Subcutaneous adipose tissue measured by computed tomography could be an independent predictor for early outcomes of patients with severe COVID-19

Early human evidence such as case series or small samples is exploring possible benefits.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Background Patients with severe Coronavirus Disease 2019 (COVID-19) can experience protein loss due to the inflammatory response and energy consumption, impairing immune function. The presence of excessive visceral and heart fat leads to chronic long-term inflammation that can adversely affect immune function and, thus, outcomes for these patients. We aimed to explore the roles of prognostic nutrition index (PNI) and quantitative fat assessment based on computed tomography (CT) scans in predicting the outcomes of patients with severe COVID-19. Methods A total of 130 patients with severe COVID-19 who were treated between December 1, 2022, and February 28, 2023, were retrospectively enrolled. The patients were divided into survival and death groups. Data on chest CT examinations following admission were collected to measure cardiac adipose tissue (CAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) and to analyze the CT score of pulmonary lesions. Clinical information and laboratory examination data were collected. Univariate and multivariate logistic regression analyses were used to explore the risk factors associated with death, and several multivariate logistic regression models were established. Results Of the 130 patients included in the study (median age, 80.5 years; males, 32%), 68 patients died and 62 patients survived. PNI showed a strong associati

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

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