Original Investigation Caring for the Critically Ill PatientMay 19, 2019

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

Christopher W. Seymour, Jason N. Kennedy, Shu Wang, et al

JAMA. Published online May 19, 2019. doi:10.1001/jama.2019.5791

Abstract 摘要

Importance 背景

Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care.

脓毒症是一种异质性综合征。确定不同的临床表型可能有助于治疗更加精准及疗效改善。

Objective 目的

To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs).

通过临床数据确定脓毒症表型，研究其可重复性以及与宿主反应生物标志物和临床预后的相关性，根据RCT结果评价可能的因果关系。

Design, Settings, and Participants 试验设计、场景及研究对象

Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737).

采用统计、机器学习及模拟工具回顾性分析数据集。根据宾夕法尼亚州12所医院(2010-2012)收治的满足sepsis-3诊断标准的20189例次住院（16552名患者）数据，针对29个参数采用一致性k均值聚类确定表型。采用第二个数据库（2013-2014; n = 43 086总住院例次， n = 31 160名患者）以及一个肺炎引起脓毒症的前瞻队列(n = 583)和3个脓毒症RCT数据(n = 4737)评价可重复性以及同生物学指标及临床预后的相关性。

Exposures 暴露因素

All clinical and laboratory variables in the electronic health record.

电子病历记录中所有临床及实验室数据。

Main Outcomes and Measures 主要预后指标

Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs.

确定表型(α, β, γ 和 δ)频率，宿主反应生物标志物，28天及365天病死率，以及RCT模拟结果等。

Results 结果

The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm).

推导队列包括20189名脓毒症患者（平均年龄 64 [SD, 17] 岁；10 022名 [50%]男性；24小时最高SOFA评分均值，3.9 [SD, 2.4]）。验证队列包括43086名患者（平均年龄 67 [SD, 17] 岁；21 993名 [51%]男性；24小时最高SOFA评分均值，3.6 [SD, 2.0]）。在确定的4种表型中，α 表型最为常见(n = 6625; 33%)，使用升压药物剂量最小；β 表型 (n = 5512; 27%) 患者年龄较大，有很多的慢性病及肾功能不全；γ 表型 (n = 5385; 27%) 患者炎症反应和呼吸功能不全更多；δ 表型 (n = 2667; 13%) 患者更多发生肝脏功能不全及感染性休克。验证队列中表型分布相似。不同表型患者生物标志物的差异一致。推导队列累积28天病死率，α 表型为287/5691 (5%)；β 表型为561/4420 (13%)；γ表型为1031/4318 (24%)；δ表型为897/2223 (40%)。在所有队列及临床试验中，δ 表型患者28天及365天病死率最高(P < .001)。在模拟模型中，报告获益、有害或无效的RCT比例变化显著（例如，早期目标指导治疗的一项RCT中，改变表型频率能够显著改变试验结果，从获益概率> 33%到有害概率> 60%）。

Conclusions and Relevance 结论与意义

In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

在这项对于脓毒症患者数据集的回顾性分析中，我们发现了与宿主反应种类及临床预后相关的4种临床表型，模拟分析提示，这些表型可能有助于了解治疗效果的异质性。需要进一步研究确定这些表型在临床工作中的用途，以及对临床试验设计及结果解读的意义。