脓毒症筛查技术进展

doi:10.16150/j.1671-2870.2025.06.001

摘要/Abstract

摘要：

脓毒症是由感染引起的全身失控性炎症反应，进而导致危及生命的器官功能障碍综合征。早期识别和精准干预对改善脓毒症患者预后至关重要。近年来，有关脓毒症的病原学鉴定、宿主反应评估与智能化辅助诊断等技术取得了显著进展，提升了脓毒症的诊治能力。本文对病原学鉴定[如阳性血培养快速质谱鉴定、宏基因组二代测序（metagenomic next-generation sequencing，mNGS）、微滴式数字聚合酶链反应（droplet digital polymerase chain reaction，ddPCR）及T2磁共振等技术进展]、宿主反应监测[如降钙素原（procalcitonin，PCT）、白细胞介素6（interleukin-6，IL-6）、人类白细胞DR抗原（monocyte human leucocyte antigen-DR，mHLA-DR）及新型非编码RNA等]在感染识别与免疫监测中的应用、多组学（转录组、蛋白质组及代谢组）与人工智能（artificial intelligence，AI）技术在脓毒症早期预警、分型与个体化治疗中的作用方面进行了系统介绍，同时也列举了当前脓毒症筛查技术存在的问题，并对未来跨学科合作推动精准筛查与临床转化提出展望。

关键词: 脓毒症, 筛查技术, 高通量测序, 多组学, 人工智能

Abstract:

Sepsis is a life-threatening syndrome of organ dysfunction caused by a dysregulated systemic inflammatory response to infection. Early recognition and precise intervention are crucial for improving the prognosis of sepsis patients. In recent years, significant progress has been made in technologies related to sepsis, such as etiological identification, host-response assessment, and intelligent-assisted diagnosis, enhancing the diagnosis and treatment capability of sepsis. This review provides a systematic overview of advances in etiological identification [such as rapid mass spectrometry identification of positive blood culture, metagenomic next-generation sequencing (mNGS), droplet digital polymerase chain reaction (ddPCR), and T2 magnetic resonance], the application of host-response monitoring markers [such as procalcitonin (PCT), interleukin-6 (IL-6), monocyte human leukocyte antigen-DR (mHLA-DR), and emerging non-coding RNAs] in infection recognition and immune monitoring, and the roles of multi-omics (transcriptomics, proteomics, and metabolomics) and artificial intelligence (AI) technologies in early warning, endotyping, and individualized therapy of sepsis. Additionally, this review highlights the current limitations of sepsis screening technologies, and outlines future directions for cross-disciplinary collaboration to promote precise screening and clinical translation.

Key words: Sepsis, Screening technologies, High-throughput sequencing, Multi-omics, Artificial intelligence

中图分类号:

R515.3
R446

刘淦, 戴媛媛, 常文娇, 马筱玲. 脓毒症筛查技术进展[J]. 诊断学理论与实践, 2025, 24(06): 567-575.

LIU Gan, DAI Yuanyuan, CHANG Wenjiao, MA Xiaoling. Advances in sepsis screening technologies[J]. Journal of Diagnostics Concepts & Practice, 2025, 24(06): 567-575.

