1
|
Yi J, Michalowska AM, Shanbhag A, Miller RJH, Geers J, Zhang W, Killekar A, Manral N, Lemley M, Buchwald M, Kwiecinski J, Zhou J, Kavanagh PB, Liang JX, Builoff V, Ruddy TD, Einstein AJ, Feher A, Miller EJ, Sinusas AJ, Berman DS, Dey D, Slomka PJ. AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.30.24311224. [PMID: 39132480 PMCID: PMC11312626 DOI: 10.1101/2024.07.30.24311224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
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.
Collapse
|
2
|
Huang Y, Yuan F, Yang L, Guo H, Jiang Y, Cun H, Mou Z, Chen J, Li C, Zhang Z, He B. Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer. Quant Imaging Med Surg 2024; 14:5737-5747. [PMID: 39144051 PMCID: PMC11320558 DOI: 10.21037/qims-24-120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/07/2024] [Indexed: 08/16/2024]
Abstract
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.
Collapse
Affiliation(s)
- Yilong Huang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Feng Yuan
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Lei Yang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Honglei Guo
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuanming Jiang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Hanxue Cun
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhanglin Mou
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Jiaxin Chen
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chunli Li
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhenguang Zhang
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bo He
- Department of Medical Imaging, the First Affiliated Hospital of Kunming Medical University, Kunming, China
| |
Collapse
|
3
|
Wang Q, Shi P, Cao L, Li H, Chen X, Wang P, Zhang J. Unveiling the detrimental vicious cycle linking skeletal muscle and COVID-19: A systematic review and meta-analysis. J Evid Based Med 2024. [PMID: 38975690 DOI: 10.1111/jebm.12629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 06/18/2024] [Indexed: 07/09/2024]
Abstract
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.
Collapse
Affiliation(s)
- Qin Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Peipei Shi
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lu Cao
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Haoran Li
- Department of Thoracic Surgery, Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Xiankai Chen
- Department of Thoracic Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peiyu Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Thoracic Surgery, Thoracic Oncology Institute, Peking University People's Hospital, Beijing, China
| | - Jianjiang Zhang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| |
Collapse
|
4
|
Liu J, Ye Z, Xiang J, Wang Q, Zhao W, Qin W, Rao J, Chen Y, Hu Z, Peng H. Association of muscle mass and radiodensity assessed by chest CT with all-cause and cardiovascular mortality in hemodialysis patients. Int Urol Nephrol 2024:10.1007/s11255-024-04113-6. [PMID: 38865001 DOI: 10.1007/s11255-024-04113-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/05/2024] [Indexed: 06/13/2024]
Abstract
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.
Collapse
Affiliation(s)
- Jianqiang Liu
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Zengchun Ye
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Juncheng Xiang
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Qian Wang
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Wenbo Zhao
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Weixuan Qin
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Jialing Rao
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Yanru Chen
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China
| | - Zhaoyong Hu
- Division of Nephrology, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Hui Peng
- Division of Nephrology, Department of Medicine, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Ave., Guangzhou, 510630, China.
| |
Collapse
|
5
|
Al-Taie A, Arueyingho O, Khoshnaw J, Hafeez A. Clinical outcomes of multidimensional association of type 2 diabetes mellitus, COVID-19 and sarcopenia: an algorithm and scoping systematic evaluation. Arch Physiol Biochem 2024; 130:342-360. [PMID: 35704400 DOI: 10.1080/13813455.2022.2086265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/25/2022] [Accepted: 05/31/2022] [Indexed: 01/08/2023]
Abstract
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.
Collapse
Affiliation(s)
- Anmar Al-Taie
- Clinical Pharmacy Department, Faculty of Pharmacy, Istinye University, Istanbul, Turkey
| | - Oritsetimeyin Arueyingho
- EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, UK
| | - Jalal Khoshnaw
- Pharmacy Department, Faculty of Pharmacy, Girne American University, Mersin, Turkey
| | - Abdul Hafeez
- Department of Pharmaceutics, Glocal School of Pharmacy, Glocal University, Saharanpur, Uttar Pradesh, India
| |
Collapse
|
6
|
Surov A, Meyer HJ, Ehrengut C, Zimmermann S, Schramm D, Hinnerichs M, Bär C, Borggrefe J. Myosteatosis predicts short-term mortality in patients with COVID-19: A multicenter analysis. Nutrition 2024; 120:112327. [PMID: 38341908 DOI: 10.1016/j.nut.2023.112327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/29/2023] [Accepted: 12/06/2023] [Indexed: 02/13/2024]
Abstract
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.
Collapse
Affiliation(s)
- Alexey Surov
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling Medical Center, Ruhr University Bochum, Germany.
| | - Hans Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Silke Zimmermann
- Department of Laboratory Medicine, University of Leipzig, Leipzig, Germany
| | - Dominik Schramm
- Department of Diagnostic and Interventional Radiology, University of Halle-Wittenberg, Halle (Saale), Germany
| | - Mattes Hinnerichs
- Department of Radiology and Nuclear Medicine, Otto von Guericke University, Magdeburg, Germany
| | - Caroline Bär
- Department of Radiology and Nuclear Medicine, Otto von Guericke University, Magdeburg, Germany
| | - Jan Borggrefe
- Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling Medical Center, Ruhr University Bochum, Germany
| |
Collapse
|
7
|
Nakagawara K, Shiraishi Y, Chubachi S, Tanabe N, Maetani T, Asakura T, Namkoong H, Tanaka H, Shimada T, Azekawa S, Otake S, Fukushima T, Watase M, Terai H, Sasaki M, Ueda S, Kato Y, Harada N, Suzuki S, Yoshida S, Tateno H, Yamada Y, Jinzaki M, Hirai T, Okada Y, Koike R, Ishii M, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Integrated assessment of computed tomography density in pectoralis and erector spinae muscles as a prognostic biomarker for coronavirus disease 2019. Clin Nutr 2024; 43:815-824. [PMID: 38350289 DOI: 10.1016/j.clnu.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/12/2024] [Accepted: 02/02/2024] [Indexed: 02/15/2024]
Abstract
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.
