Background: Metagenomic next-generation sequencing (mNGS) is a promising tool for improving antimicrobial therapy and infection control decision-making in complex infections. Secondary infection surveillance using mNGS in COVID-19 patients has rarely been reported.
Methods: Respiratory pathogen and antibiotic resistance prediction were evaluated by BALF mNGS for 192 hospitalized COVID-19 patients between December 2022 and February 2023.
Results: Secondary infection was confirmed in 83.3% (160/192) of the COVID-19 patients, with bacterial infections (45%, 72/160) predominating, followed by mixed bacterial and fungal infections (20%, 32/160), and fungal infections (17.5%, 28/160). The incidence of bacterial or viral secondary infection was significantly higher in patients who were admitted to the ICU, received mechanical ventilation, or developed severe pneumonia (all p< 0.05). Klebsiella pneumoniae (n=30, 8.4%) was the most prevalent pathogen associated with secondary infection followed by Acinetobacter baumannii (n=29, 8.1%), Candida albicans (n=29, 8.1%), Aspergillus fumigatus (n=27, 7.6%), human herpes simplex virus type 1 (n=23, 6.4%), Staphylococcus aureus (n=20, 5.6%) and Pneumocystis jiroveci (n=14, 3.9%). The overall concordance between the resistance genes detected by mNGS and the reported phenotypic resistance in 69 samples containing five clinically important pathogens (ie, K. pneumoniae, A. baumannii, S. aureus, P. aeruginosa and E. coli ) that caused secondary infection was 85.5% (59/69).
Conclusion: mNGS can detect pathogens causing secondary infection and predict antimicrobial resistance for COVID19 patients. This is crucial for initiating targeted treatment and rapidly detect unsuspected spread of multidrug-resistant pathogens.
Keywords: COVID-19, SARS-CoV-2, metagenomic next-generation sequencing, antibiotic resistance