Application of metagenomic next-generation sequencing technology in pathogen detection of severe infections in children
10.3760/cma.j.cn114452-20240330-00169
- VernacularTitle:宏基因组二代测序技术在儿童重症感染病原体检测中的应用
- Author:
Dingxiang LAI
1
;
Yun PAN
;
Ying ZHOU
;
Danyan ZHUANG
;
Haibo LI
;
Jishan ZHENG
Author Information
1. 宁波市镇海区炼化医院儿科，宁波 315000
- Keywords:
Metagenome;
Next-generation sequencing;
Infection;
Pathogen;
Child
- From:
Chinese Journal of Laboratory Medicine
2024;47(11):1340-1344
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the pathogenic spectrum of children with severe infection by metagenomic next generation sequencing (mNGS).Methods:This study was a cross-sectional study. We collected 212 cases of severely infected pediatric patients admitted to the Intensive Care Unit (ICU) of the Women and Children′s Hospital of Ningbo University from January 2022 to June 2023, and performed metagenomic next-generation sequencing (mNGS) on 249 samples to analyze the pathogenic distribution characteristics.Results:Among the 249 samples of 212 children, the positive detection rate was 49.80% (124/249), including 14 cases of mixed infections, accounting for 6.60% (14/212). According to the mNGS technology, the pathogen distribution of severely infected children showed that the most common Gram-positive bacteria were Staphylococcus aureus (3.61%, 9/249), Streptococcus pneumoniae (2.81%, 7/249), and Staphylococcus epidermidis (2.41%, 6/249); the most common Gram-negative bacteria were Klebsiella aerogenes (2.41%, 6/249), Klebsiella pneumoniae (2.41%, 6/249), and Haemophilus parainfluenzae (2.01%, 5/249). The most common fungus was Candida parapsilosis (2.01%, 5/249). The most common virus was Human Cytomegalovirus (HCMV) (6.02%, 15/249), Human Herpesvirus 1 (HHV-1) (1.61%, 4/249), and Epstein-Barr virus (EBV) (1.61%, 4/249). The most common atypical pathogen was Mycoplasma pneumoniae (3.21%, 8/249). Conclusions:This study explored the pathogen spectrum in severely infected pediatric patients through mNGS, contributing to the diagnosis of mixed infections or infections caused by uncommon or rare pathogens, which enables rapid and efficient identification of pathogens.
