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

ABSTRACT:

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

SUBMITTER: Gentilini F 

PROVIDER: S-EPMC8687578 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature