# Sarcopenia, myosteatosis and inflammation are independent prognostic factors of SARS-CoV-2 pneumonia patients admitted to the ICU

Romain Chauvot de Beauchene, Bertrand Souweine, Benjamin Bonnet, Bertrand Evrard, Yves Boirie, Lucie Cassagnes, Claire Dupuis

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Romain Chauvot de Beauchene, Bertrand Souweine, Benjamin Bonnet, Bertrand Evrard, Yves Boirie, et al.. Sarcopenia, myosteatosis and inflammation are independent prognostic factors of SARS-CoV-2 pneumonia patients admitted to the ICU. Scientific Reports, 2025, 15 (1), pp.4373. 10.1038/s41598-025-88914-4. hal-05077438

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# Sarcopenia, myosteatosis and inflammation are independent prognostic factors of SARS-CoV-2 pneumonia patients admitted to the ICU

Romain Chauvot de Beauchene<fnref n="1" />, Bertrand Souweine²,³, Benjamin Bonnet⁴,⁵, Bertrand Evrard⁴,⁵, Yves Boirie⁶,⁷, Lucie Cassagnes<fnref n="1" />,⁷ & Claire Dupuis²,⁷,⁸

The aims of our study were to assess the correlations between sarcopenia and myosteatosis assessed by CT-scan at T4 and/or L3 levels and inflammation in critically ill COVID patients on ICU admission, and their respective prognostic value on day 90 death (D90-death). It is a retrospective monocentric study. Sarcopenia was defined by skeletal muscle cross sectional surface area (CSA) and myosteatosis by skeletal muscle density (SMD) at L3 and T4 levels. Inflammatory biomarkers were collected on ICU admission. Of the 239 patients, 74 died by D90; 66.6% get sarcopenia on ICU admission. CSA at T4 level was an independent risk factor for D90-death (1.66[1.03; 2.66]; *p* = 0.04), as were procalcitonin (2.03[1.2; 3.43]; *p* = 0.01) and IL-6 levels (1.56[0.96; 2.54]; *p* = 0.07). In addition, we found correlation factors of 0.79 (*p* < 0.01) between SMD at T4 and L3 levels, and a correlation factor of 0.64 (*p* < 0.01) between CSA at T4 and L3 levels.These results indicate a poorer prognosis following a decrease in muscle surface area, a decrease in density, and an increase in inflammatory biomarkers such as IL6. It also suggests that incorporating indices of sarcopenia with inflammatory biomarkers may improve prognostic accuracy.

**Keywords** COVID 19, Inflammation, Chest CT-scan, Sarcopenia, Low muscle mass, Critical illness, Outcome

**Abbreviations**

<table><tr><td>AI</td><td>Artificial intelligence</td></tr><tr><td>ARDS</td><td>Acute respiratory distress syndrome</td></tr><tr><td>AUC</td><td>Area under curve</td></tr><tr><td>BMI</td><td>Body mass index</td></tr><tr><td>BNP</td><td>Brain natriuretic peptide</td></tr><tr><td>CHU</td><td>University Hospital Centre</td></tr><tr><td>CI</td><td>Confidence interval</td></tr><tr><td>COVID-19</td><td>Coronavirus disease 2019</td></tr><tr><td>CSA</td><td>skeletal surface Cross-Sectional Area</td></tr><tr><td>CRP</td><td>C-reactive protein</td></tr><tr><td>HAS</td><td>Haute Autorité de Santé</td></tr><tr><td>HTA</td><td>Hypertension</td></tr><tr><td>HU</td><td>Hounsfield unit</td></tr></table>

<footnote n="1">¹Radiology Department, Clermont-Ferrand University hospital, Clermont-Ferrand, France. ²Intensive Care Department, Clermont-Ferrand University Hospital, Clermont-Ferrand, France. ³CNRS, LMGE, Clermont Auvergne University, Clermont-Ferrand 63000, France. ⁴Immunology Department, Clermont-Ferrand University Hospital, Clermont-Ferrand, France. ⁵Laboratory of Immunology, ECREIN, UMR1019 UNH, UFR Médecine de Clermont-Ferrand, Clermont Auvergne University, Clermont-Ferrand, France. ⁶Clinical nutrition department, Clermont-Ferrand University hospital, Clermont-Ferrand, France. ⁷Unité de Nutrition Humaine, INRAe, CRNH Auvergne, Clermont Auvergne University, Clermont Ferrand 63000, France. ⁸Service de Médecine Intensive et Réanimation, CHU Clermont Ferrand, Clermont Ferrand, France. ☎email: cdupuis1@chu-clermontferrand.fr</footnote>
<table><tr><td>IL</td><td>Interleukin</td></tr><tr><td>mHLA-DR</td><td>Monocytic human leukocyte antigen-DR</td></tr><tr><td>RT-PCR</td><td>Reverse transcription polymerase chain reaction</td></tr><tr><td>SAPS II</td><td>Simplified acute physiology score II</td></tr><tr><td>SARS-CoV-2</td><td>Severe acute respiratory syndrome coronavirus 2</td></tr><tr><td>SFR</td><td>French Society of Radiology</td></tr><tr><td>SIT</td><td>Thoracic Imaging Society</td></tr><tr><td>SMD</td><td>Skeletal muscle density</td></tr><tr><td>SMI</td><td>Skeletal muscle index</td></tr><tr><td>SOFA</td><td>Sepsis-related organ failure assessment</td></tr><tr><td>SPILF</td><td>Société de Pathologie Infectieuse en Langue Française (French Infectious Diseases Society)</td></tr><tr><td>TNF</td><td>Tumor necrosis factor</td></tr><tr><td>VAP</td><td>Ventilator-associated pneumonia</td></tr></table>

