# Low skeletal muscle and subcutaneous fat at admission are associated with 90-day mortality in intensive care unit patients

Ronan Thibault, Bruno Pereira, Mickaël Som, Ali Ait Hssain, Sylvain Dadet, Noël Cano, Bertrand Souweine

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## ► To cite this version:

Ronan Thibault, Bruno Pereira, Mickaël Som, Ali Ait Hssain, Sylvain Dadet, et al.. Low skeletal muscle and subcutaneous fat at admission are associated with 90-day mortality in intensive care unit patients. Clinical Nutrition ESPEN, 2025, 68, pp.382-388. 10.1016/j.clnesp.2025.05.015. hal-05099411

HAL Id: hal-05099411

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Submitted on 5 Jun 2025

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conditions [2–6]. In the ICU patients, bioimpedance analysis (BIA)-
derived phase angle, reflecting in part fat-free mass, predicts 28-
day mortality [7]. There is a lack of data regarding the associa-
tion between fat mass and mortality in the ICU patients.

Body composition assessment in the ICU remains challenging.
Indeed standard equations of BIA are not reliable to assess fat-free
mass or fat mass because of rapid fluid shifts [7]. Moreover, dual-
energy X-ray absorptiometry (DXA) is not feasible routinely at
patient bed, even less in the ICU. The assessment of leg muscle
mass with ultrasound probe could be of interest to assess muscle
quality [8], but is insufficient alone to identify the ICU patients
with low muscle mass [9].

The third lumbar vertebra (L3)-targeted abdominal computed tomography (CT) scan [10] is an accurate technique to assess body composition in cancer patients comparatively to the gold standard, i.e. DXA [11]. L3-targeted abdominal CT scan assesses skeletal muscle and fat areas [12,13]. An insufficient fat-free mass determined by this technique predicts mortality [11,14,15] and chemotherapy tolerance and efficacy [16–18] in oncology patients. The analysis of the L3-targeted abdominal CT scan to assess body composition is applicable to ICU patients who were performed a routine abdominal CT. One study performed in 149 severely injured older ICU patients suggested that skeletal muscle index could predict ventilator-free days, ICU-free days, and hospital mortality, whereas total adipose tissue did not [19]. In a retrospective study of 240 mechanically ventilated critically ill patients, low muscle mass was associated with hospital mortality [20]. In older ICU trauma patients, a retrospective study found that low muscle mass (assessed by CT scan) and osteopenia were associated with higher one-year mortality, alone or in combination [21]. In severe trauma patients, acute muscle atrophy diagnosed on CT scan by psoas area measurement was strongly associated with poor outcomes at hospital [22]. However, none of these studies used mid-term mortality as an outcome, including post-ICU discharge mortality, and only one assessed the association between the different fat cross-sectional areas and mortality. Moreover, the contribution on mortality of body mass index apart from muscle and fat compartments at admission in the ICU is not well known. Therefore, using routine abdominal CT-scan, our study aims at determining the relation between skeletal muscle, subcutaneous, visceral, intramuscular and total fat cross-sectional areas at ICU admission and 90-day mortality (main), between skeletal muscle and other clinical outcomes, and between body mass index (BMI) and 90-day mortality (secondary).

## 2. Methods

### 2.1. Study design and setting

The study is a retrospective observational study performed in
the 10-bed medical ICU at Hôpital Gabriel Montpied, CHU Cler-
mont Ferrand, Clermont-Ferrand, France. According to the retro-
spective and non-interventional study design, and the French law
in 2012, the study was determined to qualify for exempt status by
the Ethics Committee of the Clermont-Ferrand University Hospital
as data was collected from existing patient records. Therefore, the
need for patient consent to participate was waived.

### 2.2. Patients’ selection

The 200 consecutive patients having performed a routine
abdominal CT scan during their ICU stay between January 1st,
2008 and December 31, 2010 were included retrospectively. The
inclusion criteria were: patients having performed a routine
abdominal CT scan within the three days following ICU admission,

and a minimal ICU length of stay of 48 h. Patients were not eligible
if they were readmitted 48 h after their discharge. Patients were
selected thanks to the dedicated software of the Department of
Radiology.

### 2.3. Study endpoints

The primary endpoint was 90-day mortality, i.e. death occur-
ing within the 90 consecutive days following ICU admission. If
patients had been discharged from the hospital or transferred to
another healthcare centre, the information of 90-day mortality
was collected by the examination of medical records, phone call to
the hospital department or to the patient's general physician. The
secondary endpoints were ICU mortality, ICU and hospital length
of stay, mechanical ventilation duration, and nosocomial in-
fections, i.e. ICU-acquired bacteremia and ventilator-associated
pneumonia.

