key: cord-0858448-jwynzvjl
authors: Osuna-Padilla, I. A.; Rodríguez-Moguel, N. C.; Rodríguez-Llamazares, S.; Orsso, C. E.; Prado, C. M.; Ríos-Ayala, M. A.; Villanueva-Camacho, O.; Aguilar-Vargas, A.; Pensado-Piedra, L. E.; Juárez-Hernández, F.; Hernández-Cárdenas, C. M.
title: Low muscle mass in COVID-19 critically-ill patients: Prognostic significance and surrogate markers for assessment
date: 2022-03-01
journal: Clin Nutr
DOI: 10.1016/j.clnu.2022.02.019
sha: 9afbfd12e7db09229ae98eec5769533fef08b3a2
doc_id: 858448
cord_uid: jwynzvjl

Introduction Low muscle mass is a common condition in the critically ill population and is associated with adverse clinical outcomes. The primary aim of this study was to analyze the prognostic significance of low muscle mass using computed tomography (CT) scans in COVID-19 critically ill patients. A second objective was to determine the accuracy and agreement in low muscle mass identification using diverse markers compared to CT as the gold standard. Methods This was a prospective cohort study of COVID-19 critically ill patients. Skeletal muscle area at the third lumbar vertebra was measured. Clinical outcomes (intensive care unit [ICU] and hospital length of stay [LOS], tracheostomy, days on mechanical ventilation [MV], and in-hospital mortality) were assessed. Phase angle, estimated fat-free mass index, calf circumference, and mid-upper arm circumference were measured as surrogate markers of muscle mass. Results Eighty-six patients were included (mean age ± SD: 48.6 ± 12.9; 74% males). Patients with low muscle mass (48%) had a higher rate of tracheostomy (50 vs 20%, p=0.01), prolonged ICU (adjusted HR 0.53, 95%CI 0.30-0.92, p=0.024) and hospital LOS (adjusted HR 0.50, 95% CI 0.29-0.86, p=0.014). Bedside markers of muscle mass showed poor to fair agreement and accuracy compared to CT-assessed low muscle mass. Conclusion Low muscle mass at admission was associated with prolonged length of ICU and hospital stays. Further studies are needed to establish targeted nutritional interventions to halt and correct the catabolic impact of COVID-19 in critically ill patients, based on standardized and reliable measurements of body composition.

Over 250 million patients worldwide have been affected by Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 1 . Studies have shown that ~30% of the hospitalized patients require ventilatory support and admission to Intensive Care Units (ICUs) 2 .

The acute phase of critical illness, immobilization, and drug administration (sedatives and neuromuscular blocking agents) results in disturbed metabolism, with a catabolic state characterized by increased protein breakdown and decreased protein synthesis, leading to a rapid wasting of skeletal muscle mass 3-5 . Muscle mass assessment at hospital admission can be useful to identify patients with higher nutritional risk, while its monitoring could offer important opportunities to guide nutritional therapy adjustments during ICU stay 6 . Magnetic resonance imaging and computed tomography (CT) are gold standard techniques for the assessment of body composition in clinical populations 7, 8 . CT employs a beam of X-rays that produces signals, that once processed by a computer, can generate cross-sectional images of the body. Using specialized software, skeletal muscle mass can be measured at the third lumbar vertebra (L3), which correlates well with whole-body skeletal muscle mass, being the preferred landmark for the estimation of whole-body measured by wrapping the tape around the widest part of the calf. Body weight and height were estimated using validated equations 22 . Body mass index (BMI) was calculated and was classified using World Health Organization criteria 23 .

Body composition was assessed by both BIA and CT scans. Regarding BIA assessment, a multi-frequency device was used (InBody S10®, InBody Co., Ltd., Seoul, Korea). Measurements were performed with the patient in a supine position.

Eight adhesive electrodes were used: one on each wrist, one on the distal part of the third metacarpal bone of each hand, one on the central part of each ankle, and one on the distal part of the second metatarsal bone in each foot. Estimated body weight and height were inputted into the device. PhA and estimated fat free mass (FFM) were recorded from the machine output. Fat-free mass index (FFMI) was calculated as FFM / height 2 (kg/m 2 ).

