Analysis of the characteristics of infectious pathogens in burn patients with sepsis based on metagenomic next-generation sequencing technology

Shi Jijing; Zhao Liang; Li Xiaoliang; Zhang Qun; Xia Chengde; Ma Chao

doi:10.3760/cma.j.cn501225-20240418-00137

Volume 40 Issue 10

Oct. 2024

Turn off MathJax

Article Contents

Article Navigation > CHINESE JOURNAL OF BURNS AND WOUNDS > 2024 > 40(10): 940-947

Shi JJ,Zhao L,Li XL,et al.Analysis of the characteristics of infectious pathogens in burn patients with sepsis based on metagenomic next-generation sequencing technology[J].Chin J Burns Wounds,2024,40(10):940-947.DOI: 10.3760/cma.j.cn501225-20240418-00137.

Citation:

PDF( 996 KB)

Analysis of the characteristics of infectious pathogens in burn patients with sepsis based on metagenomic next-generation sequencing technology

doi: 10.3760/cma.j.cn501225-20240418-00137

1.
Central Laboratory, the First People's Hospital of Zhengzhou, Zhengzhou 450004, China
2.
Department of Burns, the First People's Hospital of Zhengzhou, Zhengzhou 450004, China
3.
Department of Plastic Surgery, the First People's Hospital of Zhengzhou, Zhengzhou 450004, China

Funds:

Young and Middle-Aged Health Science and Technology Innovation Talent Project of Henan Province of China YXKC2020060

Medical science and technology research project of Henan Province of China LHGJ20230754

More Information

Corresponding author: Ma Chao, Email: Mark612163@outlook.com
Received Date: 2024-04-18

Abstract

Abstract

Objective To analyze the characteristics of infectious pathogens in burn patients with sepsis based on metagenomic next-generation sequencing (mNGS) technology. Methods This study was a retrospective observational study. From July 2021 to December 2023, 109 burn patients with sepsis who met the inclusion criteria were admitted to the Department of Burns of the First People's Hospital of Zhengzhou, including 68 males aged 57 to 92 years and 41 females aged 48 to 83 years. Blood, bronchoalveolar lavage fluid, cerebrospinal fluid, sputum, or other fluid specimens were collected from the patients during their hospital stay for microbiological culture (86 patients) and mNGS technology detection (109 patients). The types of specimens and pathogens detected by mNGS technology were counted. Patients were divided into intensive care unit (ICU) group (78 cases) who were admitted to the ICU and non-ICU group (31 cases) who were not admitted to the ICU, and the pathogens for infection in the two groups of patients were analyzed. In addition, the detection of pathogens in the specimens of 86 patients who underwent both mNGS technology detection and microbiological culture detection was analyzed. Results Among the 109 specimens detected by mNGS technology, there were 42 blood specimens, 17 bronchoalveolar lavage fluid specimens, 4 sputum specimens, 6 cerebrospinal fluid specimens, 16 pus specimens, and 24 tissue fluid specimens; a total of 39 pathogens were detected, including 13 bacteria, 12 fungi, 10 viruses, 2 parasites, and 2 mycoplasmas. The overall positive rate of pathogen detection was 88.99% (97/109). Ranked by the detection rate, the top three Gram-negative bacteria were Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas spp, the top three Gram-positive bacteria were Streptococcus pneumoniae, Staphylococcus aureus, and Enterococcus faecalis; the top three viruses were human herpesvirus, cytomegalovirus, and circovirus; the top three fungi were Aspergillus fumigatus, Candida albicans, and Aspergillus flavus. Twenty-seven patients were infected with one pathogen, 45 patients with two pathogens, and 25 patients with three or more pathogens. Compared with those in non-ICU group, the proportions of Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas spp, Streptococcus pneumoniae, Aspergillus fumigatus, and cytomegalovirus detected in the patients in ICU group were significantly higher (with χ² values of 8.62, 7.93, 3.93, 5.48, 4.28, and 5.58, respectively, P<0.05). In the pathogens detected by mNGS technology and microbiological culture method, the most common bacteria were Klebsiellapneumoniae and Acinetobacter baumannii, and the most common fungi were strains of Aspergillus and Candida. There were 19 pathogens those could only be detected by mNGS technology, such as Lichtheimia ramosa, Pneumocystis jirovecii, Mycobacterium tuberculosis, viruses, etc.; there were no pathogens detected by microbiological culture method that couldn't be detected by mNGS technology. Compared with those detected by microbiological culture method, the overall positive rate, bacterial positive rate, and fungal positive rate detected by mNGS technology were significantly increased (with χ² values of 45.52, 5.88, and 4.94, respectively, P<0.05). The 27.91% (24/86) of patients were detected positive by both methods, and 72.09% (62/86) of the patients were detected positive by mNGS technology but negative by microbiological culture method. The consistency test of the results obtained by the two detection methods showed that the difference was not statistically significant (κ=0.02, P>0.05). Conclusions The positive rate of pathogen detection in specimens using mNGS technology is higher than that detected by using conventional microbiological culture method, and it can detect pathogens those cannot be detected by the latter, such as Lichtheimia ramosa, Pneumocystis jirovidii, Mycobacterium tuberculosis, viruses, etc. Detection using mNGS technology can help clarify the types of infectious pathogens in burns patients with sepsis, and provide basis and guidance for clinical medication.
- Burns,
- Infection,
- Sepsis,
- Metagenomics,
- Pathogen