### 2.4. Body composition assessment by third lumbar vertebra (L3)-targeted abdominal computed tomography (CT)

The abdominal CT-scans of the selected patients were loaded
from the computerized Picture Archiving and Communication
System (PACS) at the Department of Radiology. Body composition
was measured by an independent non-author technician who was
already specifically trained and had the expertise of the analysis
method that was previously described [11]. L3 was chosen as a
standard landmark. Two consecutive CT images extending from L3
to the iliac crest were chosen to measure muscle and fat cross-
sectional areas [16,17]. The first image was chosen as the first
showing both transverse processes clearly visible, then the second
image, one slice below. The L3 region contains psoas, paraspinal
muscles (erector spinae, quadratus lumborum), and abdominal
wall muscles (transversus abdominus, external and internal obli-
ques, rectus abdominus). These muscles were identified by the
trained technician based on their anatomic features. Skeletal
muscles and adipose tissues were identified and quantified by use
of pre-established Hounsfield unit (HU) thresholds: -29 to +150
HU for skeletal muscles, -190 to -30 HU for subcutaneous and
intramuscular adipose tissues, and -50 to 150 HU for visceral
adipose tissue [16]. Images were analysed by use of Slice-O-matic
software, version 4.3 (Tomovision, Montreal, QC, Canada). Cross-
sectional areas (cm²) of the sum of all of these muscles were
computed for each image, and the mean value for the two images
was computed for each patient: the directly ascertained unit was
area (cm²) of total L3 skeletal muscle. Body mass index and body-
surface area (by use of the Mosteller formula [body-surface area
(cm²)=(height (cm) × weight (kg))/(3600)¹/²]) were computed. The
area (cm²) of total L3 skeletal muscle is linearly related to whole-
body muscle mass [17] and was therefore normalised for stature
(L3 skeletal muscle index, cm²/m²) [18]. The same stature
normalization was done to calculate the total fat, subcutaneous,
visceral and intramuscular fat indexes. A low skeletal muscle mass
index (SMI) was defined as <38.5 cm²/m² for women and
52.4 cm²/m² for men [14]. A high visceral fat index was defined as
≥38.7 cm²/m² in males and 24.9 cm²/m² in females, as proposed
by Baggerman et al. [23,24].

### 2.5. Other data collection

Weight and height were systematically measured at ICU
admission according to a systematic and standardized protocol.
Body mass index (BMI) was calculated as weight (kg) divided by
squared height (m²). ICU severity scores, i.e. the McCabe score [25],
taking into account the underlying comorbidities, the Sequential
Organ Failure Assessment (SOFA) [26], and the Simplified Acute Physiology Score (SAPS) II [27], were collected at ICU admission. Clinical information (primary admission diagnosis, chronic kidney insufficiency prior to ICU admission, ICU severity scores, therapies, outcomes, serum albumin and creatinine at admission, length of ICU stay and duration of mechanical ventilation) was retrieved from the electronic medical record. The diagnosis of nosocomial infections during the ICU stay was based on the usual diagnostic practices and international guidelines [28–30], without any modification during the study period. ICU-acquired bacteraemia was defined as at least one positive hemoculture (two in case of coagulase negative *Staphylococcus*) associated with infection signs, severe sepsis or septic shock [29,31]. Ventilator-associated pneumonia was diagnosed in patients with at least two of the following signs: fever >38°C, leukocytosis >10 G/l, purulent tracheal secretions, chest radiography indicating a persistent infiltrate associated with a positive respiratory secretions culture: ≥10³ UFC/ml on a protected distal collection, or ≥10⁴ UFC/ml on a bronchoalveolar lavage, or ≥10⁶ UFC/ml on a tracheal aspiration [30].