Regarding CT images, a SIEMENS brand multidetector CT (SOMATON Sensations model) with 64 detectors was used; the studies were performed with a volumetric acquisition in the supine position during maximum inspiration in the pulmonary and mediastinal windows. The main scanning parameters were as follows: tube voltage = 100 kVp, automatic modulation of the electric current tube (70 -120 mAs), pitch = 1, slice thickness = 1 mm and reconstruction matrix = 512 × 512. All images were reconstructed with a high spatial resolution algorithm and a B70 lung filter with a window amplitude of -600/1200; for the mediastinum, a B30 filter with a window width of 50/350 was applied. On each CT scan, the L3 slice was located by two experts' radiologists and was exported as DICOM files. Specific tissue demarcation using predefined thresholds in Hounsfield Units (HU) was performed at the Human J o u r n a l P r e -p r o o f Nutrition Research Unit (University of Alberta, Canada), as previously described 24, 25 .

CT images were processed with the SliceOmatic v5.0 (TomoVision, Montreal, Canada) software, and manually corrected as necessary. Cross-sectional area of skeletal muscle (i.e., skeletal muscle area [SMA]), intermuscular adipose tissue (IMAT), subcutaneous adipose tissue, visceral adipose tissue and low attenuation muscle area were determined using thresholds described elsewhere 25 . SMA was adjusted for height in meters to determine SMI (cm 2 /m 2 ). Skeletal muscle density was generated by the software as the mean radiation attenuation value of the whole muscle area at L3 26 . Low muscle radiodensity was defined as an SMD lower than 35.5 HU and lower than 32.5 HU for men and women, respectively 27 .

Low muscle mass was identified using previously published cut-off points based on sex and BMI categories 27 . For patients with a BMI <30 kg/m 2 , low muscle mass was defined as an SMI ≤ 52.3 cm 2 /m 2 for men and ≤ 38.6 cm 2 /m 2 for women. For those with a BMI ≥30 kg/m 2 , an SMI ≤ 54.3 cm 2 /m 2 for men and ≤ 46.6 cm 2 /m 2 was considered. The following surrogate markers were used for low mass identification: a) FFMI <17 kg/m 2 for males or <15 kg/m 2 in females 28, 29 indicating substantial, and >0.8 indicating almost perfect concordance 36 . The accuracy of each marker of muscle mass to predict CT-assessed low muscle mass was analyzed by sensitivity, specificity, and area under the receiver operating characteristic curve. The area under the curve (AUC) was interpreted as follows: no discrimination AUC ≤0.5, fail discrimination 0.5 to 0.6, poor discrimination 0.6 to 0.7, fair discrimination 0.7 to 0.8, good discrimination 0.8 to 0.9 and excellent discrimination ≥0.9 37 . Statistical significance was defined as p <0.05.

A total of 98 patients with available abdominal CT scans at admission were screened. Of these, 12 had CT scans with streak artifacts from metallic hardware or limited field of view. Finally, 86 critically ill patients with COVID-19 were included.

Detailed clinical and body composition characteristics of all samples and by muscle mass status are summarized in Table 1 . Mean age was 48.6 ± 12.9 years, most J o u r n a l P r e -p r o o f patients were males (73%). A total of 41 patients (48%) were classified as having low muscle mass. Patients with normal muscle mass had higher muscle radiodensity (p=0.003), lower IMAT (p=0.02) and higher values of MUAC (p=0.02), CC (p=0.02), PhA (p=<0.001) and FFMI (p=0.003).

No difference in mortality rate was observed between patients with low and normal muscle mass ( (Figure 1) .

Univariate logistic regression showed a significant association between low muscle mass and higher tracheostomy placement events in crude (OR 4.0, 95% CI 1.35-11.7, p=0.012) and adjusted model (OR 7.3, 95% CI 1.82-29.4, p=0.005) ( Table 3) .

Correlations between surrogate muscle markers and SMA were performed. Statistical significance differences were observed with MUAC, CC, PhA, and FFM (Table 4) . Poor concordance was observed for MUAC (κ 0.15, p=0.009) and fair concordance for FFMI (κ 0.20, p <0.001). PhA showed fair concordance (κ 0.34, p <0.001) and poor accuracy (AUC 0.67, 95% CI 0.57-0.77) for low muscle mass identification, with a sensitivity of 56% and specificity of 78% ( Table 5) .

Low skeletal muscle mass is a common condition in the ICU population. Our observational study showed an association between low muscle mass and prolonged ICU and hospital LOS, and a higher rate of tracheostomy.

The identification of low muscle mass at an early stage of critical illness may improve risk stratification, although little is known of this association in the context of COVID-19 patients on MV. To our knowledge, this is the first study that analyzed low muscle mass as a predictor for increased risk of prolonged LOS in a Mexican cohort of critical patients with COVID-19.

