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

Body composition assessment with ultrasound muscle measurement: optimization through the use of semi-automated tools in colorectal cancer

Colorectal cancer (CRC) is a disease with a high prevalence and major impact on global health. Body composition (BC) data are of great importance in the assessment of nutritional status. Ultrasound (US) is an emerging, accessible and non-invasive technique that could be an alternative when it is not feasible to perform computed tomography (CT). The aim of this study is to evaluate the correlation between CT, as a reference technique, and US of the rectus femoris (RF) as a "proof of concept," in a cohort of patients with CRC and assess the optimisation of results obtained by US when performed by our new semi-automated tool. A single-centre cross-sectional study including 174 patients diagnosed with CRC and undergoing surgery was carried out at the Vall d'Hebron Hospital. We found a strong correlation between CT and US of the RF area (r = 0.67; p < 0.005). The latter, is able to discriminate patients with worse prognosis in terms of length of hospital stay and discharge destination (AUC-ROC = 0.64, p 0.015). These results improve when they are carried out with the automatic tool (area AUC-ROC = 0.73, p 0.023), especially when normalised by height and eliminating patients who associate overflow. According to our results, the US could be considered as a valuable alternative for the quantitative assessment of muscle mass when CT is not feasible. These measurements are improved when measuring software is applied, such as "Bat" software.

Evaluation of muscle mass and intramuscular fatty infiltration in dogs with hypercortisolism and their association with prognosis

BACKGROUND

Muscle atrophy and intramuscular fatty infiltration, as well as their association with prognosis, have not been quantified in dogs with spontaneous hypercortisolism (HC).

OBJECTIVE

To quantitatively evaluate muscle atrophy and IM fatty infiltration in dogs with HC and determine their prognostic impact.

ANIMALS

Fifty-three dogs with HC and 66 control dogs without HC.

METHODS

Retrospective cohort study. Medical records and computed tomography images obtained between 2014 and 2021 were evaluated. Kaplan-Meier curves and log-rank tests were used to analyze the effect of muscle atrophy and IM fatty infiltration on the prognosis of dogs with HC.

RESULTS

Dogs with HC showed lower visually measured cross-sectional area (VCSA) and cross-sectional area based on attenuation (HCSA) than control dogs (median [interquartile range {IQR}]: 50.3 mm2/mm [36.2-67.8] vs 66.7 mm2/mm [48.0-85.9]; P < .001; 30.4 mm2/mm [13.7-57.2] vs 54.8 mm2/mm [39.7-71.5]; P < .001, respectively). Dogs with HC had lower epaxial muscle attenuation (L3HU) than control dogs (median [IQR]: 21.2 Hounsfield [HU] [12.4-28.2] vs 33.2 HU [22.6-43.6]; P < .001). Dogs with HC with lower HCSA or L3HU had shorter survival (median [IQR]: 670 days [222-673] vs 949 days [788-1074], P < .01; 523 days [132-670] vs 949 days [756-1074], P < .01, respectively) but not lower VCSA (median [IQR]: 673 days [132-788] vs 949 days [523 to not applicable]; P = .30).

CONCLUSION AND CLINICAL IMPORTANCE

Hypercortisolism in dogs causes muscle atrophy and IM fatty infiltration and is associated with poor prognosis.

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Myosteatosis predicts short-term mortality in patients with COVID-19: A multicenter analysis

OBJECTIVES

Body composition on computed tomography can predict prognosis in patients with COVID-19. The reported data are based on small retrospective studies. The aim of the present study was to analyze the prognostic relevance of skeletal muscle parameter derived from chest computed tomography for prediction of 30-d mortality in patients with COVID-19 in a multicenter setting.

METHODS

The clinical databases of three centers were screened for patients with COVID-19 between 2020 and 2022. Overall, 447 patients (142 female; 31.7%) were included into the study. The mean age at the time of computed tomography acquisition was 63.8 ± 14.7 y and median age was 65 y. Skeletal muscle area and skeletal muscle density were defined on level T12 of the chest.

RESULTS

Overall, 118 patients (26.3%) died within the 30-d observation period. Of the patient sample, 255 patients (57.0%) were admitted to an intensive care unit and 122 patients needed mechanical ventilation (27.3%). The mean skeletal muscle area of all patients was 96.1 ± 27.2 cm² (range = 23.2-200.7 cm²). For skeletal muscle density, the mean was 24.3 ± 11.1 Hounsfield units (range = -5.6 to 55.8 Hounsfield units). In survivors, the mean skeletal muscle density was higher compared with the lethal cases (mean 25.8 ± 11.2 versus 20.1 ± 9.6; P < 0.0001). Presence of myosteatosis was independently associated with 30-d mortality: odds ratio = 2.72 (95% CI, 1.71-4.32); P = 0.0001.

CONCLUSIONS

Myosteatosis is strongly associated with 30-d mortality in patients COVID-19. Patients with COVID-19 with myosteatosis should be considered a risk group.

