CT scan-derived pectoralis muscle parameters are closely associated with COVID-19 outcomes: A systematic review and meta-analysis

Zhang Wen, Tao Wang, Sha Luo and Yiwen Liu

PLOS ONE, 2025, vol. 20, issue 1, 1-13

Abstract: Background: The relationships between pectoralis muscle parameters and outcomes in patients with coronavirus disease 2019 (COVID-19) remain uncertain. Methods: We systematically searched PubMed, Embase, Web of Science and the Cochrane Library from 1 January 2019 to 1 May 2024 to identify non-overlapping studies evaluating pectoralis muscle-associated index on chest CT scan with clinical outcome in COVID-19 patients. Random-effects and fixed-effects meta-analyses were performed, and heterogeneity between studies was quantified using the I2 statistic. The risk of study bias was assessed using the Newcastle-Ottawa scale. Funnel plots for detecting small-study effects. Results: A total of 9 studies with 4109 COVID-19 patients were included. The meta-analysis findings revealed a correlation between pectoralis muscle parameters and COVID-19 prognosis. Specifically, patients with higher pectoralis muscle density (PMD) exhibited a lower mortality risk, with an odds ratio (OR) of 0.95 (95% CI: 0.92–0.99). The rate of intubation was lower in COVID-19 patients with a high pectoralis muscle index (PMI) (OR = 0.96, 95% CI: 0.92–1.00). Conclusion: In summary, a low PMD is associated with a marginally elevated risk of mortality, whereas a decreased PMI represents a risk factor for intubation in COVID-19 patients. These findings suggest that pectoralis muscle parameters on chest CT may be a useful prognostic tool for COVID-19 patients.

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316893 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 16893&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0316893

DOI: 10.1371/journal.pone.0316893

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().