Selected article for: "clinical sensitivity and cycle threshold"

Author: Griesemer, Sara B.; Van Slyke, Greta; St. George, Kirsten
Title: Assessment of Sample Pooling for Clinical SARS-CoV-2 Testing
  • Cord-id: qmvxj96z
  • Document date: 2021_3_19
  • ID: qmvxj96z
    Snippet: Accommodating large increases in sample workloads has presented a major challenge to clinical laboratories during the coronavirus disease 2019 (COVID-19) pandemic. Despite the implementation of automated detection systems and previous efficiencies, including barcoding, electronic data transfer, and extensive robotics, capacities have struggled to meet the demand. Sample pooling has been suggested as an additional strategy to address this need. The greatest concern with this approach in clinical
    Document: Accommodating large increases in sample workloads has presented a major challenge to clinical laboratories during the coronavirus disease 2019 (COVID-19) pandemic. Despite the implementation of automated detection systems and previous efficiencies, including barcoding, electronic data transfer, and extensive robotics, capacities have struggled to meet the demand. Sample pooling has been suggested as an additional strategy to address this need. The greatest concern with this approach in clinical settings is the potential for reduced sensitivity, particularly detection failures with weakly positive samples. To investigate this possibility, detection rates in pooled samples were evaluated, with a focus on pools containing weakly positive specimens. Additionally, the frequencies of occurrence of weakly positive samples during the pandemic were reviewed. Weakly positive specimens, with threshold cycle (C(T)) values of 33 or higher, were detected in 95% of 60 five-sample pools but only 87% of 39 nine-sample pools. The proportion of positive samples with very low viral loads rose markedly during the first few months of the pandemic, peaking in June, decreasing thereafter, and remaining level since August. At all times, weakly positive specimens comprised a significant component of the sample population, ranging from 29% to >80% for C(T) values above 31. In assessing the benefits of pooling strategies, however, other aspects of the testing process must be considered. Accessioning, result data management, electronic data transfer, reporting, and billing are not streamlined and may be complicated by pooling procedures. Therefore, the impact on the entire laboratory process needs to be carefully assessed prior to implementing such a strategy.

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