Collapse
Affiliation(s)
- Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan; Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan; Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Shimada
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hideki Terai
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Mamoru Sasaki
- Internal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan
| | - Soichiro Ueda
- Internal Medicine, JCHO (Japan Community Health Care Organization) Saitama Medical Center, Saitama, Japan
| | - Yukari Kato
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Shoji Suzuki
- Department of Respiratory Medicine, Saitama City Hospital, Saitama, Japan
| | - Shuichi Yoshida
- Department of Respiratory Medicine, Saitama City Hospital, Saitama, Japan
| | - Hiroki Tateno
- Department of Respiratory Medicine, Saitama City Hospital, Saitama, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan; Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan; Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| |
Collapse
|
8
|
Kutaiba N, Dobson J, Finnis M, Bellomo R. The identification of an optimal body size parameter to adjust skeletal muscle area on chest CT in COVID-19 patients. J Frailty Sarcopenia Falls 2024; 9:16-24. [PMID: 38444548 PMCID: PMC10910254 DOI: 10.22540/jfsf-09-016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2023] [Indexed: 03/07/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Numan Kutaiba
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
- Department of Radiology, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie Dobson
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
| | - Mark Finnis
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Critical Care, The University of Melbourne, Parkville, Victoria, Australia
- Department of Intensive Care, Austin Hospital, Heidelberg, Victoria, Australia
- Data Analytics Research and Evaluation Centre, Austin Hospital, Melbourne, Australia
- Department of Critical Care, Royal Melbourne Hospital, Melbourne, Australia
| |
Collapse
|
9
|
Wang S, Wang M, Jiang L, Zhao X. Low skeletal muscle quality extracted from CT is associated with poor outcomes in severe acute pancreatitis patients. Eur J Radiol 2024; 170:111215. [PMID: 38091663 DOI: 10.1016/j.ejrad.2023.111215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 01/16/2024]
Abstract
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.
Collapse
Affiliation(s)
- Shengqi Wang
- Department of Emergency Medicine, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Meiping Wang
- Department of Critical Care Medicine, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Li Jiang
- Department of Critical Care Medicine, Xuanwu Hospital, Capital Medical University, Beijing, PR China.
| | - Xin Zhao
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| |
Collapse
|
10
|
Yakti FAZ, Abusalah L, Ganji V. Sarcopenia and Mortality in Critically Ill COVID-19 Patients. Life (Basel) 2023; 14:24. [PMID: 38255640 PMCID: PMC10820280 DOI: 10.3390/life14010024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 11/10/2023] [Accepted: 12/15/2023] [Indexed: 01/24/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Fatima Al Zahra Yakti
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (F.A.Z.Y.); (L.A.)
| | - Lana Abusalah
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar; (F.A.Z.Y.); (L.A.)
| | - Vijay Ganji
- Department of Nutrition and Dietetics, School of Health and Human Sciences, Indiana University Indianapolis, 1050 Wishard Blvd., Indianapolis, IN 46202, USA
| |
Collapse
|
11
|
Palmisano A, Gnasso C, Cereda A, Vignale D, Leone R, Nicoletti V, Barbieri S, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannaccone M, Baffoni L, Spernadio M, de Carlini CC, Sironi S, Rapezzi C, Esposito A. Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study. Eur Radiol 2023; 33:7756-7768. [PMID: 37166497 PMCID: PMC10173240 DOI: 10.1007/s00330-023-09702-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 05/12/2023]
Abstract
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.
Collapse
Affiliation(s)
- Anna Palmisano
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Chiara Gnasso
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Alberto Cereda
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | - Davide Vignale
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Riccardo Leone
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Valeria Nicoletti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Simone Barbieri
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
| | - Marco Toselli
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | | | | | | | | | | | | | | | | | | | - Elisa Scarnecchia
- ASST Valtellina and Alto Lario, Eugenio Morelli Hospital, Sondalo, Italy
| | | | - Lucio Baffoni
- Casa Di Cura Villa Dei Pini, Civitanova Marche, Italy
| | | | | | | | - Claudio Rapezzi
- Azienda Ospedaliero-Universitaria Di Ferrara, Cona, FE, Italy
| | - Antonio Esposito
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy.
| |
Collapse
|
12
|
Tanaka H, Maetani T, Chubachi S, Tanabe N, Shiraishi Y, Asakura T, Namkoong H, Shimada T, Azekawa S, Otake S, Nakagawara K, Fukushima T, Watase M, Terai H, Sasaki M, Ueda S, Kato Y, Harada N, Suzuki S, Yoshida S, Tateno H, Yamada Y, Jinzaki M, Hirai T, Okada Y, Koike R, Ishii M, Hasegawa N, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan. Respir Res 2023; 24:241. [PMID: 37798709 PMCID: PMC10552312 DOI: 10.1186/s12931-023-02530-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Clinical Medicine (Laboratory of Bioregulatory Medicine), Kitasato University School of Pharmacy, Tokyo, Japan
- Department of Respiratory Medicine, Kitasato University, Kitasato Institute Hospital, Tokyo, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Shimada
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shuhei Azekawa
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hideki Terai
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mamoru Sasaki
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Soichiro Ueda
- Department of Respiratory Medicine, JCHO (Japan Community Health care Organization), Saitama Medical Center, Saitama, Japan
| | - Yukari Kato
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Shoji Suzuki
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shuichi Yoshida
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Hiroki Tateno
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ryuji Koike
- Health Science Research and Development Center (HeRD), Tokyo Medical and Dental University, Tokyo, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| |
Collapse
|
13
|
das Virgens IPA, Sousa IM, Bezerra ADL, Fayh APT. Assessment of body composition in adults hospitalized with acute COVID-19: a scoping review. Front Nutr 2023; 10:1176441. [PMID: 37743922 PMCID: PMC10513420 DOI: 10.3389/fnut.2023.1176441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/07/2023] [Indexed: 09/26/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Isabel Pinto Amorim das Virgens
- Graduation Program in Health Sciences, Health Sciences Center, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
- Center for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Iasmin Matias Sousa
- Graduation Program in Health Sciences, Health Sciences Center, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Agnes Denise Lima Bezerra
- Graduation Program in Health Sciences, Health Sciences Center, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Ana Paula Trussardi Fayh
- Graduation Program in Health Sciences, Health Sciences Center, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| |
Collapse
|
14
|
Emekli E, Bostancı Can EZ. Prognostic Value of Diaphragm Diameter, Muscle Volume, and Bone Mineral Density in Critically Ill COVID-19 Patients. J Intensive Care Med 2023; 38:847-855. [PMID: 37050868 PMCID: PMC10099913 DOI: 10.1177/08850666231169494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023]
Abstract
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.
Collapse
Affiliation(s)
- Emre Emekli
- Department of Radiology, Etimesgut
Şehit Sait Ertürk State Hospital, Ankara, Turkey
| | - Emine Zülal Bostancı Can
- Department of Anesthesiology and
Reanimation, Etimesgut Şehit Sait Ertürk State Hospital, Ankara, Turkey
| |
Collapse
|
15
|
Liu Y, Qi Z, Bai M, Kang J, Xu J, Yi H. Combination of Chest Computed Tomography Value and Clinical Laboratory Data for the Prognostic Risk Evaluation of Patients with COVID-19. Int J Gen Med 2023; 16:3829-3842. [PMID: 37662505 PMCID: PMC10473430 DOI: 10.2147/ijgm.s425567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/12/2023] [Indexed: 09/05/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Yali Liu
- Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| | - Zhihong Qi
- Department of Urologic Surgery, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| | - Meirong Bai
- Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| | - Jianle Kang
- Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| | - Jinxin Xu
- Department of Thoracic Surgery, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| | - Huochun Yi
- Clinical Laboratory, Zhongshan Hospital Xiamen University, Xiamen, Fujian, 361012, People’s Republic of China
| |
Collapse
|
16
|
Sumbal R, Sumbal A, Ali Baig MM. Which vertebral level should be used to calculate sarcopenia in covid-19 patients? A systematic review and meta-analysis. Clin Nutr ESPEN 2023; 56:1-8. [PMID: 37344057 DOI: 10.1016/j.clnesp.2023.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 04/15/2023] [Accepted: 04/25/2023] [Indexed: 06/23/2023]
Abstract
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.