We were able to identify low muscle mass using CT scans in 48% of our population, which is a lower frequency compared to the 65% reported in an Italian cohort using different cut-off values for low muscle identification (45.4 cm 2 /m 2 for males and 34.4 cm 2 /m 2 for females) 38 . In critically-ill septic patients, Cox Mc et al, reported a prevalence of baseline low muscle mass in 50% of patients 39 . Another study in hospitalized patients with COVID-19 showed that muscle mass was related to the need for ICU admission (17%), longer hospital LOS (mean, 10.8 days), and mortality (6.6%) 40 . Although their results are not comparable to our population of critically ill patients as muscle mass was assessed using ultrasound, their findings corroborate J o u r n a l P r e -p r o o f with ours by highlighting low muscle mass as an independent predictor of negative clinical outcomes 37 , including higher rates of extubation failure, defined as reintubation within 48 hours after extubation following long-term MV for >7 days 41 .

Notably, our lack of association with mortality can be simply due to our limited sample size to explore this specific question. In our sample, we observed a trend toward more MV days in patients with low muscle mass.

One important difference often observed across CT-based studies is the choice of thresholds to define low muscle mass 42, 43 . Some studies in patients with COVID used references derived from healthy populations 44, 45 . In this study, we used sex and BMI-adjusted thresholds proposed by Caan et al 27 

Mexican patients with COVID. We acknowledge this cutpoint is not cohort-specific, as they were derived from oncology patients. Despite differences across populations, these thresholds showed a good prognosis capacity.

In our sample, 39% of patients had low muscle mass and low muscle radiodensity.

The latter, also called myosteatosis is indicative of abnormal muscle "quality" (i.e., depicting fat infiltration into muscle). Although we have not explored the clinical implications of myosteatosis or a combined condition with low muscle mass in our study due to sample size limitations, this condition has been previously linked to extubation success 38 , less ventilator-free and ICU-free days 47 , poor survival and higher mortality in mechanically ventilated patients [48] [49] [50] [51] . The mechanism explaining the association of myosteatosis and worse outcomes is unclear, but insulin resistance, oxidative stress and inflammation responses may be implicated 52 .

Notably, although we did not fully explore the consequences of myosteatosis in our study, IMAT was included in regression analysis, which improved model adjustment.

Additionally, we also explored the impact of high adiposity and of high adiposity with low muscle mass (sarcopenic obesity) on the studied clinical outcomes, also using the definition per Caan J et al 27 . No differences between groups were detected for mortality, hospital or ICU LOS, likely due to the small sample size (data not shown).

Most of the studies carried out to date in patients with COVID-19 have not described the impact of muscle mass and the number of tracheostomies as a negative clinical result. In our analysis, we identified a 50% increase in the number of tracheostomies performed in the group of patients with low muscle mass prior to a successful withdrawal from mechanical ventilation, which may in turn impact LOS, morbidity, and mortality.

CT scan is considered a gold standard technique for body composition assessment, with the disadvantage that it is not available in all clinical settings, and not all critical patients had a CT scan for diagnosis purpose. Notably, body composition assessment is not an indication for CT scan due to its high radiation exposure.

Therefore, the identification of bedside surrogate markers for the diagnosis of low muscle mass is important when CT scans are not available. In our study, anthropometric and FFMI/BIA-derived indicators showed insufficient accuracy and agreement with SMA by CT. Despite the evidence of CC as a marker of muscle mass, the lack of accuracy between abnormal CC and CT values may be due to how cut-off points were derived, the former using DXA data from healthy subjects, the latter using CT data from patients with cancer. Similar results were obtained using the BIA-derived FFM, as our cut-off points were not device and population specific.

PhA obtained from BIA is an indicator of cell mass and membrane integrity that is adversely affected by inflammation, disease, and immobilization due to decreased electrical properties of tissues 53 . PhA has been proposed as a surrogate marker for muscle mass in different clinical settings such as patients with cirrhosis 38, 54 . In our study, cut-off values of PhA showed a fair agreement and poor accuracy. Our findings highlight the need for simple and non-invasive tools for muscle mass evaluation and monitoring.

