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

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.