## 2.6. Statistical analysis

Descriptive statistics are expressed as mean ± standard deviation or median and interquartile range for continuous data, according to their distribution, and number of patients (with the percentage in parenthesis, followed immediately by the counts for the numerator/denominator) for categorical data. The assumption of normality was assessed by using the Shapiro–Wilk test. We analyzed the relation between 90-day mortality or clinical outcomes data and study endpoints with the χ2 tests (or Fisher's exact test when appropriate) for categorical data, and the Student's t test (or Mann–Whitney–Wilcoxon test when appropriate) for continuous data. Survival analysis was estimated using Kaplan–Meier approach considering time from ICU admission to day 90 status (dead or alive). In univariate analyses, the comparisons were performed using log-rank test. For multivariable analysis, adjusted Cox model was carried out using the stepwise approach (backward and forward) on covariates fixed according to univariate results (P-value<0.2) and to their clinical relevance. The proportional-hazard hypothesis was verified using Schoenfeld's test and plotting residuals. Because dichotomizing implies loss of information and hence loss of statistical power [32,33], continuous variables were analyzed without categorizing them unless predefined clinical thresholds have been reported in the literature. A logarithmic transformation has been applied to achieve the normality of statistical distribution, when necessary. A particular attention has been paid to the study of multicollinearity and interactions between covariates 1) studying the relationships between the covariables and 2) evaluating the impact to add or delete variables on multivariable model. Results were expressed as hazard-ratios and 95 % confidence intervals (95%CI). For continuous variables, the adjusted odd ratio was expressed for 1-point increase or for 1-log point increase. All analyses were conducted using Stata software (version 15, StataCorp, College Station, Texas, USA), and two-sided P-value<0.05 was considered statistically significant.

# 3. Results

## 3.1. Patients' selection and characteristics

Among the 200 patients having performed an abdominal CT scan during the 3-year study period, 141 patients were finally analysed (71 % of the 200 patients initially screened) (Fig. 1). Patients' characteristics and diagnoses at admission are reported in

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Study flow chart. CT, abdominal computed tomography; ICU, intensive care unit.</figcaption></figure>

Table 1. There were 59 % of male, 54 % were aged of 65 years and more, and 29 % of patients were obese.

## 3.2. Assessment of skeletal muscle and fat tissue cross-sectional areas

The assessment of skeletal muscle and fat tissue cross-sectional areas are reported in Table 1. A low skeletal muscle index was observed in 45 % (64/141) of patients. As expected, mean skeletal muscle area was significantly lower in females than in males (115.5 ± 31.3 vs. 143.6 ± 33.7 cm²/m²), whereas mean subcutaneous fat area was higher (196.0 ± 121.1 vs. 128.3 ± 75.2 cm²/m²). Mean visceral fat area was higher in males than females (183.3 ± 109.3 vs. 104.3 ± 81.0 cm²/m²), whereas mean intramuscular fat area is comparable between females (40.9 ± 25.8 cm²/m²) and males (39.6 ± 22.8 cm²/m²).

## 3.3. Relation between skeletal muscle index at admission and 90-day mortality

Patients' clinical outcomes during follow up are shown in Table 2. Ninety-day mortality was 52 % (73/141). This is in part due to the severity of the underlying disease at ICU admission (mean ± SD SAPS II, 61.7 ± 21.5). ICU mortality (52 vs 31 %, P = 0.02) and 90-day mortality (61 vs. 44 %, P = 0.04) were higher in patients with low skeletal muscle index (Table 2). Kaplan–Meier analysis indicated that the survival was worse in patients with low skeletal muscle index (P < 0.05) (Fig. 2). There was no association between the levels of the skeletal muscle index and mechanical ventilation, ICU-acquired bacteraemia, ventilator-associated pneumonia, and length of stay (Table 2).

## 3.4. Multivariable analyses of factors associated with 90-day mortality

In the univariate analysis, age ≥65 years, chronic kidney insufficiency prior to ICU admission, SAPS II, SOFA, BMI 25–30, BMI ≥30, serum albumin, skeletal muscle index, total fat index, and subcutaneous fat index, were associated with 90-day mortality (p < 0.20) (Table 3). In the multivariable analysis including all the variables, only age ≥65 years, SAPS II, low skeletal muscle index and subcutaneous fat index log were significantly and independently associated with increased 90-day mortality (Table 4). There was no relation between visceral, intramuscular and total fat cross-sectional areas and 90-day mortality. Higher body mass index was associated with lower 90-day mortality, when the model excluded muscle and fat tissue levels (Table 5).

# 4. Discussion

In ICU patients presenting with chronic or acute pulmonary or renal diseases, low skeletal muscle and subcutaneous fat at
<caption>**Table 1**</caption>

Patients' characteristics at baseline and body composition assessment by 3rd lumbar vertebra-targeted computed tomography in the entire study population (n = 141) and according to the skeletal muscle index.

