CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study

Background

Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification.

Methods

We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves.

Findings

End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001).

Interpretations

CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value.

Research in context

Evidence before this study: Fully automated volumetric body composition analysis of chest computed tomography attenuation correction (CTAC) can be obtained in patients undergoing myocardial perfusion imaging. This new information has potential to significantly improve risk stratification and patient management. However, the CTAC scans have not been utilized for body composition analysis to date. We searched PubMed and Google Scholar for existing body composition related literature on June 5, 2024, using the search term ("mortality") AND ("risk stratification" OR "survival analysis" OR "prognostic prediction" OR "prognosis") AND ("body composition quantification" OR "body composition analysis" OR "body composition segmentation"). We identified 34 articles either exploring body composition segmentation or evaluating clinical value of body composition quantification. However, to date, all the prognostic evaluation is performed for quantification of three or fewer types of body composition tissues. Within the prognostic studies, only one used chest CT scans but utilized only a few specified slices selected from the scans, and not a standardized volumetric analysis. None of these previous efforts utilized CTAC scans, and none included epicardial adipose tissue in comprehensive body composition analysis.Added value of this study: In this international multi-center study, we demonstrate a novel artificial intelligence-based annotation-free approach for segmenting six key body composition tissues (bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue, epicardial adipose tissue, and visceral adipose tissue) from low-dose ungated CTAC scans, by exploiting existing CT segmentation models and image processing techniques. We evaluate the prognostic value of metrics derived from volumetric quantification of CTAC scans obtained during cardiac imaging, for all-cause mortality prediction in a large cohort of patients. We reveal strong and independent associations between several volumetric body composition metrics and all-cause mortality after adjusting for existing clinical factors, and available cardiac perfusion and atherosclerosis biomarkers.Implications of all the available evidence: The comprehensive body composition analysis can be routinely performed, at the point of care, for all cardiac perfusion scans utilizing CTAC. Automatically-obtained volumetric body composition quantification metrics provide added value over existing risk factors, using already-obtained scans to significantly improve the risk stratification of patients and clinical decision-making.

Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer

Background

Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

Methods

In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups.

Results

We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI.

Conclusions

The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.

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.

Association of muscle mass and radiodensity assessed by chest CT with all-cause and cardiovascular mortality in hemodialysis patients

PURPOSE

This study investigates the prognostic value of skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD) measured by chest CT in relation to all-cause and cardiovascular disease (CVD) mortality among hemodialysis (HD) patients.

METHODS

A retrospective study was conducted from January 2015 to December 2021 involving HD patients at a dialysis center. Chest CT scans at the twelfth thoracic vertebra level (T12) were analyzed to assess SMI and SMD. Sex-specific cut-off values for two metrics were determined using maximally selected rank statistics. Hazard ratios (HRs) were calculated to evaluate the associations of SMI and SMD with mortality. The discrimination of prognostic models was also compared.

RESULTS

The study included 603 patients with a median age of 58 years. Of these, 187 (31.0%) patients with SMI < 30.00 cm2/m2 (male) or < 25.04 cm2/m2 (female) and 192 (31.8%) patients with SMD < 32.25 HU (male) or < 30.64 HU (female) were categorized as lower SMI and SMD, respectively. Over a median follow-up of 3.8 years, 144 deaths occurred. Multivariate Cox regression analysis showed that lower SMI and SMD were independently associated with all-cause mortality (SMI: HR = 1.47, 95% CI 1.03-2.10; SMD: HR = 1.75, 95% CI 1.20-2.54) and CVD mortality (SMI: HR = 1.74, 95% CI 1.03-2.94; SMD: HR = 1.72, 95% CI 1.02-2.95). Adding SMI and SMD to the established risk model improved the C-index from 0.82 to 0.87 (P < 0.001). Decision curve analysis showed that the prognostic model incorporating both SMI and SMD offered the highest net benefit for predicting all-cause mortality.

CONCLUSIONS

Muscle metrics derived from CT scans at T12 level provide valuable prognostic information which could enhance the role of chest CT in muscle assessment among HD patients.

Clinical outcomes of multidimensional association of type 2 diabetes mellitus, COVID-19 and sarcopenia: an algorithm and scoping systematic evaluation

BACKGROUND

The aim of this study was to provide a scoping and comprehensive review for the clinical outcomes from the cross-link of Type 2 diabetes mellitus (T2DM), COVID-19, and sarcopenia.

METHODS

By using PRISMA guidelines and searching through different databases that could provide findings of evidence on the association of T2DM, COVID-19, and sarcopenia.

RESULTS

Thirty-three studies reported a relationship between sarcopenia with T2DM, twenty-one studies reported the prognosis COVID-19 in patients with T2DM, ten studies reported the prognosis of COVID-19 in patients with sarcopenia, five studies discussed the outcomes of sarcopenia in patients with COVID-19, and one study reported sarcopenia outcomes in the presence of T2DM and COVID-19.

CONCLUSION

There is an obvious multidimensional relationship between T2DM, COVID-19 and sarcopenia which can cause prejudicial effects, poor prognosis, prolonged hospitalisation, lowered quality of life and a higher mortality rate during the current COVID-19 pandemic.

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.

Integrated assessment of computed tomography density in pectoralis and erector spinae muscles as a prognostic biomarker for coronavirus disease 2019

BACKGROUND ＆ AIMS: Muscle quantification using chest computed tomography (CT) is a useful prognostic biomarker for coronavirus disease 2019 (COVID-19). However, no studies have evaluated the clinical course through comprehensive assessment of the pectoralis and erector spinae muscles. Therefore, we compared the impact of the areas and densities of these muscles on COVID-19 infection outcome.

METHODS

This multicenter retrospective cohort study was conducted by the COVID-19 Task Force. A total of 1410 patients with COVID-19 were included, and data on the area and density of the pectoralis and erector spinae muscles on chest CT were collected. The impact of each muscle parameter on the clinical outcome of COVID-19 was stratified according to sex. The primary outcome was the percentage of patients with severe disease, including those requiring oxygen supplementation and those who died. Additionally, 167 patients were followed up for changes in muscle parameters at three months and for the clinical characteristics in case of reduced CT density.

RESULTS

For both muscles, low density rather than muscle area was associated with COVID-19 severity. Regardless of sex, lower erector spinae muscle density was associated with more severe disease than pectoralis muscle density. The muscles were divided into two groups using the receiver operating characteristic curve of CT density, and the population was classified into four (Group A: high CT density for both muscles, Group B: low CT density for pectoralis and high for erector spinae muscle. Group C: high CT density for pectoralis and low for erector spinae muscle, Group D: low CT density for both muscles). In univariate analysis, Group D patients exhibited worse outcomes than Group A (OR: 2.96, 95% CI: 2.03-4.34 in men; OR: 3.02, 95% CI: 2.66-10.4 in women). Multivariate analysis revealed that men in Group D had a significantly more severe prognosis than those in Group A (OR: 1.82, 95% CI: 1.16-2.87). Moreover, Group D patients tended to have the highest incidence of other complications due to secondary infections and acute kidney injury during the clinical course. Longitudinal analysis of both muscle densities over three months revealed that patients with decreased muscle density over time were more likely to have severe cases than those who did not.

CONCLUSIONS

Muscle density, rather than muscle area, predicts the clinical outcomes of COVID-19. Integrated assessment of pectoralis and erector spinae muscle densities demonstrated higher accuracy in predicting the clinical course of COVID-19 than individual assessments.

