CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19

Unveiling the detrimental vicious cycle linking skeletal muscle and COVID-19: A systematic review and meta-analysis

OBJECTIVE

Skeletal muscle catabolism supports multiple organs and systems during severe trauma and infection, but its role in COVID-19 remains unclear. This study investigates the interactions between skeletal muscle and COVID-19.

METHODS

The PubMed, EMbase, and The Cochrane Library databases were systematically searched from January 2020 to August 2023 for cohort studies focusing on the impact of skeletal muscle on COVID-19 prevalence and outcomes, and longitudinal studies examining skeletal muscle changes caused by COVID-19. Skeletal muscle quantity (SMQN) and quality (SMQL) were assessed separately. The random-effect model was predominantly utilized for statistical analysis.

RESULTS

Seventy studies with moderate to high quality were included. Low SMQN/SMQL was associated with an increased risk of COVID-19 infection (OR = 1.62, p < 0.001). Both the low SMQN and SMQL predicted COVID-19-related mortality (OR = 1.53, p = 0.016; OR = 2.18, p = 0.001, respectively). Mortality risk decreased with increasing SMQN (OR = 0.979, p = 0.009) and SMQL (OR = 0.972, p = 0.034). Low SMQN and SMQL were also linked to the need for intensive care unit/mechanical ventilation, increased COVID-19 severity, and longer hospital stays. Significant skeletal muscle wasting, characterized by reduced volume and strength, was observed during COVID-19 infection and the pandemic.

CONCLUSIONS

This study reveals a detrimental vicious circle between skeletal muscle and COVID-19. Effective management of skeletal muscle could be beneficial for treating COVID-19 infections and addressing the broader pandemic. These findings have important implications for the management of future virus pandemics.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023395476.

The chest CT perspective on sarcopenia: Exploring reference values for muscle mass quantity/quality and its application in old adults

OBJECTIVE

To determine reference values for diagnosing sarcopenia through chest CT scans and evaluate their suitability for use among the Chinese old population.

METHODS

Chest CT scans were obtained from 500 healthy individuals aged 19-39. Skeletal muscle mass was assessed on chest CT at the level of T4 by the skeletal muscle area (T4SMA), skeletal muscle index (T4SMI), T12 erector spinae muscle area (T12ESMA), and T12 skeletal muscle index (T12SMI), as well as skeletal muscle density (SMD) at T4 and T12 levels. The diagnostic threshold for sarcopenia was defined as a gender-specific value below 2 SD of the mean value in the young group. These cutoff values were then applied to a group of older adults aged 65 and over.

RESULTS

Diagnostic thresholds for low skeletal muscle in men were 110.05 cm², 36.01 cm²/m², 29.56 cm², and 9.65 cm²/m² for T4SMA, T4SMI, T12ESMA, and T12SMI, respectively. For women, the thresholds were: 69.93 cm², 26.51 cm²/m², 17.84 cm²/m², and 6.87 cm²/m², respectively. Diagnostic thresholds for low SMD were 38.63HU in men, 34.74 HU for women at T4 level. At T12 level, the cutoff values were 40.94 HU for men and 36.63 HU for women. Sarcopenia prevalence in men, defined by T4SMA, T4SMI, T12ESMA, and T12SMI cutoffs, was 35.6%, 18.9%, 36.7%, and 23.7%, respectively. In women, sarcopenia prevalence was 5.1%, 3.2%, 3.2%, and 1.9%, respectively.

CONCLUSION

This study established reference values for sarcopenia diagnosis through chest CT scans among the Chinese population, highlighting the importance of utilizing chest CT scans for sarcopenia detection and muscle health monitoring in older adults.

Predicting COPD exacerbations based on quantitative CT analysis: an external validation study

Purpose

Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations.

Methods

Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification.

Results

A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events.

Conclusion

The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.

Malnutrition and pectoralis muscle index in medical intensive care unit patients: A matched cohort study

BACKGROUND

Muscle assessment is an important component of nutrition assessment. The Global Leadership Initiative on Malnutrition (GLIM) consortium recently underscored the need for more objective muscle assessment methods in clinical settings. Various assessment techniques are available; however, many have limitations in clinical populations. Computed tomography (CT) scans, obtained for diagnostic reasons, could serve multiple purposes, including muscle measurement for nutrition assessment. Although CT scans of the chest are commonly performed clinically, there is little research surrounding the utility of pectoralis muscle measurements in nutrition assessment. The primary aim was to determine whether CT-derived measures of pectoralis major cross-sectional area (PMA) and quality (defined as mean pectoralis major Hounsfield units [PMHU]) could be used to identify malnutrition in patients who are mechanically ventilated in an intensive care unit (ICU). A secondary aim was to evaluate the relationship between these measures and clinical outcomes in this population.

METHODS

A retrospective analysis was conducted on 33 pairs of age- and sex-matched adult patients who are being mechanically ventilated in the ICU. Patients were grouped by nutrition status. Analyses were performed to determine differences in PMA and mean PMHU between groups. Associations between muscle and clinical outcomes were also investigated.

RESULTS

Compared with nonmalnourished controls, malnourished patients had a significantly lower PMA (P = 0.001) and pectoralis major (PM) index (PMA/height in m2; P = 0.001). No associations were drawn between PM measures and clinical outcomes.

CONCLUSION

These findings regarding CT PM measures lay the groundwork for actualizing the GLIM call to action to validate quantitative, objective muscle assessment methods in clinical settings.

Visceral fat area measured by electrical bioimpedance as an aggravating factor of COVID-19: a study on body composition

INTRODUCTION

Severe forms of COVID-19 are more common in patients with abnormal fat distribution, particularly high visceral adiposity. The patient's muscle strength may be reduced during the acute phase of the infection. Electrical bioimpedance (BIA) is a non-invasive method for measuring body compartments and estimating visceral fat area (VFA) that can be used at the bedside.

OBJECTIVE

To assess the association between several body composition parameters, primarily high adipose tissue and high VFA, in patients with and without a diagnosis of COVID-19 infection, and whether it worsened the severity parameters.

METHODS

This retrospective cohort study was conducted in a private hospital in the city of São Paulo from March 2020 to August 2021. The demographic and clinical data was collected from medical reports. Body composition is assessed using the InBODY® model S10 bioelectrical impedance device and a Jamar® digital hydraulic manual dynamometer with a scale from 0 to 90 kg is used to measure handgrip strength (HGS).

RESULTS

A total of 96 patients with a mean age of 69.1 years (SD 15) were divided into two groups of 48 individuals, with and without COVID-19 infection. Body mass index (odds ratio [OR]: 4.47, 95% confidence interval [CI]: 1.69, 11.83), fat mass (OR: 2.03, 95% CI: 0.48, 8.55), and VFA (OR: 1.08, 95% CI: 0.33, 3.53) were all higher in the infection group. When COVID-19 patients were evaluated, those with higher VFA had longer hospital stays (OR: 0.99, 95% CI: 0.97, 1.01) and used more vasoactive drugs (p = 0.043). Patients with COVID-19 with poor handgrip strength were 3.29 times more likely to require a prolonged intensive care unit (ICU) stay.

CONCLUSION

The study concluded that excess weight and body fat are significantly associated with COVID-19 involvement, but the severity is primarily related to a greater area of visceral fat. The use of bioimpedance for visceral fat measurement was effective, as it is a simple method performed in the hospital setting that does not require the use of radiation.