FullText(HTML)

References(36)

References

[1]	马琪敏, 汤文彬, 李孝建, 等. 危重烧伤老年患者早期临床特征的多中心回顾分析及预后的危险因素分析[J]. 中华烧伤与创面修复杂志, 2024, 40(3): 249-257. DOI: 10.3760/cma.j.cn501225-20230808-00042.
[2]	BelbaMK, PetrelaEY, BelbaAG. Epidemiology and outcome analysis of sepsis and organ dysfunction/failure after burns[J]. Burns, 2017, 43(6): 1335-1347. DOI: 10.1016/j.burns.2017.02.017.
[3]	TorresMJM, PetersonJM, WolfSE. Detection of infection and sepsis in burns[J]. Surg Infect (Larchmt), 2021, 22(1): 20-27. DOI: 10.1089/sur.2020.348.
[4]	WilsonMR, NaccacheSN, SamayoaE, et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing[J]. N Engl J Med, 2014, 370(25): 2408-2417. DOI: 10.1056/NEJMoa1401268.
[5]	WangL, LiS, QinJ, et al. Clinical diagnosis application of metagenomic next-generation sequencing of plasma in suspected sepsis[J]. Infect Drug Resist, 2023,16:891-901. DOI: 10.2147/IDR.S395700.
[6]	OverbeekR, LeitlCJ, StollSE, et al. The value of next-generation sequencing in diagnosis and therapy of critically ill patients with suspected bloodstream infections: a retrospective cohort study[J]. J Clin Med, 2024, 13(2): 306. DOI: 10.3390/jcm13020306.
[7]	BloemsmaGC, DokterJ, BoxmaH, et al. Mortality and causes of death in a burn centre[J]. Burns, 2008, 34(8): 1103-1107. DOI: 10.1016/j.burns.2008.02.010.
[8]	LavrentievaA, VoutsasV, KonoglouM, et al. Determinants of outcome in burn ICU patients with septic shock[J]. J Burn Care Res, 2017, 38(1): e172-e179. DOI: 10.1097/BCR.0000000000000337.
[9]	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.
[10]	曹钰, 柴艳芬, 邓颖, 等. 中国脓毒症/脓毒性休克急诊治疗指南(2018)[J]. 感染、炎症、修复, 2019, 20(1): 3-22. DOI: 10.3969/j.issn.1672-8521.2019.01.001.
[11]	MillerS, NaccacheSN, SamayoaE, et al. Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid[J]. Genome Res, 2019,29(5):831-842. DOI: 10.1101/gr.238170.118.
[12]	BolgerAM, LohseM, UsadelB. Trimmomatic: a flexible trimmer for Illumina sequence data[J]. Bioinformatics, 2014, 30(15): 2114-2120. DOI: 10.1093/bioinformatics/btu170.
[13]	LiH, DurbinR. Fast and accurate short read alignment with Burrows-Wheeler transform[J]. Bioinformatics, 2009, 25(14):1754-1760. DOI: 10.1093/bioinformatics/btp324.
[14]	ZinterMS, DvorakCC, MaydayMY, et al. Pulmonary metagenomic sequencing suggests missed infections in immunocompromised children[J]. Clin Infect Dis, 2019,68(11):1847-1855. DOI: 10.1093/cid/ciy802.
[15]	NapolitanoLM. Sepsis 2018: definitions and guideline changes[J]. Surg Infect (Larchmt), 2018,19(2):117-125. DOI: 10.1089/sur.2017.278.
[16]	ChiuCY, MillerSA. Clinical metagenomics[J]. Nat Rev Genet, 2019, 20(6): 341-355. DOI: 10.1038/s41576-019-0113-7.
[17]	WilsonMR, SampleHA, ZornKC, et al. Clinical metagenomic sequencing for diagnosis of meningitis and encephalitis[J]. N Engl J Med, 2019, 380(24): 2327-2340. DOI: 10.1056/NEJMoa1803396.
[18]	ChenY, FengW, YeK, et al. Application of metagenomic next-generation sequencing in the diagnosis of pulmonary infectious pathogens from bronchoalveolar lavage samples[J]. Front Cell Infect Microbiol, 2021,11:541092. DOI: 10.3389/fcimb.2021.541092.
[19]	GuW, DengX, LeeM, et al. Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids[J]. Nat Med, 2021,27(1):115-124. DOI: 10.1038/s41591-020-1105-z.
[20]	QinC, ZhangS, ZhaoY, et al. Diagnostic value of metagenomic next-generation sequencing in sepsis and bloodstream infection[J]. Front Cell Infect Microbiol, 2023, 10 (13): 1117987. DOI: 10.3389/fcimb.2023.1117987.
