Metagenomic next-generation sequencing vs. conventional detection methods for detecting the pulmonary infections

OBJECTIVE: The absence of proper pathogen treatment in the early stages can result in missing out on treatment chances or the overuse of antibiotics, both of which are the primary factors behind fatalities caused by lung infections. In this study, we aimed to investigate the efficacy of metagenomic next-generation sequencing (mNGS) in comparison to conventional detection methods in detecting infectious pathogens. PATIENTS AND METHODS: In this retrospective study, the infection pathogens of 104 patients were examined, and 86 bronchoalveolar lavage fluid (BALF), eight pleural effusions, and ten sputum samples were collected. The conventional detection approaches and mNGS analysis were used to determine the infection pathogen profiles and their detection rates were analyzed. RESULTS: Our study showed that mNGS was more sensitive (89.42%) than the conventional detection methods (56.73%) (p < 0.001), with a 32.69% improvement in sensitivity. The efficacy of mNGS in detecting mixed infections was significantly higher than that of conventional detection methods, with a detection rate of 85.29% compared to 17.65% (p < 0.001). The study demonstrated that mNGS had a higher sensitivity than the conventional detection methods when it came to diagnosing pulmonary infections, making it a potentially useful tool for clinical diagnosis. CONCLUSIONS: Combining mNGS with other pathogenic detection techniques can be an effective way to increase the rate of detecting pulmonary infections, as well as to provide guidance for treatment adjustments. Furthermore, the timing of sample collection and antibiotic administration can influence the effectiveness of mNGS when used on BALF specimens.