脓毒症分型：精准治疗之基石

姚咏明; 张卉; 董宁

doi:10.3760/cma.j.cn501225-20240529-00203

脓毒症分型：精准治疗之基石

doi: 10.3760/cma.j.cn501225-20240529-00203

解放军总医院医学创新研究部、第四医学中心,北京　　100853

基金项目:

国家自然科学基金重点项目 82130062, 82241062

国家重点研发计划 2022YFA1104604

详细信息

通讯作者:
姚咏明,Email：c_ff@sina.com

计量
- 文章访问数: 85
- HTML全文浏览量: 46
- PDF下载量: 19
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-29

Classification of sepsis: the cornerstone for precise treatment

Medical Innovation Research Division and the Fourth Medical Center of PLA General Hospital, Beijing 100853, China

Funds:

Key Program of National Natural Science Foundation of China 82130062, 82241062

National Key Research and Development Program of China 2022YFA1104604

More Information

Corresponding author: Yao Yongming, Email: c_ff@sina.com

摘要

摘要: 脓毒症是异质性较大的临床综合征。对脓毒症患者进行分型,是提高脓毒症临床处置效率的前提,也是实现脓毒症精准治疗的基础。近年来,国内外许多研究根据脓毒症患者的临床特征、实验室指标、基因组学等数据对患者进行分型。该文简要分析了目前认可度较高的几种脓毒症分型方式,以期为构建脓毒症规范化诊断和治疗体系提供新思路。
- 脓毒症 /
- 内表型 /
- 外表型 /
- 精准治疗
Abstract: Sepsis appears to be a heterogeneous clinical syndrome. The classification of patients with sepsis is the prerequisite for improving the efficiency of clinical management and is also the basis for achieving precise treatment of sepsis. In recent years, many studies at home and abroad have classified patients with sepsis based on data including their clinical characteristics, laboratory biomarkers, and genomics. This article briefly analyzed several sepsis classification methods that are currently well recognized, with a view to providing new ideas for building a standardized diagnosis and treatment system for sepsis.
- Sepsis /
- Endotype /
- Phenotype /
- Precise treatment
所有作者均声明不存在利益冲突

HTML全文

参考文献(19)

[1]	SingerM,DeutschmanCS,SeymourCW,et al.The third international consensus definitions for sepsis and septic shock (sepsis-3)[J].JAMA,2016,315(8):801-810.DOI: 10.1001/jama.2016.0287.
[2]	张卉,冯永文,姚咏明.深刻理解烧创伤脓毒症发病机制的网络效应[J].中华医学杂志,2020,100(12):881-885.DOI: 10.3760/cma.j.cn112137-20191129-02603.
[3]	HasegawaD,NishidaO.Patient selection in sepsis: precision medicine using phenotypes and its implications for future clinical trial design[J].J Thorac Dis,2019,11(9):3672-3675.DOI: 10.21037/jtd.2019.09.31.
[4]	SeymourCW,GomezH,ChangCH,et al.Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness[J].Crit Care,2017,21(1):257.DOI: 10.1186/s13054-017-1836-5.
[5]	SeymourCW,KennedyJN,WangS,et al.Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis[J].JAMA,2019,321(20):2003-2017.DOI: 10.1001/jama.2019.5791.
[6]	ChenX,LiJ,LiuG,et al.Identification of distinct clinical phenotypes of heterogeneous mechanically ventilated ICU patients using cluster analysis[J].J Clin Med,2023,12(4):1499. DOI: 10.3390/jcm12041499.
[7]	水鹏飞,王妍秀,王彬,等.人工智能方式推导脓毒症临床表型及临床结局的研究[J].智慧健康,2023,9(18):218-221.DOI: 10.19335/j.cnki.2096-1219.2023.18.052.
[8]	CalfeeCS,DelucchiK,ParsonsPE,et al.Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials[J].Lancet Respir Med,2014,2(8):611-620.DOI: 10.1016/S2213-2600(14)70097-9.
[9]	SinhaP,DelucchiKL,ThompsonBT,et al.Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study[J].Intensive Care Med,2018,44(11):1859-1869.DOI: 10.1007/s00134-018-5378-3.
[10]	BhavaniSV,CareyKA,GilbertER,et al.Identifying novel sepsis subphenotypes using temperature trajectories[J].Am J Respir Crit Care Med,2019,200(3):327-335.DOI: 10.1164/rccm.201806-1197OC.
[11]	BhavaniSV,WolfeKS,HruschCL,et al.Temperature trajectory subphenotypes correlate with immune responses in patients with sepsis[J].Crit Care Med,2020,48(11):1645-1653.DOI: 10.1097/CCM.0000000000004610.
[12]	ZhangZ,HoKM,GuH,et al.Defining persistent critical illness based on growth trajectories in patients with sepsis[J].Crit Care,2020,24(1):57.DOI: 10.1186/s13054-020-2768-z.
[13]	VolkHD,ReinkeP,KrauschD,et al.Monocyte deactivation--rationale for a new therapeutic strategy in sepsis[J].Intensive Care Med,1996,22Suppl 4:S474-481.DOI: 10.1007/BF01743727.
[14]	DavenportEE,BurnhamKL,RadhakrishnanJ,et al.Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study[J].Lancet Respir Med,2016,4(4):259-271.DOI: 10.1016/S2213-2600(16)00046-1.
[15]	ZhangZ,PanQ,GeH,et al.Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values[J].EBioMedicine,2020,62:103081.DOI: 10.1016/j.ebiom.2020.103081.
[16]	SciclunaBP,van VughtLA,ZwindermanAH,et al.Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study[J].Lancet Respir Med,2017,5(10):816-826.DOI: 10.1016/S2213-2600(17)30294-1.
[17]	van AmstelR,KennedyJN,SciclunaBP,et al.Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes[J].Intensive Care Med,2023,49(11):1360-1369.DOI: 10.1007/s00134-023-07239-w.
[18]	MaP,LiuJ,ShenF,et al.Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen[J].Crit Care,2021,25(1):243.DOI: 10.1186/s13054-021-03682-7.
[19]	BhavaniSV,SemlerM,QianET,et al.Development and validation of novel sepsis subphenotypes using trajectories of vital signs[J].Intensive Care Med,2022,48(11):1582-1592.DOI: 10.1007/s00134-022-06890-z.