Research Trends on Living Donors for Liver Transplantation: A Text Network Analysis and Topic Modeling

Abstract

Purpose: This study aimed to identify research topics and trends on living liver donors over time through text network analysis and topic modeling.Methods: Five electronic databases (PubMed, CINAHL, Embase, Web of Science, and PsycINFO) were reviewed for studies published through September 2023, and 392 studies were included. Text network analysis was used to identify the basic characteristics and centrality of the network. The topics were named after extracting meaningful topics through topic modeling using latent Dirichlet allocation.Results: A total of 1,111 keywords were extracted from the abstracts of 392 selected studies, among which “length of stay,” “morbidity,” “mortality,” “pain,” and “quality of life” showed high frequency and centrality. Through topic modeling analysis, the following four topics were derived: objective health indicators (topic 1), subjective health indicators (topic 2), hepatobiliary-related indicators (topic 3), and early health indicators (topic 4). An analysis of trends in these topics over time showed that the proportion of topics 1, 3, and 4 increased or remained stable. In contrast, there was no significant change in topic 2, representing subjective health indicators.Conclusion: This study explored research trends on living liver donors using text network analysis and topic modeling. Based on the main topics derived, research on postoperative outcomes for living liver donors has focused on objective health indicators, hepatobiliary-related indicators, and early health indicators compared to subjective health indicators. We suggest that future studies utilize integrated indicators of physical and psychosocial aspects.