The identification of an optimal body size parameter to adjust skeletal muscle area on chest CT in COVID-19 patients

Objectives

The most efficient way to adjust skeletal muscle area (SMA) derived from chest CT to body size remains unclear. We hypothesized that vertebral body area (VBA) measurement would allow such efficient adjustment.

Methods

We conducted a retrospective observational study of chest CT imaging in a cohort of critically ill COVID-19 patients. We measured paravertebral SMA at T5 level and T5 vertebral body anteroposterior length, width, and area. We used linear regression and multivariable modelling to assess the association of VBA with SMA.

Results

In 48 COVID-19 patients in ICU, T5 VBA could be easily derived from simple width and anteroposterior length linear measurements. T5 VBA (measured manually or estimated from width and length) performed similarly to height (R2 of 0.22) as an adjustment variable for SMA, with R2 of 0.23 and 0.22, respectively. Gender had the strongest correlation with SMA (R2 = 0.28). Adding height or age to a model using gender and VBA did not improve correlation.

Conclusions

Gender and estimated VBA from simple linear measurements at T5 level on CT images can be utilized for adjustment of SMA without the need for height. Validation of these findings in larger cohorts of critically ill patients is now needed.

Low skeletal muscle quality extracted from CT is associated with poor outcomes in severe acute pancreatitis patients

PURPOSE

To evaluate the association between body composition parameters derived from computed tomography (CT) scans and clinical outcomes in patients with severe acute pancreatitis (AP).

METHODS

Patients who have been diagnosed AP with a CT scan at ICU admission were included. Body composition parameters were measured on a single slice at L2-3 of the unenhanced CT scans. The intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), skeletal muscle area (SMA) and skeletal muscle density (SMD) were assessed using HUs by image analysis software. Univariable and multivariable analyses were performed to analyze the association between body composition parameters and clinical outcomes including all-cause mortality or prolonged ICU stay. The area under the curve (AUC) of a receiver operating characteristic curve was used to explore the predictive value of the body composition on treatment clinical outcomes.

RESULTS

A total of 158 patients were included. The IMAT (8.3 cm2 vs 6.0 cm2, P = 0.001) and VAT (190.3 cm2 vs 143.7 cm2, P < 0.001) were significantly higher in the severe AP group than in the moderately severe group, but were notassociatedwithoutcomes. For 1 HU of SMD increased, the risk of poor clinical outcomes decresed 11 % (adjusted OR 0.892, 95 %CI 0.806-0.987, P = 0.026), while an SMD below the median value (32.1 HU for males and 28.5 HU for females) was independently associated with worse outcomes in the multivariable analysis (adjusted OR 8.868, 95 % CI 2.146-36.650, P = 0.003). The SMD had a good predictive ability for clinical outcomes, AUC was 0.824 (95 % CI, 0.715-0.933) for males and 0.803 (95 % CI, 0.639-0.967) for females.

CONCLUSION

Low SMD was associated with poor outcomes in patients with severe and moderately severe AP and might be used as a novel marker to predict outcomes in patients suffering from severe and moderately severe AP.

Sarcopenia and Mortality in Critically Ill COVID-19 Patients

COVID-19 can manifest as either asymptomatic or progressing to a severe phase in some patients, which may require hospitalization. These patients may experience dyspnea and hypoxia, leading to the development of acute respiratory distress syndrome. Studies have reported an increased risk of severe sarcopenia in COVID-19 patients during and after recovery. This narrative review aimed to summarize and synthesize available studies on the association between sarcopenia and mortality in critically ill COVID-19 patients. A total of 22 studies conducted on hospitalized COVID-19 patients were included in this review. Of those, 17 studies reported a direct association, while 5 studies showed no association between sarcopenia and mortality in severe COVID-19 patients. It is important to maintain muscle quality and quantity in defense against COVID-19. The measurement of lean muscle mass should be included in the risk assessment of severely ill COVID-19 patients as part of the therapy plan.

Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

OBJECTIVE

To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.

METHODS

In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.

RESULTS

The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).

CONCLUSION

Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.

CLINICAL RELEVANCE STATEMENT

In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.

KEY POINTS

• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.

Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan

BACKGROUND

Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.

METHODS

This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.

RESULTS

The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.

CONCLUSIONS

AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Assessment of body composition in adults hospitalized with acute COVID-19: a scoping review

Introduction

Body composition (BC) assessment can supply accurate information for in-hospital nutritional evaluation. The aim of this study was to explore in the literature how the studies assessed BC, for what purpose, and investigate the role of BC findings in COVID-19 hospitalized patients' outcomes.

Methods

A scoping review was conducted according to the methodology available on the Joanna Briggs Institute website. We used the PCC acronym for the systematic search (population: adults with COVID-19, concept: assessment of BC, context: hospital setting) and performed it on PubMed, Scopus, and the Web of Science on 16 September 2022. Eligibility criteria consisted of the utilization of BC assessment tools in COVID-19 patients. Studies in which BC was solely measured with anthropometry (perimeters and skinfolds) were excluded. No language restriction was applied.

Results

Fifty-five studies were eligible for the review. Out of the 55 studies, 36 used computed tomography (CT), 13 used bioelectrical impedance (BIA), and 6 used ultrasound (US). No studies with D3-creatinine, 24  h urine excretion, dual-energy X-ray absorptiometry, or magnetic resonance were retrieved. BC was mainly assessed to test associations with adverse outcomes such as disease severity and mortality.

Discussion

Studies assessing BC in hospitalized patients with COVID-19 used mainly CT and BIA and associated the parameters with severity and mortality. There is little evidence of BC being assessed by other methods, as well as studies on BC changes during hospitalization.

Prognostic Value of Diaphragm Diameter, Muscle Volume, and Bone Mineral Density in Critically Ill COVID-19 Patients

Objective: We aimed to analyze the prognostic value of muscle volume (MV), bone mineral density (BMD), and diaphragm diameter (DD) in COVID-19. Method: The study included 498 patients admitted to the intensive care unit (ICU) with a positive polymerase chain reaction test for COVID-19 from March 11, 2020, through August 31, 2022. The patients' laboratory and demographic data of the patients at the time of ICU admission were recorded. MV, DD, and BMD measurements were performed using computed tomography examinations. Muscle index (MI) was calculated as MV/height2. Quartiles were determined for all 4 measurements. Patients in the lowest quartile were recorded as having low MV, MI, DD, and BMD values. The parameters were evaluated for the whole group and compared according to gender and mortality. The multiple regression analysis was performed for the prediction of mortality. Results: In the mortality group, the male and female patients had significantly statistically lower values in MV (P < .001 and P = .002, respectively), MI (P < .001 and P = .005, respectively), DD (P < .001 and P < .001, respectively), and BMD (P = .002 and P < .001, respectively). In the multiple regression analysis, low MI (OR: 2.03, 95% CI: 1.14-3.61, P = .016) and DD (OR: 10.47, 95% CI: 5.59-19.59, P < .001) values remained significant for the prediction of mortality. Conclusion: BMD is a risk factor for mortality in patients with severe COVID-19, but cannot be used as an independent predictor. However, MI and DD can be used as independent predictors of mortality even in severe cases.