A novel body composition risk score (B-Score) and overall survival among patients with nonmetastatic breast cancer

BACKGROUND & AIMS

Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in patients with breast cancer. However, they were not typically combined to develop an overall risk score, which can identify patients at high risk of death and prioritize patients in need of dietary and lifestyle interventions. Thus, we aimed to develop a novel composite body composition risk score (B-Score).

METHODS

We included 3105 patients with stage II or III breast cancer at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we assessed areas and radiodensity of muscle and adipose tissue at the third lumber vertebrae. We considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent prognostic factors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (<40.1 cm2/m2), high SATI (≥75.7 cm2/m2), and high SAT radiodensity (≥-97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors.

RESULTS

Mean (standard deviation) age was 53.9 (11.8) years, 70.3% were Non-Hispanic White, and 60.5% were stage II. Most patients (60.6%) had only one body composition risk factor (B-Score = 1). Compared to those with no risk factors (B-Score = 0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend < 0.001).

CONCLUSIONS

More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may improve risk stratification and inform dietary and lifestyle interventions following breast cancer diagnosis.

Effect of mild cortisol cosecretion on body composition and metabolic parameters in patients with primary hyperaldosteronism

OBJECTIVE

To investigate the effects of simultaneous cortisol cosecretion (CCS) on body composition in computed tomography (CT)-imaging and metabolic parameters in patients with primary aldosteronism (PA) with the objective of facilitating early detection.

DESIGN

Retrospective cohort study.

PATIENTS

Forty-seven patients with PA and CCS confirmed by 1-mg dexamethasone suppression test (DST) with a cutoff of ≥1.8 µg/dL were compared with PA patients with excluded CCS (non-CCS, n = 47) matched by age and sex.

METHODS

Segmentation of the fat compartments and muscle area at the third lumbar region was performed on non-contrast-enhanced CT images with dedicated segmentation software. Additionally, liver, spleen, pancreas and muscle attenuation were compared between the two groups.

RESULTS

Mean cortisol after DST was 1.2 µg/dL (33.1 nmol/L) in the non-CCS group and 3.2 µg/dL (88.3 nmol/L) in the CCS group with mild autonomous cortisol excess (MACE). No difference in total, visceral and subcutaneous fat volumes was observed between the CCS and non-CCS group (p = .7, .6 and .8, respectively). However, a multivariable regression analysis revealed a significant correlation between total serum cholesterol and results of serum cortisol after 1-mg DST (p = .026). Classification of the patients based on visible lesion on CT and PA-lateralization via adrenal venous sampling also did not show any significant differences in body composition.

CONCLUSION

MACE in PA patients does not translate into body composition changes on CT-imaging. Therefore, early detection of concurrent CCS in PA is currently only attainable through biochemical tests. Further investigation of the long-term clinical adverse effects of MACE in PA is necessary.

The association of body composition on chemotherapy toxicities in non-metastatic colorectal cancer patients: a systematic review

BACKGROUND

In recent years, certain body composition measures, assessed by computed tomography (CT), have been found to be associated with chemotherapy toxicities. This review aims to explore available data on the relationship between skeletal muscle and adiposity, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular and intermuscular adipose tissue and their association with chemotherapy toxicity in non-metastatic colorectal cancer (CRC) patients.

METHODS

A systematic literature search following PRISMA guidelines was conducted in Medline, Embase, Cochrane and Web of Science, for papers published between 2011 and 2023. The search strategy combined keywords and MESH terms relevant to 'body composition', 'chemotherapy toxicities', and 'non-metastatic colorectal cancer'.

RESULTS

Out of 3868 studies identified, six retrospective studies fulfilled the inclusion criteria with 1024 eligible patients. Low skeletal muscle mass was strongly associated with increased incidence of both chemotherapy toxicities and dose-limiting toxicity (DLT). The association of VAT, intramuscular and intermuscular adiposity was heterogeneous and inconclusive. There was no association between SAT and chemotherapy intolerance. No universal definitions or cut-offs for sarcopenia and obesity were noted. All studies utilized 2-dimensional (2D) CT slices for CT body composition assessment with varied selection on the vertebral landmark and inconsistent reporting of tissue-defining Hounsfield unit (HU) measurements.

CONCLUSION

Low skeletal muscle is associated with chemotherapy toxicities in non-metastatic CRC. However, quality evidence on the role of adiposity is limited and heterogeneous. More studies are needed to confirm these associations with an emphasis on a more coherent body composition definition and an approach to its assessment, especially regarding sarcopenia.

Body composition analysis via spatially resolved NIR spectroscopy with multifrequency bioimpedance precision

Near-infrared spectroscopy (NIRS) is often criticized due to its insufficient accuracy in determining body composition compared to the gold standard methods. In this work, we show that the use of multiple source-detector distances, as well as the simultaneous use of physiological and optical features, can significantly improve the accuracy of determination of fat and lean mass percentage in the human body using NIR spectroscopy. The study performed on the n = 292 cohort revealed the mean absolute errors of 3.5% for fat content and 3.3% for soft lean mass percentage prediction (r = 0.93) using the multifrequency bioimpedance analysis (BIA) as a reference. Hence, NIRS can serve as an independent reliable method for body composition analysis with precision close to that of advanced multifrequency BIA.