Our study has several limitations: 1) our cohort was enrolled at a single center; 2) the number of female participants was limited; 3) we did not include a non-COVID-19 control group which would allow us to distinguish potential differences associated with COVID-19 or with low muscle mass per se; 4) thresholds for low muscle mass identification were derived from another clinical population, in absence of a Mexican references population or a stablished cut-off for critically-ill patients; 5) Long-term survivorship was not accessible due to the impact of COVID-19 on the workload of the nutrition department, and 6) analysis of additional body composition phenotypes was limited by our small sample size. However, clinical data obtained in this study supports the use of CT a safe non-invasive and reliable technique to detect low muscle mass in critical care patients with COVID-19. J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f

World Health Organization Coronavirus Disease (COVID-19) Dashboard. World Health Organization

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Sarcopenia in critically ill patients

Influence of sarcopenia focused on critically ill patients

Body Composition Findings by Computed Tomography in SARS-CoV-2 Patients: Increased Risk of Muscle Wasting in Obesity

Measuring and monitoring lean body mass in critical illness

Sarcopenia: revised European consensus on definition and diagnosis

Methodology, clinical applications, and future directions of body composition analysis of computed tomography (CT) images: A review

Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image

Body Composition Technology: Implications for the ICU

Recent Issues on Body Composition Imaging for Sarcopenia Evaluation

Psoas as a sentinel muscle for sarcopenia: a flawed premise

The impact of chest CT body composition parameters on clinical outcomes in COVID-19 patients

The prognostic value of pneumonia severity score and pectoralis muscle Area on chest CT in adult COVID-19 patients

Evaluation of Bioelectrical Impedance Analysis in Critically Ill Patients: Results of a Multicenter Prospective Study

Update on body composition tools in clinical settings: computed tomography, ultrasound, and bioimpedance applications for assessment and monitoring

Effect of Intravenous Infusion Solutions on Bioelectrical Impedance Spectroscopy. J Parenter Enteral Nutr prognostic factor for mortality at 90 days in patients with COVID-19: a longitudinal cohort study

Low Phase angle is associated with 60-day mortality in COVID-19 Critically illpatients

Identifying critically-ill patients who will benefit most from nutritional therapy: Further validation of the "modified NUTRIC" nutritional risk assessment tool

Anthropometric Standardization Reference Manual

Validation of predictive equations for weight and height using a metric tape

Obesity: preventing and managing the global epidemic

The emerging role of computerized tomography in assessing cancer cachexia

Sarcopenia and cachexia in the era of obesity: clinical and nutritional impact

Measurement of skeletal muscle radiation attenuation and basis of its biological variation

Explaining the obesity paradox: The association between body composition and colorectal cancer survival (C-SCANS study)

Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98

Diagnostic criteria for malnutrition -An ESPEN Consensus Statement

GLIM criteria for the diagnosis of malnutrition -A consensus report from the global clinical nutrition community

Calf circumference: cutoff values from the NHANES 1999-2006

Anthropometric Reference Data for Children and Adults: United States

Nutrition Therapy in Critically Ill Patients with Coronavirus Disease (COVID-19)

ESPEN expert statements and practical guidance for nutritional management of individuals with SARS-CoV-2 infection

Nutritional management of individuals with obesity and COVID-19: ESPEN expert statements and practical J o u r n a l P r e -p r o o f guidance

The measurement of observer agreement for categorical data

Assessing the Accuracy of Diagnostic Tests

Influence of reduced muscle mass and quality on ventilator weaning and complications during intensive care unit stay in COVID-19 patients

The impact of sarcopenia and acute muscle mass loss on long-term outcomes in critically ill patients with intra-abdominal sepsis

Muscle strength and muscle mass as predictors of hospital length of stay in patients with moderate to severe COVID-19: a prospective observational study. J Cachexia Sarcopenia Muscle 2021

Evaluation of the association between decreased skeletal muscle mass and extubation failure after long-term mechanical ventilation

CT-defined low skeletal muscle mass as a prognostic marker for short-term mortality in critical ill patients. A systematic review and meta analysis

Sarcopenia as a predictor of mortality among the critically ill in an intensive care unit: a systematic review and meta-analysis

Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US

Cut-off values of skeletal muscle index and psoas muscle index at L3 vertebra level by computerized tomography to assess low muscle mass

Myosteatosis in the Context of Skeletal Muscle Function Deficit: An Interdisciplinary Workshop at the National Institute on Aging

Nutrition and Rehabilitation Investigators Consortium (NUTRIC)

Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients

Early high protein intake and mortality in critically ill ICU patients with low skeletal muscle area and -density

Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients

Skeletal muscle quality as assessed by CT-derived skeletal muscle density is associated with 6-month mortality in mechanically ventilated critically ill patients

Skeletal Muscle Composition Predicts Outcome in Critically Ill Patients

Skeletal muscle mass and adipose tissue alteration in critically ill patients

Exploring the potential role of phase angle as a marker of oxidative stress: A narrative review