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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8687578 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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