Pathogen Detection in Spinal Infections: Next-Generation Sequencing Versus Conventional Microbiological Methods

Abstract

Objective and Background

Early and accurate diagnosis of spinal infections, including spinal tuberculosis, is pivotal for effective treatment but remains challenging. This study aims to assess the diagnostic yield of metagenomic next-generation sequencing (mNGS) compared with that of conventional microbiological tests (CMTs) in identifying pathogens associated with spinal pathologies, with a special focus on infections leading to surgical interventions.

Methods

We enrolled 85 patients who underwent spinal surgery, comprising 63 patients with clinically diagnosed spinal infections, including patients with spinal tuberculosis, and 22 patients with noninfectious spinal conditions. The procedures involved irrigation and debridement for persistent wound drainage, with subsequent DNA extraction from plasma and joint fluid for mNGS and CMT analysis.

Results

Significantly increased C-reactive protein (CRP) levels were observed in patients with infections. The mNGS approach showed greater diagnostic sensitivity (92.06%) for detecting pathogens, including Mycobacterium tuberculosis, than did CMTs (36.51%). Despite its low specificity, mNGS had considerable negative predictive value (70.59%), underscoring its utility in ruling out infections.

Conclusions

The mNGS offers superior sensitivity over CMTs in the diagnosis of a variety of spinal infections, notably spinal tuberculosis. This study highlights the potential of mNGS in enhancing the diagnosis of complex spinal infections, thereby informing targeted treatment strategies.

Keywords

Metagenomic next-generation sequencing / Conventional microbiological tests / Spinal infections / Sensitivity / Specificity / Predictive value / D-dimers

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Funding

Hubei Provincial Natural Science Foundation of China ((2023AFB646))

Innovation Program of Wuhan Basic Research((No. 2023020201010155))

Educational Research Program of Huazhong University of Science and Technology((2022135))

RIGHTS & PERMISSIONS

The Author(s), under exclusive licence to Huazhong University of Science and Technology