<table><thead><tr><th></th><th>All n = 141</th><th>Low skeletal muscle index n = 64</th><th>Normal skeletal muscle index n = 77</th><th>P-value</th></tr></thead><tbody><tr><td>Male gender – n (%)</td><td>83 (59)</td><td>43 (68)</td><td>40 (52)</td><td>0.07</td></tr><tr><td>Age (years) – mean ± SD</td><td>65.2 ± 14.9</td><td>67.4 ± 13.8</td><td>63.4 ± 13.8</td><td>0.09</td></tr><tr><td>Age &gt;65 years – n (%)</td><td>76 (54)</td><td>40 (62)</td><td>36 (47)</td><td>0.06</td></tr><tr><td>Admission diagnosis – n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Emergency surgery</td><td>19 (13)</td><td>8 (12)</td><td>11 (14)</td><td>0.92</td></tr><tr><td>Scheduled surgery</td><td>3 (2)</td><td>1 (2)</td><td>2 (3)</td><td></td></tr><tr><td>Medical</td><td>119 (84)</td><td>55 (86)</td><td>64 (83)</td><td></td></tr><tr><td>Acute kidney insufficiency – n (%)</td><td>107 (76)</td><td>49 (77)</td><td>58 (75)</td><td>0.86</td></tr><tr><td>Chronic kidney insufficiency prior to ICU admission</td><td>13 (9.2)</td><td>4 (6.3)</td><td>9 (11.7)</td><td>0.28</td></tr><tr><td>SAPS II – mean ± SD</td><td>61.7 ± 21.5</td><td>62.9 ± 20.1</td><td>60.7 ± 22.8</td><td>0.54</td></tr><tr><td>SOFA – mean ± SD</td><td>10.2 ± 4.5</td><td>10.1 ± 4.6</td><td>10.3 ± 4.5</td><td>0.78</td></tr><tr><td>McCabe score – n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>I</td><td>114 (81)</td><td>50 (78)</td><td>64 (83)</td><td>0.42</td></tr><tr><td>2</td><td>20 (14)</td><td>10 (16)</td><td>10 (13)</td><td></td></tr><tr><td>3</td><td>7 (5)</td><td>4 (6)</td><td>3 (4)</td><td></td></tr><tr><td>Body mass index (kg/m²) – mean ± SD</td><td>27.9 ± 6.4</td><td>26.3 ± 5.4</td><td>29.3 ± 6.9</td><td>0.005</td></tr><tr><td>Body mass index – n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;20</td><td>1 (1)</td><td>0 (0)</td><td>1 (1)</td><td>0.07</td></tr><tr><td>≥20 and &lt; 25</td><td>48 (34)</td><td>24 (37)</td><td>24 (32)</td><td></td></tr><tr><td>≥25 and &lt; 30</td><td>50 (36)</td><td>27 (42)</td><td>23 (30)</td><td></td></tr><tr><td>≥30</td><td>42 (29)</td><td>13 (20)</td><td>29 (37)</td><td></td></tr><tr><td>Serum albumin at admission (g/l) – mean ± SD</td><td>22.9 ± 6.4</td><td>22.4 ± 5.9</td><td>23.3 ± 6.8</td><td>0.41</td></tr><tr><td>Serum albumin at admission &gt;27 g/l – n (%)</td><td>32 (25)</td><td>13 (22)</td><td>19 (27)</td><td>0.54</td></tr><tr><td>Serum creatinine at admission – median [interquartile range]</td><td>213 [106–403]</td><td>245 [119.5–398.5]</td><td>202 [103–452]</td><td>0.87</td></tr><tr><td>Renal replacement therapy – n (%)</td><td>86 (61)</td><td>38 (59)</td><td>48 (62)</td><td>0.72</td></tr><tr><td>Vasoactive drugs – n (%)</td><td>109 (77)</td><td>49 (77)</td><td>60 (78)</td><td>0.85</td></tr><tr><td>Body composition data</td><td></td><td></td><td></td><td></td></tr><tr><td>Skeletal muscle index (cm²/m²) – mean ± SD</td><td>48.6 ± 11.8</td><td>40.3 ± 7.3</td><td>55.4 ± 10.4</td><td>&lt;0.001</td></tr><tr><td>Skeletal muscle area (cm²) – mean ± SD</td><td>132.1 ± 35.5</td><td>113.3 ± 27.1</td><td>147.6 ± 34.2</td><td>&lt;0.001</td></tr><tr><td>Subcutaneous fat area (cm²) – mean ± SD</td><td>156.1 ± 101.9</td><td>150.6 ± 90.7</td><td>160.8 ± 110.8</td><td>0.54</td></tr><tr><td>Subcutaneous fat index (cm²/m²) – mean ± SD</td><td>58.6 ± 40.1</td><td>54.9 ± 34.4</td><td>61.7 ± 44.2</td><td>0.30</td></tr><tr><td>Visceral fat area (cm²) – mean ± SD</td><td>150.8 ± 105.8</td><td>172.5 ± 109.7</td><td>132.8 ± 99.5</td><td>0.03</td></tr><tr><td>Visceral fat index (cm²/m²) – mean ± SD</td><td>54.7 ± 37.2</td><td>61.1 ± 38.3</td><td>49.4 ± 35.6</td><td>0.052</td></tr><tr><td>Intramuscular fat area (cm²) – mean ± SD</td><td>40.2 ± 24.0</td><td>39.7 ± 21.8</td><td>40.5 ± 25.8</td><td>0.84</td></tr><tr><td>Intramuscular fat index (cm²/m²) – mean ± SD</td><td>15.0 ± 9.2</td><td>14.4 ± 8.1</td><td>15.5 ± 10.1</td><td>0.46</td></tr><tr><td>Total fat area (cm²) – mean ± SD</td><td>347.1 ± 185.5</td><td>362.8 ± 178.5</td><td>334.1 ± 191.3</td><td>0.36</td></tr><tr><td>Total fat index (cm²/m²) – mean ± SD</td><td>128.3 ± 68.5</td><td>130.4 ± 64.5</td><td>126.6 ± 72.0</td><td>0.75</td></tr></tbody></table>

ICU, intensive care unit; SAPS, Simplified Acute Physiology Score; SD, standard deviation; SOFA, Sequential Organ Failure Assessment.

<caption>**Table 2**</caption>

Clinical outcomes of the study patients in the entire study population (n = 141) and according to the skeletal muscle index.

<table><thead><tr><th></th><th>All n = 141</th><th>Low skeletal muscle index n = 64</th><th>Normal skeletal muscle index n = 77</th><th>P-value</th></tr></thead><tbody><tr><td>Mechanical ventilation – n (%)</td><td>106 (75)</td><td>46 (71)</td><td>60 (78)</td><td>0.41</td></tr><tr><td>Mechanical ventilation duration (day) – median [IQR]</td><td>9 [4–17]</td><td>8 [3.5–15]</td><td>11 [5–18]</td><td>0.28</td></tr><tr><td>ICU-acquired bacteraemia – n (%)</td><td>13 (9)</td><td>7 (11)</td><td>6 (8)</td><td>0.52</td></tr><tr><td>Ventilator-associated pneumonia – n (%)</td><td>16 (11)</td><td>6 (9)</td><td>10 (13)</td><td>0.18</td></tr><tr><td>Length of stay (day) – median [IQR]</td><td></td><td></td><td></td><td></td></tr><tr><td>ICU</td><td>10 [5–19]</td><td>9.5 [4.5–16.5]</td><td>11 [5–21]</td><td>0.37</td></tr><tr><td>Hospital</td><td>18 [10–36]</td><td>16.5 [6–35.5]</td><td>21 [10–39]</td><td>0.44</td></tr><tr><td>Mortality – n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>ICU</td><td>57 (40)</td><td>33 (52)</td><td>24 (31)</td><td>0.02</td></tr><tr><td>90-day</td><td>73 (52)</td><td>39 (61)</td><td>34 (44)</td><td>0.04</td></tr></tbody></table>

ICU, intensive care unit; IQR, interquartile range.

admission were independently associated with 90-day mortality. In our study, we found no association between the other fat cross-sectional areas, i.e. visceral, intramuscular and total, and 90-day mortality. Skeletal muscle area was not associated with length of stay, duration of mechanical ventilation, or infections. In the multivariable model excluding body composition parameters, BMI was associated with 90-day mortality. Despite the Global Leadership Initiative on Malnutrition criteria were not used, these results suggest that protein-energy malnutrition at ICU admission is a marker of mortality.

In our study, we used the thresholds reported in oncology patients [17] to consider low skeletal muscle index. Forty-five % of

our patients had low skeletal muscle index. The studies by Looijgard et al. [34] and Moisey et al. [19] found more patients (65 % and 71 %, respectively) with low muscle mass. Looijgard et al. [34] used cut offs of low skeletal muscle area (in cm²) coming from their own previous work [20]. As did Moisey et al. [19], we choose to use cut-offs that are well validated in the oncology area [11,14,16]. The proportion of 45 % of the included patients having low skeletal muscle mass is in line with the well-known figures of malnutrition prevalence (30–50 %) at hospital [35].