Combination of Chest Computed Tomography Value and Clinical Laboratory Data for the Prognostic Risk Evaluation of Patients with COVID-19

Objective

This study aims to investigate the independent prognostic factors of patients with coronavirus disease 2019 (COVID-19) and thereafter construct a related prognostic model.

Methods

The subjects were screened following the COVID-19 diagnostic criteria. The independent prognostic factors were selected based on the indicators, including medical history, clinical manifestation, laboratory tests, imaging examination and clinical prognosis. Subsequently, we constructed a nomogram model to predict short-term prognosis.

Results

Clinical information was obtained from 393 COVID-19 patients admitted to Zhongshan Hospital at Xiamen University between December 2022 and January 2023. The independent risk factors determined by Cox multivariate regression analysis included gender (OR: 0.355, 95% CI: 0.16~0.745), age (OR: 3.938, 95% CI: 1.221~15.9), pectoral muscle index (PMI, OR: 4.985, 95% CI: 2.336~11.443), pneumonia severity score (PSS, OR: 6.486, 95% CI: 2.082~21.416) and lactate dehydrogenase (LDH, OR: 3.857, 95% CI: 1.571~10.266). A short-term prognostic nomogram was developed based on the five independent risk factors above. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram model was 0.857. The calibration curve confirmed the outcomes of the prognostic model, which exhibited excellent consistency with the actual results.

Conclusion

In summary, gender, age, pectoral muscle index, pneumonia severity score, and lactate dehydrogenase are all independent risk factors for COVID-19 mortality. Thus, the nomogram based on the above indicators can predict the risk of mortality in COVID-19 patients. This may have the potential of being clinical application in prognostic evaluation of COVID-19.

Which vertebral level should be used to calculate sarcopenia in covid-19 patients? A systematic review and meta-analysis

BACKGROUND & AIMS

Evidence shows that CT-derived sarcopenia can predict adverse outcomes in COVID-19 patients. However, discrepancies exist as to which vertebral level can be used to calculate sarcopenia which can effectively serve as a prognostic tool. Thus, we aim to investigate the difference in sarcopenia calculated at the Thoracic and Lumbar vertebral levels.

METHODS

An online literature search was conducted on Electronic databases such as PubMed, Cochrane CENTRAL, and Google scholar. Meta-analysis was performed by using Revman 5.3 software.

RESULTS

A total of 14 articles were selected for meta-analysis. The prevalence of sarcopenia calculated at the Thoracic level was 31% (95%CI 24%-37%; p < 0.00001; I² = 86%), while sarcopenia calculated at the Lumbar vertebral level was 63% (95%CI 51%-75%; p < 0.00001; I² = 88%). Meanwhile, sarcopenia calculated at the Upper thoracic level was a significant predictor of mortality OR 3.47 (95%CI 1.74-6.91; p = 0.0004; I² = 56%)as compared to sarcopenia calculated at the lower thoracic OR 1.74 (95%Cl 0.91-3.33; p = 0.10; I² = 60%)or lumbar level OR 2.49 (95%CI 0.45-13.72; p = 0.30; I² = 57%). In addition to this sarcopenia calculated at the Upper thoracic level was also a significant predictor of severe illness OR 3.92 (95%CI 2.33-6.58; p < 0.00001; I² = 0%) as compared to lower thoracic OR 1.40 (95%CI 0.78-2.53; p = 0.26; I² = 67%) or lumbar level OR 1.64 (95%CI 0.26-10.50; p = 0.60; I² = 81%) CONCLUSIONS: Sarcopenia calculated at the thoracic vertebrae and lumber level has different prognostic values. Sarcopenia is prevalent at the lumbar level. Sarcopenia at the thoracic level has a higher mortality and severity rate.

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.

Radiologic Features of T10 Paravertebral Muscle Sarcopenia: Prognostic Factors in COVID-19

Background

Sarcopenia, defined as a small cross-sectional area (CSA) in computed tomography (CT) measurements of skeletal muscles, serves as a disease severity marker in various clinical scenarios, including pulmonary conditions and critical illness. Another parameter of sarcopenia, the level of myosteatosis, reflected by the tissue's radiodensity, in the thoracic skeletal muscles group, has been linked to disease progression in coronavirus disease 2019 (COVID-19) patients. We hypothesize that CT-derived measurements of the skeletal muscle density (SMD) and the CSA of thoracic skeletal muscles can predict outcomes in COVID-19 pneumonia.

Methods

We retrospectively reviewed the CT scans of 84 patients with COVID-19 pneumonia admitted to two of Greece's largest academic teaching hospitals between April 2020 and February 2021. CSA and SMD at the level of the T10 vertebra were measured using computational imaging methods. The patient population was stratified according to survival status and CT severity score (CT-SS). Correlations were drawn between the radiologic features of sarcopenia, CT severity subgroups, serum inflammatory markers, and adverse events, e.g., death and intubation.

Results

Thoracic muscles' CSA measurements correlate with CT-SS and prominent inflammatory markers, such as white blood cell (WBC), C-reactive protein (CRP), fibrinogen, and D-dimers. Moreover, according to linear regression analysis, CSA seems to predict CT-SS variation significantly (β = -0.266, P = 0.018). CSA proved to differ significantly across survivors (P = 0.027) but not between CT severity categories and intubation subgroups. The AUC (area under the curve) of the receiver operating characteristic (ROC) curve for the predictive value of thoracic muscles' CSA in mortality is 0.774 (95% confidence interval (CI): 0.66 - 0.83, P < 0.000). The optimal cut-off value (Youden index = 0.57) for mortality prognosis, with a sensitivity of 66.7% and a specificity of 88.9%, is 15.55. Thoracic muscles' SMD analyses did not reveal any significant correlations.

Conclusions

Easy to obtain and accurately calculated, radiologic features can provide a reliable alternative to laboratory methods for predicting survival in COVID-19. Thoracic muscles' CSA measurement in the level of the T10 vertebra, an acclaimed prognostic imaging assessment that relates directly to CT-SS and inflammatory markers in COVID-19 pneumonia, is a fairly specific tool for survival prognosis.

Ketogenic Diet and Ketone Bodies as Clinical Support for the Treatment of SARS-CoV-2-Review of the Evidence

One of the proposed nutritional therapies to support drug therapy in COVID-19 is the use of a ketogenic diet (KD) or ketone bodies. In this review, we summarized the evidence from tissue, animal, and human models and looked at the mechanisms of action of KD/ketone bodies against COVID-19. KD/ketone bodies were shown to be effective at the stage of virus entry into the host cell. The use of β-hydroxybutyrate (BHB), by preventing the metabolic reprogramming associated with COVID-19 infection and improving mitochondrial function, reduced glycolysis in CD4+ lymphocytes and improved respiratory chain function, and could provide an alternative carbon source for oxidative phosphorylation (OXPHOS). Through multiple mechanisms, the use of KD/ketone bodies supported the host immune response. In animal models, KD resulted in protection against weight loss and hypoxemia, faster recovery, reduced lung injury, and resulted in better survival of young mice. In humans, KD increased survival, reduced the need for hospitalization for COVID-19, and showed a protective role against metabolic abnormalities after COVID-19. It appears that the use of KD and ketone bodies may be considered as a clinical nutritional intervention to assist in the treatment of COVID-19, despite the fact that numerous studies indicate that SARS-CoV-2 infection alone may induce ketoacidosis. However, the use of such an intervention requires strong scientific validation.

Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients

Purpose

Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data.

Approach

We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity.

Results

Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs consistently better than predictive models developed using only imaging or non-imaging features, with area under the receiver operating characteristics curve of 0.76, 0.90, and 0.75 for discharge from hospital, mortality, and ICU admission, respectively. External validation was performed on data collected from the Mayo Clinic. Our scheme highlights known biases in the model prediction, such as bias against patients with alcohol abuse history and bias based on insurance status.

Conclusions

Our study signifies the importance of the fusion of multiple data modalities for the accurate prediction of clinical trajectories. The proposed graph structure can model relationships between patients based on non-imaging EHR data, and graph convolutional networks can fuse this relationship information with imaging data to effectively predict future disease trajectory more effectively than models employing only imaging or non-imaging data. Our graph-based fusion modeling frameworks can be easily extended to other prediction tasks to efficiently combine imaging data with non-imaging clinical data.

Abdominal imaging associates body composition with COVID-19 severity

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n = 967) and matched controls (n = 913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.

Prevalence and clinical implications of abnormal body composition phenotypes in patients with COVID-19: a systematic review

BACKGROUND

The impact of body composition (BC) abnormalities on coronavirus disease 2019 (COVID-19) outcomes remains to be determined.

OBJECTIVE

We summarized the evidence on BC abnormalities and their relationship with adverse clinical outcomes in patients with COVID-19.

METHODS

A systematic search was conducted up until September 26, 2022 for observational studies using BC techniques to quantify skeletal muscle mass (or related compartments), muscle radiodensity or echo intensity, adipose tissue (AT, or related compartments), and phase angle (PhA) in adults with COVID-19. Methodological quality of studies was assessed using the Newcastle-Ottawa Scale. A synthesis without meta-analysis was conducted to summarize the prevalence of BC abnormalities and their significant associations with clinical outcomes.

RESULTS

We included 62 studies (69.4% low risk of bias) with 12 to 1,138 participants, except three with up to 490,301 participants. Using computed tomography and different cut-offs, prevalence ranged approximately from 22-90% for low muscle mass, 12-85% for low muscle radiodensity, 16-70% for high visceral AT. Using bioelectrical impedance analysis (BIA), prevalence of high fat mass was 51% and low PhA was 22-88%. Mortality was inversely related to PhA (3/4 studies) and positively related to intramuscular AT (4/5 studies), muscle echo intensity (2/2 studies), and BIA-estimated fat mass (2/2 studies). Intensive care unit admission was positively related to visceral AT (6/7 studies) and total AT (2/3 studies). Disease severity and hospitalization outcomes were positively related to intramuscular AT (2/2 studies). Inconsistent associations were found for the rest of BC measures and hospitalization outcomes.

CONCLUSIONS

Abnormalities in BC were prevalent in patients with COVID-19. Although conflicting associations were observed among certain BC abnormalities and clinical outcomes, higher muscle echo intensity (reflective of myosteatosis) and lower PhA were more consistently associated with greater mortality risk. Likewise, high IMAT and VAT were associated with mortality and ICU admission, respectively.

Relationship between Muscle Mass, Bone Density and Vascular Calcifications in Elderly People with SARS-CoV-2 Pneumonia

BACKGROUND

Little is known about the changes in organs and tissues that may make elder patients more vulnerable to acute stressors such as SARS-CoV-2 infection.

METHODS

In 80 consecutive elderly patients with SARS-CoV-2 infection, we evaluated the association between the descending thoracic aorta calcium score, L1 bone density and T12 skeletal muscle density measured on the same scan by high-resolution computed tomography.

RESULTS

At median regression, the ln-transformed DTA calcium score was inversely associated with L1 bone density (-0.02, 95%CI -0.04 to -0.01 ln-Agatston units for an increase of 1 HU) and with T12 muscle density (-0.03, -0.06 to -0.001 ln-Agatston units for an increase of 1 HU). At penalized logistic regression, an increase of 1 ln-Agatston unit of DTA calcium score was associated with an OR of death of 1.480 (1.022 to 2.145), one of 1 HU of bone density with an OR of 0.981 (0.966 to 0.996) and one of 1 HU of muscle density with an OR of 0.973 (0.948 to 0.999). These relationships disappeared after correction for age and age was the stronger predictor of body composition and death.

CONCLUSIONS

Age has a big effect on the relationship between vascular calcifications, L1 bone density and T12 muscle density and on their relationship with the odds of dying.

Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study

OBJECTIVE

To evaluate the association of body composition parameters with outcomes in Covid-19.

METHODS

173 patients hospitalized for Covid-19 infection in 6 European centers were included in this retrospective study. Measurements were performed at L3-level and comprised skeletal muscle index (SMI), muscle density (MD), and adipose tissue measurements [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral-to-subcutaneous-adipose-tissue-area-ratio (VSR)]. The association with mortality, the need for intubation (MV), and the need for admission to ICU within 30 days were evaluated.

RESULTS

Higher SAT density was associated with a greater risk of MV (OR = 1.071, 95%CI=(1.034;1.110), p < 0.001). Higher VAT density was associated with admission to ICU (OR = 1.068, 95%CI=(1.029;1.109), p < 0.001). Higher MD was a protective factor for MV and ICU admission (OR = 0.914, 95%CI=(0.870;0.960), p < 0.001; OR = 0.882, 95%CI=(0.832;0.934), p = 0.028). Higher VSR was associated with mortality (OR = 2.147, 95%CI=(1.022;4.512), p = 0.044). Male sex showed the strongest influence on the risk of ICU admission and MV. SMI was not associated with either parameter.

CONCLUSION

In patients hospitalized for Covid-19 infection, higher VSR seems to be a strong prognostic factor of short-term mortality. Weak associations with clinical course were found for MD and adipose tissue measurements. Male sex was the strongest prognostic factor of adverse clinical course.

ADVANCES IN KNOWLEDGE

VSR is a prognostic biomarker for 30-day mortality in patients hospitalized for Covid-19 disease.

Low thoracic skeletal muscle index is associated with negative outcomes in 244 patients with respiratory COVID-19

BACKGROUND

Sarcopenia is associated with negative outcomes in intensive care unit (ICU) patients and during chronic diseases. We aimed to evaluate if low skeletal muscle index (SMI) measured by computed tomography (CT) at the thoracic level is associated with poor outcomes in hospitalized patients with respiratory COVID-19.

METHODS

Patients admitted to the hospital between March 1st and June 9, 2020 with a confirmed diagnosis of respiratory COVID-19 in the Emergency Department were included in this retrospective cohort study. SMI was assessed from a transverse CT image at the T12 level. We analysed the association between thoracic SMI and mortality, ICU admissions, infections, length of stay and gravity scores.

RESULTS

We included 244 patients, whose median age was 62 (20-95) years, mean body mass index was 28,6 kg/m2, and 34% were obese patients. 102 patients (41,8%) had low thoracic SMI. On multivariable analysis, low thoracic SMI was associated with more infections (OR = 1,88 [1,06-2,98]) and increased length of stay (OR = 1,87 [1,14-3,49]) but not with mortality (OR = 1.37 [0.54-3.52]), whereas it was inversely associated with ICU admission (OR = 5,56 [1,96-16,67].

CONCLUSION

Low SMI measured by CT at the thoracic level T12 is associated with negative outcomes in patients with respiratory COVID-19.

