Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis.

Autor:	O'Connor K; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Weissenbacher D; Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA., Elyaderani A; Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ, USA., Lautenbach E; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Scotch M; Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ, USA.; College of Health Solutions, Arizona State University, Tempe, AZ, USA., Gonzalez-Hernandez G; Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Mar 05. Date of Electronic Publication: 2024 Mar 05.
DOI:	10.1101/2023.07.14.23292681
Abstrakt:	Background: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored. Methods: The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted. Discussion: This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases. Competing Interests: Competing interests The authors declare that they have no competing interests.
Databáze:	MEDLINE
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