Our study confirmed previous data that body composition assessment by L3-targeted CT is feasible in ICU patients, but a dedicated computed software is needed [19–22,34]. In our study,
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Kaplan-Meier actuarial curve showing mortality in the 90 days following intensive care unit admission according to skeletal muscle index (n = 140). The information on mortality was unavailable for one patient.</figcaption></figure>

92 % of CT-scans were exploitable. Some CT scans are non-interpretable because of artefacts. The software SliceOmatic® needs an expertise for its use and remains time consuming (15 min

per slide for a trained technician). Therefore, it could be not easily transferred into the daily clinical practice since dedicated radiology personal is needed. Also, L3-targeted CT scan is only useable in patients for whom the abdominal CT is performed for routine diagnosis purpose, and, therefore, could not be implemented widely for daily body composition assessment in the routine practice. Nevertheless, at a time where reliable tools to assess malnutrition are lacking in the ICU, body composition assessed by CT-scan could be a reliable method to diagnose patients with low skeletal muscle in the ICU. This is of utmost interest as DEXA and bioimpedance analysis are difficult to implement or non-interpretable for body composition analysis in the ICU setting [15].

The measurement of skeletal muscle mass by L3-targeted CT scan has been developed in the oncology [17] and hemodialysis [36] settings. Several studies already identified that low skeletal muscle was significantly associated with oncology patients' prognosis and mortality [11], including obese patients [14,37]. To our knowledge, four other retrospective studies have already evaluated the predictive value of skeletal muscle areas assessed by L3-targeted CT scan in the ICU on short term and/or hospital mortality. One study performed in 149 severely injured older ICU patients suggested that skeletal muscle levels could predict ventilator-free days, ICU-free days, and mortality [19]. In two retrospective studies from the same group [20,34], including respectively 240 and 491 mechanically ventilated critically ill patients, low muscle mass, but also muscle density [34], as assessed

<table><caption>**Table 3**
Univariate analysis of factors associated with 90-day mortality (n = 140). The information on mortality was unavailable for one patient.</caption><thead><tr><th></th><th>Alive at 90-day (n = 65)</th><th>Dead at 90-day (n = 75)</th><th>Hazard ratio [95 % CI]</th><th>P value</th></tr></thead><tbody><tr><td>Gender – M vs F – n (%)</td><td>40 (61.5)/25 (38.5)</td><td>43 (57.3)/32 (42.7)</td><td>0.93 [0.58–1.47]</td><td>0.75</td></tr><tr><td>Age ≥65 years – n (%)</td><td>25 (38.5)</td><td>50 (66.7)</td><td>2.14 [1.32–3.47]</td><td>0.002</td></tr><tr><td>Admission diagnosis – medical vs surgical – n (%)</td><td>55 (84.6)/10 (15.4)</td><td>63 (84)/12 (16)</td><td>0.96 [0.52–1.78]</td><td>0.90</td></tr><tr><td>Acute kidney insufficiency</td><td>47 (72.3)</td><td>60 (80)</td><td>1.38 [0.77–2.47]</td><td>0.28</td></tr><tr><td>Chronic kidney insufficiency prior to ICU admission</td><td>3 (4.6)</td><td>10 (13.3)</td><td>1.75 [0.90–3.42]</td><td>0.10</td></tr><tr><td>SAPS II – mean ± SD</td><td>51.3 ± 18.9</td><td>70.8 ± 19.7</td><td>1.03 [1.02–1.04]</td><td>&lt;0.001</td></tr><tr><td>SOFA – mean ± SD</td><td>8.4 ± 4.0</td><td>11.9 ± 4.4</td><td>1.14 [1.08–1.20]</td><td>&lt;0.001</td></tr><tr><td>Body mass index – n (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;25</td><td>14 (21.5)</td><td>33 (44.6)</td><td>–</td><td>–</td></tr><tr><td>25–30</td><td>25 (38.5)</td><td>25 (33.8)</td><td>0.62 [0.37–1.05]</td><td>0.08</td></tr><tr><td>≥30</td><td>26 (40.0)</td><td>16 (21.6)</td><td>0.41 [0.23–0.76]</td><td>0.004</td></tr><tr><td>Serum albumin at admission &gt;27 g/l – n (%)</td><td>22 (35.5)</td><td>10 (15.4)</td><td>0.49 [0.25–0.96]</td><td>0.04</td></tr><tr><td>Skeletal muscle index: Normal vs low – n (%)</td><td>42 (64.6)/23 (35.4)</td><td>35 (46.7)/40 (53.3)</td><td>0.60 [0.38–0.95]</td><td>0.03</td></tr><tr><td>Visceral fat index–normal/low vs high – n (%)</td><td>17 (26.1)/48 (73.9)</td><td>21 (28.0)/54 (72.0)</td><td>0.97 [0.58–1.62]</td><td>0.90</td></tr><tr><td>Total fat index (cm²/m²)</td><td>140.5 ± 71.3</td><td>117.6 ± 65.0</td><td>0.79 [0.57–1.09]<sup>#</sup></td><td>0.15</td></tr><tr><td>Subcutaneous fat index (cm²/m²)</td><td>67.3 ± 43.4</td><td>50.7 ± 35.6</td><td>0.78 [0.62–0.98]<sup>#</sup></td><td>0.04</td></tr><tr><td>Visceral fat index (cm²/m²)</td><td>59.1 ± 37.4</td><td>51.3 ± 36.9</td><td>0.94 [0.76–1.17]<sup>#</sup></td><td>0.60</td></tr><tr><td>Intramuscular fat index (cm²/m²)</td><td>14.0 ± 7.9</td><td>15.7 ± 10.3</td><td>1.14 [0.78–1.69]<sup>#</sup></td><td>0.50</td></tr></tbody></table>