Which Body Composition Parameters on Computed Tomography Are More Successful in Predicting the Prognosis of COVID-19 Patients?

OBJECTIVES

The aims of the study are to compare the body composition parameters (BCPs), which have been reported to have a prognostic impact, in COVID-19 patients, and to determine the most influential BCP(s) on the prognosis.

METHODS

Unenhanced chest computed tomography examinations of COVID-19 cases were assessed regarding the severity of pneumonia, pectoralis muscle area and density (PMA, PMD), visceral adipose tissue area (VAA), waist circumference, waist to paravertebral muscle circumference ratio, coronary artery calcification severity, and paravertebral muscle area at the T5 vertebral level. A second observer repeated measurements and an intraclass correlation coefficient score were used for interobserver agreement. The relationship between data and patient outcomes (intubation, death) was investigated using multivariable logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of BCPs in predicting outcomes.

RESULTS

A total of 238 (121 males, median age, 48 years [interquartile range, 36-63 years]) consecutive COVID-19 patients with chest computed tomography were investigated. Twenty-four patients (10.08%) were intubated, and 15 patients (6.3%) died during at least 1 month of follow-up. Waist to paravertebral muscle circumference ratio, PMA, PMD, and T5 vertebral level were significantly associated with intubation in the multivariable analysis. Pectoralis muscle density and PMA were significantly associated with death. Pectoralis muscle density showed the highest AUC for the prediction of intubation and death (AUC of 0.814 and 0.871, respectively). There was a good to excellent agreement between observers (intraclass correlation coefficient range, 0.899-0.998).

CONCLUSIONS

Pectoralis muscle density is the most influential BCP in predicting intubation and death in COVID-19 patients.

Sarcopenia and COVID-19 Outcomes

Coronavirus disease 2019 (COVID-19) spread rapidly and became a severe global public health threat. Older adults have a high risk of COVID-19 and its associated mortality. Sarcopenia has emerged as a predictor of poor outcomes in COVID-19 patients, including lengthy hospital stays, mortality, intensive care unit admission, need for invasive mechanical ventilation, and poor rehabilitation outcomes. Chronic inflammation, immune dysfunction, respiratory muscle dysfunction, and swallowing dysfunction may underlie the association between sarcopenia and the poor outcomes of COVID-19 patients. Interleukin 6 receptor blockers (tocilizumab or sarilumab) are recommended for treating patients with severe COVID-19, and their therapeutic effects on sarcopenia are of great interest. This review aimed to analyze the current reports on the association between sarcopenia and COVID-19 and provide an update on the contribution of sarcopenia to the severity and adverse outcomes of COVID-19 and its underlying mechanisms. We also aimed to explore the different screening tools for sarcopenia concurrent with COVID-19, and advocate for early diagnosis and treatment of sarcopenia. Given that the fight against the COVID-19 pandemic may be long-term, further research into understanding the effects of sarcopenia in patients infected with the Omicron variant is necessary.

Prognostic Role of the Pectoralis Musculature in Patients with COVID-19. A Multicenter Study

RATIONALE AND OBJECTIVES

To evaluate the impact of low skeletal muscle mass in patients with COVID-19 on relevant outcomes like 30-day mortality, need for intubation and need for intensive care unit admission.

MATERIALS AND METHODS

For this study, data from six centers were acquired. The acquired sample comprises 1138 patients. There were 547 women (48.1%) and 591 men (51.9%) with a mean age of 54.5 ± 18.8 years; median age, 55 years; range, 18-84 years). In every case, thoracic CT without intravenous application of contrast medium was performed. The following parameters of the pectoralis muscles were estimated: muscle area as a sum of the bilateral areas of the pectoralis major and minor muscles, muscle density, muscle index (PMI) (pectoralis muscle area divided by the patient's body height square) as a ratio pectoralis major and minor muscles divided by the patient's body height², and muscle gauge as PMI x muscle density.

RESULTS

Overall, 220 patients (19.33%) were admitted to the intensive care unit. In 171 patients (15.03%), mechanical lung ventilation was performed. Finally, 154 patients (13.53%) died within the observation time of 30-day. All investigated parameters of pectoralis muscle were lower in the patients with unfavorable courses of Covid-19. All pectoralis muscle parameters were associated with 30-day mortality in multivariate analyses adjusted for age and sex: pectoralis muscle area, HR = 0.93 CI 95% (0.91-0.95) p < 0.001; pectoralis muscle density, HR = 0.94 CI 95% (0.93-0.96) p < 0.001; pectoralis muscle index, HR = 0.79 CI 95% (0.75-0.85) p < 0.001, pectoralis muscle gauge, HR = 0.995 CI 95% (0.99-0.996) p < 0.001.

CONCLUSION

in COVID-19, survivors have larger areas and higher index, gauge and density of the pectoralis muscles in comparison to nonsurvivors. However, the analyzed muscle parameters cannot be used for prediction of disease courses.

Respiratory sarcopenia: A position paper by four professional organizations

We defined respiratory sarcopenia as a coexistence of respiratory muscle weakness and decreased respiratory muscle mass. Although respiratory muscle function is indispensable for life support, its evaluation has not been included in the regular assessment of respiratory function or adequately evaluated in clinical practice. Considering this situation, we prepared a position paper outlining basic knowledge, diagnostic and assessment methods, mechanisms, involvement in respiratory diseases, intervention and treatment methods, and future perspectives on respiratory sarcopenia, and summarized the current consensus on respiratory sarcopenia. Respiratory sarcopenia is diagnosed when respiratory muscle weakness and decreased respiratory muscle mass are observed. If respiratory muscle mass is difficult to measure, we can use appendicular skeletal muscle mass as a surrogate. Probable respiratory sarcopenia is defined when respiratory muscle weakness and decreased appendicular skeletal muscle mass are observed. If only respiratory muscle strength is decreased without a decrease in respiratory function, the patient is diagnosed with possible respiratory sarcopenia. Respiratory muscle strength is assessed using maximum inspiratory pressure and maximum expiratory pressure. Ultrasonography and computed tomography are commonly used to assess respiratory muscle mass; however, there are insufficient data to propose the cutoff values for defining decreased respiratory muscle mass. It was jointly prepared by the representative authors and authorized by the Japanese Society for Respiratory Care and Rehabilitation, Japanese Association on Sarcopenia and Frailty, Japanese Society of Respiratory Physical Therapy and Japanese Association of Rehabilitation Nutrition. Geriatr Gerontol Int 2023; 23: 5-15.

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis

Background and objectives

We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19.

Methods

A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test.

Results

Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04.

Conclusion

ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality.

Nutritional parameters associated with prognosis in non-critically ill hospitalized COVID-19 patients: The NUTRI-COVID19 study

BACKGROUND & AIMS

To investigate the association between the parameters used in nutritional screening assessment (body mass index [BMI], unintentional weight loss [WL] and reduced food intake) and clinical outcomes in non-critically ill, hospitalized coronavirus disease 2019 (COVID-19) patients.

METHODS

This was a prospective multicenter real-life study carried out during the first pandemic wave in 11 Italian Hospitals. In total, 1391 patients were included. The primary end-point was a composite of in-hospital mortality or admission to ICU, whichever came first. The key secondary end-point was in-hospital mortality.

