Impact of viral load on sample pooling for reverse-transcription polymerase chain reaction detection-based diagnosis of coronavirus disease 2019 in Nigeria

病毒载量对尼日利亚基于逆转录聚合酶链式反应检测的冠状病毒病2019诊断中样本混合的影响

阅读:1

Abstract

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic strained diagnostic testing capacities globally, particularly in low- and middle-income countries like Nigeria. Reverse-transcription polymerase chain reaction (RT-PCR) remains the gold standard for COVID-19 detection, but limited testing resources caused bottlenecks in Nigeria's response during the pandemic. Sample pooling offers a cost-effective strategy to enhance testing capacity during future outbreaks. OBJECTIVE: This study determined the maximum number of COVID-19 samples that can be pooled for RT-PCR testing in Nigeria without compromising the detection sensitivity of a single positive sample. METHODS: A total of 1222 nasopharyngeal samples from symptomatic COVID-19 patients in Nasarawa State, Nigeria, collected between March 2021 and August 2022, were retrieved from the laboratory biorepository and analysed from November 2022 to February 2023. These included five positive samples with cycle threshold (Ct) values ranging from ≤ 20 to 40, and 1217 negative samples. Positive samples were pooled with negative ones at increasing dilution ratios (1:4-1:64), to assess detection sensitivity on the GeneXpert platform. RESULTS: A positive sample with a Ct value ≤ 25 could be pooled with up to 64 negative samples while maintaining a detectable positive result. However, samples with Ct values of 36-40 could only be pooled with a maximum of eight negative samples. Higher Ct values reduced pooling effectiveness. CONCLUSION: Sample pooling is a feasible method for scaling up COVID-19 RT-PCR testing in resource-limited settings like Nigeria. The Ct value is critical in determining optimal pool sizes for accurate detection. WHAT THIS STUDY ADDS: The findings provide critical guidelines for determining the optimal pool sizes based on Ct values, aiding in effective COVID-19 testing strategies. By optimising sample pooling based on viral load, health authorities can improve their response to future COVID-19 outbreaks and similar public health emergencies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。