Collapse
Affiliation(s)
- Ramish Sumbal
- Dow University of Health and Science, Baba-E-Urdu Road, 74200, Karachi, Pakistan.
| | - Anusha Sumbal
- Dow University of Health and Science, Baba-E-Urdu Road, 74200, Karachi, Pakistan
| | | |
Collapse
|
17
|
van Bakel SIJ, Gietema HA, Stassen PM, Gosker HR, Gach D, van den Bergh JP, van Osch FHM, Schols AMWJ, Beijers RJHCG. CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19. Chest 2023; 164:314-322. [PMID: 36894133 PMCID: PMC9990885 DOI: 10.1016/j.chest.2023.02.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/09/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Sophie I J van Bakel
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Hester A Gietema
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, the Netherlands; Grow School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Patricia M Stassen
- Section Acute Medicine, Division of General Internal Medicine, Department of Internal Medicine, CARIM School for Cardiovascular Diseases, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Harry R Gosker
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Debbie Gach
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Clinical Epidemiology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Joop P van den Bergh
- Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Internal Medicine, VieCuri Medical Centre, Venlo, the Netherlands
| | - Frits H M van Osch
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands; Department of Clinical Epidemiology, VieCuri Medical Centre, Venlo, the Netherlands
| | - Annemie M W J Schols
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Rosanne J H C G Beijers
- Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, the Netherlands.
| |
Collapse
|
18
|
Schinas G, Dimakopoulou V, Dionysopoulos K, Fezoulidi G, Vlychou M, Vassiou K, Gatselis NK, Samakidou A, Giannoulis G, Tzouvelekis A, Marangos M, Gogos C, Dalekos GN, Kalogeropoulou C, Akinosoglou K. Radiologic Features of T10 Paravertebral Muscle Sarcopenia: Prognostic Factors in COVID-19. J Clin Med Res 2023; 15:368-376. [PMID: 37575354 PMCID: PMC10416190 DOI: 10.14740/jocmr4963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Georgios Schinas
- School of Medicine, University of Patras, Patras, Greece
- These authors contributed equally to this article
| | - Vasiliki Dimakopoulou
- Department of Internal Medicine, University Hospital of Patras, Patras, Greece
- These authors contributed equally to this article
| | | | | | - Marianna Vlychou
- Department of Radiology, General University Hospital of Larissa, Larissa, Greece
| | - Katerina Vassiou
- Department of Anatomy, Medical School, University of Thessaly, Larisa, Greece
| | - Nikolaos K. Gatselis
- Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece
| | - Anna Samakidou
- Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece
| | - Georgios Giannoulis
- Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece
| | - Argyrios Tzouvelekis
- Division of Respiratory Medicine, Department of Internal Medicine, University Hospital of Patras, Patras, Greece
| | - Markos Marangos
- Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece
| | - Charalambos Gogos
- Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece
| | - George N. Dalekos
- Department of Medicine and Research Laboratory of Internal Medicine, Expertise Center of Greece in Autoimmune Liver Diseases, European Reference Network on Hepatological Diseases (ERN RARE-LIVER), General University Hospital of Larissa, Larissa, Greece
| | | | - Karolina Akinosoglou
- Division of Infectious Diseases, Department of Internal Medicine, University Hospital of Patras, Patras, Greece
| |
Collapse
|
19
|
Bolesławska I, Kowalówka M, Bolesławska-Król N, Przysławski J. Ketogenic Diet and Ketone Bodies as Clinical Support for the Treatment of SARS-CoV-2-Review of the Evidence. Viruses 2023; 15:1262. [PMID: 37376562 PMCID: PMC10326824 DOI: 10.3390/v15061262] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Izabela Bolesławska
- Department of Bromatology, Poznan University of Medical Sciences, 60-806 Poznan, Poland; (M.K.); (J.P.)
| | - Magdalena Kowalówka
- Department of Bromatology, Poznan University of Medical Sciences, 60-806 Poznan, Poland; (M.K.); (J.P.)
| | - Natasza Bolesławska-Król
- Student Society of Radiotherapy, Collegium Medicum, University of Zielona Gora, Zyta 28, 65-046 Zielona Góra, Poland;
| | - Juliusz Przysławski
- Department of Bromatology, Poznan University of Medical Sciences, 60-806 Poznan, Poland; (M.K.); (J.P.)