Relationship Between Pretreatment Body Composition and Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Receiving First-Line Ipilimumab Plus Nivolumab

INTRODUCTION

Biomarkers are needed to identify patients with metastatic renal cell carcinoma (mRCC) most likely to benefit from immune checkpoint inhibitors. We examined associations between radiographically assessed body composition (BC) variables and body mass index (BMI) with clinical outcomes for patients with mRCC receiving first-line ipilimumab + nivolumab (ipi/nivo).

PATIENTS AND METHODS

We retrospectively reviewed all patients with mRCC treated with first-line ipi/nivo at one institution before June 1, 2021 with an analyzable baseline computed tomography (CT) scan. BC variables (skeletal muscle index [SMI], subcutaneous adipose tissue index [SATI], and visceral adipose tissue index [VATI]) were measured using baseline CT scans. Relationships between BC variables and clinical outcomes were examined using Cox proportional hazard regression models.

RESULTS

Ninety-nine patients were analyzed (74% male, 64% overweight/obese, 75% low SMI). Controlling for age, IMDC risk, and sex (for BMI analyses), high vs. low SMI (HR=2.433, CI: 1.397-4.238, P=.0017), high vs. low SATI (HR=1.641, CI: 1.023-2.632, P=.0398), and obese BMI (≥ 30 kg/m2) vs. normal/overweight BMI (<30 kg/m2) (HR=1.859, CI: 1.156-2.989, P=.0105) were significantly associated with progression-free survival (PFS). Median overall survival (OS) for low SMI patients was higher (42.74 months, CI: 26.84, NR) than median OS for high SMI patients (27.01 months, CI: 15.28, NR) (adjusted HR=1.728, CI: 0.909-3.285, P=.0952). No BC variables were significantly associated with OS or objective response rate.

CONCLUSIONS

Low SMI and low SATI were associated with significantly better PFS for patients with mRCC receiving first-line ipi/nivo. Radiographic BC variables may be useful prognostic biomarkers in this setting.

Standardization of dual-energy x-ray visceral adipose tissue measures for comparison across clinical imaging systems

OBJECTIVE

Excess visceral adipose tissue (VAT) is a major risk factor for metabolic syndrome (MetS) and clinical guidelines have been proposed to define VAT levels associated with increased risk. The aim was to standardize VAT measures between two dual-energy x-ray absorptiometry (DXA) manufacturers who provide different VAT estimates to support standardization of measures across imaging modalities.

METHODS

Scans from 114 individuals (ages 18-81 years) on GE HealthCare (GEHC) and Hologic DXA systems were compared via Deming regression to standardize VAT between the two systems, validated in a separate sample (n = 15), with κ statistics to assess agreement of VAT measurements for classifying patients into risk categories.

RESULTS

The GEHC and Hologic VAT measures were highly correlated and validated in the separate data set (r2  = 0.97). VAT area measures substantially agreed for metabolic risk classification (weighted κ = 0.76) with no significant differences in the population mean values.

CONCLUSIONS

VAT measures can be estimated from GEHC and Hologic scans that classify individuals in a substantially similar way into metabolic risk categories, and systematic bias between the measures can be removed using simple regression equations. These findings allow for DXA VAT measures to be used in complement to other imaging modalities, regardless of whether scans used GEHC or Hologic systems.

Prognostic utility of body composition parameters based on computed tomography analysis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors

OBJECTIVE

The purpose of this study was to evaluate the prognostic impact of body composition parameters based on computed tomography (CT) in patients with non-small cell lung cancer (NSCLC) who received ICI treatment.

METHODS

This retrospective study analyzed the data from advanced NSCLC patients treated with ICI therapy between 2013 and 2019. We included patients with NSCLC who underwent baseline CT scans. The exclusion criteria included patients who received three or more lines of chemotherapy, those with insufficient clinical information, or those without treatment response evaluation.

RESULTS

A total of 136 patients were enrolled. Among the volumetric body composition parameters, patients in the highest quartiles (Q2-4) of the visceral fat index (VFI) exhibited a higher response rate to ICI therapy than those in the lowest quartile (Q1) of VFI (Q1 vs. Q2-4: 18.2% vs. 43.1%, p = 0.012). Patients with a VFI in Q2-4 had significantly prolonged progression-free survival (PFS) and overall survival (OS) (PFS, Q1 vs. Q2-4: 3.0 months vs. 6.4 months, p = 0.043; OS, Q1 vs. Q2-4: 5.6 months vs. 16.3 months, p = 0.004). Kaplan-Meier analysis based on the VFI and visceral fat Hounsfield unit (HU) revealed that patients with VFI in Q1 and HU in Q2-4 had the worst prognosis.

CONCLUSIONS

Visceral fat volume is significantly associated with treatment outcomes in ICI-treated patients with NSCLC. Moreover, fat quality may impact the treatment outcomes. This finding underscores the potential significance of both fat compartments and fat quality as prognostic indicators.

