Sample pooling for SARS-CoV-2 RT-PCR screening. (December 2020) - de Salazar, Adolfo; Aguilera, Antonio; Trastoy, Rocio; Fuentes, Ana; Alados, Juan Carlos; Causse, Manuel; Galán, Juan Carlos; Moreno, Antonio; Trigo, Matilde; Pérez-Ruiz, Mercedes; Roldán, Carolina; Pena, Maria José; et al.
Abstract: Objective: To evaluate the efficacy of sample pooling compared to the individual analysis for the diagnosis of coronavirus disease 2019 (COVID-19) by using different commercial platforms for nucleic acid extraction and amplification. Methods: A total of 3519 nasopharyngeal samples received at nine Spanish clinical microbiology laboratories were processed individually and in pools (342 pools of ten samples and 11 pools of nine samples) according to the existing methodology in place at each centre. Results: We found that 253 pools (2519 samples) were negative and 99 pools (990 samples) were positive; with 241 positive samples (6.85%), our pooling strategy would have saved 2167 PCR tests. For 29 pools (made out of 290 samples), we found discordant results when compared to their correspondent individual samples, as follows: in 22 of 29 pools (28 samples), minor discordances were found; for seven pools (7 samples), we found major discordances. Sensitivity, specificity and positive and negative predictive values for pooling were 97.10% (95% confidence interval (CI), 94.11–98.82), 100%, 100% and 99.79% (95% CI, 99.56–99.90) respectively; accuracy was 99.80% (95% CI, 99.59–99.92), and the kappa concordant coefficient was 0.984. The dilution of samples in our pooling strategy resulted in a median loss of 2.87 (95% CI, 2.46–3.28) cycle threshold ( C t ) for E gene, 3.36 (95% CI, 2.89–3.85) C t for the RdRP gene and 2.99 (95% CI, 2.56–3.43) C t for the N gene. Conclusions: We found a high efficiency of pooling strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA testing across different RNA extraction and amplification platforms, with excellent performance in terms of sensitivity, specificity and positive and negative predictive values.