NDLI: SARS-CoV-2 Viral dynamics (Dataset)

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SARS-CoV-2 Viral dynamics (Dataset)

Content Provider	Dryad
Author	Alamri, Maha
Abstract	Background: Cases of undiagnosed pneumonia have emerged, and sequencing of respiratory samples indicated the presence of SARS-CoV-2 causing COVID-19. Patients with higher viral load and lower Ct values tend to have progressive disease and severe lung injury. The objective of this study is to describe the clinical manifestation and disease outcomes of COVID-19 patients in relation to their Ct values. Methods: A retrospective, single-center observational study was performed, including patients admitted to King Faisal Specialist Hospital and Research Centre (KFSH&RC) Riyadh in one month in 2020 and have a confirmed diagnosis of COVID-19. The Ct value was identified to determine the viral load. All patients were treated according to KFSH&RC guidelines. Patients were divided into HCQ/AZI and non HCQ/AZI treated groups. Results: There were many days where Ct values were not available. An attempt at imputing information for missing Ct values was made using logic. The logic leads to an ordinal Ct score 1, 2, 3, or 4. As a result, complete Ct score profiles were available. There was no evidence of statistically significant difference between the two groups in regard to clinical severity, duration to negative test, or changes in Ct values. Conclusion: There is little knowledge known to the time profile of Ct values and their relation to disease course of COVID-19. This study provides insight on how Ct values might be used to determine treatment efficacy. As it might be difficult to obtain Ct values at all times, this study provides an imputation method that may be used with conservative statistical assumptions for analyses of Ct profiles.
File Size	461981
File Format	HTM / HTML
ISSN	20461402
DOI	10.5061/dryad.15dv41nwm
Alternate Webpage(s)	https://datadryad.org/stash/downloads/file_stream/1271409
Alternate Webpage(s)	https://datadryad.org/stash/downloads/file_stream/1271417
Language	English
Publisher Date	2022-01-24
Access Restriction	Open
Content Type	Text
Resource Type	Data Set
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Pharmacology, Toxicology and Pharmaceutics

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