CRITICAL RELEVANCE STATEMENT

Visceral fat volume is significantly associated with treatment outcomes in ICI-treated patients with non-small cell lung cancer. Moreover, fat quality may impact the treatment outcomes. This finding underscores the potential significance of both fat compartments and fat quality as prognostic indicators.

KEY POINTS

• We found that visceral fat volume positively correlated with treatment response and survival in patients with non-small cell lung cancer receiving immune checkpoint inhibitors. • Additionally, a trend toward a negative correlation between visceral fat attenuation and survival was observed. • The findings highlight the prognostic utility of fat compartments and fat quality.

CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease

Purpose

Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD).

Materials and Methods

A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRMemph), PRM-derived functional small airway disease (PRMfSAD), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson's correlation analysis.

Results

The volume and area of muscle and subcutaneous fat were negatively associated with PRMemph and PRMfSAD (p < 0.05). Bone density at T12 was negatively associated with PRMemph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001).

Conclusion

Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.

Abnormal distribution of fat tissue and its association with intestinal failure-associated liver disease in children and adolescents with long-time parenteral nutrition support: A case-control study

BACKGROUND

Patients with intestinal failure (IF) often present with abnormal body composition characterized by high fat mass. However, the distribution of fat and its association with the development of IF-associated liver disease (IFALD) remain unclear. This study aims to investigate the body composition and its relationship with IFALD in older children and adolescents with IF.

METHODS

This retrospective case-control study enrolled patients with IF receiving parenteral nutrition (PN) at Keio University Hospital who initiated PN before the age of 20 years (cases). The control group included patients with abdominal pain, with available computed tomography (CT) scan and anthropometric data. CT scan images of the third lumbar vertebra (L3) were used for body composition analysis and compared between the groups. Liver histology was compared with CT scan findings in IF patients who underwent biopsy.

RESULTS

Nineteen IF patients and 124 control patients were included. To account for age distribution, 51 control patients were selected. The median skeletal muscle index was 33.9 (29.1-37.3) in the IF group and 42.1 (39.1-45.7) in the control group (P < 0.01). The median visceral adipose tissue index (VATI) was 9.6 (4.9-21.0) in the IF group and 4.6 (3.0-8.3) in the control group (P = 0.018). Among the 13 patients with IF who underwent liver biopsies, 11 (84.6%) had steatosis, and there was a tendency for fibrosis to correlate with VATI.

CONCLUSION

Patients with IF exhibit low skeletal muscle mass and high visceral fat, which may be related to liver fibrosis. Routine monitoring of body composition is recommended.

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

BACKGROUND

COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19.

RESEARCH QUESTION

Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score?

STUDY DESIGN AND METHODS

This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records.

RESULTS

Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038).

INTERPRETATION

CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

Association between Computed Tomography-Determined Loss of Muscle Mass and Impaired Three-Month Survival in Frail Older Adults with Cancer

As patients with solid (non-hematological) cancers and a life expectancy of <3 months rarely benefit from oncological treatment, we examined whether the CT-determined loss of muscle mass is associated with an impaired 3-month overall survival (OS) in frail ≥75-year-old patients with cancer. Frailty was assessed with G8-screening and comprehensive geriatric assessment in older adults at risk of frailty. The L3-level skeletal (SMI) and psoas (PMI) muscle indexes were determined from routine CT scans. Established and optimized SMI and PMI cut-offs were used. In the non-curative treatment group (n = 58), 3-month OS rates for normal and low SMI were 95% and 64% (HR 9.28; 95% CI 1.2-71) and for PMI 88%, and 60%, respectively (HR 4.10; 1.3-13). A Cox multivariable 3-month OS model showed an HR of 10.7 (1.0-110) for low SMI, 2.34 (0.6-9.8) for ECOG performance status 3-4, 2.11 (0.5-8.6) for clinical frailty scale 5-9, and 0.57 (0.1-2.8) for males. The 24-month OS rates in the curative intent group (n = 21) were 91% and 38% for the normal and low SMI groups, respectively. In conclusion, CT-determined low muscle mass is independently associated with an impaired 3-month OS and, alongside geriatric assessment, could aid in oncological versus best supportive care decision-making in frail patients with non-curable cancers.

Comparison of computed tomography and dual-energy X-ray absorptiometry in the evaluation of body composition in patients with obesity

Objective

a) To evaluate the accuracy of the pre-existing equations (based on cm2 provided by CT images), to estimate in kilograms (Kg) the body composition (BC) in patients with obesity (PwO), by comparison with Dual-energy X-ray absorptiometry (DXA). b) To evaluate the accuracy of a new approach (based on both cm2 and Hounsfield Unit parameters provided by CT images), using an automatic software and artificial intelligence to estimate the BC in PwO, by comparison with DXA.