RESULTS

Multivariable models were based on 1183 patients with complete data. Reduced self-reported food intake before hospitalization and/or expected by physicians in the next days since admission was found to have a negative prognostic impact for both the primary and secondary end-point (P < .001 for both). No association with BMI and WL was observed. Other predictors of outcomes were age and presence of multiple comorbidities. A significant interaction between obesity and multi-morbidity (≥2) was detected. Obesity was found to be a risk factor for composite end-point (HR = 1.36 [95%CI, 1.03-1.80]; P = .031) and a protective factor against in-hospital mortality (HR = 0.32 [95%CI, 0.20-0.51]; P < .001) in patients with and without multiple comorbidities, respectively. Secondary analysis (patients, N = 829), further adjusted for high C-reactive protein (>21 mg/dL) and LDH (>430 mU/mL) levels yielded consistent findings.

CONCLUSIONS

Reduced self-reported food intake before hospitalization and/or expected by physicians in the next days since admission was associated with negative clinical outcomes in non-critically ill, hospitalized COVID-19 patients. This simple and easily obtainable parameter may be useful to identify patients at highest risk of poor prognosis, who may benefit from prompt nutritional support. The presence of comorbidities could be the key factor, which may determine the protective or harmful role of a high body mass index in COVID-19.

Breath-holding physiology, radiological severity and adverse outcomes in COVID-19 patients: A prospective validation study

BACKGROUND AND OBJECTIVE

COVID-19 remains a major cause of respiratory failure, and means to identify future deterioration is needed. We recently developed a prediction score based on breath-holding manoeuvres (desaturation and maximal duration) to predict incident adverse COVID-19 outcomes. Here we prospectively validated our breath-holding prediction score in COVID-19 patients, and assessed associations with radiological scores of pulmonary involvement.

METHODS

Hospitalized COVID-19 patients (N = 110, three recruitment centres) performed breath-holds at admission to provide a prediction score (Messineo et al.) based on mean desaturation (20-s breath-holds) and maximal breath-hold duration, plus baseline saturation, body mass index and cardiovascular disease. Odds ratios for incident adverse outcomes (composite of bi-level ventilatory support, ICU admission and death) were described for patients with versus without elevated scores (>0). Regression examined associations with chest x-ray (Brixia score) and computed tomography (CT; 3D-software quantification). Additional comparisons were made with the previously-validated '4C-score'.

RESULTS

Elevated prediction score was associated with adverse COVID-19 outcomes (N = 12/110), OR[95%CI] = 4.54[1.17-17.83], p = 0.030 (positive predictive value = 9/48, negative predictive value = 59/62). Results were diminished with removal of mean desaturation from the prediction score (OR = 3.30[0.93-11.72]). The prediction score rose linearly with Brixia score (β[95%CI] = 0.13[0.02-0.23], p = 0.026, N = 103) and CT-based quantification (β = 1.02[0.39-1.65], p = 0.002, N = 45). Mean desaturation was also associated with both radiological assessment. Elevated 4C-scores (≥high-risk category) had a weaker association with adverse outcomes (OR = 2.44[0.62-9.56]).

CONCLUSION

An elevated breath-holding prediction score is associated with almost five-fold increased adverse COVID-19 outcome risk, and with pulmonary deficits observed in chest imaging. Breath-holding may identify COVID-19 patients at risk of future respiratory failure.

How the COVID-19 Pandemic Affected Attendance at a Tertiary Orthopedic Center Emergency Department: A Comparison between the First and Second Waves

Italy was the first European country to face the SARS-CoV-2 virus (COVID-19) pandemic in 2020. The country quickly implemented strategies to contain contagions and re-organize medical resources. We evaluated the COVID-19 effects on the activity of a tertiary-level orthopedic emergency department (ED) during the first and second pandemic waves. We retrospectively collected and compared clinical radiological data of ED admissions during four periods: period A, first pandemic wave; period B, second pandemic wave; period C, three months before the COVID-19 outbreak; period D, same timeframe of the first wave but in 2019. During period A, we found a reduction in ED admissions (-68.2% and -59.9% compared with periods D and C) and a decrease in white codes (non-urgent) (-7.5%) compared with pre-pandemic periods, with a slight increase for all other codes: +6.3% green (urgent, not critical), +0.8% yellow (moderately critical) and +0.3% red (highly urgent, risk of death). We observed an increased rate of fracture diagnosis in period A: +14.9% and +13.3% compared with periods D and C. Our study shows that the COVID-19 pandemic caused a drastic change in the ED patient flow and clinical radiological activity, with a marked reduction in admissions and an increased rate of more severe triage codes and diagnosed fractures.

Comparison between magnetic resonance imaging and electrical impedance myography for evaluating lumbar skeletal muscle composition

Background

To compare electrical impedance myography (EIM) and MRI in assessing lumbar skeletal muscle composition.

Methods

One hundred forty-one patients (78 females, mean age 57 ± 19 years) were prospectively enrolled and underwent lumbar spine MRI, EIM with Skulpt®, and clinical evaluation including the questionnaire SARC-F. MRIs were reviewed to assess the Goutallier score of paravertebral muscles at L3 level and to calculate the cross sectional area (CSA) of both psoas, quadratus lumborum, erector spinae, and multifidus muscles on a single axial slice at L3 level, in order to calculate the skeletal muscle index (SMI=CSA/height2). We tested the correlation between EIM-derived parameters [body fat percentage (BF%) and muscle quality] and body mass index (BMI), Goutallier score (1–4), SMI, and SARC-F scores (0–10) using the Pearson correlation coefficient. The strength of association was considered large (0.5 to 1.0), medium (0.3 to 0.5), small (0.1 to 0.3).

Results

Pearson’s correlation coefficient showed small (0.26) but significant (p < 0.01) positive correlation between BF% obtained with EIM and Goutallier score. Small negative correlation (− 0.22, p < 0.01) was found between EIM muscle quality and Goutallier Score. Large negative correlation (− 0.56, p < 0.01) was found between SMI and Goutallier Score, while SMI showed small negative correlation with SARC-F (− 0.29, p < 0.01). Medium positive correlation was found between Goutallier Score and SARC-F (0.41, p < 0.01). BMI showed medium positive correlation with SMI (r = 0.369, p < 0.01) and small correlation with EIM muscle quality (r = − 0.291, p < 0.05) and BF% (r = 0.227, p < 0.05). We found a substantial increase of the strength of associations of BF% and muscle quality with Goutallier in the 18–40 years (r = 0.485 and r = − 0.401, respectively) and in the 41–70 years group (r = 0.448 and r = − 0.365, respectively).

Conclusions

Muscle quality and BF% measured by EIM device showed only small strength of correlation with other quantitative parameters for assessing muscle mass and fat infiltration. Interesting results have been found in younger patients, but Skulpt Chisel™ should be applied cautiously to assess lumbar skeletal muscle composition. This point deserves further investigation and other studies are warranted.

Trial registration

The registration number of this study is 107/INT/2019.

Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro–Wilk and two-sided Mann–Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female (p_severity ≤ 0.001, p_mortality ≤ 0.0001) and male patients (p_severity = 0.018, p_mortality ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) (p ≤ 0.0001), epicardial adipose tissue (EAT) (p ≤ 0.001) and pericardial adipose tissue (PAT) (p ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle (p ≤ 0.0001), IMAT (p ≤ 0.001), EAT (p = 0.011) and PAT (p = 0.003) remained significant. For female patients, bone (p ≤ 0.001), IMAT (p = 0.032) and PAT (p = 0.047) were significant in univariate analyses regarding the severity and bone (p = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker (p ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p_female = 0.014, p_male ≤ 0.0001) and for mortality (p_female ≤ 0.0001, p_male ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant (p_severity = 0.006, p_mortality = 0.002) for both endpoints (OR_severity = 0.42, 95% CI_severity: 0.23–0.78, OR_mortality = 0.34, 95% CI_mortality: 0.17–0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06–1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.

Knee Muscles Composition Using Electrical Impedance Myography and Magnetic Resonance Imaging

We evaluated the correlation of electrical impedance myography (EIM) measurements of knee muscles composition using Skulpt ChiselTM with MRI data retrieved from muscles segmentation. A total of 140 patients (71 females, 52 ± 21 years) underwent knee MRI, EIM with Skulpt®, and clinical evaluation (SARC-F questionnaire). MRIs were reviewed to assess the cross-sectional area (CSA) and skeletal muscle index (SMI = CSA/height2) of vastus medialis, vastus lateralis, biceps, semimembranosus, and sartorius. We tested the correlations of EIM-derived parameters [body fat-percentage (BF%) and muscle quality] with total CSA, CSA of each muscle, SMI, and SARC-F scores (0−10) using Pearson correlation coefficient. We found medium negative correlation of BF% with SMI (r = −0.430, p < 0.001) and total CSA (r = −0.445, p < 0.001), particularly with biceps (r = −0.479, p < 0.001), sartorius (r = −0.440, p < 0.001), and semimembranosus (r = −0.357, p < 0.001). EIM-derived muscle quality showed small-to-medium positive correlation with MRI measurements, ranging from r = 0.234 of biceps (p = 0.006) to r = 0.302 of total CSA (p < 0.001), except for vastus lateralis (r = 0.014, p = 0.873). SARC-F scores showed small correlations with EIM and MRI data, ranging from r = −0.132 (p = 0.121) with EIM muscle quality to r = −0.288 (p = 0.001) with CSA of vastus medialis. Hence, we observed small-to-medium correlations of muscle parameters derived from Skulpt ChiselTM with SARC-F scores and MRI parameters. We recommend using Skulpt ChiselTM with caution for assessing knee skeletal muscles composition.

From the Ketogenic Diet to the Mediterranean Diet: The Potential Dietary Therapy in Patients with Obesity after CoVID-19 Infection (Post CoVID Syndrome)

PURPOSE OF REVIEW

This review primarily examines the evidence for areas of consensus and on-going uncertainty or controversy about diet and physical exercise approaches for in the post-CoVID. We propose an ideal dietary and physical activity approach that the patient with obesity should follow after CoVID-19 infection in order to reduce the clinical conditions associated with post-CoVID syndrome.

RECENT FINDINGS

The CoVID-19 disease pandemic, caused by the severe acute respiratory syndrome coronavirus-2, has spread all over the globe, infecting hundreds of millions of individuals and causing millions of death. It is also known to be is associated with several medical and psychological complications, especially in patients with obesity and weight-related disorders who in general pose a significant global public health problem, and in specific affected individuals are on a greater risk of developing poorer CoVID-19 clinical outcomes and experience a higher rate of mortality. Little is still known about the best nutritional approach to be adopted in this disease especially in the patients post-CoVID syndrome. To the best of our knowledge, no specific nutritional recommendations exist to manage in the patients post-CoVID syndrome. We report a presentation of nutritional therapeutic approach based on a ketogenic diet protocol followed by a transition to the Mediterranean diet in patients post-infection by CoVID, combined to a physical activity program to address conditions associated with post-CoVID syndrome.

Correlation of CT-derived pectoralis muscle status and COVID-19 induced lung injury in elderly patients

OBJECTIVES

To explore the association between CT-derived pectoralis muscle index (PMI) and COVID-19 induced lung injury.

METHODS

We enrolled 116 elderly COVID-19 patients linked to the COVID-19 outbreak in Nanjing Lukou international airport. We extracted three sessions of their CT data, including one upon admission (T1), one during the first 2 weeks when lung injury peaked (T2) and one on day 14 ± 2 (T3). Lung injury was assessed by CT severity score (CTSS) and pulmonary opacity score (POS). Pneumonia evolution was evaluated by changes of CT scores at T2 from T1(Δ).

RESULTS

The maximum CT scores in low PMI patients were higher than those of normal PMI patients, including CTSS1 (7, IQR 6-10 vs. 5, IQR 3-6, p < 0.001), CTSS2 (8, IQR 7-11 vs. 5, IQR 4-7, p < 0.001) and POS (2, IQR 1-2.5 vs. 1, IQR 1-2, p < 0.001). Comorbidity (OR = 6.15, p = 0.023) and the presence of low PMI (OR = 5.43, p = 0.001) were predictors of lung injury aggravation with ΔCTSS1 > 4. The presence of low PMI (OR = 5.98, p < 0.001) was the predictor of lung injury aggravation with ΔCTSS2 > 4. Meanwhile, presence of low PMI (OR = 2.82, p = 0.042) and incrementally increasing D-dimer (OR = 0.088, p = 0.024) were predictors of lung injury aggravation with ΔPOS = 2.

CONCLUSIONS

PMI can be easily assessed on chest CT images and can potentially be used as one of the markers to predict the severity of lung injury in elderly COVID-19 patients.

Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study

Objective:

To evaluate anthropometric and clinical data, muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease related to COVID-19, quantification of pulmonary emphysema, coronary calcium, and hepatic steatosis using chest computed tomography of hospitalized patients with confirmed diagnosis of COVID-19 pneumonia and verify its association with disease severity.

Methods:

A total of 123 adults hospitalized due to COVID-19 pneumonia were enrolled in the present study, which evaluated the anthropometric, clinical and chest computed tomography data (pectoral and paravertebral muscle area and density, subcutaneous fat, thoracic vertebral bodies density, degree of pulmonary involvement by disease, coronary calcium quantification, liver attenuation measurement) and their association with poorer prognosis characterized through a combined outcome of intubation and mechanical ventilation, need of intensive care unit, and death.

Results:

Age (p=0.013), body mass index (p=0.009), lymphopenia (p=0.034), and degree of pulmonary involvement of COVID-19 pneumonia (p<0.001) were associated with poor prognosis. Extent of pulmonary involvement by COVID-19 pneumonia had an odds ratio of 1,329 for a poor prognosis and a cutoff value of 6.5 for increased risk, with a sensitivity of 64.9% and specificity of 67.1%.

Conclusion:

The present study found an association of high body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with severity of COVID-19 pneumonia in hospitalized patients.

Nutritional Risk Assessment Scores Effectively Predict Mortality in Critically Ill Patients with Severe COVID-19

Background: Malnutrition predicts a worse outcome for critically ill patients. However, quick, easy-to-use nutritional risk assessment tools have not been adequately validated. Aims and Methods: The study aimed to evaluate the role of four biological nutritional risk assessment instruments (the Prognostic Nutritional Index—PNI, the Controlling Nutritional Status Score—CONUT, the Nutrition Risk in Critically Ill—NUTRIC, and the modified NUTRIC—mNUTRIC), along with CT-derived fat tissue and muscle mass measurements in predicting in-hospital mortality in a consecutive series of 90 patients hospitalized in the intensive care unit for COVID-19-associated ARDS. Results: In-hospital mortality was 46.7% (n = 42/90). Non-survivors had a significantly higher nutritional risk, as expressed by all four scores. All scores were independent predictors of mortality on the multivariate regression models. PNI had the best discriminative capabilities for mortality, with an area under the curve (AUC) of 0.77 for a cut-off value of 28.05. All scores had an AUC above 0.72. The volume of fat tissue and muscle mass were not associated with increased mortality risk. Conclusions: PNI, CONUT, NUTRIC, and mNUTRIC are valuable nutritional risk assessment tools that can accurately predict mortality in critically ill patients with COVID-19-associated ARDS.

