The identification of tuberculosis using CRISPR technique: A systematic review and meta-analysis

Rapid and precise detection of Mycobacterium tuberculosis (MTB) is essential for effective management and control of tuberculosis. The diagnostic challenge is particularly acute for extrapulmonary TB, which accounts for approximately 15-20% of cases and often presents with paucibacillary samples. Clustered regularly interspaced short palindromic repeats (CRISPR) technology has emerged as a promising tool for pathogenic diagnosis across diverse sample types owing to its specificity and adaptability. This systematic review and meta-analysis aimed to appraise the diagnostic accuracy of CRISPR-based techniques in identifying MTB.

A comprehensive search was conducted in Medline, Scopus, Embase, and ISI Web of Science to retrieve relevant studies, adhering to PRISMA guidelines. Quality was assessed using the Joanna Briggs comprehensive checklist. Data synthesis and analyses, including subgroup analyses, were performed with Meta-Disc 1.4, examining variables like CRISPR variants, gene targets, pre-amplification techniques, and signal readout methods.

From 341 identified studies, 13 met the inclusion criteria, encompassing 1,572 MTB strains. The pooled sensitivity and specificity of CRISPR-based techniques were 0.91 (95%CI: 0.89-0.92) and 0.97 (95%CI: 0.95-0.98), respectively. The pooled diagnostic odds ratio was 498.67 (95%CI: 255.1-974.7), with an AUC of 0.99 in the SROC curve, denoting excellent diagnostic accuracy. Subgroup analysis by sample type revealed that sputum samples achieved sensitivity and specificity of 0.92 (95%CI: 0.89-0.94) and 0.97 (95%CI: 0.94-0.99), while extrapulmonary samples (BALF, pus, CSF) showed comparable performance with sensitivity of 0.89 (95%CI: 0.84-0.93) and specificity of 0.98 (95%CI: 0.94-0.99).

CRISPR-based methods exhibit substantial diagnostic sensitivity and specificity for detecting MTB across both pulmonary and extrapulmonary samples, with notable variances across different CRISPR variants and methodological approaches.