参考文献 50

[1]	GIAMARELLOS-BOURBOULIS E J, EUROPEAN SEPSIS A, ZINKERNAGEL A S, et al. Sepsis, a call for inclusion in the work plan of the European Center for Di-sease Prevention and Control[J]. Intensive Care Med, 2023, 49(9): 1138-1142. doi: 10.1007/s00134-023-07127-3
[2]	RUDD K E, JOHNSON S C, AGESA K M, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study[J]. Lancet, 2020, 395(10219):200-211. doi: S0140-6736(19)32989-7 pmid: 31954465
[3]	MORRISSEY R, LEE J, BARAL N, et al. Demographic and regional trends of sepsis mortality in the United States, 1999-2022[J]. BMC Infect Dis, 2025, 25(1):504. doi: 10.1186/s12879-025-10921-7
[4]	WENG L, XU Y, YIN P, et al. National incidence and mortality of hospitalized sepsis in China[J]. Crit Care, 2023, 27(1):84. doi: 10.1186/s13054-023-04385-x
[5]	DONG R, LIU W, WENG L, et al. Temporal trends of sepsis-related mortality in China, 2006-2020: a population-based study[J]. Ann Intensive Care, 2023, 13(1):71. doi: 10.1186/s13613-023-01166-1 pmid: 37578609
[6]	付晶, 张瑞鹏, 许美馨, 等. 中国西南地区某大型三甲综合医院脓毒症住院患者的流行病学分析[J]. 中华危重病急救医学, 2024, 36(6):574-577.
	FU J, ZHANG R P, XU M X, et al. Epidemiological analysis of in patients with sepsis in a large tertiary general hospital in Southwest China[J]. Chin Crit Care Med, 2024, 36(6):574-577.
[7]	MILLER J M, BINNICKER M J, CAMPBELL S, et al. Guide to utilization of the microbiology laboratory for diagnosis of infectious diseases: 2024 update by the Infectious Diseases Society of America (IDSA) and the American Society for Microbiology (ASM)[J]. Clin Infect Dis, 2024.
[8]	RYDZAK T, GROVES R A, ZHANG R, et al. Metabolic preference assay for rapid diagnosis of bloodstream infections[J]. Nat Commun, 2022, 13(1):2332. doi: 10.1038/s41467-022-30048-6 pmid: 35484129
[9]	PHILIPPON A L, LEBAL S, CANCELLA DE ABREU M, et al. Association between time to antibiotic and mortality in patients with suspected sepsis in the emergency department: post hoc analysis of the 1-BED randomized clinical trial[J]. Eur J Emerg Med, 2025, 32(2):109-115. doi: 10.1097/MEJ.0000000000001212 URL
[10]	LI Z, LIU S, CHEN H, et al. Comparative evaluation of BACTEC FX, BacT/ALERT 3D, and BacT/ALERT VIRTUO automated blood culture systems using simulated blood cultures[J]. Acta Clin Belg, 2022, 77(1):71-78. doi: 10.1080/17843286.2020.1797343 URL
[11]	FITTS E C, DENT E A, BURD E M. Validation of short culture method for rapid bacterial identification of blood cultures via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry[J]. J Antimicrob Chemother, 2024, 79(12Suppl 2):i9-i12.
[12]	TSENG H Y, CHEN C L, CHEN W C, et al. Reduced mortality with antimicrobial stewardship guided by BioFire FilmArray Blood Culture Identification 2 panel in critically ill patients with bloodstream infection: A retrospective propensity score-matched study[J]. Int J Antimicrob Agents, 2024, 64(4):107300. doi: 10.1016/j.ijantimicag.2024.107300 URL
[13]	MAO J Y, LI D K, ZHANG D, et al. Utility of paired plasma and drainage fluid mNGS in diagnosing acute intra-abdominal infections with sepsis. BMC Infect Dis. 2024; 24(1):409. doi: 10.1186/s12879-024-09320-1
[14]	PAN T J, LUO W W, ZHANG S S, et al. The clinical application value of multi-site mNGS detection of patients with sepsis in intensive care units[J]. BMC Infect Dis, 2024, 24(1):920. doi: 10.1186/s12879-024-09822-y
[15]	XU F, CHEN C, LU S, et al. Impact of metagenomics next-generation sequencing on etiological diagnosis and early outcomes in sepsis[J]. J Transl Med, 2025, 23(1):394. doi: 10.1186/s12967-025-06332-6 pmid: 40181443
