[Metagenomic next - generation sequencing for diagnosis of infection of unknown origin in intensive care units: a bibliometric analysis].

Abstract

Objective: To investigate the scientific outputs of metagenomic next-generation sequencing (mNGS) for diagnosis of infection of unknown origin in intensive care units (ICUs), and to decipher the latest advances, frontier trends and spatiotemporal evolution of research hotpots in mNGS for diagnosis of infection of unknown origin in ICUs.

Methods: Publications pertaining to the application of mNGS in diagnosis of infection of unknown origin in ICUs were retrieved from Web of Science Core Collection (WOSCC) from January 1, 2015 to December 31, 2024. The software Scimago Graphica 1.0.30 was employed to generate the network maps of collaboration relationships between countries, international collaborative relationships, author collaborations, institutional collaborative relationships, and a heatmap of journals, and the software VOSviewer 1.6.18 was used to create a heatmap of keywords, and maps of keyword co-occurrence clustering and keyword clustering timelines. In addition, the keyword burst map was created using the software CiteSpace 6.3.R3.

Results: A total of 1 707 publications were included in the final analysis, and the number of publications appeared an overall tendency towards a rise from 2015 to 2024, with the largest number of publications seen in 2024 (545 publications). The largest number of publications was recorded in China (1 390 publications), followed by in USA (190 publications) and United Kingdom (31 publications), and China led the global research in this field, with 81% of global related researches linked with China. Frontiers in Cellular and Infection and Microbiology published the largest number of articles (212 publications, 12.42%), and Joseph Derisi was the most productive author (33 publications). Author collaborations occurred within groups; however, there was a lack of close inter-group collaborations, with University of California, San Francisco and Chan Zuckerberg Biohub-based group seen as the largest collaborative group. High-frequency co-occurrence keywords included mNGS, infection, diagnosis, case report, community-acquired pneumonia and bronchoalveolar lavage fluid, and the 100 most common high-frequency co-occurrence keywords were assigned into four clusters. Keyword clustering timeline analysis revealed that the research hotspots in this field shifted from virus sequencing and sequence alignment to severe pulmonary infections, and keyword burst analysis showed identification, mNGS and virus as top three keywords with the highest burst intensity.

Conclusions: mNGS was mainly used for identification of viruses among patients with infections of unknown origins in ICUs from 2015 to 2024, and future research priority shifted to pathogen detection for severe pulmonary infections.