Sarcopenia is a condition characterized by a progressive loss of muscle mass and strength that is particularly prevalent in older individuals and those with comorbidities¹. It is closely related to frailty and has been identified as an adverse prognostic factor in various diseases, including chronic respiratory failure²,³ and cancer⁴,⁵, acute conditions such as pneumonia and critical illness⁶⁻⁸. Sarcopenia has also been identified as predictors for outcomes in COVID-19 pneumonia⁹⁻¹² including ICU admission, need for invasive mechanical ventilation (IMV), and mortality¹³⁻¹⁵.

At the end of a prolonged ICU stay, most of the patients can present ICU acquired weakness (ICU-AW)¹⁶, which is associated with longer duration of invasive mechanical ventilation, ICU stay and increased risk of death¹⁷. Sarcopenia is a major player of the ICU-AW¹⁸ and is closely linked in ICU to preexisting denutrition, calory and protein intakes during ICU stay, but also hypercatabolism and inflammation¹⁹.

COVID-19 patients with acute hypoxic respiratory failure (COVID-AHRF)²⁰,²¹, are characterized by a prolonged hypercatabolism²² and inflammation that can result in the formation of a “cytokine storm” with the secretion of multiple inflammatory mediators, including interleukins. This inflammation can result in COVID-AHRF and increased risk of death²³,²⁴. In addition, other markers like under-expression of monocytic human leukocyte antigen-DR (mHLA-DR)²⁵ have revealed patients with severe SARS-CoV 2 pneumonia admitted to the intensive care unit (ICU).

CT-scan has been rapidly recommended for all severe COVID-19 patients to assess respiratory status¹¹,²⁶. On those CT-scan, it is possible to obtain a standardized analysis of myosteatosis using skeletal muscle density (SMD) and the skeletal surface cross-sectional area (CSA) using dedicated software⁹. Even if not recommended at the present time to define sarcopenia, due to lack of universal threshold adapted for all patients, either for surgical patients²⁷, or the last consensus from the Global Leadership Initiative in Sarcopenia (GLIS)²⁸, the CSA and SMD measurements obtained at T4 (4th thoracic vertebra) and L3 (3rd lumbar vertebrae) can be used as markers for assessing sarcopenia and myosteatosis. Sarcopenia is related to a decrease of the CSA. Myosteatosis is defined by a reduction in SMD as measured in Hounsfield units (HU)²⁹. Those criteria have been used most of the time in oncologic and older patients³⁰, but also in general cohort of hospitalized patients³¹.

Until nowadays, the association between inflammation and muscle loss and their mutual consequences on outcomes have not yet been fully explored for COVID-19 patients admitted in ICU. Furthermore, the correlation between CSA and SMD assessed on T4 and/or L3 have not been also fully explored.

In that context, the aims of our study were to assess among critically ill COVID-19 patients on ICU admission the association between sarcopenia and myosteatosis assessed on CT-scan on L3 and or T4, inflammatory biomarkers and the risk of death at day 90. We also assessed the correlations between the indices of sarcopenia and myosteatosis obtained on CT-scan at the levels L3 and T4.

# Materials and methods

## Type of study

We conducted a single-centre retrospective observational study, achieved on a database collected prospectively, in the medical ICU of Clermont Ferrand University Hospital, following the TRIPOD guidelines³². All patients or their relatives received clear, fair and appropriate information before the consent. Then, all the patients and/or their legal guardian(s) gave their written informed consent for the storage and use for research purposes of the blood samples of the patients collected during routine examinations during the hospital stay. Our study received approval from the ethics committee (Institutional review board (IRB) No. 20.03.20.56342 from the ethic committee called in french: “Comités de protection des personnes (CPP)”-Ile-de-France VI Groupe Hospitalier Pitié-Salpetrière) in accordance with our local regulations. All the procedures were followed in accordance with the ethical standards of the responsible committee on human experimentation (institutional or regional) and with the Helsinki Declaration of 1975.

## Population

We included patients over 18 years of age admitted for severe COVID-19 documented by RT PCR in the medical ICU between July 1, 2020 and November 1, 2021 who had a thoracic CT scan more than 72 h before or after their ICU admission. We excluded patients referred from another ICU, those who decided to limit or stop active therapies in the first two days following ICU admission, and those whose ICU length of stay (LOS) was less than 72 h.
# Diagnosis of SARS-CoV-2 infection

The diagnosis of SARS-Co-2 pneumonia was confirmed by a positive PCR test, irrespective of the patient's serological status.

