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In a recent study published in Metabolic Diseases, the authoritative journal Metabolic Diseases, researchers aimed to assess the relationship between the severity and adverse outcomes of pneumonia in patients with COVID-19 quantified epidural adipose tissue (EAT) on chest computerized fault scans (CT).
researchers conducted an ex post facto analysis of the prospective international registration study, which included 109 continuous patients (aged 64±16 years; 62% male), who were diagnosed with COVID-19 by laboratory examinations and chest CT imaging.
using semi-automated software, the researchers quantified the abnormal lung burden (%) associated with COVID-19 pneumonia and used deep learning software to measure EAT volume (mL) and attenuation (Hounsfield units).
the main outcome of the study was clinical deterioration (hospitalization in the intensive care unit, invasive mechanical aeration or boost therapy) or hospital death.
in the multivariable linear regression analysis of patient colitis, the total burden of COVID-19 pneumonia was related to the amount of EAT (β s 10.6, p s 0.005) and EAT attenuation (β s 5.2, p s 0.004).
dietary levels were associated with serum lactic acid dehydrogenase levels (r=0.361, p=0.001) and C-reactive proteins (r=0.450, p<0.001).
23 patients (21.1%) experienced clinical deterioration or death 3 days after chest CT examination (IQR was 1-13 days).
in multivarivariating logistic regression analysis, EAT volume (for each double, OR is 5.1 (95% CI is 1.8-14.1) p=0.011) and EAT decay Subtract (for each additional 5 Hounsfield units, OR is 3.4 (95% CI is 1.5-7.5) and p=0.003) is an independent predictor of clinical deterioration or death, e.g. total Pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p-0.002), chronic lung disease (OR 1.3 (95% CI 1.1-1.7', p=0.011) and history of heart failure (OR is 3.5 (95%1.1-8.2) and p=0.037).
this, it can be seen that chest CT quantified EAT is independently associated with the severity and adverse outcomes of pneumonia in COVID-19 patients, which provides evidence for its use in clinical risk stratification.
