 10.1109/TCBB.2021.3091972. Epub 2022 Jun 3.
Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.
脑震荡，也称为轻度创伤性脑损伤（mTBI），是一个日益严重的健康挑战。在美国，每年大约有 400 万人被诊断出患有脑震荡。脑震荡在病因、症状和结果上存在异质性，这使得精准医学方法在这种疾病上显得很重要。在一小部分 mTBI 患者中，持续存在的致残症状有时会延迟康复，而这些患者的康复情况难以预测。尽管有大量数据，但临床医生仍需要更好的工具来评估和预测康复情况。由于数据驱动的决策支持能够识别复杂数据集隐藏的相关性，因此它为 mTBI 的准确临床预测工具提供了希望。我们应用了一种局部敏感哈希模型，并通过各种统计方法对其进行了增强，以对在多个时间点采集的血液生物标志物水平轨迹进行聚类。使用自动编码器提取来自人口统计学、损伤背景、神经认知评估和姿势稳定性评估的附加特征，以增强模型。该数据来自于 FITBIR，包含 301 名脑震荡患者（运动员和学员）。聚类识别出了 11 种不同的生物标志物轨迹。其中两种轨迹（GFAP 升高和 NF-L 升高）与发病时意识丧失或创伤后遗忘的风险增加有关。聚类血液生物标志物轨迹的能力增强了对 mTBI 进行精准医学方法的可能性。
