ABSTRACT
Purpose: Severe community-acquired pneumonia (SCAP) is the leading cause of death among patients with infectious diseases worldwide. This study aimed to evaluate the effectiveness of metagenomic next-generation sequencing (mNGS) through detecting pathogens in bronchoalveolar lavage fluid (BALF) and identifying risk factors for recovery in SCAP patients. Patients and Methods: This prospective study recruited 158 SCAP patients admitted to respiratory intensive care unit that were randomly divided into control and study groups, with receiving conventional tests and the same conventional tests plus mNGS, respectively. The diagnostic efficiency of mNGS was evaluated by comparing with conventional tests. Furthermore, univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for recovery in SCAP patients, and a nomogram prediction model was established based on these factors. Results: Within the study group, the pathogen detection rate was significantly higher with mNGS than that with conventional tests (84.81% vs 45.57%, P < 0.001), with a positive coincidence rate of 94.44%. Acinetobacter baumannii (21.52%, 17/79), Candida albicans (17.72%, 14/79), and Klebsiella pneumonia (15.19%, 12/79) were the top three common pathogens detected by mNGS. Of note, the improvement rate of patients in the study group was significantly higher than that in the control group. The further analysis revealed that the increased levels of interleukin-6, blood urea nitrogen, procalcitonin, the longer length of hospital stay, and bacterial infection were independent risk factors for recovery of SCAP patients, while mNGS detection status was a protective factor. The predictive model showed a good performance for the modeling and validation sets. Conclusion: Early mNGS exhibited a superior diagnostic efficiency to conventional tests in SCAP patients, which can reduce the risk of death in SCAP patients. Moreover, the clinical factors could also be used for the management and prognosis prediction of SCAP patients.