| |
Collapse
|
20
|
Tariq A, Tang S, Sakhi H, Celi LA, Newsome JM, Rubin DL, Trivedi H, Gichoya JW, Banerjee I. Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients. J Med Imaging (Bellingham) 2023; 10:034004. [PMID: 37388280 PMCID: PMC10306115 DOI: 10.1117/1.jmi.10.3.034004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 06/07/2023] [Accepted: 06/13/2023] [Indexed: 07/01/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Amara Tariq
- Mayo Clinic, Department of Administration, Phoenix, Arizona, United States
| | - Siyi Tang
- Stanford University, Department of Electrical Engineering, Stanford, California, United States
| | - Hifza Sakhi
- Philadelphia College of Osteopathic Medicine - Georgia Campus, Swanee, Georgia, United States
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Boston, Massachusetts, United States
| | - Janice M. Newsome
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United States
| | - Daniel L. Rubin
- Stanford University, Department of Biomedical Data Science, Stanford, California, United States
- Stanford University, Department of Radiology, Stanford, California, United States
| | - Hari Trivedi
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United States
| | - Judy Wawira Gichoya
- Emory University, School of Medicine, Department of Radiology and Imaging Sciences, Atlanta, Georgia, United States
| | - Imon Banerjee
- Mayo Clinic, Department of Radiology, Phoenix, Arizona, United States
- Arizona State University, Ira A. Fulton School of Engineering, Department of Computer Engineering, Tempe, Arizona, United States
| |
Collapse
|
21
|
Basty N, Sorokin EP, Thanaj M, Srinivasan R, Whitcher B, Bell JD, Cule M, Thomas EL. Abdominal imaging associates body composition with COVID-19 severity. PLoS One 2023; 18:e0283506. [PMID: 37053189 PMCID: PMC10101472 DOI: 10.1371/journal.pone.0283506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/10/2023] [Indexed: 04/14/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Elena P. Sorokin
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, United States of America
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, United Kingdom
| |
Collapse
|
22
|
Montes-Ibarra M, Orsso CE, Limon-Miro AT, Gonzalez MC, Marzetti E, Landi F, Heymsfield SB, Barazonni R, Prado CM. Prevalence and clinical implications of abnormal body composition phenotypes in patients with COVID-19: a systematic review. Am J Clin Nutr 2023:S0002-9165(23)46332-0. [PMID: 37037395 PMCID: PMC10082471 DOI: 10.1016/j.ajcnut.2023.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 03/01/2023] [Accepted: 04/03/2023] [Indexed: 04/12/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Montserrat Montes-Ibarra
- Human Nutrition Research Unit, Department of Agricultural, Food, & Nutritional Science, University of Alberta, Edmonton, Alberta, Canada. (MMI, CEO, ATLM and CMP)
| | - Camila E Orsso
- Human Nutrition Research Unit, Department of Agricultural, Food, & Nutritional Science, University of Alberta, Edmonton, Alberta, Canada. (MMI, CEO, ATLM and CMP)
| | - Ana Teresa Limon-Miro
- Human Nutrition Research Unit, Department of Agricultural, Food, & Nutritional Science, University of Alberta, Edmonton, Alberta, Canada. (MMI, CEO, ATLM and CMP); Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. (ATLM)
| | - Maria Cristina Gonzalez
- Postgraduate Program in Health and Behavior, Catholic University of Pelotas, Pelotas, Rio Grande do Sul, Brazil. (MCG)
| | - Emanuele Marzetti
- Department of Geriatrics and Orthopedics, Università Cattolica del Sacro Cuore, Rome, Italy. (EM and FL); Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy. (EM and FL)
| | - Francesco Landi
- Department of Geriatrics and Orthopedics, Università Cattolica del Sacro Cuore, Rome, Italy. (EM and FL); Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy. (EM and FL)
| | - Steven B Heymsfield
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, United States. (SBH)
| | - Rocco Barazonni
- Department of Medical, Surgical and Health Sciences, University of Trieste, Italy; Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), Cattinara Hospital, Trieste, Italy. (RB)
| | - Carla M Prado
- Human Nutrition Research Unit, Department of Agricultural, Food, & Nutritional Science, University of Alberta, Edmonton, Alberta, Canada. (MMI, CEO, ATLM and CMP).
| |
Collapse
|
23
|
Del Toro R, Palmese F, Feletti F, Zani G, Minguzzi MT, Maddaloni E, Napoli N, Bedogni G, Domenicali M. Relationship between Muscle Mass, Bone Density and Vascular Calcifications in Elderly People with SARS-CoV-2 Pneumonia. J Clin Med 2023; 12:jcm12062372. [PMID: 36983372 PMCID: PMC10059976 DOI: 10.3390/jcm12062372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/12/2023] [Accepted: 03/13/2023] [Indexed: 03/30/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Rossella Del Toro
- Department of Primary Health Care, Internal Medicine Unit Addressed to Frailty and Aging, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
| | - Francesco Palmese
- Department of Primary Health Care, Internal Medicine Unit Addressed to Frailty and Aging, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Francesco Feletti
- Department of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, Italy
- Department of Diagnostic Imaging, Radiology Unit, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
| | - Gianluca Zani
- Department of Anesthesia and Intensive Care, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
| | - Maria Teresa Minguzzi
- Department of Diagnostic Imaging, Radiology Unit, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
| | - Ernesto Maddaloni
- Department of Experimental Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Nicola Napoli
- Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Campus Bio-Medico University of Rome, 00128 Rome, Italy
| | - Giorgio Bedogni
- Department of Primary Health Care, Internal Medicine Unit Addressed to Frailty and Aging, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Marco Domenicali
- Department of Primary Health Care, Internal Medicine Unit Addressed to Frailty and Aging, Santa Maria delle Croci Hospital, AUSL Romagna, 48121 Ravenna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| |
Collapse
|
24
|
Surov A, Thormann M, Kardas H, Hinnerichs M, Omari J, Cingöz E, Cingöz M, Dursun M, Kormaz İ, Orhan Ç, Yıldız Ö, Hocaoğlu E, Inci E, Önder H, Erk H, Chousein O, Sasani H, Gönen KA, Pech M, Wienke A. Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study. Br J Radiol 2023; 96:20220869. [PMID: 36744766 PMCID: PMC10078869 DOI: 10.1259/bjr.20220869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Alexey Surov
- Department of Radiology,, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany
| | - Maximilian Thormann
- Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Hakan Kardas
- Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Mattes Hinnerichs
- Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Jazan Omari
- Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Eda Cingöz
- Istanbul Medical Faculty Radiology Department, Istanbul University, Istanbul, Turkey
| | - Mehmet Cingöz
- Department of Radiology, Istanbul Cam and Sakura City Hospital, Istanbul, Turkey
| | - Memduh Dursun
- Istanbul Medical Faculty Radiology Department, Istanbul University, Istanbul, Turkey
| | - İnan Kormaz
- Department of Radiology, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey
| | - Çağrı Orhan
- Department of Radiology, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey
| | - Ömer Yıldız
- Radiology Department, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Bakirkoy, Istanbul, Turkey
| | - Elif Hocaoğlu
- Radiology Department, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Bakirkoy, Istanbul, Turkey
| | - Ercan Inci
- Radiology Department, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Bakirkoy, Istanbul, Turkey
| | - Hakan Önder
- Radiology Department, Health Science University, Prof. Dr. Cemil Tascioğlu City Hospital, Istanbul, Turkey
| | - Hamdullah Erk
- Radiology Department, Health Science University, Prof. Dr. Cemil Tascioğlu City Hospital, Istanbul, Turkey
| | - Ougkour Chousein
- Department of Radiology, Tekirdag Namik Kemal University, Tekirdag, Turkey
| | - Hadi Sasani
- Department of Radiology, Tekirdag Namik Kemal University, Tekirdag, Turkey
| | - Korcan Aysun Gönen
- Department of Radiology, Tekirdag Namik Kemal University, Tekirdag, Turkey
| | - Maciej Pech
- Clinic for Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biometry, and Informatics, Martin- Luther- University, Halle-Wittenberg, Germany
| |
Collapse
|
25
|
Grigioni S, Lvovschi VE, Tamion F, Joly LM, Coëffier M, Van Elslande H, Galmiche M, Gâté M, Traore-Ouattara U, Marais B, Michel J, Dechelotte P, Savoye-Collet C, Achamrah N. Low thoracic skeletal muscle index is associated with negative outcomes in 244 patients with respiratory COVID-19. Clin Nutr 2023; 42:102-107. [PMID: 36521254 PMCID: PMC9674398 DOI: 10.1016/j.clnu.2022.11.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/27/2022] [Accepted: 11/09/2022] [Indexed: 11/20/2022]
Abstract
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.