[16]	PENG J, BAI H, LI Y, et al. ddPCR enhances early diagnosis, treatment, prognosis, and pathogen verification in elderly BSI[J]. Front Cell Infect Microbiol, 2025, 15:1605795. doi: 10.3389/fcimb.2025.1605795 URL
[17]	JIANG S, ZHAO D, WANG C, et al. Clinical evaluation of droplet digital PCR in the early identification of suspected sepsis patients in the emergency department: a prospective observational study[J]. Front Cell Infect Microbiol, 2024, 14:1358801. doi: 10.3389/fcimb.2024.1358801 URL
[18]	LIN K, ZHAO Y, XU B, et al. Clinical diagnostic performance of droplet digital PCR for suspected bloodstream infections[J]. Microbiol Spectr, 2023, 11(1):e0137822.
[19]	KITAGAWA H, KOJIMA M, TADERA K, et al. Clinical diagnostic performance of droplet digital PCR for pathogen detection in patients with Escherichia coli bloodstream infection: a prospective observational study[J]. BMC Infect Dis, 2025, 25(1):22. doi: 10.1186/s12879-024-10396-y pmid: 39757158
[20]	MYLONAKIS E, CLANCY C J, OSTROSKY-ZEICHNER L, et al. T2 magnetic resonance assay for the rapid diagnosis of candidemia in whole blood: a clinical trial[J]. Clin Infect Dis, 2015, 60(6):892-899. doi: 10.1093/cid/ciu959 pmid: 25586686
[21]	BONURA C, GRACEFFA D, DISTEFANO S, et al. Evaluation of T 2 magnetic resonance (T2MR®) techno-logy for the early detection of ESKAPEc pathogens in septic patients[J]. Antibiotics (Basel), 2024, 13(9):885.
[22]	BOLANAKI M, WINNING J, SLAGMAN A, et al. Biomarkers improve diagnostics of sepsis in adult patients with suspected organ dysfunction based on the quick Sepsis-Related Organ Failure Assessment (qSOFA) score in the emergency department[J]. Crit Care Med, 2024, 52(6):887-899. doi: 10.1097/CCM.0000000000006216 pmid: 38502804
[23]	CHRISTENSEN E E, BINDE C, LEEGAARD M, et al. Diagnostic accuracy and added value of infection biomarkers in patients with possible sepsis in the emergency department[J]. Shock, 2022, 58(4):251-259. doi: 10.1097/SHK.0000000000001981 pmid: 36130401
[24]	MONNERET G, LAFON T, GOSSEZ M, et al. Monocyte HLA-DR expression in septic shock patients: insights from a 20-year real-world cohort of 1023 cases[J]. Intensive Care Med, 2025, 51(10):1820-1832. doi: 10.1007/s00134-025-08110-w
[25]	YANG L, GAO Q, LI Q, et al. PD-L1 blockade improves survival in sepsis by reversing monocyte dysfunction and immune disorder[J]. Inflammation, 2024, 47(1):114-128. doi: 10.1007/s10753-023-01897-0
[26]	ANDERSSON M, FRÖDERBERG SCHOONER K, KARLSSON WERTHER V, et al. Prehospital lactate analysis in suspected sepsis improves detection of patients with increased mortality risk: an observational study[J]. Crit Care, 2025, 29(1):38. doi: 10.1186/s13054-024-05225-2
[27]	WANG Y, GAO S, HONG L, et al. Prognostic impact of blood urea nitrogen to albumin ratio on patients with sepsis: a retrospective cohort study[J]. Sci Rep, 2023, 13(1): 10013. doi: 10.1038/s41598-023-37127-8 pmid: 37340147
[28]	GARCÍA-CONCEJO A, SÁNCHEZ-QUIRÓS B, GÓMEZ-SÁNCHEZ E, et al. Study on the diagnostic role of exosome-derived miRNAs in postoperative septic shock and non-septic shock patients[J]. Crit Care, 2025, 29(1):96. doi: 10.1186/s13054-025-05320-y
[29]	XU L, LI J, LI L, et al. LncRNA CYP1B1-AS1 as a clinical biomarker exacerbates sepsis inflammatory response via targeting miR- 18a- 5p[J]. BMC Immunol, 2025, 26(1):32. doi: 10.1186/s12865-025-00712-9 pmid: 40234801
[30]	SEYMOUR C W, LIU V X, IWASHYNA T J, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3)[J]. JAMA, 2016, 315(8):762-774. doi: 10.1001/jama.2016.0288 pmid: 26903335
[31]	PLEČKO D, BENNETT N, UKOR I F, et al. A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis[J]. Crit Care Resusc, 2025, 27(1):100105.
