Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes
Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes
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DOI：
10.1089/neu.2019.6705
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发表时间：
2020-03-11
影响因子：
4.2
通讯作者：
Masino, Aaron J.
中科院分区：
文献类型：
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作者：
Folweiler, Kaitlin A.;Sandsmark, Danielle K.;Masino, Aaron J.
创伤性脑损伤（TBI）的异质性仍然是介入性临床试验成功的核心挑战。数据驱动的患者分层方法可能有助于确定急性损伤期TBI患者的表型，并促进有针对性的试验患者登记和治疗效果分析。在这项研究中，我们实施了一种无监督的机器学习方法，使用1213名参加胞胆碱脑损伤治疗试验（COBRIT）试验的TBI患者的数据来识别损伤基线的TBI亚群。利用广义低秩模型的包装器框架自动选择相关的临床特征，随后使用围绕中间点的划分聚类算法对患者进行聚类。使用这种方法，我们根据急性损伤特征的子集确定了三种具有独特临床损伤概况的患者表型。在损伤后3个月和6个月分别观察到长期功能结局轨迹的表型特异性差异。相比之下，当患者按基线格拉斯哥昏迷量表（GCS）分组时，基线临床特征概况或长期结局没有观察到差异。为了测试外部验证数据集中表型的可重复性，我们使用k近邻算法将创伤性脑损伤转化研究和临床知识（TRACK-TBI）试点数据集中的受试者分类为相应的表型，然后测量TRACK-TBI和COBRIT受试者在每种表型上的高尔差异。在两种表型的受试者之间没有发现显着差异，这表明这些表型可能在大范围的TBI严重程度中具有普遍性。此外，TRACK-TBI数据集中的扩展格拉斯哥结果量表（GOS-E）结果同样显示了长期结果的表型特异性差异。我们的研究结果表明，与传统的基于gcs的方法相比，无监督机器学习是一种有前途和有效的方法，可以发现新的损伤亚群，并可能改善未来TBI临床试验中患者的选择。
The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches for patient stratification may help to identify TBI patient phenotypes during the acute injury period as well as facilitate targeted trial patient enrollment and analysis of treatment efficacy. In this study, we implemented an unsupervised machine learning approach to identify TBI subpopulations at injury baseline using data from 1213 TBI patients who participated in the Citicoline Brain Injury Treatment Trial (COBRIT) Trial. A wrapper framework utilizing generalized low-rank models automatically selected relevant clinical features that were subsequently used to cluster patients using a partitioning around medoids clustering algorithm. Using this approach, we identified three patient phenotypes with unique clinical injury profiles based on a subset of acute injury features. Phenotype-specific differences in long-term functional outcome trajectories were respectively observed at 3 and 6 months after injury. In comparison, when patients were grouped by baseline Glasgow Coma Scale (GCS), no differences in baseline clinical feature profiles or long-term outcomes were observed. To test phenotype reproducibility in an external validation data set, we used a K-nearest neighbors algorithm to classify subjects in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot data set into corresponding phenotypes, then measured the Gower's dissimilarities between TRACK-TBI and COBRIT subjects in each phenotype. No significant differences were found between trial subjects within two phenotypes, suggesting that these phenotypes may be generalizable within a broad range of TBI severity. Further, Extended Glasgow Outcome Scale (GOS-E) outcomes in the TRACK-TBI data set similarly demonstrated phenotype-specific differences in long-term outcomes. Our results suggest that unsupervised machine learning is a promising and effective approach for discovery of novel injury subpopulations over the conventional GCS-based method, and may improve patient selection in future TBI clinical trials.