A team of scientists from the United Kingdom has demonstrated that clinical metagenomic sequencing-based detection of microbial communities in respiratory samples can improve the management of life-threatening hospital-acquired infections in coronavirus disease 2019 (COVID-19) patients.
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The technique is also effective in detecting multidrug resistance genes in microbes. The study is currently available on the MedRxiv* preprint server.
Extremely high infectivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of coronavirus disease 2019 (COVID-19), has put a lot of burden on the healthcare systems of many countries across the world.
An increased number of hospital admissions due to COVID-19 related complications have caused a significant drop in medical resources, including therapeutics and medical equipment. Such increased hospital occupancies have in turn increased the risk of secondary or hospital-acquired infections among COVID-19 patients.
Also, medical equipment used to treat critical COVID-19 patients can introduce secondary infections. Studies have shown that COVID-19 patients are highly susceptible to acquire pneumonia from invasive ventilation, which is associated with a significantly high mortality rate. Similarly, reports are demonstrating a high incidence of Gram-negative bacterial infections and fungal infections (Invasive pulmonary aspergillosis) among hospitalized COVID-19 patients.
Generally, initial treatment of hospitalized patients with suspected secondary infection is done by guideline-directed empiric antibiotics until the identification of specific pathogens through microbial culture. However, microbial culture typically takes 2 to 4 days to provide results, and thereby, a delay in initiating the exact treatment frequently occurs in hospital setups. This subsequently can increase the risk of more severe complications and death.
In the current study, the scientists aimed to evaluate the potency of nanopore technology-based clinical metagenomic sequencing approach in identifying a microbial community in a patient’s sample. They used this real-time data acquisition approach for faster identification of microbes.
Current study design
Initially, a total of 763 respiratory samples collected from 225 invasively-ventilated COVID-19 patients were cultured to detect secondary bacterial and fungal infections.
Clinical metagenomics using nanopore sequencing was performed on 43 respiratory samples obtained from 34 COVID-19 patients who were strongly suspected to have secondary infections.
The microbial culture data revealed that the most common Gram-negative bacteria in COVID-19 patients were Klebsiella spp., Citrobacter spp., E. coli, and P. aeruginosa, and the most frequently detected Gram-positive bacteria were S. aureus, Enterococcus spp., and C. striatum. In addition, C. Albicans and Aspergillus spp. were identified as the most common fungal pathogens.
About 63% of samples tested by clinical metagenomics were found to have respiratory pathogens. The method took only 8 hours to provide results. Of 27 pathogens detected through microbial culture, 25 were detected by clinical metagenomics, indicating that the method is 93% sensitive in detecting pathogens in respiratory samples.
Moreover, the method identified 3 extra pathogens in 3 samples that remained undetected in microbial culture. This indicates that the method is 81% specific in detecting pathogens compared to microbial culture.
Most importantly, A 2-hour clinical metagenomics workflow effectively detected the presence or absence of beta-lactam resistance genes in Enterobacterales, which is a large family of Gram-negative bacteria. Clinical metagenomics-based information on multidrug-resistant bacteria could be used by medical professionals to modify the initial treatment guideline and to introduce more accurate therapeutics for the better management of severe COVID-19 patients with secondary infections.
Clinical metagenomics also showed 100% efficiency in detecting A. fumigatus, the causative pathogen of Invasive pulmonary aspergillosis. Single nucleotide polymorphism analysis of 24-hour metagenomics sequencing data identified the outbreaks of multidrug-resistant K. pneumoniae and C. striatum in patients admitted to intensive care units. These findings indicate that the clinical metagenomic approach helps assess patient to patient transmission of pathogens in hospital setups.
The current study findings indicate that clinical metagenomic sequence analysis has the potential to be routinely used in hospital setups for faster and accurate detection of secondary or hospital-acquired infections in COVID-19 patients.
Faster detection of infectious pathogens can improve the management of patients and curb infection transmission in hospitals.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.