Collapse
Affiliation(s)
- S Grigioni
- Nutrition Department, CHU Rouen, Rouen, France; Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Clinical Investigation Centre CIC 1404, INSERM and CHU Rouen, France
| | - V-E Lvovschi
- Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Emergency Department, CHU Rouen, Rouen, France
| | - F Tamion
- Medical Intensive Care Unit, CHU Rouen, France; Normandie Univ, UNIROUEN, INSERM U1096, F-76000 Rouen, France
| | - L-M Joly
- Emergency Department, CHU Rouen, Rouen, France
| | - M Coëffier
- Nutrition Department, CHU Rouen, Rouen, France; Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Clinical Investigation Centre CIC 1404, INSERM and CHU Rouen, France
| | - H Van Elslande
- Nutrition Department, CHU Rouen, Rouen, France; Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Clinical Investigation Centre CIC 1404, INSERM and CHU Rouen, France
| | | | - M Gâté
- Nutrition Department, CHU Rouen, Rouen, France
| | | | - B Marais
- Medical Intensive Care Unit, CHU Rouen, France
| | - J Michel
- Emergency Department, CHU Rouen, Rouen, France
| | - P Dechelotte
- Nutrition Department, CHU Rouen, Rouen, France; Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Clinical Investigation Centre CIC 1404, INSERM and CHU Rouen, France
| | | | - N Achamrah
- Nutrition Department, CHU Rouen, Rouen, France; Normandie Univ, UNIROUEN, INSERM UMR 1073, Nutrition, Inflammation et axe microbiote-intestin-cerveau, IRIB, Rouen, France; Clinical Investigation Centre CIC 1404, INSERM and CHU Rouen, France.
| |
Collapse
|
26
|
Which Body Composition Parameters on Computed Tomography Are More Successful in Predicting the Prognosis of COVID-19 Patients? J Comput Assist Tomogr 2023; 47:58-66. [PMID: 36219743 DOI: 10.1097/rct.0000000000001387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
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.
Collapse
|
27
|
Wang Y, Tan S, Yan Q, Gao Y. Sarcopenia and COVID-19 Outcomes. Clin Interv Aging 2023; 18:359-373. [PMID: 36923269 PMCID: PMC10010141 DOI: 10.2147/cia.s398386] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/02/2023] [Indexed: 03/18/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Yuhan Wang
- Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shuwen Tan
- Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Qihui Yan
- Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Ying Gao
- Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, People's Republic of China
| |
Collapse
|
28
|
Surov A, Kardas H, Besutti G, Pellegrini M, Ottone M, Onur MR, Atak F, Erdemir AG, Hocaoglu E, Yıldız Ö, Inci E, Cingöz E, Cingöz M, Dursun M, Korkmaz İ, Orhan Ç, Strobel A, Wienke A, Pech M. Prognostic Role of the Pectoralis Musculature in Patients with COVID-19. A Multicenter Study. Acad Radiol 2023; 30:77-82. [PMID: 35667979 PMCID: PMC9108033 DOI: 10.1016/j.acra.2022.05.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 01/28/2023]
Abstract
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.
Collapse
Affiliation(s)
- Alexey Surov
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg (A.S., H.K., M.P.).
| | - Hakan Kardas
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg (A.S., H.K., M.P.)
| | - Giulia Besutti
- Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy (G.B., M.P., M.O.)
| | - Massimo Pellegrini
- Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy (G.B., M.P., M.O.)
| | - Marta Ottone
- Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy (G.B., M.P., M.O.)
| | - Mehmet Ruhi Onur
- Department of Radiology, University of Hacettepe School of Medicine, Ankara, Turkey (M.R.O., F.A., A.G.E.)
| | - Firat Atak
- Department of Radiology, University of Hacettepe School of Medicine, Ankara, Turkey (M.R.O., F.A., A.G.E.)
| | - Ahmet Gurkan Erdemir
- Department of Radiology, University of Hacettepe School of Medicine, Ankara, Turkey (M.R.O., F.A., A.G.E.)
| | - Elif Hocaoglu
- Department of Radiology, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Radiology (E.H., O.Y., E.I.)
| | - Ömer Yıldız
- Department of Radiology, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Radiology (E.H., O.Y., E.I.)
| | - Ercan Inci
- Department of Radiology, University of Health Sciences, Bakirkoy Dr. Sadi Konuk Research and Training Hospital, Radiology (E.H., O.Y., E.I.)
| | - Eda Cingöz
- İstanbul Medical Faculty Radiology Department, Istanbul Turkey (E.C., M.C., M.D.)
| | - Mehmet Cingöz
- İstanbul Medical Faculty Radiology Department, Istanbul Turkey (E.C., M.C., M.D.),Basaksehir Cam and Sakura City Hospital Radiology Department (M.C.)
| | - Memduh Dursun
- İstanbul Medical Faculty Radiology Department, Istanbul Turkey (E.C., M.C., M.D.)
| | - İnan Korkmaz
- Hatay Mustafa Kemal University, Faculty of Medicine, Department of Radiology, Antakya, Hatay, Turkey (I.K., C.O.)
| | - Çağrı Orhan
- Hatay Mustafa Kemal University, Faculty of Medicine, Department of Radiology, Antakya, Hatay, Turkey (I.K., C.O.)
| | - Alexandra Strobel
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Profile Area Clinical Studies & Biostatistics, Martin-Luther-University Halle-Wittenberg, Halle, Germany (A.S., A.W.)
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Profile Area Clinical Studies & Biostatistics, Martin-Luther-University Halle-Wittenberg, Halle, Germany (A.S., A.W.)
| | - Maciej Pech
- Department of Radiology and Nuclear Medicine, Otto-von-Guericke University Magdeburg (A.S., H.K., M.P.)
| |
Collapse
|
29
|
Sato S, Miyazaki S, Tamaki A, Yoshimura Y, Arai H, Fujiwara D, Katsura H, Kawagoshi A, Kozu R, Maeda K, Ogawa S, Ueki J, Wakabayashi H. Respiratory sarcopenia: A position paper by four professional organizations. Geriatr Gerontol Int 2023; 23:5-15. [PMID: 36479799 DOI: 10.1111/ggi.14519] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/16/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022]
Abstract
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.