Severe COVID-19 was defined according to WHO criteria by an oxygen saturation <90% on room air and/or signs of severe respiratory distress (accessory muscle use, inability to complete full sentences, respiratory rate > 30 breaths per minute).

Patients with previous immunosuppression on ICU admission gathered patients with organ transplants, acquired immunodeficiency syndrome (AIDS), non-AIDS HIV, corticoids > 1 month or > 2 mg/kg/j, chemotherapy, aplasia, or other immunodepression.

# Data collection

All data were prospectively collected including demographics, chronic disease/comorbidities as assessed by the Knaus Scale³³, baseline severity indexes, and simplified acute physiology score (SAPS) II³⁴. Sequential Organ Failure Assessment (SOFA)³⁵ scores, treatment and laboratory parameters on admission, including blood count, plasma coagulability markers (D-dimer and fibrinogen levels) and biomarkers of inflammation (CRP, ferritin, IL-1β, IL-6, IL-10, Procalcitonin, mHLA-DR).

Several data were collected throughout ICU stay, including the need for organ assistance including IMV, vasopressor drugs, renal replacement therapy (RRT). The prognostic criteria collected were ICU and hospital LOS, and vital status at ICU discharge and on D90. We consider all-cause mortality at day 90.

# Extent of lung parenchymal lesions on CT-scan

Most of the patients underwent thoracic and/or abdominal CT-scan. The percentage of the extent of parenchymal pulmonary lesions was assessed with Thoracic VCAR software (GE Healthcare), a post-processing software that uses an automatic thresholding method based on density measurements in HU. A density threshold greater than –686 HU (Hounsfield unity) was thus able to quantify areas of condensation or ground glass associated with COVID-19 pneumopathy and differentiate them from healthy lung parenchyma. The software has the advantage of allowing reproducible standardization of the measurement³⁶.

# Measuring Sarcopenia and myosteatosis

We measured muscle surfaces and densities on sections passing through levels L3 and T4 considering all muscles (Figure S1). The measurements were made with SliceOmatic V 5.0 software (Tomovision, Magog, Canada). The HU thresholds for muscle were defined from –29 to +150 HU³⁷, which means that the lower the density of a muscle surface the more it will be considered to be in a state of myosteatosis.

Sarcopenia was defined according to the skeletal muscle index (SMI) (cm²/m²). A reduced SMI at level L3 corresponded to a value of <41 cm²/m² for women and <43 cm²/m² for men with a body mass index (BMI) < 30 kg/m², and <53 cm²/m² for men with a BMI > 30 kg/m²²³,³⁹. At T4 level, a reduced SMI corresponded to a value of <51.9 cm²/m² for women and <65.2 cm²/m² for men independently of BMI⁴⁰.

Myosteatosis was defined according to SMD. A reduced SMD was defined, at level L3, as <41 HU for a BMI < 25 kg/m² and <33 HU for a BMI > 25 kg/m²²³,³⁹. At level T4, a reduced SMD corresponded to a value of <36.2 HU for women and <48 HU for men⁴⁰.

# Statistical analysis

Patient characteristics were expressed as numbers (percentage) for categorical variables and as median (interquartile range) for continuous variables. Linearity of the continuous covariates was checked carefully. Comparisons were made with Fisher's exact test for categorical variables and the Wilcoxon test for continuous variables, which were dichotomized according to their medians or usual threshold used into the literature if necessary.

The correlations between the variables were explored using Pearson's correlation coefficient and represented by a correlation matrix. The biases between the different indices were investigated with the Bland-Altman plot.

Risk factors for death at D90 were investigated using a Cox survival model. Since we knew the outcomes for all patients at day 90, no competitive risk models were considered. We defined mortality as all-cause mortality. After imputation, uni- and multivariate analyses were performed. Only covariates with less than 15% of missing values were considered into the analyses. Variables with a p-value < 0.1 in univariate analysis were tested for inclusion in the multivariate models. A backward selection of the co-variate was used retaining only variables with a p-value < 0.05 to determine the final multivariate model. The proportionality of the risks was carefully checked. A p-value of less than 0.05 was considered statistically significant. Missing data were imputed linearly. Several sensitivity analyses were performed. First, we conducted complete case analyses, and then we adjusted for all covariates that had a p-value < 0.05 in the univariate analyses. All statistical analyses were performed using SAS software, version 9.4 (SAS Institute, Cary, NC) and R (Version 3.4.0; R Core Team, Wien, Austria). The following packages were used for the analyses with R: ggcorrplot, ggplot2, ggsci, gridExtra.

