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Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies

OBJECTIVES: To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL. METHODS: A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression mo...

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Autores principales: Gentilini, Fabio, Turba, Maria Elena, Taddei, Francesca, Gritti, Tommaso, Fantini, Michela, Dirani, Giorgio, Sambri, Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687578/
https://www.ncbi.nlm.nih.gov/pubmed/34928966
http://dx.doi.org/10.1371/journal.pone.0260884
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author Gentilini, Fabio
Turba, Maria Elena
Taddei, Francesca
Gritti, Tommaso
Fantini, Michela
Dirani, Giorgio
Sambri, Vittorio
author_facet Gentilini, Fabio
Turba, Maria Elena
Taddei, Francesca
Gritti, Tommaso
Fantini, Michela
Dirani, Giorgio
Sambri, Vittorio
author_sort Gentilini, Fabio
collection PubMed
description OBJECTIVES: To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL. METHODS: A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy. RESULTS: The model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance. CONCLUSION: The modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.
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spelling pubmed-86875782021-12-21 Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies Gentilini, Fabio Turba, Maria Elena Taddei, Francesca Gritti, Tommaso Fantini, Michela Dirani, Giorgio Sambri, Vittorio PLoS One Research Article OBJECTIVES: To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL. METHODS: A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy. RESULTS: The model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance. CONCLUSION: The modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity. Public Library of Science 2021-12-20 /pmc/articles/PMC8687578/ /pubmed/34928966 http://dx.doi.org/10.1371/journal.pone.0260884 Text en © 2021 Gentilini et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gentilini, Fabio
Turba, Maria Elena
Taddei, Francesca
Gritti, Tommaso
Fantini, Michela
Dirani, Giorgio
Sambri, Vittorio
Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title_full Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title_fullStr Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title_full_unstemmed Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title_short Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies
title_sort modelling rt-qpcr cycle-threshold using digital pcr data for implementing sars-cov-2 viral load studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687578/
https://www.ncbi.nlm.nih.gov/pubmed/34928966
http://dx.doi.org/10.1371/journal.pone.0260884
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