CI, confidence interval; F, females; ICU, intensive care unit; M, males; SAPS, Simplified Acute Physiology Score; SOFA, Sequential Organ Failure Assessment.

\# Expressed as per log cm²/m² increment.

<table><caption>**Table 4**
Multivariable analysis of factors associated with 90-day mortality (n = 140). The information on mortality was unavailable for one patient. The model includes body mass index together with muscle and fat tissue levels.</caption><thead><tr><th></th><th>Hazard ratio [95 % confidence interval]</th><th>P value</th></tr></thead><tbody><tr><td>Gender: Male vs female</td><td>0.64 [0.37–1.10]</td><td>0.10</td></tr><tr><td>Age ≥65 years: Yes vs no</td><td>1.79 [1.01–3.19]</td><td>0.047</td></tr><tr><td>Body mass index ≥30 vs. &lt;30</td><td>0.56 [0.30–1.07]</td><td>0.10</td></tr><tr><td>Chronic kidney insufficiency prior to ICU admission</td><td>1.65 [0.81–3.39]</td><td>0.08</td></tr><tr><td>SAPS II</td><td>1.04 [1.02–1.05]</td><td>&lt;0.001</td></tr><tr><td>Skeletal muscle mass index: Normal vs low</td><td>0.48 [0.29–0.82]</td><td>0.007</td></tr><tr><td>Intramuscular fat index log</td><td>1.36 [0.77–2.40]<sup>#</sup></td><td>0.29</td></tr><tr><td>Subcutaneous fat index log</td><td>0.67 [0.51–0.89]<sup>#</sup></td><td>0.006</td></tr><tr><td>Visceral fat index log</td><td>0.79 [0.55–1.14]<sup>#</sup></td><td>0.21</td></tr></tbody></table>

ICU, intensive care unit; SAPS, Simplified Acute Physiology Score.

\# expressed as per log cm²/m² increment.
<caption>**Table 5**</caption>

**Multivariable analysis of factors associated with 90-day mortality (n = 140).** The information on mortality was unavailable for one patient. The model excludes muscle and fat tissue levels.

<table><thead><tr><th></th><th>Hazard ratio [95 % confidence interval]</th><th>P value</th></tr></thead><tbody><tr><td>Gender: Male vs female</td><td>0.77 [0.48–1.22]</td><td>0.26</td></tr><tr><td>Age ≥65 years: Yes vs no</td><td>1.93 [1.16–3.19]</td><td>0.01</td></tr><tr><td>Body mass index #</td><td>0.92 [0.88–0.96]</td><td>&lt;0.001</td></tr><tr><td>SAPS II #</td><td>1.03 [1.02–1.04]</td><td>&lt;0.001</td></tr><tr><td>Chronic kidney insufficiency prior to ICU admission</td><td>1.54 [0.77–3.02]</td><td>0.22</td></tr></tbody></table>

ICU, intensive care unit; SAPS, Simplified Acute Physiology Score.

