ArticleIndian journal of medical microbiology
Comparison of SARS-CoV-2 diagnosis by rapid antigen detection kit with RT-qPCR in a tertiary care setup in North Eastern India.
Article in Indian journal of medical microbiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association Between Rapid Antigen Detection Tests and Real-Time Reverse Transcription-Polymerase Chain Reaction Assay for SARS-CoV-2: A Systematic Review and Meta-Analyses.International journal of public health · 2023Pooled it
- Coronavirus Anatomy and Its Analytical Approaches for Targeting COVID-19.Advances in experimental medicine and biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeReal time reverse transcriptase polymerase chain reaction (RT-qPCR) is still considered a gold standard for the diagnosis of COVID-19. However, due to several limitations, use of RT-qPCR is limited in a resource poor setting like North East India. Rapid antigen detection testing kit has revolutionized the diagnosis and management of COVID-19 in India. However, conflicting reports exist regarding the efficacy of the kits for diagnosis of COVID-19. This study aims to highlight the performance of Standard Q COVID-19® Antigen detection kit (SD Biosensor) compared with RT-qPCR in the setup of North East India.
methodsNasopharyngeal and oropharyngeal swab samples were collected from consenting patients attending the flu clinic in the period from 1st July to December 31, 2020. Samples were transferred to Viral Research and Diagnostic Laboratory (VRDL) for RT-qPCR test. Antigen detection from the patient samples were undertaken using Standard Q ® COVID-19 antigen detection kit (SD Biosensor, Republic of Korea). Data were then analyzed for comparison between RT-qPCR and antigen kit results.
resultsDuring the study period, 189 samples were collected, out of which 119 were positive by RT-qPCR. Out of 119 positive samples, calculated sensitivity and specificity of the rapid antigen kit was 63% and 100% respectively. Sensitivity and diagnostic accuracy increases in symptomatic patients as compared to asymptomatic patients. Cohen's Kappa coefficient showed a moderate association (0.6) between the kit and RT-qPCR test. The kit performed optimally at a CT value of ≤32.5 for N gene with a predicted sensitivity of 77.3% and specificity of 93.3%.
conclusionThe study shows an overall acceptable sensitivity and specificity of the testing kit, with a better performance in symptomatic patients. The association of the kit result is moderate with the results obtained in RT-qPCR. In this study, the rapid antigen test kit performed optimally at N gene qRT PCR cut off value of ≤32.5.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.