# Results
## Population

A total of 316 patients were eligible over the inclusion period. The 63 patients who did not have imaging within the defined period and the 16 who were transferred from another ICU were excluded. Finally, 239 patients were included in the study (Figure S2). Their characteristics are presented in Table 1. They had a median age of 68.8 years [60.4; 74.8], a median BMI of 28.8 kg/m² [24.6; 32] and were 66.8% male. The main comorbidities were cardiovascular disease (18%) and type 2 diabetes (14%). Overall, 17% of patients were receiving
<table><thead><tr><th>Median [IQR]/number (%)</th><th>All (N=239)</th><th>Alive at D90 (N=165)</th><th>D90 mortality (N=74)</th><th>Pvalue</th></tr></thead><tbody><tr><td>Number of patients</td><td>239</td><td>165</td><td>74</td><td></td></tr><tr><td>Admission before december 2020</td><td>103 (43)</td><td>67 (40.6)</td><td>36 (48.6)</td><td>0.30</td></tr><tr><td>Age (yr)</td><td>68.8 [60.4; 74.8]</td><td>66.2 [57.4; 72.5]</td><td>74.5 [69; 79.7]</td><td>&lt;0.01</td></tr><tr><td>Sex, men</td><td>159 (66.6)</td><td>109 (66.1)</td><td>50 (67.6)</td><td>0.82</td></tr><tr><td>Comorbidities*</td><td></td><td></td><td></td><td></td></tr><tr><td>BMI &gt; 30 kg/m² (body mass index)</td><td>106 (44.4)</td><td>77 (46.7)</td><td>29 (39.2)</td><td>0.28</td></tr><tr><td>Cardiovascular disease</td><td>42 (17.6)</td><td>20 (12.1)</td><td>22 (29.7)</td><td>&lt;0.01</td></tr><tr><td>Respiratory chronic disease</td><td>19 (8)</td><td>11 (6.7)</td><td>8 (10.8)</td><td>0.27</td></tr><tr><td>Chronic kidney disease</td><td>19 (8)</td><td>8 (4.8)</td><td>11 (14.9)</td><td>&lt;0.01</td></tr><tr><td>Chronic liver disease</td><td>4 (1.6)</td><td>0 (0)</td><td>4 (5.4)</td><td>&lt;0.01</td></tr><tr><td>Immunosuppression§</td><td>40 (16.8)</td><td>25 (15.2)</td><td>15 (20.3)</td><td>0.33</td></tr><tr><td>Diabetes mellitus</td><td>33 (13.8)</td><td>19 (11.5)</td><td>14 (18.9)</td><td>0.13</td></tr><tr><td>Time from 1st symptoms to ICU admi (days)</td><td>9 [6; 12]</td><td>10 [8; 12]</td><td>8 [5; 10]</td><td>&lt;0.01</td></tr><tr><td>Time from hospital to ICU admi (days)</td><td>2 [1; 4]</td><td>2 [1; 4]</td><td>2 [1; 4]</td><td>0.59</td></tr><tr><td>Remdesivir</td><td>48 (20)</td><td>36 (21.8)</td><td>12 (16.2)</td><td>0.32</td></tr><tr><td>Steroids</td><td>212 (88.8)</td><td>144 (87.3)</td><td>68 (91.9)</td><td>0.30</td></tr><tr><td>Tocilizumab</td><td>17 (7.2)</td><td>14 (8.5)</td><td>3 (4.1)</td><td>0.22</td></tr><tr><td>SAPS II</td><td>35 [29; 43]</td><td>34 [28; 40]</td><td>38.5 [34; 47]</td><td>&lt;0.01</td></tr><tr><td>SOFA</td><td>4 [3; 6]</td><td>4 [3; 5]</td><td>5 [4; 7]</td><td>&lt;0.01</td></tr><tr><td>PaO2/FiO2</td><td>98 [71; 257.2]</td><td>105.7 [78.8; 290]</td><td>76.1 [61.1; 147.1]</td><td>&lt;0.01</td></tr><tr><td>Extent of lung lesions (%)</td><td>45.6 [33.8; 59]</td><td>44 [31.8; 57]</td><td>47.8 [35.5; 61.9]</td><td>0.10</td></tr><tr><td>SMI L3 (cm²/m²)</td><td>43 [36; 50.4]</td><td>43.9 [36.8; 52.1]</td><td>40.3 [34.4; 47]</td><td>0.02</td></tr><tr><td>Low SMI L3</td><td>159 (66.6)</td><td>103 (62.4)</td><td>56 (75.7)</td><td>0.04</td></tr><tr><td>SMI T4 (cm²/m²)</td><td>61 [50.6; 70]</td><td>62.4 [51.1; 71.8]</td><td>56.7 [45; 66.9]</td><td>&lt;0.01</td></tr><tr><td>Low SMI T4</td><td>124 (51.8)</td><td>77 (46.7)</td><td>47 (63.5)</td><td>0.02</td></tr><tr><td>SMD L3 (HU)</td><td>29 [24; 37]</td><td>31 [26; 39]</td><td>26 [22; 31]</td><td>&lt;0.01</td></tr><tr><td>Low SMD L3</td><td>168 (70.2)</td><td>105 (63.6)</td><td>63 (85.1)</td><td>&lt;0.01</td></tr><tr><td>SMD T4 (HU)</td><td>39 [33; 44]</td><td>40 [35; 45]</td><td>35 [28.4; 40]</td><td>&lt;0.01</td></tr><tr><td>Low SMD T4</td><td>168 (70.2)</td><td>106 (64.2)</td><td>62 (83.8)</td><td>&lt;0.01</td></tr><tr><td>IMV</td><td>17 (7.2)</td><td>8 (4.8)</td><td>9 (12.2)</td><td>0.04</td></tr><tr><td>Vasopressors</td><td>20 (8.4)</td><td>7 (4.2)</td><td>13 (17.6)</td><td>&lt;0.01</td></tr><tr><td>Renal replacement therapy</td><td>6 (2.6)</td><td>3 (1.8)</td><td>3 (4.1)</td><td>0.31</td></tr><tr><td>Antimicrobial therapy</td><td>73 (30.6)</td><td>46 (27.9)</td><td>27 (36.5)</td><td>0.18</td></tr><tr><td>Laboratory features on admission</td><td></td><td></td><td></td><td></td></tr><tr><td>Neutrophils (G/L) (miss = 33)</td><td>6.6 [4.8; 9.8]</td><td>6.2 [4.8; 9.1]</td><td>7.3 [4.9; 10.7]</td><td>0.10</td></tr><tr><td>Lymphocytes (G/L) (miss = 33)</td><td>0.6 [0.4; 1]</td><td>0.7 [0.5; 1.1]</td><td>0.6 [0.4; 0.9]</td><td>0.28</td></tr><tr><td>Procalcitonin (µg/L) (miss = 16)</td><td>0.2 [0.2; 0.6]</td><td>0.2 [0.1; 0.5]</td><td>0.4 [0.2; 1.2]</td><td>&lt;0.01</td></tr><tr><td>C-reactive protein (mg/L) (miss = 73)</td><td>120 [73; 165]</td><td>113 [70; 161]</td><td>130.5 [82; 184]</td><td>0.19</td></tr><tr><td>D-dimers (µg/L) (miss = 4)</td><td>1215.6 [773.6; 2009.6]</td><td>1130 [715; 1877]</td><td>1494 [1100; 2546]</td><td>&lt;0.01</td></tr><tr><td>Fibrinogen (g/L) (miss = 10)</td><td>7.2 [6.4; 8]</td><td>7.3 [6.6; 8]</td><td>7.2 [6; 7.9]</td><td>0.17</td></tr><tr><td>Ferritin (µg/L) (miss = 20)</td><td>1060.6 [625.6; 1793]</td><td>1035 [652.5; 1678]</td><td>1167.5 [542; 1934]</td><td>0.55</td></tr><tr><td>IL-10 (pg/mL) (miss = 33)</td><td>4.4 [2; 8.2]</td><td>3.5 [1.5; 6.6]</td><td>6.3 [3.2; 11.4]</td><td>&lt;0.01</td></tr><tr><td>IL-12(pg/mL) (miss = 34)</td><td>0.4 [0; 1.6]</td><td>0.3 [0; 1.8]</td><td>0.5 [0; 1.5]</td><td>0.76</td></tr><tr><td>IL-1b (pg/mL) (miss = 34)</td><td>0 [0; 1]</td><td>0 [0; 0.8]</td><td>0 [0; 1.1]</td><td>0.36</td></tr><tr><td>IL-6 (pg/mL) (miss = 26)</td><td>39.2 [14; 87.6]</td><td>26.2 [10.4; 87.6]</td><td>47.7 [27.4; 87.6]</td><td>&lt;0.01</td></tr><tr><td>IL-8 (pg/mL) (miss = 34)</td><td>27.8 [16.8; 47.8]</td><td>22.7 [15.3; 36.4]</td><td>36.7 [25.6; 63.8]</td><td>&lt;0.01</td></tr><tr><td>TNFalpha (miss = 34)</td><td>0 [0; 1.2]</td><td>0 [0; 1.2]</td><td>0 [0; 1.3]</td><td>0.62</td></tr><tr><td>mHLA-DR (Ab/c) (miss = 56)</td><td>10,182 [6717; 14340]</td><td>11,138 [7663.5; 15821]</td><td>7750 [6322; 12032]</td><td>&lt;0.01</td></tr><tr><td>IL-6/mHLA DR (x 1000) (miss = 63)</td><td>4.2 [1.2; 8.4]</td><td>3 [0.8; 7.6]</td><td>5.8 [3.5; 9.6]</td><td>&lt;0.01</td></tr><tr><td>During the whole ICU stay</td><td></td><td></td><td></td><td></td></tr><tr><td>Vasopressors</td><td>64 (26.8)</td><td>16 (9.7)</td><td>48 (64.9)</td><td>&lt;0.01</td></tr><tr><td>Invasive mechanical ventilation</td><td>64 (26.8)</td><td>19 (11.5)</td><td>45 (60.8)</td><td>&lt;0.01</td></tr><tr><td>Renal replacement therapy</td><td>34 (14.2)</td><td>7 (4.2)</td><td>27 (36.5)</td><td>&lt;0.01</td></tr><tr><td>Pulmonary embolism</td><td>10 (4.2)</td><td>5 (3)</td><td>5 (6.8)</td><td>0.18</td></tr><tr><td>Ventilator-associated pneumonia</td><td>24 (10)</td><td>8 (4.8)</td><td>16 (21.6)</td><td>&lt;0.01</td></tr><tr><td>Steroids</td><td>222 (92.8)</td><td>151 (91.5)</td><td>71 (95.9)</td><td>0.22</td></tr><tr><td>Continued</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th>Median [IQR]/number (%)</th><th>All (N=239)</th><th>Alive at D90 (N=165)</th><th>D90 mortality (N=74)</th><th>Pvalue</th></tr></thead><tbody><tr><td>ICU death</td><td>65 (27.2)</td><td>0 (0)</td><td>65 (87.8)</td><td>&lt;0.01</td></tr><tr><td>ICU LOS</td><td>6 [4; 10]</td><td>6 [4; 9]</td><td>10 [5; 16]</td><td>&lt;0.01</td></tr><tr><td>Hospital LOS</td><td>13 [9; 22]</td><td>14 [10; 24]</td><td>13 [8; 18]</td><td>0.02</td></tr></tbody></table>

