Radiologic Features of T10 Paravertebral Muscle Sarcopenia: Prognostic Factors in COVID-19.

COVID-19 Chest computed tomography Cross-sectional anatomy Muscle mass Prognostic factors SAR-CoV-2

Journal

Journal of clinical medicine research
ISSN: 1918-3003
Titre abrégé: J Clin Med Res
Pays: Canada
ID NLM: 101538301

Informations de publication

Date de publication:
Jul 2023
Historique:
received: 28 05 2023
accepted: 12 07 2023
medline: 14 8 2023
pubmed: 14 8 2023
entrez: 14 8 2023
Statut: ppublish

Résumé

Sarcopenia, defined as a small cross-sectional area (CSA) in computed tomography (CT) measurements of skeletal muscles, serves as a disease severity marker in various clinical scenarios, including pulmonary conditions and critical illness. Another parameter of sarcopenia, the level of myosteatosis, reflected by the tissue's radiodensity, in the thoracic skeletal muscles group, has been linked to disease progression in coronavirus disease 2019 (COVID-19) patients. We hypothesize that CT-derived measurements of the skeletal muscle density (SMD) and the CSA of thoracic skeletal muscles can predict outcomes in COVID-19 pneumonia. We retrospectively reviewed the CT scans of 84 patients with COVID-19 pneumonia admitted to two of Greece's largest academic teaching hospitals between April 2020 and February 2021. CSA and SMD at the level of the T10 vertebra were measured using computational imaging methods. The patient population was stratified according to survival status and CT severity score (CT-SS). Correlations were drawn between the radiologic features of sarcopenia, CT severity subgroups, serum inflammatory markers, and adverse events, e.g., death and intubation. Thoracic muscles' CSA measurements correlate with CT-SS and prominent inflammatory markers, such as white blood cell (WBC), C-reactive protein (CRP), fibrinogen, and D-dimers. Moreover, according to linear regression analysis, CSA seems to predict CT-SS variation significantly (β = -0.266, P = 0.018). CSA proved to differ significantly across survivors (P = 0.027) but not between CT severity categories and intubation subgroups. The AUC (area under the curve) of the receiver operating characteristic (ROC) curve for the predictive value of thoracic muscles' CSA in mortality is 0.774 (95% confidence interval (CI): 0.66 - 0.83, P < 0.000). The optimal cut-off value (Youden index = 0.57) for mortality prognosis, with a sensitivity of 66.7% and a specificity of 88.9%, is 15.55. Thoracic muscles' SMD analyses did not reveal any significant correlations. Easy to obtain and accurately calculated, radiologic features can provide a reliable alternative to laboratory methods for predicting survival in COVID-19. Thoracic muscles' CSA measurement in the level of the T10 vertebra, an acclaimed prognostic imaging assessment that relates directly to CT-SS and inflammatory markers in COVID-19 pneumonia, is a fairly specific tool for survival prognosis.

Sections du résumé

Background UNASSIGNED
Sarcopenia, defined as a small cross-sectional area (CSA) in computed tomography (CT) measurements of skeletal muscles, serves as a disease severity marker in various clinical scenarios, including pulmonary conditions and critical illness. Another parameter of sarcopenia, the level of myosteatosis, reflected by the tissue's radiodensity, in the thoracic skeletal muscles group, has been linked to disease progression in coronavirus disease 2019 (COVID-19) patients. We hypothesize that CT-derived measurements of the skeletal muscle density (SMD) and the CSA of thoracic skeletal muscles can predict outcomes in COVID-19 pneumonia.
Methods UNASSIGNED
We retrospectively reviewed the CT scans of 84 patients with COVID-19 pneumonia admitted to two of Greece's largest academic teaching hospitals between April 2020 and February 2021. CSA and SMD at the level of the T10 vertebra were measured using computational imaging methods. The patient population was stratified according to survival status and CT severity score (CT-SS). Correlations were drawn between the radiologic features of sarcopenia, CT severity subgroups, serum inflammatory markers, and adverse events, e.g., death and intubation.
Results UNASSIGNED
Thoracic muscles' CSA measurements correlate with CT-SS and prominent inflammatory markers, such as white blood cell (WBC), C-reactive protein (CRP), fibrinogen, and D-dimers. Moreover, according to linear regression analysis, CSA seems to predict CT-SS variation significantly (β = -0.266, P = 0.018). CSA proved to differ significantly across survivors (P = 0.027) but not between CT severity categories and intubation subgroups. The AUC (area under the curve) of the receiver operating characteristic (ROC) curve for the predictive value of thoracic muscles' CSA in mortality is 0.774 (95% confidence interval (CI): 0.66 - 0.83, P < 0.000). The optimal cut-off value (Youden index = 0.57) for mortality prognosis, with a sensitivity of 66.7% and a specificity of 88.9%, is 15.55. Thoracic muscles' SMD analyses did not reveal any significant correlations.
Conclusions UNASSIGNED
Easy to obtain and accurately calculated, radiologic features can provide a reliable alternative to laboratory methods for predicting survival in COVID-19. Thoracic muscles' CSA measurement in the level of the T10 vertebra, an acclaimed prognostic imaging assessment that relates directly to CT-SS and inflammatory markers in COVID-19 pneumonia, is a fairly specific tool for survival prognosis.

Identifiants

pubmed: 37575354
doi: 10.14740/jocmr4963
pmc: PMC10416190
doi:

Types de publication

Journal Article

Langues

eng

Pagination

368-376

Informations de copyright

Copyright 2023, Schinas et al.

Déclaration de conflit d'intérêts

None to declare.

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Auteurs

Georgios Schinas (G)

School of Medicine, University of Patras, Patras, Greece.
These authors contributed equally to this article.

Vasiliki Dimakopoulou (V)

Department of Internal Medicine, University Hospital of Patras, Patras, Greece.
These authors contributed equally to this article.

Konstantinos Dionysopoulos (K)

Department of Radiology, University Hospital of Patras, Patras, Greece.

Georgia Fezoulidi (G)

Faculty of Medicine, University of Thrace, Alexandroupolis, Greece.

Marianna Vlychou (M)

Department of Radiology, General University Hospital of Larissa, Larissa, Greece.

Katerina Vassiou (K)

Department of Anatomy, Medical School, University of Thessaly, Larisa, Greece.

Nikolaos K Gatselis (NK)

Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece.

Anna Samakidou (A)

Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece.

Georgios Giannoulis (G)

Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece.

Argyrios Tzouvelekis (A)

Division of Respiratory Medicine, Department of Internal Medicine, University Hospital of Patras, Patras, Greece.

Markos Marangos (M)

Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece.

Charalambos Gogos (C)

Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece.

George N Dalekos (GN)

Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece.

Christina Kalogeropoulou (C)

Department of Radiology, University Hospital of Patras, Patras, Greece.

Karolina Akinosoglou (K)

Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece.

Classifications MeSH