Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis
Authors: O’Connor Karen , Weissenbacher Davy , Elyaderani Amir , Lautenbach Ebbing , Scotch Matthew , Gonzalez-Hernandez Graciela 
Published: July 2023

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.

Strengths and Limitations

Classification of papers using machine learning will help pinpoint more relevant papers reducing time to sift through articles for inclusion.

Study will provide an overview of the availability of information required to facilitate genomic epidemiology studies of SARS-CoV-2

The bibliometric analysis will analyze publication trends, author affiliations, citation metrics, and other bibliographic information to provide several insights into the broader landscape of research that includes patient metadata reporting in genomic viral sequencing studies of SARS-CoV-2

As with any search method for relevant studies, some may be missed by both the automated methods proposed and manually developed queries.