Clinical and pathogen features of COVID-19-associated infections during an Omicron strain outbreak in Guangzhou, China

Abstract

Although the Omicron variant has been associated with greater transmissibility and tropism of the upper respiratory tract, the clinical and pathogenic features of patients infected with the Omicron variant during an outbreak in China have been unclear. Adults with COVID-19 were retrospectively enrolled from seven medical centers in Guangzhou, China, and clinical information and specimens ( BALF, sputum, and throat swabs) from participants were collected. Conventional detection methods, metagenomics next-generation sequencing (mNGS), and other methods were used to detect pathogens in lower respiratory tract samples. From December 2022 to January 2023, we enrolled 836 patients with COVID-19, among which 56.7% patients had severe/critical illness. About 91.4% of patients were infected with the Omicron strain (BA.5.2). The detection rate of possible co-infection pathogens was 53.4% by mNGS, including Klebsiella pneumoniae (16.3%), Aspergillus fumigatus (12.2%), and Pseudomonas aeruginosa (11.8%). The co-infection rate was 19.5%, with common pathogens being Streptococcus pneumoniae (11.5%), Haemophilus influenzae (9.2%), and Adenovirus (6.9%). The superinfection rate was 75.4%, with common pathogens such as Klebsiella pneumoniae (26.1%) and Pseudomonas aeruginosa (19.4%). Klebsiella pneumoniae (27.1%% vs 6.1%, P < 0.001), Aspergillus fumigatus (19.6% vs 5.3%, P = 0.001), Acinetobacter baumannii (18.7% vs 4.4%, P = 0.001), Pseudomonas aeruginosa (16.8% vs 7.0%, P = 0.024), Staphylococcus aureus (14.0% vs 5.3%, P = 0.027), and Streptococcus pneumoniae (0.9% vs 10.5%, P = 0.002) were more common in severe cases. Co-infection and superinfection of bacteria and fungi are common in patients with severe pneumonia associated with Omicron variant infection. Sequencing methods may aid in the diagnosis and differential diagnosis of pathogens.

Importance: Our study has analyzed the clinical characteristics and pathogen spectrum of the lower respiratory tract associated with co-infection or superinfection in Guangzhou during the outbreak of the Omicron strain, particularly after the relaxation of the epidemic prevention and control strategy in China. This study will likely prompt further research into the specific issue, which will benefit clinical practice.

Keywords: Aspergillus; COVID-19; Klebsiella pneumoniae; Omicron strain; co-infection; diabetes mellitus; superinfection.

Fig 1

Analysis flow chart.

Fig 2

The pathogen spectrum of co-infection and/or superinfection in patients with COVID-19 during the Omicron outbreak. The pathogen spectrum in patients with COVID-19 detected by mNGS and conventional methods are shown in the bar graph. Red bars represent the proportion of pathogens detected by mNGS only, green bars represent the proportion of pathogens detected by conventional methods only, and blue bars represent the proportion of pathogens detected by both conventional methods and mNGS. (A) Pathogens of COVID-19-associated infections. (B) pathogen of Co-infection associated with COVID-19. (C) Pathogen of superinfection associated with COVID-19.

Fig 3

The pathogen spectrum of COVID-19-associated infections between the severe and non-severe groups. Bar graph showing the detection rate (%) of different pathogens in the severe and non-severe groups. The colors red and blue represent the severe and non-severe groups, respectively. The pathogens with the highest detection rates in the severe group included Klebsiella pneumoniae, Acinetobacter baumannii, and Aspergillus fumigatus. The pathogens with the highest detection rates in the non-severe group consisted of Streptococcus pneumoniae, Pseudomonas aeruginosa, Haemophilus influenzae, and Klebsiella pneumoniae. In addition, Klebsiella pneumoniae, Aspergillus fumigatus, Acinetobacter baumannii, and Pseudomonas aeruginosa were more common in the severe group than in the non-severe group.

Fig 4

The influence of underlying diabetes on the pathogen spectrum in patients with COVID-19. A bar graph compares the detection rate of various pathogens in COVID-19 patients with and without diabetes. Red represents patients with diabetes, while green represents those without. Patients with diabetes had a higher detection rate of Streptococcus pneumoniae.