**Table 1.** Description of the cohort and bivariate analysis comparing patients according to whether they were alive or dead at day 90. BMI: Body Mass Index; ICU: Intensive care unit; SAPS II: Simplified Acute Physiology Score; SOFA: Sequential Organ Failure Assessment; SMI: Skeletal muscle index; SMD: Skeletal Muscle density; HU: Hounsfield unit); II: Interleukin; mHLA-DR: monocytic human leukocyte antigen-DR; LOS: length of stay. *According to Knauss scale. §: Organ transplants, AIDS, non-AIDS HIV, corticoids > 1 month or > 2 mg/ kg/j, chemotherapy, aplasia, or other immunodepression.

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Univariate</th><th colspan="3">Multivariate*</th></tr><tr><th>HR</th><th>95% CI</th><th>P-value</th><th>HR</th><th>95% CI</th><th>P-value</th></tr></thead><tbody><tr><td>Low SMI L3</td><td>1.74</td><td>[1.04; 2.93]</td><td>0.04</td><td>1.28</td><td>[0.76; 2.16]</td><td>0.36</td></tr><tr><td>Low SMI T4</td><td>1.85</td><td>[1.16; 2.94]</td><td>0.01</td><td>1.66</td><td>[1.03; 2.66]</td><td>0.04</td></tr><tr><td>Low SMD L3</td><td>2.77</td><td>[1.46; 5.23]</td><td>&lt;0.01</td><td>1.47</td><td>[0.7; 3.07]</td><td>0.31</td></tr><tr><td>Low SMD T4</td><td>2.51</td><td>[1.37; 4.61]</td><td>&lt;0.01</td><td>1.55</td><td>[0.86; 2.79]</td><td>0.14</td></tr></tbody></table>

**Table 2.** Myosteatose and sarcopenia defined by CT-scan and risks of mortality at Day-90: uni- and multivariate analyses. SMI: Skeletal muscle index; SMD: Skeletal Muscle density; L: Lumber; T: Thoracic. *adjustment on age, requirement for vasopressors, procalcitonin, IL6 (See table S1 and S2). Sarcopenia was defined according to the skeletal muscle index (SMI) (cm²/m²). A reduced SMI at level L3 corresponded to a value of < 41 cm²/m² for women and < 43 cm²/m² for men with a body mass index (BMI) < 30 kg/m², and < 53 cm²/m² for men with a BMI > 30 kg/m²³⁸; Wang et al. (2020). At T4 level, a reduced SMI corresponded to a value of < 51.9 cm²/m² for women and < 65.2 cm²/m² for men independently of BMI⁴⁰. Myosteatosis was defined according to the skeletal muscle density (SMD). A reduced SMD was defined, at level L3, as < 41 HU for a BMI < 25 kg/m² and < 33 HU for a BMI > 25 kg/m²³⁸; Wang et al. (2020). At level T4, a reduced SMD corresponded to a value of < 36.2 HU for women and < 48 HU for men⁴⁰.

immunomodulatory therapy and 89% corticosteroids. Only 7% of patients on admission were on IMV, 8% on vasopressors and 2.6% on RRT. The average percentage of parenchymal involvement was 45.6% (Table 2).

During ICU stay, 27% of patients required vasopressors, 27% IMV and 14% RRT. The two main complications were VAP, in 10% of cases, and pulmonary embolism, in 4% of patients. Finally, by D90, 72 (30.2%) of the patients had died.

Correlations between patient characteristics at admission are shown in Fig. 1.

Muscle densities at L3 and T4 were highly correlated (r²=0.79, *p* < 0.001). To a lesser extent, we found a correlation coefficient of 0.64 between the surface area at L3 and T4 (*p* < 0.001). All the correlations between CT-scan markers and inflammatory biomarkers were less than 0.2.