# for 1-point increase.

by CT scan, was associated with hospital [20] and 6-month mortality [34]. In older ICU trauma patients, a retrospective study found that low skeletal muscle index and osteopenia were associated with higher risks of 1-year mortality alone and in combination [21]. Interestingly, the acute constitution of muscle atrophy during the first days of ICU stay and diagnosed on CT scan by the evolution of psoas area levels is strongly associated with poor outcomes at hospital in severe trauma patients [22]. Our study confirms the association between low skeletal muscle index and mortality, but is the first to show this relation with 90-day mortality including mortality after ICU discharge. Moreover, our study is the first to show that lower subcutaneous fat cross-sectional area is also associated with 90-day mortality, together with low skeletal muscle index. Indeed, Moisey et al. found no association between any fat cross-sectional areas and ICU mortality, length of stay or duration of mechanical ventilation [19]. Our findings that lower muscle mass and subcutaneous fat are both associated with mortality corroborates older observations that depletion of energy reserves during acute illness is associated with decreased survival [38]. This strongly suggests that protein-energy malnutrition at ICU admission is associated with mortality. When BMI is associated with skeletal muscle and fat cross-sectional areas in the model, BMI is not associated with mortality. This suggests that BMI was unreliable to detect low muscle mass in ICU patients, since BMI is not sensitive enough to identify patients with low muscle mass; indeed, in our study, about 70 % (n = 92) of patients had BMI ≥25, with 43 % of them (n = 40) having a low skeletal muscle mass (data not shown). Our study confirms that BMI alone should not be recommended to diagnose malnutrition or prognosis in ICU patients [39]. However, our study suggests that, in the absence of body composition assessment, lower BMI is associated with higher 90-day mortality. This reinforces previous knowledge that obesity with BMI between 30 and 35 is associated with decreased mortality in the ICU [40].

To determine high visceral fat, we used the cut-offs associated with the metabolic risk in the general population [23]. However, the previous study by Baggerman et al. applying these cut-offs to ICU patients reported that they were not associated to 90-mortality [24]. Our data confirm this result showing that the visceral fat area and index were not associated with 90-day mortality. Contrary to Baggerman et al. [41], we found that chronic kidney insufficiency prior to ICU admission was not independently associated with 90-day mortality, whereas low muscle mass was. Even though the study designs and sample size were closed, this difference could be explained by different ICU populations since Baggerman et al. [41] only included patients with abdominal sepsis.

In our study, low skeletal muscle was not associated with the other clinical outcomes, while Moisey et al., showed a relation between low skeletal muscle and less ventilator-free days, and ICU-free days [19]. Others studies found a relation between energy deficit in ICU and clinical outcomes, nevertheless nutritional status at admission was not considered [42–44]. These results could be

explained by the fact that, due to our recruitment, our study population composed of a high prevalence of chronic organ insufficiency such as respiratory failure or chronic renal insufficiency, has bad outcomes (52 % of 90-day mortality), independently from body composition parameters. Selecting the patients that have been performed a routine abdominal CT scan could have increased the bias of selecting the most severe critically ill patients.

In conclusion, our study showed that low skeletal muscle and subcutaneous fat assessed by L3 CT scan were significantly associated with 90-day mortality. Despite the Global Leadership Initiative on Malnutrition criteria were not used, these results suggest that protein-energy malnutrition at ICU admission is a strong marker of mortality. Trials should be realized to determine whether optimized nutritional interventions based on the routine assessment of body composition in the ICU patients could improve the clinical outcomes of ICU patients.

## Statement of authorship

Ronan Thibault conceived and designed the study, obtained funding, recruited patients, collected, analyzed and interpreted the data, managed the database, and wrote the manuscript. Bruno Pereira performed the statistical analysis. Mickaël Som analyzed and interpreted the data, managed the database, and drafted the manuscript. Ali Ait Hssain recruited patients, collected the data, and managed the database. Sylvain Dadet collected the data, and managed the database. Noël Cano conceived and designed the study, obtained funding, analyzed and interpreted the data. Bertrand Souweine designed the study, recruited patients, collected, analyzed and interpreted the data, and drafted the manuscript.

## Funding sources

A research grant of 4'050 € was obtained from Fresenius-Kabi France, Sèvres, France.

## Declaration of competing interest

Research grant from Fresenius-Kabi France, Sèvres, France. Ronan Thibault, Bruno Pereira, Mickaël Som, Ali Ait Hssain, Sylvain Dadet, Noël Cano, and Bertrand Souweine, declare no other conflict of interest regarding this article. Ronan Thibault received consulting and conference fees from Aguettant, Baxter, B Braun, Fresenius-Kabi, Nestlé Health Science, Nutricia, Roche; and conference fees from Astra-Zeneca, Homeperf, Lactalis, Shire, and Theradial.

## Acknowledgements

The authors gratefully thank Mr Philippe Bonnet and Dr Eric Dumousset, for their help in CT scan loading and recording, Mrs
Audrey Clément, Maison-Alfort, France, and Dr Sami Antoun, Institut Gustave Roussy, Villejuif, France, for their technical expertise in body composition analysis.

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