Methods

Single-centre cross-sectional study including consecutive PwO, matched by gender with subjects with normal BMI. All the subjects underwent BC assessment by Dual-energy X-ray absorptiometry (DXA) and skeletal-CT at L3 vertebrae. CT images were processed using FocusedON-BC software. Three different models were tested. Model 1 and 2, based on the already existing equations, estimate the BC in Kg based on the tissue area (cm2) in the CT images. Model 3, developed in this study, includes as additional variables, the tissue percentage and its average Hounsfield unit.

Results

70 subjects (46 PwO and 24 with normal BMI) were recruited. Significant correlations for BC were obtained between the three models and DXA. Model 3 showed the strongest correlation with DXA (r= 0.926, CI95% [0.835-0.968], p<0.001) as well as the best agreement based on Bland - Altman plots.

Conclusion

This is the first study showing that the BC assessment based on skeletal CT images analyzed by automatic software coupled with artificial intelligence, is accurate in PwO, by comparison with DXA. Furthermore, we propose a new equation that estimates both the tissue quantity and quality, that showed higher accuracy compared with those currently used, both in PwO and subjects with normal BMI.

High prevalence of pre-existing sarcopenia in critically ill patients with hematologic malignancies admitted to the intensive care unit for sepsis or septic shock

BACKGROUND & AIMS

We aimed to evaluate body composition (BC) by computed tomography (CT) in hematologic malignancy (HM) patients admitted to the intensive care unit (ICU) for sepsis or septic shock.

METHODS

We retrospectively assessed BC and its impact on outcome of 186 patients at the 3rd lumbar (L3) and 12th thoracic vertebral levels (T12) using CT-scan performed before ICU admission.

RESULTS

The median patient age was 58.0 [47; 69] years. Patients displayed adverse clinical characteristics at admission with median [q1; q3] SAPS II and SOFA scores of 52 [40; 66] and 8 [5; 12], respectively. The mortality rate in the ICU was 45.7%. Overall survival rates at 1 month after admission in the pre-existing sarcopenic vs. non pre-existing sarcopenic patients were 47.9% (95% CI [37.6; 61.0]) and 55.0% (95% CI [41.6; 72.8]), p = 0.99), respectively, at the L3 level and 48.4% (95% CI [40.4; 58.0]) vs. 66.7% (95% CI [51.1; 87.0]), p = 0.062), respectively, at the T12 level.

CONCLUSIONS

Sarcopenia is assessable by CT scan at both the T12 and L3 levels and is highly prevalent in HM patients admitted to the ICU for severe infections. Sarcopenia may contribute to the high mortality rate in the ICU in this population.

A Trend towards Diaphragmatic Muscle Waste after Invasive Mechanical Ventilation in Multiple Trauma Patients-What to Expect?

Considering the prioritization of life-threatening injuries in trauma care, secondary dysfunctions such as ventilator-induced diaphragmatic dysfunction (VIDD) are often overlooked. VIDD is an entity induced by muscle inactivity during invasive mechanical ventilation, associated with a profound loss of diaphragm muscle mass. In order to assess the incidence of VIDD in polytrauma patients, we performed an observational, retrospective, longitudinal study that included 24 polytraumatized patients. All included patients were mechanically ventilated for at least 48 h and underwent two chest CT scans during their ICU stay. Diaphragmatic thickness was measured by two independent radiologists on coronal and axial images at the level of celiac plexus. The thickness of the diaphragm was significantly decreased on both the left and right sides (left side: -0.82 mm axial p = 0.034; -0.79 mm coronal p = 0.05; right side: -0.94 mm axial p = 0.016; -0.91 coronal p = 0.013). In addition, we obtained a positive correlation between the number of days of mechanical ventilation and the difference between the two measurements of the diaphragm thickness on both sides (r =0.5; p = 0.02). There was no statistically significant correlation between the body mass indexes on admission, the use of vitamin C or N-acetyl cysteine, and the differences in diaphragmatic thickness.

Predicting dry weight in patients with cirrhotic ascites undergoing large-volume paracentesis

BACKGROUND & AIMS

Ascites impairs the correct diagnosis and nutritional management in patients with cirrhosis, because the body weight, which is needed for nutritional assessment and calculation of nutritional needs, is overestimated. To adjust the weight in patients with ascites, dietetic guidances indicate substracting 2.2-14 kg or 5-15% of the measured body weight according to the degree of ascites, however, there is a lack of evidence to substantiate these values. The aim of this study was to develop new prediction equations to estimate the dry weight, comparing them with the currently used weight adjustments in patients with refractory cirrhotic ascites.

METHODS

Cross-sectional study, that included patients with decompensated cirrhosis undergoing large-volume paracentesis. Patients were submitted to nutritional risk screening, nutritional assessment, and anthropometric measurements that included body weight, abdominal circumference (both measured before and after paracentesis) height, and upper mid-arm circumference. The volume of ascitic fluid drained was also registered. For the predictions of dry weight, linear regression models were performed using as predictor variables: height, pre-paracentesis weight, pre-paracentesis abdominal circumference, or mid-upper arm circumference, and as response variable: post-paracentesis weight. The capacity of these models to predict the post-paracentesis weight was evaluated by comparing it with the currently used predictions through the intraclass correlation coefficient (ICC) and the mean squared error (MSE).

