Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Recently, researchers have hypothesized that sepsis consists of a heterogeneous spectrum of distinct subtypes, motivating several studies to identify clusters of sepsis patients that correspond to subtypes, with the long-term goal of using these clusters to design subtype-specific treatments. Therefore, clinicians rely on clusters having a concrete medical interpretation, usually corresponding to clinically meaningful regions of the sample space that have a concrete implication to practitioners. In this article, we propose Clustering Around Meaningful Regions (CLAMR), a Bayesian clustering approach that explicitly models the medical interpretation of each cluster center. CLAMR favors clusterings that can be summarized via meaningful feature values, leading to medically significant sepsis patient clusters. We also provide details on measuring the effect of each feature on the clustering using Bayesian hypothesis tests, so one can assess what features are relevant for cluster interpretation. Our focus is on clustering sepsis patients from Moshi, Tanzania, where patients are younger and the prevalence of HIV infection is higher than in previous sepsis subtyping cohorts.

翻译：脓毒症是一种由宿主对感染的失调反应引起的危及生命的疾病。近年来，研究人员假设脓毒症由异质性谱系的不同亚型组成，这促使多项研究识别与亚型相对应的脓毒症患者聚类，其长期目标是利用这些聚类设计针对特定亚型的治疗方案。因此，临床医生依赖具有具体医学解释的聚类，这些聚类通常对应样本空间中具有临床意义的区域，对临床实践具有具体指导意义。本文提出"基于有意义区域的聚类方法"（Clustering Around Meaningful Regions, CLAMR），这是一种贝叶斯聚类方法，显式建模每个聚类中心的医学解释。CLAMR倾向于能够通过有意义的特征值进行总结的聚类结果，从而产生具有医学重要性的脓毒症患者聚类。我们还提供了使用贝叶斯假设检验测量每个特征对聚类影响的细节，以便评估哪些特征与聚类解释相关。本研究聚焦于坦桑尼亚莫希地区的脓毒症患者聚类，该地区患者比以往脓毒症亚型队列更年轻，且HIV感染率更高。