发表机构
Leiden University Medical Center; UMC Utrecht; Antwerp University Hospital; University of Antwerp（莱顿大学医学中心; 乌得勒支大学医学中心; 安特卫普大学医院; 安特卫普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多宇宙分析揭示，CENTER-TBI 队列中无监督聚类的不同算法、距离和聚类数选择导致结果高度不稳定，且判别性能不如监督模型，提示聚类结果不应被盲目解释为潜在结构。
AI 中文摘要
理解患者异质性是改善创伤性脑损伤（TBI）预后建模的关键。无监督聚类被广泛用于探索可能定义亚组的患者特征模式。然而，聚类涉及众多决策，包括算法选择、距离度量以及确定“最优”聚类数的方法。本研究旨在探讨这些选择如何影响最终的聚类结果。我们分析了来自欧洲 TBI 协作研究（CENTER-TBI）的 4,509 名患者数据。采用 K-medoids、凝聚聚类和谱聚类，在完整的 3 X 2 X 2 因子设计中，结合欧几里得距离或 Gower 距离，并使用轮廓系数或间隙统计量来选择聚类数。我们使用 UpSet 图考察聚类方案的一致性，并使用（调整后）兰德指数考察稳定性。比较既在原始数据集上跨方法进行，也在方法内通过自助重采样进行。聚类结果因分析选择的不同而差异显著。建议的聚类数从 1 到 25 不等。调整后兰德指数证实了方法间的一致性较低。此外，在分类患者恢复方面，没有任何聚类方案表现出可与监督逻辑回归模型相媲美的判别性能，这说明了聚类在此目的上的有限实用性。聚类结果的高度不稳定性损害了可解释性，并强调此类方案不应被盲目解释为潜在结构。
英文摘要
Understanding patient heterogeneity is key to improving prognostic modeling in traumatic brain injury (TBI). Unsupervised clustering is widely used to explore patterns in patient characteristics that may define subgroups. However, it involves a multitude of decisions, including the choice of algorithm, the distance metric, and the method used to determine the "optimal" number of clusters. The aim of this study is to investigate how these choices influence the resulting clustering solution. We analyzed data from 4,509 patients enrolled in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study. K-medoids, agglomerative, and spectral clustering were applied in a complete 3 X 2 X 2 factorial design, in combination with Euclidean or Gower's distances, and silhouette score or gap statistic to choose the number of clusters. We investigated the agreement of clustering solutions with UpSet Plots and stability with the (adjusted) Rand index. Comparisons were made both across approaches using the original dataset and within approaches using bootstrap resampling. Clustering results varied substantially depending on the analysis choices. The number of suggested clusters varied widely, from one to twenty-five. Adjusted Rand indices confirmed low concordance between methods. Moreover, none of the clustering solutions demonstrated discriminatory performance comparable to a supervised logistic regression model in classifying patient recovery illustrating the limited usefulness of clustering for this purpose. The high instability in clustering results compromises interpretability and underscores that such solutions should not be blindly interpreted as underlying structure.
