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

Z Zhang Wen (Center of Energy Storage Materials & Technology, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, National Laboratory of Solid State Microstructures and Collaborative Innovation Center of Advanced Microstructures) T Tao Wang S Sha Luo (Biomedical Pioneering Innovation Center, School of Life Science, Peking University) Y Yiwen Liu

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.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 1
Published January 28, 2025
Pages e0316893
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (4)

Z

Zhang Wen

Center of Energy Storage Materials & Technology, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, National Laboratory of Solid State Microstructures and Collaborative Innovation Center of Advanced Microstructures

T

Tao Wang

S

Sha Luo

Biomedical Pioneering Innovation Center, School of Life Science, Peking University

Y

Yiwen Liu