RESULTS

Nineteen patients were included, 15 male, and 18 with high nutritional risk and malnutrition. The difference between post-paracentesis weight and pre-paracentesis weight was -5.0 (-3.6 to -9.9) kg, similar to ascitic fluid volume drained. Two equations were developed to predict post-paracentesis weight. ICC values showed that both prediction equations were strongly correlated (r > 0.94) with post-paracentesis weight. Our models also showed lower MSEs (<17.97), compared with the current predictions (MSEs <64.19, when the pre-paracentesis weight is adjusted from absolute values and MSEs <33.24 when adjusted from percentage values), indicating a more accurate prediction.

CONCLUSION

The predictive equations from this study may be better options for dry weight estimation in patients with refractory cirrhotic ascites since they showed higher reliability compared to the currently used weight adjustment. External validation in a larger sample is still needed to confirm the clinical applicability of these equations.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

[Routine computed tomography body composition analysis-experience in intensive care patients]

The assessment of the nutritional status of patients in the intensive care unit is recommended in current guidelines and should include the assessment of muscle status. A suitable method is the analysis of routine computed tomography (CT) scans, which are frequently performed in critically ill patients. With the help of special software, individual CT slices are processed and various parameters such as muscle area, muscle density or even the percentage of adipose tissue are displayed and quantified. It has been shown that cross-sectional acquisition of skeletal muscle in the lumbar spine correlates very well with total body muscle. There are defined, albeit population-based, cut-off values that can be used to establish diagnosis of sarcopenia. Monitoring of individualized nutritional therapy can be accomplished by assessment of repetitive CT examinations. The steadily growing body of data confirms that the method can make a valuable contribution to the assessment of body composition in intensive care medicine. Most of the currently available software requires time-consuming processing of the CT. Automated programs, which are now occasionally available and eliminate the need for most manual processing, may make the method even more attractive in the future. Ultimately, the risk of intensive transport to the CT or radiation exposure may be only justified for medical indications. Nevertheless, whenever CT is available for medical reasons, it should also be exploited for composition analysis.

Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS)

Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases. Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data. Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69. Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy.

Comparison of CT derived body composition at the thoracic T4 and T12 with lumbar L3 vertebral levels and their utility in patients with rectal cancer

BACKGROUND

Computed tomography (CT) derived body composition measurements of sarcopenia are an emerging form of prognostication in many disease processes. Although the L3 vertebral level is commonly used to measure skeletal muscle mass, other studies have suggested the utilisation of other segments. This study was performed to assess the variation and reproducibility of skeletal muscle mass at vertebral levels T4, T12 and L3 in pre-operative rectal cancer patients. If thoracic measurements were equivalent to those at L3, it will allow for body composition comparisons in a larger range of cancers where lumbar CT images are not routinely measured.

RESEARCH METHODS

Patients with stage I - III rectal cancer, undergoing curative resection from 2010 - 2014, were assessed. CT based quantification of skeletal muscle was used to determine skeletal muscle cross sectional area (CSA) and skeletal muscle index (SMI). Systematic differences between the measurements at L3 with T4 and T12 vertebral levels were evaluated by percentile rank differences to assess distribution of differences and ordinary least product regression (OLP) to detect and distinguish fixed and proportional bias.

RESULTS

Eighty eligible adult patients were included. Distribution of differences between T12 SMI and L3 SMI were more marked than differences between T4 SMI and L3 SMI. There was no fix or proportional bias with T4 SMI, but proportional bias was detected with T12 SMI measurements. T4 CSA duplicate measurements had higher test-retest reliability: coefficient of repeatability was 34.10 cm² for T4 CSA vs 76.00 cm² for T12 CSA. Annotation time (minutes) with L3 as reference, the median difference was 0.85 for T4 measurements and -0.03 for T12 measurements. Thirty-seven patients (46%) had evidence of sarcopenia at the L3 vertebral level, with males exhibiting higher rates of sarcopenia. However, there was no association between sarcopenia and post-operative complications, recurrence or hospital LOS (length of stay) in patients undergoing curative resection.

CONCLUSIONS

Quantifying skeletal muscle mass at the T4 vertebral level is comparable to measures achieved at L3 in patients with rectal cancer, notwithstanding annotation time for T4 measurements are longer.

Serum creatinine/cystatin C ratio as a muscle mass evaluating tool and prognostic indicator for hospitalized patients: A meta-analysis

Objective

Sarcopenia is a syndrome of decreased muscle mass and deficits in muscle strength and physical function. We aimed to investigate the relationship between creatinine/cystatin C ratio (CCR) and sarcopenia and the prognostic value of CCR in hospitalized patients.

Materials and methods

We searched for relevant studies in PubMed, EMBASE, and the Cochrane Database up to August 25, 2022. Meta-analyses were performed to evaluate the relationship between CCR and skeletal muscle [computed tomography-assessed skeletal muscle (CTASM), muscle strength, and physical performance], prognosis and important clinical outcomes in hospitalized adults. The pooled correlation coefficient, the area under the receiver operating characteristic (ROC) curves, and hazard ratio (HR) together with their 95% confidence intervals (CIs) were calculated. We also conducted subgroup analyses to explore the sources of heterogeneity.

