Enhanced diagnostic yield and clinical impact of metagenomic next-generation sequencing in infective endocarditis: a continuous and prospective surgical cohort study
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Traditional culture-based techniques for diagnosing infective endocarditis (IE) suffer from limited sensitivity, particularly in blood culture-negative endocarditis (BCNE). Metagenomic next-generation sequencing (mNGS) has emerged as a promising alternative to improve pathogen detection and guide treatment. This study aimed to evaluate the diagnostic performance of mNGS in IE and assess its impact on clinical decision-making. METHODS: We conducted a continuous and prospective cohort study of 352 patients undergoing cardiac surgery for suspected IE. Each patient underwent blood culture, tissue culture, and mNGS testing on matched arterial blood, venous blood, and valve tissue. RESULTS: Traditional tissue culture had low sensitivity [accuracy 24%, area under the receiver operating characteristic curve (AUC) 0.57], while blood culture performed better (accuracy 48%, AUC 0.71). In contrast, clinically adjusted valve tissue mNGS showed higher accuracy (84%) and AUC (0.91), outperforming traditional methods. Streptococcus species were most common, but mNGS also detected fastidious pathogens like Coxiella burnetii , especially in BCNE cases. mNGS results prompted antibiotic adjustments in 30% of all patients and 45% of blood culture-negative patients, enabling targeted or de-escalated therapy. Notably, no IE relapses were observed at 1-year follow-up, supporting the effectiveness of mNGS-guided treatment. CONCLUSION: mNGS significantly enhances microbiological diagnosis in IE, especially in BCNE, complements traditional methods, and improves clinical decision-making. However, it requires expert interpretation to mitigate false positives.
Source: International Journal of Surgery
Authors: Weiteng Wang, Fanyu Chen, Lanxin Ye, Oudi Chen, Hongkun Qing, Lequan Zhuang, Lixi Gan, Xuejiao Hu, Qingyan Ma, Xuhua Jian
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W4416617015
