Literature DB >> 34866554

Epicardial and pericoronary adipose tissue in severe COVID-19 infection.

Pınar Turker Duyuler1, Serkan Duyuler2, Bekir Demirtaş3, Velihan Çayhan4.   

Abstract

OBJECTIVE: To investigate the association between epicardial and pericoronary adipose tissue thicknesses measured with computed tomography (CT) and severity of COVID-19 infection.
METHODS: We recruited 504 patients admitted with RT-PCR-proven diagnosis of COVID-19 infection and underwent simultaneous Chest CT scanning. Epicardial adipose tissue thickness (EAT) and pericardial adipose tissue thickness (PCAT) were measured by CT. Comparisons were performed between ICU admitting and non-ICU admitting patients were performed.
RESULTS: Of 504 patients, 423 patients were hospitalised in normal wards or followed as outpatient, and 81 patients were admitted to ICU. EAT and PCAT were significantly increased in ICU patients (5.98[5.06-7.13] mm vs. 8.05[6.90-9.89] mm, p < 0.001 and 9.3[7.4-11.5] mm vs. 11.2[10.3-13.2] mm, p < 0.001, respectively). In multiple logistic regression analyses, EAT and PCAT were independent predictors of ICU admission. A cut-off point of 6.64 mm EAT has a sensitivity of 82.7% and a specificity of 66.7% (AUC = 0.789, 95% CI: 0.744-0.833, p < 0.001) and a cut-off point of 9.85 mm PCAT has a sensitivity of 91.4% and a specificity of 61.2% (AUC = 0.744, 95% CI: 0.700-0.788, p < 0.001).
CONCLUSION: We found that both increased EAT and PCAT were associated with the severity of COVID-19 infection defined as the need for ICU admission.

Entities:  

Keywords:  COVID-19; computed tomography; epicardial adipose tissue; intensive care; pericoronary adipose tissue

Year:  2021        PMID: 34866554     DOI: 10.1080/00015385.2021.2010009

Source DB:  PubMed          Journal:  Acta Cardiol        ISSN: 0001-5385            Impact factor:   1.718


  3 in total

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Authors:  Kaiwei Liu; Xin Wang; Guang Song
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2.  Epicardial adipose tissue in coronary microvascular dysfunction.

Authors:  Serkan Duyuler; Pınar Türker Duyuler
Journal:  Int J Obes (Lond)       Date:  2022-04-25       Impact factor: 5.551

3.  Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19.

Authors:  Axel Bartoli; Joris Fournel; Léa Ait-Yahia; Farah Cadour; Farouk Tradi; Badih Ghattas; Sébastien Cortaredona; Matthieu Million; Adèle Lasbleiz; Anne Dutour; Bénédicte Gaborit; Alexis Jacquier
Journal:  Cells       Date:  2022-03-18       Impact factor: 6.600

  3 in total

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