Results

A total of 38 studies with 20,362 patients were eligible. These studies were of moderate to high quality. Our results showed that CCR was significant correlations with all CTASM types (Fisher's Z ranged from 0.35 to 0.5; P values ranged from < 0.01 to 0.01), handgrip strength (Fisher's Z = 0.39; 95% CI, 0.32-0.45; P < 0.001) and gait speed (Fisher's Z = 0.25; 95% CI, 0.21-0.30; P < 0.001). The ROC curves suggested that CCR had good diagnostic efficacy (0.689; 95% CI, 0.632-0.746; P < 0.01) for sarcopenia. CCR can reliably predict mortality in hospitalized patients, which was confirmed by regression analysis of CCR as both continuous (HR 0.78; 95% CI, 0.72-0.84; P < 0.01) and categorical variables (HR 2.05; 95% CI, 1.58-2.66; P < 0.0001). In addition, less evidence showed that higher CCR was independently associated with a shorter duration of mechanical ventilation, reduced length of stay in the intensive care unit and hospital, less nutritional risk, and decreased complications in hospitalized patients.

Conclusion

CCR could be a simple, economical, and effective screening tool for sarcopenia in hospitalized patients, and it is a helpful prognostic factor for mortality and other important clinical outcomes.

Systematic review registration

https://inplasy.com/inplasy-2022-9-0097/, identifier INPLASY202290097.

Low muscle mass in COVID-19 critically-ill patients: Prognostic significance and surrogate markers for assessment

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.

Assessment of Standing Multi-Frequency Bioimpedance Analyzer to Measure Body Composition of the Whole Body and Limbs in Elite Male Wrestlers

We investigated differences in body composition measurements for the whole body and limb segments in elite male wrestlers between results of multi-frequency bioelectrical impedance analyses (MF_BIA) and dual energy X-ray absorptiometry (DXA). Sixty-six elite male wrestlers from Taiwan were recruited. Wrestlers' body fat percentage (PBF_WB), whole body fat-free mass (FFM_WB), whole body lean soft tissue mass (LSTM_WB), and fat-free mass of arms, legs and trunk (FM_Arms, FFM_Legs, FFM_Trunk) were measured by MF_BIA and DXA, and analyzed using Pearson correlation coefficient and Bland-Altman plot. Correlations of FFM_WB, LSTM_WB, and PBF_WB between devices were 0.958, 0.954, and 0.962, respectively. Limits of agreement (LOA) of Bland-Altman plot were -4.523 to 4.683 kg, -4.332 to 4.635 kg and -3.960 to 3.802%, respectively. Correlations of body composition parameters FFM_Arms, FFM_Legs and FFM_Turnk between devices in each limb segment were 0.237, 0.809, and 0.929, respectively; LOAs were -2.877 to 2.504 kg, -7.173 to -0.015 kg and -5.710 to 0.777 kg, respectively. Correlation and consistency between the devices are high for FFM, LSTM and PBF but relatively low for limb segment FFM. MF_BIA may be an alternative device to DXA for measuring male wrestlers' total body composition but limb segment results should be used cautiously.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

INTRODUCTION

Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice.

MATERIALS AND METHODS

A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale.

RESULTS

Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating.

CONCLUSION

A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.

Unsupervised Domain Adaptation for Vertebrae Detection and Identification in 3D CT Volumes Using a Domain Sanity Loss

A variety of medical computer vision applications analyze 2D slices of computed tomography (CT) scans, whereas axial slices from the body trunk region are usually identified based on their relative position to the spine. A limitation of such systems is that either the correct slices must be extracted manually or labels of the vertebrae are required for each CT scan to develop an automated extraction system. In this paper, we propose an unsupervised domain adaptation (UDA) approach for vertebrae detection and identification based on a novel Domain Sanity Loss (DSL) function. With UDA the model’s knowledge learned on a publicly available (source) data set can be transferred to the target domain without using target labels, where the target domain is defined by the specific setup (CT modality, study protocols, applied pre- and processing) at the point of use (e.g., a specific clinic with its specific CT study protocols). With our approach, a model is trained on the source and target data set in parallel. The model optimizes a supervised loss for labeled samples from the source domain and the DSL loss function based on domain-specific “sanity checks” for samples from the unlabeled target domain. Without using labels from the target domain, we are able to identify vertebra centroids with an accuracy of 72.8%. By adding only ten target labels during training the accuracy increases to 89.2%, which is on par with the current state-of-the-art for full supervised learning, while using about 20 times less labels. Thus, our model can be used to extract 2D slices from 3D CT scans on arbitrary data sets fully automatically without requiring an extensive labeling effort, contributing to the clinical adoption of medical imaging by hospitals.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT scans for staging, treatment response, and surveillance, providing the opportunity for performing quantitative body composition assessment as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semi-automated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.

