Clinical diagnostic value of metagenomic next-generation sequencing in patients with acute infection in emergency department

宏基因组二代测序在急诊科急性感染患者中的临床诊断价值

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Abstract

OBJECTIVE: To explore the value of metagenomic next-generation sequencing (mNGS) and culture in microbial diagnosis of patients with acute infection. METHODS: We retrospectively analyzed 206 specimens from 163 patients who were admitted to the emergency department of The First Affiliated Hospital of Sun Yat-sen University between July 2020, and July 2021. We evaluated the diagnostic efficacy of mNGS and in-hospital traditional culture. RESULTS: The total positive rate of mNGS was significantly higher than that culture methods (71.4 % vs 40.8 %, p < 0.001), while the sensitivity and accuracy of mNGS were found to be 92.9 % and 88.2 % respectively. However, culture exhibited superior specificity with a value of 92.6 % compared to 75.9 % for mNGS. The detection efficiency of mNGS and culture for fungi was comparable, but mNGS showed superior performance for bacterial detection. In the analysis of sepsis samples, mNGS outperformed traditional culture methods in diagnosing various types of samples, especially for sputum and bronchoalveolar lavage fluid. Among the identified infections, bacterial infections were the most common single infection (37.5 %). Additionally, bacterial-fungal infections represented the most prevalent form of mixed infection (77.3 %). Candida albicans and Staphylococcus aureus were identified as the predominant pathogens in the survival and death groups, respectively. No significant differences in microbial diversity were observed. CONCLUSION: Compared to culture methods, mNGS demonstrates superior positive rates, sensitivity, and accuracy in the rapid detection of acute infections, particularly in critically ill patients such as those with sepsis. This capability establishes a foundation for the swift and precise identification of pathogens, allowing for the analysis of clinical indicators and patient prognosis based on the extensive data generated from mNGS.

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