Department of Infectious Diseases, Karolinska Hospital, S-14186 Stockholm, Sweden.
Center for the study of Aging and Human Development, Department of Biostatistics and Bioinformatics, Northern Arizona University, PO Box 15106, Flagstaff, AZ 86011, United States.
J Crit Care. 2018 Oct;47:70-79. doi: 10.1016/j.jcrc.2018.06.012. Epub 2018 Jun 8.
Septic shock is a highly heterogeneous condition which is part of the challenge in its diagnosis and treatment. In this study we aim to identify clinically relevant subphenotypes of septic shock using a novel statistic al approach.
Baseline patient data from a large global clinical trial of septic shock (n = 1696) was analysed using latent class analysis (LCA). This approach allowed investigators to identify subgroups in a heterogeneous population by estimating a categorical latent variable that detects relatively homogeneous subgroups within a complex phenomenon.
LCA identified six different, clinically meaningful subphenotypes of septic shock each with a typical profile: (1) "Uncomplicated Septic Shock, (2) "Pneumonia with adult respiratory distress syndrome (ARDS)", (3) "Postoperative Abdominal", (4) "Severe Septic Shock", (5): "Pneumonia with ARDS and multiple organ dysfunction syndrome (MODS)", (6) "Late Septic Shock". The 6-class solution showed high entropy approaching 1 (i.e., 0.92), indicating there was excellent separation between estimated classes.
LCA appears to be an applicable statistical tool in analysing a heterogenous clinical cohort of septic shock. The results may lead to a better understanding of septic shock complexity and form a basis for considering targeted therapies and selecting patients for future clinical trials.
感染性休克是一种高度异质的病症,这也是其诊断和治疗面临的挑战之一。本研究旨在采用一种新的统计学方法来确定感染性休克的临床相关亚表型。
对一项大型全球感染性休克临床试验(n=1696)的基线患者数据采用潜在类别分析(LCA)进行分析。这种方法允许研究人员通过估计一个分类潜在变量来识别异质人群中的亚组,该潜在变量可以检测到复杂现象中相对同质的亚组。
LCA 确定了六种不同的、具有临床意义的感染性休克亚表型,每个亚表型都有典型的特征:(1)“单纯性感染性休克”;(2)“肺炎合并成人呼吸窘迫综合征(ARDS)”;(3)“术后腹部”;(4)“严重感染性休克”;(5)“肺炎合并 ARDS 和多器官功能障碍综合征(MODS)”;(6)“晚期感染性休克”。6 类解决方案显示出接近 1 的高熵(即 0.92),表明估计类之间有很好的分离。
LCA 似乎是一种适用于分析感染性休克异质临床队列的统计学工具。研究结果可能有助于更好地了解感染性休克的复杂性,并为考虑靶向治疗和为未来临床试验选择患者奠定基础。