Preoperative Pectoralis Muscle Index Predicts Distant Metastasis-Free Survival in Breast Cancer Patients

Background

Breast cancer is one of the most commonly diagnosed cancers, and the fourth leading cause of cancer deaths in females worldwide. Sarcopenia is related to adverse clinical outcomes in patients with malignancies. Muscle index is a key parameter in evaluating sarcopenia. However, there is no data investigating the association between muscle index and distant metastasis in breast cancer. The aim of this study was to explore whether muscle index can effectively predict distant metastasis and death outcomes in breast cancer patients.

Study Design

The clinical data of 493 breast cancer patients at the Harbin Medical University Cancer Hospital between January 2014 and December 2015 were retrospectively analyzed. Quantitative measurements of pectoralis muscle area and skeletal muscle area were performed at the level of the fourth thoracic vertebra (T4) and the eleventh thoracic vertebra (T11) of the chest computed tomography image, respectively. The pectoralis muscle index (PMI) and skeletal muscle index (SMI) were assessed by the normalized muscle area (area/the square of height). Survival analysis was performed using the log-rank test and Cox proportional hazards regression analysis.

Result

The patients with metastases had lower PMI at T4 level (PMI/T4) and SMI at T11 level (SMI/T11) compared with the patients without metastases. Moreover, there were significant correlations between PMI/T4 and lymphovascular invasion, Ki67 expression, multifocal disease, and molecular subtype. In addition, multivariate analysis revealed that PMI/T4, not SMI/T11, was an independent prognostic factor for distant metastasis-free survival (DMFS) and overall survival (OS) in breast cancer patients.

Conclusions

Low PMI/T4 is associated with worse DMFS and OS in breast cancer patients. Future prospective studies are needed.

[Assessment and technical monitoring of nutritional status of patients in intensive and intermediate care units : Position paper of the Section Metabolism and Nutrition of the German Interdisciplinary Association for Intensive and Emergency Medicine (DIVI)]

Die Erhebung des Ernährungsstatus zum Zeitpunkt der Aufnahme im Intensiv- oder Intermediate Care Bereich hat sowohl prognostische als auch therapeutische Relevanz im Hinblick auf die Planung einer individualisierten medizinischen Ernährungstherapie (engl. „medical nutrition therapy“, MNT). Diese Planung wird im Rahmen der Erstversorgung eines vital bedrohlichen Krankheitsbilds nachvollziehbar nicht priorisiert, jedoch im weiteren Verlauf häufig auch oft nicht mehr angemessen durchgeführt. Vor allem bei längerer Verweildauer besteht das Risiko einer Mangelernährung mit Aufbau eines kumulativen, prognoserelevanten Makro- und/oder Mikronährstoffdefizits. Bisher gibt es für Patient*innen auf Intensiv- und Intermediate Care Einheiten keine strukturierten Empfehlungen zur Erhebung des Ernährungsstatus. Das vorliegende Positionspapier der Sektion Metabolismus und Ernährung der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin (DIVI) beinhaltet konsensbasierte Empfehlungen zur Erfassung und zum apparativen Monitoring des Ernährungsstatus von Patient*innen auf Intensiv- und Intermediate Care Stationen. Diese Empfehlungen ergänzen die aktuelle S2k-Leitlinie „Klinische Ernährung in der Intensivmedizin“ der Deutschen Gesellschaft für Ernährungsmedizin (DGEM) und der DIVI.

The applicability of the Visceral Adiposity Index (VAI) for predicting visceral fat

Abstract As obesity has reached epidemic proportions and given the current recognition of central adiposity as an important cardiometabolic risk factor, several researchers have focused on developing and validating predictive indexes and equations to evaluate Visceral Adipose Tissue (VAT). This study evaluates the applicability of the Visceral Adiposity Index (VAI) for predicting cardiometabolic risk in individuals treated in a hospital In the northeast region of Brazil. The VAT was evaluated by computed tomography (CT) and the VAI was calculated through specific equations for each gender. The sample involved adult and elderly patients of both genders followed up in a cardiology outpatient clinic. The following cardiometabolic parameters were collected: fasting glycemia, glycated hemoglobin, lipid profile, C-reactive protein (CRP) and uric acid. The simple linear regression was used to evaluate the explanatory power of the VAI in relation to the volume of VAT determined by CT. The predictive capacity of VAI in relation to the volume of VAT determined by CT was 25.8% (p=0.004) for males and 19.9% (p<0.001) for females. VAI correlated strongly with the triglyceride (TG) (p<0.001) and TG/high-density lipoprotein (HDL) ratio (p<0.001) and inversely correlated with HDL (p<0.001). Moreover, VAI showed low correlation with the following variables: abdominal circumference, total cholesterol, low density lipoprotein, fasting glycemia, and glycated hemoglobin (p<0.05). VAI was associated with variables considered as cardiometabolic risk factors, but exhibited a low predictive capacity regarding the volume of VAT determined by CT. Thus, caution is recommended in its use in Brazilian individuals.