Collapse
Affiliation(s)
- Susumu Sato
- Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Akira Tamaki
- Department of Physical Therapy, School of Rehabilitation, Hyogo Medical University, Kobe, Japan
| | - Yoshihiro Yoshimura
- Center for Sarcopenia and Malnutrition Research, Kumamoto Rehabilitation Hospital, Kumamoto, Japan
| | - Hidenori Arai
- National Center for Geriatrics and Gerontology, Obu, Japan
| | - Dai Fujiwara
- Department of Rehabilitation Medicine, Saka General Hospital, Miyagi, Japan
| | - Hideki Katsura
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | | | - Ryo Kozu
- Department of Physical Therapy Science, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Keisuke Maeda
- Department of Geriatric Medicine, Hospital, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Sumito Ogawa
- Department of Geriatric Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Jun Ueki
- Clinical Research Unit of Respiratory Pathophysiology, Graduate School of Health Care and Nursing, Juntendo University, Chiba, Japan
| | - Hidetaka Wakabayashi
- Department of Rehabilitation Medicine, Tokyo Women's Medical University Hospital, Tokyo, Japan
| |
Collapse
|
30
|
Padilha DM, Garcia GR, Liveraro GS, Mendes MC, Takahashi ME, Lascala F, Silveira MN, Pozzuto L, Carrilho LA, Guerra LD, Moreira RC, Branbilla SR, Dertkigil SS, Takahashi J, Carvalheira JB. Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis. INFORMATICS IN MEDICINE UNLOCKED 2023; 36:101138. [PMID: 36474601 PMCID: PMC9715454 DOI: 10.1016/j.imu.2022.101138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/17/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Daniela M.H. Padilha
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Gabriel R. Garcia
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Gianni S.S. Liveraro
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Maria C.S. Mendes
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil,Department of Internal Medicine, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Maria E.S. Takahashi
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - Fabiana Lascala
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Marina N. Silveira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Lara Pozzuto
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Larissa A.O. Carrilho
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Lívia D. Guerra
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Rafaella C.L. Moreira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Sandra R. Branbilla
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Sérgio S.J. Dertkigil
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil
| | - Jun Takahashi
- Institute of Physics “Gleb Wataghin”, University of Campinas, Campinas, SP, Brazil
| | - José B.C. Carvalheira
- Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Campinas, SP, Brazil,Corresponding author. Division of Oncology, Department of Anesthesiology, Oncology and Radiology, School of Medical Sciences, University of Campinas, Rua Vital Brasil, 80, Cidade Universitária, ZIP Code: 13, 083-888, Campinas, SP, Brazil
| |
Collapse
|
31
|
Caccialanza R, Formisano E, Klersy C, Ferretti V, Ferrari A, Demontis S, Mascheroni A, Masi S, Crotti S, Lobascio F, Cerutti N, Orlandoni P, Dalla Costa C, Redaelli E, Fabbri A, Malesci A, Corrao S, Bordandini L, Cereda E. Nutritional parameters associated with prognosis in non-critically ill hospitalized COVID-19 patients: The NUTRI-COVID19 study. Clin Nutr 2022; 41:2980-2987. [PMID: 34353669 PMCID: PMC8233046 DOI: 10.1016/j.clnu.2021.06.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 01/27/2023]
Abstract
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.
Collapse
Affiliation(s)
- Riccardo Caccialanza
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy,Corresponding author. Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Viale Golgi 19, 27100, Pavia, Italy. Fax: +39 0382 502801
| | - Elena Formisano
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Catherine Klersy
- Biometry and Clinical Epidemiology Service, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Virginia Ferretti
- Biometry and Clinical Epidemiology Service, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Alessandra Ferrari
- Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | | | - Annalisa Mascheroni
- Clinical Nutrition and Dietetics Unit - ASST Melegnano e Martesana, Melegnano (Milano), Italy
| | - Sara Masi
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Silvia Crotti
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Federica Lobascio
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Nadia Cerutti
- Medicine and Dietetics Unit, ASST Pavia, Pavia, Italy
| | | | - Cloè Dalla Costa
- Clinical Nutrition Unit, Michele e Pietro Ferrero Hospital, Verduno (Cuneo), Italy
| | - Elena Redaelli
- UOSD Endocrinology and Clinical Nutrition ASST Lecco, Lecco Hospital, Lecco, Italy
| | - Alessandra Fabbri
- Artificial Nutrition Interprofessional Group, Local Health Unit-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Alberto Malesci
- Division of Gastroenterology and Digestive Endoscopy, Department of Gastroenterology, Humanitas Research Hospital, Rozzano, Milano, Italy
| | - Salvatore Corrao
- Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, Italy,COVID Unit, Department of Internal Medicine, National Relevance and High Specialization Hospital Trust ARNAS Civico, Di Cristina, Benfratelli, Palermo, Italy
| | - Lorella Bordandini
- Clinical Nutrition and Dietetic Unit, Department of Internal Medicine, AUSL Della Romagna, S.Maria Delle Croci Hospital, Ravenna, Italy
| | - Emanuele Cereda
- Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - NUTRI-COVID19 Collaborative Working Group1CaccialanzaRiccardooFormisanoElenaoKlersyCatherineoFerrettiVirginiaoFerrariAlessandraoMasiSaraoCrottiSilviaoLobascioFedericaoCeredaEmanueleoBrunoRaffaeleoMontecuccoCarlo MauriziooCorsicoAngelo GuidooBelliatoMirkooDi SabatinoAntoniooLudovisiSerenaoBoglioloLauraoMarianiFrancescaoMuggiaChiaraoCroceGabrieleoBarteselliChiaraoMambellaJacopooDi TerlizziFrancescooCostaCloè DallaPLentaElenaPNigroEmanuelaPMascheroniAnnalisaqMerelliElisaqMisottiAlessandro Mariaqde MonteAndreaqRedaelliElenarLauraLaura IoriorRossiPaolarCeruttiNadiasFavaretoFlaviasPisocriElisasCimorelliManuelasOrlandoniPaolotVenturiniClaudiatFabbriAlessandrauVaccaroSalvatoreuBodecchiSimonauMonzaliElisauMalesciAlbertovCraviottoVincenzovOmodeiPaolo DariovPreatoniPaolettavPastoreManuelavDa RioLeonardovDemontisStefaniawIvaldiCeciliawSferrazzoElsawArietaLorenzinawNattaErikawCorraoSalvatorexBocchioRaffaella MollacixBordandiniLorellayPalmeseFrancescoyGrazianiAlessandroyFondazione IRCCS Policlinico San Matteo, Pavia, ItalyMichele e Pietro Ferrero Hospital, Verduno (Cuneo), ItalyASST Melegnano-Martesana, Melegnano (MI), ItalyASST Lecco, Lecco Hospital, Lecco, ItalyASST Pavia, Pavia, ItalyIRCCS-INRCA, Ancona, ItalyLocal Health Unit-IRCCS of Reggio Emilia, Reggio Emilia, ItalyHumanitas Research Hospital and Humanitas University, Rozzano, Milano, ItalyGiovanni Borea Civil Hospital, Sanremo, ItalyNational Relevance and High Specialization Hospital Trust ARNAS Civico, Di Cristina, Benfratelli, Palermo, ItalyAUSL della Romagna, S.Maria delle Croci Hospital, Ravenna, Italy
| |
Collapse
|
32
|
Messineo L, Fanfulla F, Pedroni L, Pini F, Borghesi A, Golemi S, Vailati G, Kerlin K, Malhotra A, Corda L, Sands S. Breath-holding physiology, radiological severity and adverse outcomes in COVID-19 patients: A prospective validation study. Respirology 2022; 27:1073-1082. [PMID: 35933689 PMCID: PMC9539071 DOI: 10.1111/resp.14336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/18/2022] [Indexed: 12/13/2022]
Abstract
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.
