Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients

Mingyu Gan; Yanyan Zhang; Gangfeng Yan; Yixue Wang; Guoping Lu; Bingbing Wu; Weiming Chen; Wenhao Zhou

doi:10.1186/s12941-024-00690-7

Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients

Ann Clin Microbiol Antimicrob. 2024 Apr 15;23(1):33. doi: 10.1186/s12941-024-00690-7.

Authors

Mingyu Gan^#¹, Yanyan Zhang^#², Gangfeng Yan³, Yixue Wang³, Guoping Lu³, Bingbing Wu¹, Weiming Chen⁴, Wenhao Zhou^{5

6}

Affiliations

¹ Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China.
² Department of Neonatology, Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
³ Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China.
⁴ Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China. Weiming_chen@fudan.edu.cn.
⁵ Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China. zhouwenhao@fudan.edu.cn.
⁶ Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510005, China. zhouwenhao@fudan.edu.cn.

^# Contributed equally.

Abstract

Background: Antimicrobial resistance (AMR) is a major threat to children's health, particularly in respiratory infections. Accurate identification of pathogens and AMR is crucial for targeted antibiotic treatment. Metagenomic next-generation sequencing (mNGS) shows promise in directly detecting microorganisms and resistance genes in clinical samples. However, the accuracy of AMR prediction through mNGS testing needs further investigation for practical clinical decision-making.

Methods: We aimed to evaluate the performance of mNGS in predicting AMR for severe pneumonia in pediatric patients. We conducted a retrospective analysis at a tertiary hospital from May 2022 to May 2023. Simultaneous mNGS and culture were performed on bronchoalveolar lavage fluid samples obtained from pediatric patients with severe pneumonia. By comparing the results of mNGS detection of microorganisms and antibiotic resistance genes with those of culture, sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

Results: mNGS detected bacterial in 71.7% cases (86/120), significantly higher than culture (58/120, 48.3%). Compared to culture, mNGS demonstrated a sensitivity of 96.6% and a specificity of 51.6% in detecting pathogenic microorganisms. Phenotypic susceptibility testing (PST) of 19 antibiotics revealed significant variations in antibiotics resistance rates among different bacteria. Sensitivity prediction of mNGS for carbapenem resistance was higher than penicillins and cephalosporin (67.74% vs. 28.57%, 46.15%), while specificity showed no significant difference (85.71%, 75.00%, 75.00%). mNGS also showed a high sensitivity of 94.74% in predicting carbapenem resistance in Acinetobacter baumannii.

Conclusions: mNGS exhibits variable predictive performance among different pathogens and antibiotics, indicating its potential as a supplementary tool to conventional PST. However, mNGS currently cannot replace conventional PST.

Keywords: Antimicrobial resistance; Metagenomic next-generation sequencing; Pediatric; Severe pneumonia.

MeSH terms

Anti-Bacterial Agents* / pharmacology
Anti-Bacterial Agents* / therapeutic use
Bronchoalveolar Lavage Fluid
Carbapenems
Child
Drug Resistance, Bacterial / genetics
High-Throughput Nucleotide Sequencing
Humans
Pneumonia*
Retrospective Studies
Sensitivity and Specificity

Substances

Anti-Bacterial Agents
Carbapenems

Abstract

MeSH terms

Substances

Grants and funding