Diagnostic Efficacy of Metagenomic Next-Generation Sequencing in Patients with Spinal Infections: A Retrospective Study
Year: 2023
Type: article
Study design: Observational Study
Abstract: Purpose: Early diagnosis of spinal infections remains challenging, and emerging metagenomic next-generation sequencing (mNGS) technology holds promise in addressing this issue. The aim of this study is to investigate the diagnostic efficacy of mNGS in spinal infections. Patients and Methods: A total of 78 cases with suspected spinal infections were enrolled in this study, all of whom underwent laboratory, histopathological and mNGS examinations upon admission. Lesion samples were obtained by surgical or C-arm-guided puncture. Sensitivity, specificity, positive predictive value and negative predictive value of culture and mNGS were calculated for statistical analysis. Results: With histopathological results as the reference, the included 78 patients were categorized into 50 cases in the spinal infection group and 28 cases in the aseptic group. The sensitivity (84%) and negative predictive value (77.14%) of mNGS were significantly higher than those of culture (32% and 44.26%, respectively), whereas no significant differences were observed in terms of specificity and positive predictive value. In the subgroup analysis for Mycobacterium tuberculosis, the sensitivity of mNGS (90.91%) and T-spot (90.91%) was significantly higher than that of culture (0). Additionally, mNGS demonstrated markedly higher specificity (100%) compared to T-spot (85.07%). Conclusion: This study underscores the substantial advantages of mNGS in terms of diagnostic accuracy and bacterial coverage for spinal infections. The findings provide compelling clinical evidence that supports the enhanced diagnostic efficacy of mNGS.
Source: Infection and Drug Resistance
DOI: 10.2147/idr.s435466
Authors: Hanwen Cheng, Haoyu Wu, Ni Tan, Zhuojie Liu, Ning Wang, Ningyi Chen, Chunhai Li
Institutions: Sun Yat-sen University, Sun Yat-sen Memorial Hospital
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,836
OpenAlex ID: W4389628231