[32]	MORENO R, RHODES A, RANZANI O, et al. Rationale and methodological approach underlying the development of the Sequential Organ Failure Assessment (SOFA)-2 score: A consensus statement[J]. JAMA Netw Open, 2025, 8(10):e2545040.
[33]	RANZANI O T, SINGER M, SALLUH J I F, et al. Deve-lopment and validation of the Sequential Organ Failure Assessment (SOFA)-2 score[J/OL]. JAMA,2025-08-29. https://pubmed.ncbi.nlm.nih.gov/41159833/.
[34]	CHENOWETH J G, COLANTUONI C, STRIEGEL D A, et al. Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes[J]. Nat Commun, 2024, 15(1):4606. doi: 10.1038/s41467-024-48821-0 pmid: 38816375
[35]	KALANTAR K L, NEYTON L, ABDELGHANY M, et al. Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults[J]. Nat Microbiol, 2022, 7(11):1805-1816. doi: 10.1038/s41564-022-01237-2 pmid: 36266337
[36]	SCHLAPBACH L J, GANESAMOORTHY D, WILSON C, et al. Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study[J]. Lancet Child Adolesc Health, 2024, 8(5):325-338. doi: 10.1016/S2352-4642(24)00017-8 pmid: 38513681
[37]	BRANDES-LEIBOVITZ R, RIZA A, YANKOVITZ G, et al. Sepsis pathogenesis and outcome are shaped by the balance between the transcriptional states of systemic inflammation and antimicrobial response[J]. Cell Rep Med, 2024, 5(11):101829.
[38]	PALMOWSKI L, WEBER M, BAYER M, et al. Mortality-associated plasma proteome dynamics in a prospective multicentre sepsis cohort[J]. EBioMedicine, 2025, 111:105508. doi: 10.1016/j.ebiom.2024.105508 URL
[39]	BRACHT T, KAPPLER K, BAYER M, et al. Plasma proteomics identifies molecular subtypes in sepsis[J]. Crit Care, 2025, 29(1):392. doi: 10.1186/s13054-025-05639-6
[40]	HUANG P, LIU Y, LI Y, et al. Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury[J]. BMC Med, 2025, 23(1):79. doi: 10.1186/s12916-025-03920-7 pmid: 39934788
[41]	MAYERS J R, VARON J, ZHOU R R, et al. A metabolomics pipeline highlights microbial metabolism in bloodstream infections[J]. Cell, 2024, 187(15):4095-4112.e21. doi: 10.1016/j.cell.2024.05.035 pmid: 38885650
[42]	EVANS L, RHODES A, ALHAZZANI W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021[J]. Crit Care Med, 2021, 49(11):e1063-e1143.
[43]	GOH K H, WANG L, YEOW A Y K, et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare[J]. Nat Commun, 2021, 12(1):711. doi: 10.1038/s41467-021-20910-4 pmid: 33514699
[44]	SHASHIKUMAR S P, MOHAMMADI S, KRISHNAMOORTHY R, et al. Development and prospective implementation of a large language model based system for early sepsis prediction[J]. NPJ Digit Med, 2025, 8(1):290. doi: 10.1038/s41746-025-01689-w pmid: 40379845
[45]	MALIC L, ZHANG P G Y, PLANT P J, et al. A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis[J]. Nat Commun, 2025, 16(1):4442. doi: 10.1038/s41467-025-59227-x
[46]	BHARGAVA A, LÓPEZ-ESPINA C, SCHMALZ L, et al. FDA-authorized AI/ML tool for sepsis prediction: Deve-lopment and validation[J]. NEJM AI, 2024,1,AIoa2400867.
[47]	DIWAN S, GANDHI V, BAIDYA KAYAL E, et al. Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries[J]. Intensive Care Med Exp, 2025, 13(1):56. doi: 10.1186/s40635-025-00765-5 pmid: 40459817
[48]	BRAKENRIDGE S C, CHEN U I, LOFTUS T, et al. Evaluation of a multivalent transcriptomic metric for diagnosing surgical sepsis and estimating mortality among critically ill patients[J]. JAMA Netw Open, 2022, 5(7):e2221520.
[49]	WANG S, LIU X, YUAN S, et al. Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study[J]. NPJ Digit Med, 2025, 8(1):228. doi: 10.1038/s41746-025-01643-w pmid: 40295871
[50]	DE BACKER D, DEUTSCHMAN C S, HELLMAN J, et al. Surviving sepsis campaign research priorities 2023[J]. Crit Care Med, 2024, 52(2):268-296. doi: 10.1097/CCM.0000000000006135 pmid: 38240508