[21]	HuangJ, JiangE, YangD, et al. Metagenomic next-generation sequencing versus traditional pathogen detection in the diagnosis of peripheral pulmonary infectious lesions[J]. Infect Drug Resist, 2020,13:567-576. DOI: 10.2147/IDR.S235182.
[22]	HanD, LiZ, LiR, et al. mNGS in clinical microbiology laboratories: on the road to maturity[J]. Crit Rev Microbiol, 2019,45(5/6):668-685. DOI: 10.1080/1040841X.2019.1681933.
[23]	SimnerPJ, MillerS, CarrollKC. Understanding the promises and hurdles of metagenomic next-generation sequencing as a diagnostic tool for infectious diseases[J]. Clin Infect Dis, 2018,66(5):778-788. DOI: 10.1093/cid/cix881.
[24]	AfshinnekooE, ChouC, AlexanderN, et al. Precision metagenomics: rapid metagenomic analyses for infectious disease diagnostics and public health surveillance[J]. J Biomol Tech, 2017,28(1):40-45. DOI: 10.7171/jbt.17-2801-007.
[25]	WangY, BeekmanJ, HewJ, et al. Burn injury: challenges and advances in burn wound healing, infection, pain and scarring[J]. Adv Drug Deliv Rev, 2018,123:3-17. DOI: 10.1016/j.addr.2017.09.018.
[26]	OryanA, AlemzadehE, MoshiriA. Burn wound healing: present concepts, treatment strategies and future directions[J]. J Wound Care, 2017, 26(1): 5-19. DOI: 10.12968/jowc.2017.26.1.5.
[27]	袁志强, 彭毅志. 烧伤重症监护病房多重耐药菌感染的应对策略及思考[J]. 中华烧伤杂志, 2021, 37(6): 524-529. DOI: 10.3760/cma.j.cn501120-20210413-00129.
[28]	SalzerHJF, BurchardG, CornelyOA, et al. Diagnosis and management of systemic endemic mycoses causing pulmonary disease[J]. Respiration, 2018, 96(3): 283-301. DOI: 10.1159/000489501.
[29]	GuarnerJ, BrandtME. Histopathologic diagnosis of fungal infections in the 21st century[J]. Clin Microbiol Rev, 2011,24(2):247-280. DOI: 10.1128/CMR.00053-10.
[30]	LiH, GaoH, MengH, et al. Detection of pulmonary infectious pathogens from lung biopsy tissues by metagenomic next-generation sequencing[J]. Front Cell Infect Microbiol, 2018,8:205. DOI: 10.3389/fcimb.2018.00205.
[31]	SokulskaM, KiciaM, WesołowskaM, et al. Pneumocystis jirovecii--from a commensal to pathogen: clinical and diagnostic review[J]. Parasitol Res, 2015,114(10):3577-3585. DOI: 10.1007/s00436-015-4678-6.
[32]	CantalupoPG, PipasJM. Detecting viral sequences in NGS data[J]. Curr Opin Virol, 2019,39:41-48. DOI: 10.1016/j.coviro.2019.07.010.
[33]	SalterSJ, CoxMJ, TurekEM, et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses[J]. BMC Biol, 2014,12:87. DOI: 10.1186/s12915-014-0087-z.
[34]	Conceição-NetoN, ZellerM, LefrèreH, et al. Modular approach to customise sample preparation procedures for viral metagenomics: a reproducible protocol for virome analysis[J]. Sci Rep, 2015,5:16532. DOI: 10.1038/srep16532.
[35]	HengX, CaiP, YuanZ, et al. Efficacy and safety of extracorporeal membrane oxygenation for burn patients: a comprehensive systematic review and meta-analysis[J/OL]. Burns Trauma, 2023,11:tkac056[2024-04-18]. https://pubmed.ncbi.nlm.nih.gov/36873286/. DOI: 10.1093/burnst/tkac056.
[36]	HuangJ, ChenY, GuoZ, et al. Prospective study and validation of early warning marker discovery based on integrating multi-omics analysis in severe burn patients with sepsis[J/OL]. Burns Trauma, 2023,11:tkac050[2024-04-18]. https://pubmed.ncbi.nlm.nih.gov/36659877/. DOI: 10.1093/burnst/tkac050.