Collapse
Affiliation(s)
- Ludovico Messineo
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Francesco Fanfulla
- Sleep Medicine and Respiratory Function Unit, Maugeri Clinical and Scientific Institutes IRCCS, Pavia, Italy
| | - Leonardo Pedroni
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy
| | - Floriana Pini
- Department of General Medicine, Mellini Hospital, Chiari, Italy
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Salvatore Golemi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Guido Vailati
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy
| | - Kayla Kerlin
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Atul Malhotra
- University of California San Diego, La Jolla, CA, USA
| | - Luciano Corda
- Respiratory Medicine and Sleep Laboratory, Department of Experimental and Clinical Sciences, University of Brescia and Spedali Civili, Brescia, Italy,Department of Internal Medicine, Spedali Civili, Brescia, Italy
| | - Scott Sands
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA,Department of Allergy Immunology and Respiratory Medicine and Central Clinical School, The Alfred and Monash University, Melbourne, Victoria, Australia
| |
Collapse
|
33
|
How the COVID-19 Pandemic Affected Attendance at a Tertiary Orthopedic Center Emergency Department: A Comparison between the First and Second Waves. Diagnostics (Basel) 2022; 12:diagnostics12112855. [PMID: 36428919 PMCID: PMC9689342 DOI: 10.3390/diagnostics12112855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/19/2022] Open
Abstract
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.
Collapse
|
34
|
Albano D, Gitto S, Vitale J, Bernareggi S, Aliprandi A, Sconfienza LM, Messina C. Comparison between magnetic resonance imaging and electrical impedance myography for evaluating lumbar skeletal muscle composition. BMC Musculoskelet Disord 2022; 23:970. [DOI: 10.1186/s12891-022-05902-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/20/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
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.
Collapse
|
35
|
Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity. Sci Rep 2022; 12:16411. [PMID: 36180519 PMCID: PMC9524347 DOI: 10.1038/s41598-022-20419-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/13/2022] [Indexed: 12/03/2022] Open
Abstract
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 (pseverity ≤ 0.001, pmortality ≤ 0.0001) and male patients (pseverity = 0.018, pmortality ≤ 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 (pfemale = 0.014, pmale ≤ 0.0001) and for mortality (pfemale ≤ 0.0001, pmale ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant (pseverity = 0.006, pmortality = 0.002) for both endpoints (ORseverity = 0.42, 95% CIseverity: 0.23–0.78, ORmortality = 0.34, 95% CImortality: 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.
Collapse
|
36
|
Albano D, Gitto S, Vitale J, Bernareggi S, Lamorte S, Aliprandi A, Sconfienza LM, Messina C. Knee Muscles Composition Using Electrical Impedance Myography and Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12092217. [PMID: 36140617 PMCID: PMC9497744 DOI: 10.3390/diagnostics12092217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/06/2022] [Accepted: 09/10/2022] [Indexed: 11/29/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy
- Correspondence: ; Tel.: +39-333-240-1189
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, 20122 Milan, Italy
| | - Jacopo Vitale
- IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy
| | | | - Sveva Lamorte
- Scuola di Specializzazione in Radiodiagnostica, Università di Parma, 43121 Parma, Italy
| | - Alberto Aliprandi
- Unità Operativa di Radiologia, Istituti Clinici Zucchi, 20052 Monza, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, 20122 Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, 20161 Milan, Italy
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, 20122 Milan, Italy
| |
Collapse
|
37
|
Barrea L, Vetrani C, Caprio M, Cataldi M, Ghoch ME, Elce A, Camajani E, Verde L, Savastano S, Colao A, Muscogiuri G. From the Ketogenic Diet to the Mediterranean Diet: The Potential Dietary Therapy in Patients with Obesity after CoVID-19 Infection (Post CoVID Syndrome). Curr Obes Rep 2022; 11:144-165. [PMID: 35524067 PMCID: PMC9075143 DOI: 10.1007/s13679-022-00475-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 01/08/2023]
Abstract
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.
Collapse
Affiliation(s)
- Luigi Barrea
- Dipartimento Di Scienze Umanistiche, Centro Direzionale, Università Telematica Pegaso, Via Porzio, isola F2, 80143, Napoli, Italy.
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy.
| | - Claudia Vetrani
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Dipartimento Di Medicina Clinica E Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
| | - Massimiliano Caprio
- Laboratory of Cardiovascular Endocrinology, IRCCS San Raffaele Roma, 00166, Rome, Italy
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166, Rome, Italy
| | - Mauro Cataldi
- Department of Neuroscience, Reproductive Medicine and Dentistry, Section of Pharmacology, Medical School of Naples, Federico II University, 80131, Naples, Italy
| | - Marwan El Ghoch
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Beirut Arab University, P.O. Box 11-5020, Riad El Solh, Beirut, 11072809, Lebanon
| | - Ausilia Elce
- Dipartimento Di Scienze Umanistiche, Centro Direzionale, Università Telematica Pegaso, Via Porzio, isola F2, 80143, Napoli, Italy
| | - Elisabetta Camajani
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166, Rome, Italy
- PhD Programme in Endocrinological Sciences, Sapienza University of Rome, 00161, Rome, Italy
| | - Ludovica Verde
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Dipartimento Di Medicina Clinica E Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
| | - Silvia Savastano
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Dipartimento Di Medicina Clinica E Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
| | - Annamaria Colao
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Dipartimento Di Medicina Clinica E Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Cattedra Unesco "Educazione Alla Salute E Allo Sviluppo Sostenibile", University Federico II, Naples, Italy
| | - Giovanna Muscogiuri
- Department of Clinical Medicine and Surgery, Endocrinology Unit, Centro Italiano Per La Cura E Il Benessere del Paziente Con Obesità (C.I.B.O), University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Dipartimento Di Medicina Clinica E Chirurgia, Unit of Endocrinology, Federico II University Medical School of Naples, Via Sergio Pansini 5, 80131, Naples, Italy
- Cattedra Unesco "Educazione Alla Salute E Allo Sviluppo Sostenibile", University Federico II, Naples, Italy
| |
Collapse
|
38
|
Ying-Hao P, Hai-Dong Z, Yuan F, Yong-Kang L, Sen L, Wei-Long X, Yu-Shan Y, Jun-Feng Z, Hai-Qi Z, Hua J. Correlation of CT-derived pectoralis muscle status and COVID-19 induced lung injury in elderly patients. BMC Med Imaging 2022; 22:144. [PMID: 35962312 PMCID: PMC9372984 DOI: 10.1186/s12880-022-00872-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/26/2022] [Indexed: 12/15/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Pei Ying-Hao
- Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhang Hai-Dong
- Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Fang Yuan
- Department of Geriatrics, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Liu Yong-Kang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Liang Sen
- Department of Neurology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Xu Wei-Long
- Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Yang Yu-Shan
- First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhu Jun-Feng
- First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
| | - Zhou Hai-Qi
- Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
| | - Jiang Hua
- Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
| |
Collapse
|
39
|
do Amaral e Castro A, Yokoo P, Fonseca EKUN, Otoni JC, Haiek SL, Shoji H, Chate RC, Pereira AZ, Queiroz MRGD, Batista MC, Szarf G. Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study. EINSTEIN-SAO PAULO 2022; 20:eAO6953. [PMID: 35649055 PMCID: PMC9126606 DOI: 10.31744/einstein_journal/2022ao6953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/16/2021] [Indexed: 11/15/2022] Open
Abstract
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.
