Sample pooling for SARS-COV-2 RT-PCR screening

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; José Pena, Ma; Bernal, Samuel; Serrano-Conde, Esther; Barbeito, Gema; Torres, Eva; Riazzo, Cristina; Cortes-Cuevas, Jose Luis; Chueca, Natalia; Coira, Amparo; Sanchez-Calvo, Juan M.; Marfil, Eduardo; Becerra, Federico; Gude, María José; Pallarés, Ángeles; Pérez Del Molino, María Luisa; García, Federico

OBJECTIVE: To evaluate the efficacy of sample pooling compared to the individual analysis for the diagnosis of COVID-19, by using different commercial platforms for nucleic acid extraction and amplification. METHODS: 3519 nasopharyngeal samples received at 9 Spanish clinical microbiology laboratories were processed individually and in pools (342 pools of 10 samples and 11 pools of 9 samples) according to the existing methodology in each of the centres. 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: in 22/29 pools (28 samples), minor discordances were found; for seven pools (7 samples), we found major discordances. Sensitivity, specificity, positive and negative predictive values for pooling were 97.10% (CI95%; 94.11-98.82), 100%, 100% and 99.79% (CI95%; 99.56-99.90) respectively; accuracy was 99.80% (CI95%; 99.59-99.92) and kappa concordant coefficient was 0.984. The dilution of samples in our pooling strategy resulted into a median loss of 2.87 (CI95%; 2.46-3.28) CTs for E gene, 3.36 (CI95%; 2.89-3.85) CTs for RdRP gene and 2.99 (CI95%; 2.56-3.43) CTs for N gene. CONCLUSION: we show a high efficiency of pooling strategies for 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.