## Muscle surface area and density

The median SMI at L3 was 43 [36; 50.4] cm²/m²: 159 patients (66.6%) had a reduced SMI at L3, with a significantly higher number in the group who died at D90 (75.7%) than in the group alive at D90 (62.4%, *p* = 0.04). The mean SMI at T4 was 61 [50.6; 70] cm²/m², and a total of 124 patients (51.8%) had a reduced SMI at T4, with a significant difference between the two groups: 46.7% of patients were alive at D90 as against 63.5% who had died (*p* = 0.02).

The mean value of SMD at L3 was 29 HU and a total of 168 patients (70.2%) had reduced SMD at L3 with, once again, a significant difference between the two groups: 63.6% of patients alive at D90 compared with 85.1% deceased (*p* < 0.01).

In T4, the mean SMD was 39 HU, and a total of 168 patients (70.2%) had reduced SMD in T4. There was a significant difference between the two groups: 4.2% of patients alive at D90 compared with 83.8% deceased (*p* < 0.01).

The Bland-Altman difference diagram presented in Fig. 2 shows a mean systematic difference of 18.4 cm²/ m² [-4.9;41.6] in SMI between T4 and L3. There was a systematic difference of 7.94 [-3.22;19.1] UH in SMD between T4 and L3.

## Risk factors of D90 mortality

The results of the uni- and multivariate analyses are presented in Table S1 and 2. Finally, the factors independently associated with D90 mortality were age, the requirement for vasopressors, procalcitonin and il-6 levels, and reduced muscle surface area in T4 was associated with D90 mortality (HR 1.66 *p* = 0.04). These results are consistent with the sensitivity analyses conducted after imputation (Tables S2 and S3).
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Correlation between variables on ICU admission. SMI: Skeletal muscle index; SMD: Skeletal Muscle density; Il: Interleukin; mHLA-DR: monocytic human leukocyte antigen-DR; UH: Hounsfield unity; L: Lumbar; T: Thoracic; PCT: Procalcitonin.</figcaption></figure>

Muscle surface area at L3, muscle density at T4 and L3 and the extent of pulmonary lesions were not independently associated with D90 mortality.

Figure 3 shows the areas under the curve (AUC) for the prediction of mortality at D90 as a function of the different laboratory and CT-scan findings of patients on ICU admission.

The best performance was obtained for T4 SMD (0.69, CI [0.61–0.76]).

Discussion

Our study shows that sarcopenia tended to be an independent risk factor for mortality at D90 in patients admitted to ICU care for severe COVID-19. Interestingly, we did not observe a direct correlation between inflammation and sarcopenia. However, incorporating indices of sarcopenia with inflammatory biomarkers improved prognostic accuracy. These findings align with previous research. We also report that assessment of sarcopenia could be achieved at T4 and L3.

In a cohort of ICU patients in the study of Schinas et al.⁴¹, the T10 CSA was significantly associated with survival. The authors also showed a correlation with inflammatory markers. This association could be attributed to increased catabolism during severe COVID-19, driven by elevated inflammation levels⁴²,⁴³. Data from intubated patients indicated that higher pectoralis muscle area (PMA) and pectoralis muscle density (PMD) at T4 were linked to shorter ICU stays and successful extubation⁴³,⁴⁴ whereas lower muscle density correlated with longer hospitalization and higher in-hospital mortality. This could be partly explained by the fact that sarcopenia impairs the ability to produce adequate respiratory volumes after IMV thus causing a slower recovery of spontaneous breathing⁴⁵.

SMI and SMD at T5 and T12 levels had a predictive value for ICU admission and death in COVID-19 patients admitted to the emergency department (ED)⁴⁶. Additionally, thoracic muscle steatosis at the T10 vertebra level was suggested to predict the progression to severe COVID-19⁴⁷. Koehler et al., in COVID-19 patients admitted to the ED, showed also that sarcopenia assessed at T4 and L3 levels was associated with an increased risk of hospitalization in ICU¹⁵. A meta-analysis conducted by Sumbal et al.⁴⁸ shows that the risk of mortality and severity of COVID-19 spikes up to twice in patients with sarcopenia. Thus, chest CT-scans should be analyzed to identify patients at risk of intubation, who should then be kept in a department under continuous monitoring.

Our study specifically highlights the significance of CSA at T4, but not at L3, as a risk factor for mortality. However, reduced CSA and density at both T4 and L3 showed an overall trend towards increased mortality risk. The good correlation between SCA at T4 and L3 levels and of SMD at T4 and L3 levels attests to the reproducibility of this assessment method. This is interesting because most studies assessing sarcopenia are based
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Bland-Altman difference diagram for muscle density and surface area at L3 and T4. SMI: Skeletal muscle index; SMD: Skeletal Muscle density; UH: Hounsfield unity; L: Lumbar; T: Thoracic.</figcaption></figure>

on CSA and SMD at L3, but this level of slice is not always available, particularly with thoracic CT-scans. Sumbal et al.⁴⁹ performed a meta-analysis of 14 articles to assess which vertebral level to use in COVID-19 patients to calculate sarcopenia. Their findings showed that in 31% of cases the thoracic level was most appropriate and in 63% the lumbar vertebral level. They found that sarcopenia calculated at the thoracic vertebral and lumbar levels has different prognostic values, with sarcopenia at the thoracic level showing higher mortality rates than at the lumbar level.

