Researchers create a clinical scoring system to predict viable viral shedding after COVID-19 infection

By Nidhi Saha, BDSReviewed by Aimee MolineuxOct 6 2022

Personalized scoring systems are an efficient way of monitoring disease progression, persistence, and after-effects.

A recent study published in the Journal of Clinical Virology developed an individualized scoring system to predict the shedding of viable severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from coronavirus disease 2019 (COVID-19) positive patients, which would help to determine personalized isolation strategies.

Background

According to the Centers for Disease Control and Prevention (CDC), COVID-19-positive patients should self-isolate for 5-10 days, based on how long the symptoms last and how severe they are. However, the isolation period does not consider the duration of viral shedding, which varies between patients and may be influenced by pre-existing diseases.

In addition, despite evidence of a shorter duration of viable viral shedding by vaccinated individuals, the recommended isolation guidelines do not account for an individual’s vaccination status. 

An individualized approach to ascertain the apt isolation period for COVID-19 patients is warranted. Viral culture to evaluate the viable virus shedding is impractical due to the expense and time involved.

Therefore, an individualized clinical prediction scoring system was proposed to estimate the shedding of viable viruses - to customize the isolation and precaution period that must be followed.

The study

The new study entailed four prospective studies conducted over the last two years, from February 2020 to January 2022, to examine the viable virus-shedding kinetics of SARS-CoV-2. The clinical prediction scoring system was developed based on the findings.

The first cohort comprised patients from a tertiary hospital in Seoul, all hospitalized in 2020. The second cohort included patients with asymptomatic to mild disease from a community isolation facility in a South Korean city between January and February 2021. Patients of the third cohort were selected from the community isolation facility from July to August 2021. The fourth cohort recruited patients during the Delta wave in Korea between September 2021 and January 2022.

Data on the vaccination status and COVID-19 disease severity and duration were collected. Subjects for whom at least two weeks had elapsed since the last CDC-recommended dose of COVID-19 vaccine - two doses of mRNA vaccine for the general population and three doses of mRNA vaccine for immunocompromised individuals - were deemed fully vaccinated. Subjects who had never been vaccinated were categorized as unvaccinated. The remaining were deemed partially vaccinated.

Results

A total of 612 samples were collected from 121 patients; among these, 315 samples belonging to 66 patients were analyzed.

The results depicted notable heterogeneity amongst the cohorts. It was found that compared to cohorts 1 and 4, cohorts 2 and 3 – hailing from the community isolation facility, comprised younger patients who presented with less severe disease.

Negative conversion of viral culture was associated with moderate to critically severe disease, viral copy number, fully vaccinated status, and organ transplant recipients. The predictive model of viable viral shedding included these four variables.

The culture-negative rates predictions at day 10 of symptom onset in patients with scores -5, 0, and 6 were 100%, 86%, and 6%, respectively.

Discussion 

Culture-based isolation is the gold standard for determining the infectivity of SARS-CoV-2. 

This prediction model used the number of genomic ribonucleic acid (RNA) copies, disease severity, the time elapsed since symptom onset, and vaccination status. Of note, males exhibited a shorter period of viable virus shedding than females. However, on multivariable analysis, the statistical significance was minimal. 

Conclusion 

The prediction clinical scoring model provides a customized approach to determine the isolation period, thereby reducing the burden on the healthcare system. The model can be easily integrated into an infection control or medical calculator application.