Frailty and senile apathy in the everyday clinical practice in the conditions of COVID-19

The article covers the pathogenesis, clinical manifestations, and diagnostic criteria of frailty and senile apathy in the elderly. Special attention is paid to sarcopenia: the phenotypic classification and modern approaches to the treatment are discussed. The knowledge and understanding of the main pathogenetic links of sarcopenia, frailty and senile apathy, as well as the development of a single therapeutic line for these pathological conditions can significantly improve the life quality and expectancy of the elderly.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19's possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction.

VLCKD in Combination with Physical Exercise Preserves Skeletal Muscle Mass in Sarcopenic Obesity after Severe COVID-19 Disease: A Case Report

The prevalence of sarcopenic obesity is increasing worldwide, with a strong impact on public health and the national health care system. Sarcopenic obesity consists of fat depot expansion and associated systemic low-grade inflammation, exacerbating the decline in skeletal muscle mass and strength. Dietary approach and physical exercise represent essential tools for reducing body weight and preserving muscle mass and function in subjects with sarcopenic obesity. This case report describes the effects of a dietary intervention, based on a Very-Low-Calorie Ketogenic Diet (VLCKD) combined with physical exercise, on body composition, cardiometabolic risk factors, and muscle strength in a woman with sarcopenic obesity, two weeks after hospitalization for bilateral interstitial pneumonia due to COVID-19. To our knowledge, this is the first case report to describe the efficacy of a combined approach intervention including VLCKD along with physical exercise, in reducing fat mass, improving metabolic profile, and preserving skeletal muscle performance in a patient with obesity, soon after severe COVID-19 disease.

Chest CT-Derived Muscle Analysis in COVID-19 Patients

Background: sarcopenia is a predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is not well known. To evaluate the association between the chest computed tomography (CT) derived muscle analysis of sarcopenia and clinical-radiological outcomes in coronavirus disease 2019 (COVID-19). Methods: in this retrospective study were revised the medical records of patients admitted to the intensive care unit (ICU) and intubated for COVID-19. All patients had undergone chest CT scan prior to intubation, and the cross-sectional areas of the pectoralis muscles (PMA, cm²) and density (PMD, HU) were measured at the level of the fourth thoracic vertebral. The relationship between PMA and PMD and CT severity pneumonia, length of ICU, extubation failure/success, and mortality were investigated. Results: a total of 112 patients were included (82 M; mean age 60.5 ± 11.4 years). Patients with successful extubation had higher PMA compared to patients with failure extubation, 42.1 ± 7.9 vs. 37.8 ± 6.4 cm² (p = 0.0056) and patients with shorter ICU had higher PMA and PMD compared to those with longer, respectively, 41.6 ± 8.7 vs. 37.2 ± 6.7 cm² (p = 0.0034) and 30.2 ± 6.2 vs. 26.1 ± 4.9 HU (p = 0.0002). No statistical difference in PMA and PMD resulted in CT severity pneumonia and mortality. Conclusion: sarcopenia in COVID-19 patients, evaluated by CT-derived muscle analysis, could be associated with longer ICU stay and failure extubation.

Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019

PURPOSE OF REVIEW

This review aims to explore the different imaging modalities, such as chest radiography (CXR), computed tomography (CT), ultrasound, PET/CT scan, and MRI to describe the main features for the evaluation of the chest in COVID-19 patients with ARDS.

RECENT FINDINGS

This article includes a systematic literature search, evidencing the different chest imaging modalities used in patients with ARDS from COVID-19. Literature evidences different possible approaches going from the conventional CXR and CT to the LUS, MRI, and PET/CT.

SUMMARY

CT is the technique with higher sensitivity and definition for studying chest in COVID-19 patients. LUS or bedside CXR are critical in patients requiring close and repeated monitoring. Moreover, LUS and CXR reduce the radiation burden and the risk of infection compared with CT. PET/CT and MRI, especially in ARDS patients, are not usually used for diagnostic or follow-up purposes.

Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: A Meta-analysis and Meta-Regression

Background

Sarcopenia has been associated with patients' poor quality of life, disability, and hospitalization. As of today, evidence that highlights the association between sarcopenia and Covid-19 outcomes remains unclear. This study sought to analyze whether patients with sarcopenia are at higher risk for developing poor Covid-19 outcomes.

Methods

Using specific keywords, we comprehensively go through the potential articles on medRxiv, Europe PMC, and PubMed sources until July 31st, 2021. All published studies on sarcopenia and coronavirus disease 2019 were collected. We were using Review Manager 5.4 and Comprehensive Meta-Analysis 3 software to conduct statistical analysis.

Results

There were 9 studies with 492,245 Covid-19 patients included in the analysis. Evaluation of the data gathered yielded an association between sarcopenia and increased severity of Covid-19 (OR 1.99; 95%CI: 1.37–2.90, p = 0.0003, I² = 79%, random-effect modelling); and mortality from Covid-19 (OR 1.96; 95%CI: 1.11–3.46, p = 0.020, I² = 49%, random-effect modelling). The increased risk of developing severe Covid-19 in a sarcopenic patient is also further influenced by cancer.

Conclusions

This study proposes that patients with sarcopenia are at risk of developing poor Covid-19 outcomes. Patients with sarcopenia need special attention and should be prioritized to receive the SARS-CoV-2 vaccine.

Registration details

PROSPERO (CRD42021270725).

Sarcopenia: imaging assessment and clinical application

Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.

Extrapulmonary CT Findings Predict In-Hospital Mortality in COVID-19. A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

Several prognostic factors have been identified for COVID-19 disease. Our aim was to elucidate the influence of non-pulmonary findings of thoracic computed tomography (CT) on unfavorable outcomes and in-hospital mortality in COVID-19 patients based on a large patient sample.

MATERIALS AND METHODS

MEDLINE library, Cochrane and SCOPUS databases were screened for the associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 22 studies were suitable for the analysis, and included into the present analysis. Overall, data regarding 4 extrapulmonary findings could be pooled: pleural effusion, pericardial effusion, mediastinal lymphadenopathy, and coronary calcification.

RESULTS

The included studies comprised 7859 patients. The pooled odds ratios for the effect of the identified extrapulmonary findings on in-hospital mortality are as follows: pleural effusion, 4.60 (95% CI 2.97-7.12); pericardial effusion, 1.29 (95% CI 0.83-1.98); coronary calcification, 2.68 (95% CI 1.78-4.04); mediastinal lymphadenopathy, 2.02 (95% CI 1.18-3.45).

CONCLUSION

Pleural effusion, mediastinal lymphadenopathy and coronary calcification have a relevant association with in-hospital mortality in COVID-19 patients and should be included as prognostic biomarker into clinical routine.