Collapse
|
40
|
Nutritional Risk Assessment Scores Effectively Predict Mortality in Critically Ill Patients with Severe COVID-19. Nutrients 2022; 14:nu14102105. [PMID: 35631246 PMCID: PMC9144143 DOI: 10.3390/nu14102105] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 12/23/2022] Open
Abstract
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.
Collapse
|
41
|
Frailty and senile apathy in the everyday clinical practice in the conditions of COVID-19. КЛИНИЧЕСКАЯ ПРАКТИКА 2022. [DOI: 10.17816/clinpract104831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
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.
Collapse
|
42
|
Schiaffino S. Editorial comment to artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis. Eur J Radiol 2022; 151:110292. [PMID: 35397407 DOI: 10.1016/j.ejrad.2022.110292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 03/31/2022] [Indexed: 11/03/2022]
Affiliation(s)
- Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy.
| |
Collapse
|
43
|
Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022; 29:243-262. [PMID: 35048222 PMCID: PMC8769787 DOI: 10.1007/s10140-021-02008-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/03/2021] [Indexed: 01/08/2023]
Abstract
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.
Collapse
Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Dara Joseph Lundon
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| |
Collapse
|
44
|
Brandi N, Ciccarese F, Rimondi MR, Balacchi C, Modolon C, Sportoletti C, Renzulli M, Coppola F, Golfieri R. An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review. Diagnostics (Basel) 2022; 12:846. [PMID: 35453894 PMCID: PMC9032937 DOI: 10.3390/diagnostics12040846] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 01/08/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Nicolò Brandi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Federica Ciccarese
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Maria Rita Rimondi
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Caterina Balacchi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Cecilia Modolon
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Camilla Sportoletti
- Cardio-Thoracic Radiology Unit, University Hospital S.Orsola-Malpighi, 40138 Bologna, Italy; (M.R.R.); (C.M.); (C.S.)
| | - Matteo Renzulli
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Albertoni 15, 40138 Bologna, Italy; (F.C.); (C.B.); (M.R.); (F.C.); (R.G.)
| |
Collapse
|
45
|
Camajani E, Feraco A, Basciani S, Gnessi L, Barrea L, Armani A, Caprio M. VLCKD in Combination with Physical Exercise Preserves Skeletal Muscle Mass in Sarcopenic Obesity after Severe COVID-19 Disease: A Case Report. Healthcare (Basel) 2022; 10:healthcare10030573. [PMID: 35327051 PMCID: PMC8950622 DOI: 10.3390/healthcare10030573] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/25/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Elisabetta Camajani
- PhD Program in Endocrinological Sciences, University of Rome “La Sapienza”, 00161 Rome, Italy;
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy; (A.F.); (A.A.)
| | - Alessandra Feraco
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy; (A.F.); (A.A.)
- Laboratory of Cardiovascular Endocrinology, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Sabrina Basciani
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (S.B.); (L.G.)
| | - Lucio Gnessi
- Department of Experimental Medicine, University of Rome “La Sapienza”, 00161 Rome, Italy; (S.B.); (L.G.)
| | - Luigi Barrea
- Endocrinology Unit, Department of Clinical Medicine and Surgery, University Federico II, 80131 Naples, Italy;
| | - Andrea Armani
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy; (A.F.); (A.A.)
- Laboratory of Cardiovascular Endocrinology, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Massimiliano Caprio
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy; (A.F.); (A.A.)
- Laboratory of Cardiovascular Endocrinology, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Correspondence:
| |
Collapse
|
46
|
Chest CT-Derived Muscle Analysis in COVID-19 Patients. Tomography 2022; 8:414-422. [PMID: 35202199 PMCID: PMC8878548 DOI: 10.3390/tomography8010034] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/16/2022] [Accepted: 01/29/2022] [Indexed: 11/24/2022] Open
Abstract
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.
Collapse
|
47
|
Di Meglio L, Carriero S, Biondetti P, Wood BJ, Carrafiello G. Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019. Curr Opin Crit Care 2022; 28:17-24. [PMID: 34864792 PMCID: PMC8711303 DOI: 10.1097/mcc.0000000000000906] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
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.
Collapse
Affiliation(s)
- Letizia Di Meglio
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano
| | - Serena Carriero
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano
| | - Pierpaolo Biondetti
- Operative Unit of Radiology, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Bradford J. Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Gianpaolo Carrafiello
- Operative Unit of Radiology, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| |
Collapse
|
48
|
Siahaan YMT, Hartoyo V, Hariyanto TI, Kurniawan A. Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: A Meta-analysis and Meta-Regression. Clin Nutr ESPEN 2022; 48:158-166. [PMID: 35331486 PMCID: PMC8785332 DOI: 10.1016/j.clnesp.2022.01.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 11/18/2022]
Abstract
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).
Collapse
Affiliation(s)
- Yusak Mangara Tua Siahaan
- Department of Neurology, Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang, 15811, Indonesia
| | - Vinson Hartoyo
- Department of Neurology, Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang, 15811, Indonesia
| | | | - Andree Kurniawan
- Department of Internal Medicine, Faculty of Medicine, Pelita Harapan University, Karawaci, Tangerang, 15811, Indonesia.
| |
Collapse
|
49
|
Chianca V, Albano D, Messina C, Gitto S, Ruffo G, Guarino S, Del Grande F, Sconfienza LM. Sarcopenia: imaging assessment and clinical application. Abdom Radiol (NY) 2022; 47:3205-3216. [PMID: 34687326 PMCID: PMC8536908 DOI: 10.1007/s00261-021-03294-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
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.
Collapse
Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland ,Ospedale Evangelico Betania, Napoli, Italy
| | - Domenico Albano
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy ,grid.10776.370000 0004 1762 5517Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | - Carmelo Messina
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Salvatore Gitto
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy
| | - Gaetano Ruffo
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | | | | | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milano, Italy. .,Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano, Italy.
| |
Collapse
|
50
|
Meyer HJ, Wienke A, Surov A. Extrapulmonary CT Findings Predict In-Hospital Mortality in COVID-19. A Systematic Review and Meta-Analysis. Acad Radiol 2022; 29:17-30. [PMID: 34772618 PMCID: PMC8516661 DOI: 10.1016/j.acra.2021.10.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/15/2022]
Abstract
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.
Collapse
Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
| |
Collapse
|