Various other studies have explored associations between body composition using methods other than CT-scan and COVID-19 outcomes which indicated that sarcopenia is linked to an elevated risk of mortality and severity⁵⁰. Looijaard et al.⁵¹ explored the relation between CT-scan and indices obtained by bio-impedance analysis (BIA). CT-derived skeletal muscle area and density were significantly lower in patients with low compared to normal phase angle. One meta-analysis of 62 studies was performed to assess the association between body composition and COVID-19 outcomes. Low phase angle, low muscle echo intensity, and low BIA-estimated muscle mass were associated with increased risk of mortality and ICU admission. Of note, low
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Prediction of mortality at D90 - ROC curve. SMI: Skeletal muscle index; SMD: Skeletal Muscle density; Il: Interleukin; mHLA-DR: monocytic human leukocyte antigen-DR; UH: Hounsfield unity; L: Lumbar; T: Thoracic; PCT: Procalcitonin.</figcaption></figure>

phase angle has been associated with increased inflammation and oxidative stress, which may affect COVID-19 pathogenesis and severity.

Incorporating biological biomarkers and radiological findings significantly enhanced the prediction of D60 mortality. Notably, high IL-6 levels were associated with an increased risk of mortality, aligning with the concept of a cytokine storm in severe COVID-19 cases.

Prognostication of COVID-19 patients should consider clinical and biological parameters. Systematic assessment of IL-6 levels in patients admitted to intensive care would be useful to plan more active treatment for patients with high levels. After adjustment, we were unable to demonstrate such a correlation with the other specific markers of inflammation, most likely owing to a lack of power in our study. Some studies have also assessed the combined performance of radiological findings and biomarkers to predict COVID-19 outcomes⁵². For instance, the RadScore proposed by Wu et al.⁵³ was as reliable as their proposed clinical score, and prognostication was improved when clinical and radiological findings were considered together.

Despite these insights, our study has limitations, such as its retrospective nature, limited sample size, and potential selection bias. First, our definition of sarcopenia was solely based on muscle loss without incorporating assessment of muscle function. Second, the cut-off values to define sarcopenia and myosteatosis were derived from oncological cohort that can be not adapted to our cohort. Hence, this may have led to over-classification of patients as sarcopenic yet under representation of the disease burden of comorbidities³⁹. Then, we must
acknowledge that we didn't know if among included patients, some of them add already sarcopenia, before COVID-19. Furthermore, because of missing data, some covariates could not be tested into the multivariate analyses.

The main strength of the present study is the quality of the data mostly collected prospectively, including inflammatory cytokine and mHLA DR levels. It is one of the first studies focusing on patients with SARS-CoV2 pneumonia admitted to the ICU that has used reliable and reproducible AI software to measure SMD and SCA values at T4 and L3 levels. We also acknowledge that we were able to assess muscle surface area and muscle density at both the L3 and T4 levels, allowing for a comparison between these two regions.

## Conclusions

Our findings underscore the independent prognostic value of the muscle area at T4 level in predicting death at D90 in ICU-admitted COVID-19 patients. Although muscle density at T4 and muscle density and area at L3 did not show significance in multivariate analysis, they were almost associated significantly with death at D90 in univariate analysis, highlighting the importance of muscle assessment in managing COVID-19 in the ICU. Combining IL-6 levels with T4 muscle surface area and density analysis could provide valuable insights into the prognosis of COVID-19 patients in intensive care. The correlation between muscle damage assessments at T4 and L3 requires further validation.

## Data availability

All the data are available on request at the corresponding author.

Received: 10 June 2024; Accepted: 31 January 2025

Published online: 05 February 2025

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## Acknowledgements

We thank Jeffrey Watts for help in preparing the manuscript. We thank the Michelin Corporate Foundation for the grant.

## Author contributions

“Conceptualization, RCdB, BS, CD, LC; methodology, BS, CD; software, CD; validation, BS., CD, LC, RCdB; formal analysis, CD; investigation, RCdB, BS, CD; resources, BS.; data curation, CD, RCdB, BB, BE, YB, LC, BS, CD writing—original draft preparation, RCdB, CD.; LC writing—review and editing, BB, BE, YB, BS visualization, CD, RCdB; supervision, LC, CD.; project administration, BS; funding acquisition, BS. All authors have read and agreed to the published version of the manuscript.”

## Funding

This study was supported by a grant from the Michelin Corporate Foundation. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Competing interests**

The authors declare no competing interests.

**Consent for publication**

All patients or their relatives received clear, fair and appropriate information and gave their written consent for the storage and use for research purposes of the blood samples of the patients collected during routine examinations during the hospital stay (IRB No. 20.03.20.56342 from the CPP-Ile-de-France VI Groupe Hospitalier Pitié-Salpetrière).

**Additional information**

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-88914-4.

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