Diagnostic accuracy of GeneXpert in the diagnosis of spinal tuberculosis: A systematic review and meta-analysis
DOI:
https://doi.org/10.52225/narra.v4i2.925Keywords:
Tuberculosis, spinal tuberculosis, GeneXpert, mycobacterial culture, Mycobacterium tuberculosisAbstract
Tuberculosis remains a significant global health issue, with spinal tuberculosis being a severe form of extrapulmonary tuberculosis. Despite the high morbidity associated with spinal tuberculosis, effective and rapid diagnostic methods are limited. The aim of this study was to evaluate the diagnostic accuracy of the GeneXpert compared to other microbiological methods in diagnosing spinal tuberculosis. A systematic review and meta-analysis were conducted following the PRISMA guidelines. Six databases (PubMed, Scopus, EBSCO, EMBASE, ScienceDirect, and Cochrane Central) were searched for relevant studies as of August 31, 2023. Studies were selected based on predefined inclusion criteria, focusing on patients diagnosed with spinal tuberculosis and comparing GeneXpert to microbiological culture, acid-fast bacilli (AFB) staining, and polymerase chain reaction (PCR). Two authors independently performed data extraction and quality assessment, and the meta-analysis was conducted using Meta-DiSc 2.0. Fourteen studies comprising retrospective cohort, prospective cohort, and cross-sectional designs were included. GeneXpert demonstrated a pooled sensitivity of 92% (85–96%) and specificity of 71% (51–86%) compared to culture. AFB smear had the highest specificity at 80% (70–88%) but the lowest sensitivity at 27% (20–35%). The PCR had sensitivity and specificity of 83% (67–92%) and 58% (31–81%), respectively. Substantial heterogeneity was noted across the studies. This study highlighted that GeneXpert had high sensitivity and moderate specificity in diagnosing spinal tuberculosis, making it an alternative to conventional methods. However, further validation through larger, interventional studies is necessary to standardize its use in clinical practice.
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Copyright (c) 2024 Karya T. Biakto, I GPY. Kusmawan, Muhammad N. Massi, Muhammad A. Usman, Jainal Arifin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.