Background
Timely and accurate identification of pathogens is crucial for appropriate treatment and prognosis of infectious diseases. As an increasingly popular pathogen detection method, the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens in febrile patients with suspected infection requires further exploration.
Methods
This study included 368 febrile patients with suspected infections who were admitted to the Infectious Disease Department of Qilu Hospital, Shandong University between January 5, 2021 and April 14, 2023. Both mNGS testing and conventional culture were performed in all patients. Clinical data of enrolled patients were collected, and the diagnostic performances of mNGS and culture were compared.
Results
Of the 368 enrolled patients, 231 were finally diagnosed with infection and 137 were with diseases other than infection. The sensitivity (58.01% vs. 21.65%, p < 0.001) and negative predictive value (54.67% vs. 42.9%) of mNGS were superior to those of culture. In contrast, the culture exhibited higher specificity (99.27% vs. 85.40%, p < 0.001) and positive predictive value (98.84% vs. 87.01%) than mNGS. Among infected patients with positive mNGS results, 64 received adjusted antibiotic therapy including treatment transitions, antibiotic downgrading, and combination therapy. Among them, 9 had additional antifungal drugs and 21 patients had a treatment turning point based on the mNGS results and these patients recovered and discharged due to timely antibiotic adjustment. Both positive rates of puncture fluid mNGS and tissue mNGS were higher than those of culture in the patients who had prior antibiotic use, and this difference was statistically significant (p = 0.000).
Conclusion
mNGS is more sensitive and accurate than traditional culture, making it ideal for identifying pathogens and screening infectious diseases, especially for those with uncultivated or difficult-to-cultivate species. Early diagnosis allows for prompt treatment with targeted antibiotics, and mNGS is recommended when samples are limited.
