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Meta-analysis

Diagnostic accuracy of CRISPR technology for detecting SARS-CoV-2: a systematic review and meta-analysis

, , , , , & show all
Pages 655-663 | Received 23 Oct 2021, Accepted 26 Jul 2022, Published online: 05 Aug 2022
 

ABSTRACT

Objective

To evaluate the diagnostic accuracy of CRISPR-Cas technology for SARS-CoV-2.

Methods

RT-qPCR is defined as the reference standard. Data was collected and assessed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. A bivariate model for pooling was employed and subgroups analysis was used to explore heterogeneity.

Results

2264 samples from 28 articles were extracted for evaluating the accuracy of CRISPR technology for diagnosing SARS-CoV-2. The pooled sensitivity and specificity of CRISPR technology were 0.98 (95% CI: 0.95–0.99) and 1.0 (95% CI: 0.98–1.00), respectively. High risks in patient selection bias and unclear risk of index test bias may affect accuracy. Subgroup analysis showed that CRISPR-Cas12 is applicable for molecular diagnostics for its active editing characteristics. RT-LAMP and RT-RPA are usually used for pre-amplification and fluorescence detection to output results quantitatively. Nasopharyngeal swabs and dual-genes perform greatly in our study.

Conclusion

The results concluded from all studies showed that CRISPR technology is a promising molecular method for detecting SARS-CoV-2. Standard methods including comparable sample material, patient selection, operating procedure and operators should be established.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewers Disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

The study was supported by the Jilin Province Science and Technology Development Plan Item (20200403035SF).

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