CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19
2023; Elsevier BV; Volume: 164; Issue: 2 Linguagem: Inglês
10.1016/j.chest.2023.02.048
ISSN1931-3543
AutoresS.I.J. van Bakel, Hester A. Gietema, Patricia M. Stassen, Harry R. Gosker, Debbie Gach, Joop P. van den Bergh, Frits van Osch, Annemie M.W.J. Schols, Rosanne J.H.C.G. Beijers,
Tópico(s)COVID-19 and healthcare impacts
ResumoBackgroundCOVID-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 QuestionAre 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 MethodsThis 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.ResultsData 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).InterpretationCT 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. 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. Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score? 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. 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). 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. FOR EDITORIAL COMMENT, SEE PAGE 269Take-home PointsStudy Question: Are CT scan-derived muscle and adipose tissue cross-sectional areas (CSAs) associated with 30-day in-hospital mortality in patients with COVID-19, independent of 4C Mortality Score?Results: In multivariate analyses, low pectoralis muscle CSA was associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score.Interpretation: Low CT scan-derived pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score. FOR EDITORIAL COMMENT, SEE PAGE 269 Study Question: Are CT scan-derived muscle and adipose tissue cross-sectional areas (CSAs) associated with 30-day in-hospital mortality in patients with COVID-19, independent of 4C Mortality Score? Results: In multivariate analyses, low pectoralis muscle CSA was associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score. Interpretation: Low CT scan-derived pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score. COVID-19 caused by the SARS-CoV-2 presents with a highly variable disease course varying from asymptomatic disease to severe illness requiring hospitalization, ICU admission, mechanical ventilation, and eventually death.1Chen S. Sun H. Heng M. et al.Factors predicting progression to severe COVID-19: a competing risk survival analysis of 1753 patients in community isolation in Wuhan, China.Engineering (Beijing). 2022; 13: 99-106PubMed Google Scholar,2Adjei S. Hong K. Molinari N.M. et al.Mortality risk among patients hospitalized primarily for COVID-19 during the omicron and delta variant pandemic periods—United States, April 2020-June 2022.MMWR Morb Mortal Wkly Rep. 2022; 71: 1182-1189Crossref PubMed Scopus (50) Google Scholar However, the high prevalence of SARS-CoV-2 infections resulted in very high absolute numbers of severely ill patients requiring hospitalization and high mortality rates of up to 20% to 25% in several European regions, putting a high burden on hospitals and health-care systems worldwide.3Nijman G. Wientjes M. Ramjith J. et al.Risk factors for in-hospital mortality in laboratory-confirmed COVID-19 patients in the Netherlands: a competing risk survival analysis.PLoS One. 2021; 16e0249231Crossref Scopus (14) Google Scholar,4Giorgi Rossi P. Marino M. Formisano D. et al.Characteristics and outcomes of a cohort of COVID-19 patients in the province of Reggio Emilia, Italy.PLoS One. 2020; 15e0238281Crossref PubMed Scopus (96) Google Scholar Early diagnosis of COVID-19 and identification of patients at high risk for severe illness and mortality are essential for adequate clinical decision-making and managing the large numbers of severely ill patients. For this purpose, chest CT scan imaging was found useful from a very early stage in the pandemic onward.5Simpson S. Kay F.U. Abbara S. et al.Radiological Society of North America Expert Consensus Statement on reporting chest CT findings related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA—Secondary Publication.J Thorac Imaging. 2020; 35: 219-227Crossref PubMed Scopus (429) Google Scholar, 6Bai Y. Yao L. Wei T. et al.Presumed asymptomatic carrier transmission of COVID-19.JAMA. 2020; 323: 1406-1407Crossref PubMed Scopus (2950) Google Scholar, 7Prokop M. van Everdingen W. van Rees Vellinga T. et al.CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19—definition and evaluation.Radiology. 2020; 296: E97-E104Crossref PubMed Scopus (528) Google Scholar, 8Schalekamp S. Bleeker-Rovers C.P. Beenen L.F.M. et al.Chest CT in the emergency department for diagnosis of COVID-19 pneumonia: Dutch experience.Radiology. 2021; 298: E98-E106Crossref PubMed Google Scholar Based on systematic classification of intrapulmonary abnormalities, these chest CT scans can provide a likelihood of COVID-19 with high diagnostic accuracy.7Prokop M. van Everdingen W. van Rees Vellinga T. et al.CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19—definition and evaluation.Radiology. 2020; 296: E97-E104Crossref PubMed Scopus (528) Google Scholar, 8Schalekamp S. Bleeker-Rovers C.P. Beenen L.F.M. et al.Chest CT in the emergency department for diagnosis of COVID-19 pneumonia: Dutch experience.Radiology. 2021; 298: E98-E106Crossref PubMed Google Scholar, 9Liu G. Chen Y. Runa A. Liu J. Diagnostic performance of CO-RADS for COVID-19: a systematic review and meta-analysis.Eur Radiol. 2022; 32: 4414-4426Crossref PubMed Scopus (5) Google Scholar, 10Turcato G. Zaboli A. Panebianco L. et al.Clinical application of the COVID-19 Reporting and Data System (CO-RADS) in patients with suspected SARS-CoV-2 infection: observational study in an emergency department.Clin Radiol. 2021; 76: 74 e23-74 e29Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar Next to the pulmonary abnormalities, CT scans contain relevant information on muscle and adipose tissue mass and distribution.11Shen W. Punyanitya M. Wang Z. et al.Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image.J Appl Physiol (1985). 2004; 97: 2333-2338Crossref PubMed Scopus (1155) Google Scholar Quantification of muscle cross-sectional area (CSA) at the level of the third lumbar vertebra is considered the reference for estimating whole body muscle mass.11Shen W. Punyanitya M. Wang Z. et al.Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image.J Appl Physiol (1985). 2004; 97: 2333-2338Crossref PubMed Scopus (1155) Google Scholar,12Tolonen A. Pakarinen T. Sassi A. et al.Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review.Eur J Radiol. 2021; 145109943Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar However, analyses at higher vertebral levels available on chest CT scan images, for example, at the level of the first lumbar vertebra or the pectoralis muscle, also recently were validated for assessment of clinically relevant muscle mass.12Tolonen A. Pakarinen T. Sassi A. et al.Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review.Eur J Radiol. 2021; 145109943Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar,13Sanders K.J.C. Degens J. Dingemans A.C. Schols A. Cross-sectional and longitudinal assessment of muscle from regular chest computed tomography scans: L1 and pectoralis muscle compared to L3 as reference in non-small cell lung cancer.Int J Chron Obstruct Pulmon Dis. 2019; 14: 781-789Crossref PubMed Scopus (14) Google Scholar Additionally, the levels of first and third lumbar vertebrae appeared to be comparable for assessment of adipose tissue mass and distribution.12Tolonen A. Pakarinen T. Sassi A. et al.Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review.Eur J Radiol. 2021; 145109943Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar Therefore, chest CT scans obtained for the diagnosis and assessment of severity of pulmonary involvement also can be used to gain insight into body composition of these patients. Multiple authors have investigated the possible prognostic value of CT scan-derived body composition parameters on COVID-19 outcomes.14Siahaan Y.M.T. Hartoyo V. Hariyanto T.I. Kurniawan A. Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: a meta-analysis and meta-regression.Clin Nutr ESPEN. 2022; 48: 158-166Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar, 15Pranata R. Lim M.A. Huang I. et al.Visceral adiposity, subcutaneous adiposity, and severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis.Clin Nutr ESPEN. 2021; 43: 163-168Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar, 16Foldi M. Farkas N. Kiss S. et al.Visceral adiposity elevates the risk of critical condition in COVID-19: a systematic review and meta-analysis.Obesity (Silver Spring). 2021; 29: 521-528Crossref PubMed Scopus (46) Google Scholar Methodology and exact anatomic levels at which these parameters were quantified varied among studies. Still, meta-analyses showed that low skeletal muscle mass predicts short-term mortality and high visceral adipose tissue (VAT), but not subcutaneous adipose tissue (SAT), is associated with more severe disease in patients with COVID-19.14Siahaan Y.M.T. Hartoyo V. Hariyanto T.I. Kurniawan A. Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: a meta-analysis and meta-regression.Clin Nutr ESPEN. 2022; 48: 158-166Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,15Pranata R. Lim M.A. Huang I. et al.Visceral adiposity, subcutaneous adiposity, and severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis.Clin Nutr ESPEN. 2021; 43: 163-168Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar Currently, clinical decision-making in the management of patients with COVID-19 is based on vital parameters and blood analyses that are available rapidly and commonly in the ED. With these parameters, a multitude of models predicting adverse outcomes in COVID-19 have been developed. Based on a living systematic review, the 4C Mortality Score was developed and validated by Knight et al17Knight S.R. Ho A. Pius R. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370: m3339Crossref PubMed Scopus (605) Google Scholar in derivation and validation cohorts of 35,463 and 22,361 patients, respectively, and has been identified as the most extensively validated and best model to predict in-hospital mortality in COVID-19 after 30 days.17Knight S.R. Ho A. Pius R. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370: m3339Crossref PubMed Scopus (605) Google Scholar, 18Wynants L. Van Calster B. Collins G.S. et al.Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.BMJ. 2020; 369: m1328Crossref PubMed Scopus (1745) Google Scholar, 19BMJ. Update to living systematic review on prediction models for diagnosis and prognosis of covid-19.BMJ. 2021; 372: n236PubMed Google Scholar The 4C Mortality Score is a risk stratification score based on the following highly predictive clinical items; sex, age, number of comorbidities, vital signs, blood urea level, and C-reactive protein (CRP) level. Essentially, it combines information on the acute clinical state of the patient (eg, vital signs, blood urea level, and CRP level) and the preexisting condition of the patient (eg, age and number of comorbidities). Because the CT scan-derived body composition parameters inherently are associated with clinical parameters commonly used to reflect patients' preexisting conditions, it can be questioned whether CT scan-derived body composition is associated with mortality independent of the 4C Mortality Score. Therefore, this study aimed to evaluate the association of CT scan-derived body composition parameters independent of a validated set of predictive clinical parameters on 30-day in-hospital mortality in patients with COVID-19. This was a retrospective, multicenter cohort analysis of patients with COVID-19 from the Maastricht University Medical Centre+ (MUMC+) and the VieCuri Medical Centre in the province of Limburg, The Netherlands. Both cohorts consisted of consecutive adult patients who sought treatment at the ED of the concerning hospital with a primary clinical suspicion of COVID-19 during the first wave of the pandemic and underwent chest CT scan imaging at presentation. All cases of COVID-19 either were confirmed by reverse-transcription polymerase chain reaction testing or had a high clinical likelihood in combination with a COVID-19 Reporting and Data System (CO-RADS) score of ≥ 4 (eg, a high likelihood based on CT scan abnormalities) and no alternative diagnosis.7Prokop M. van Everdingen W. van Rees Vellinga T. et al.CO-RADS: a categorical CT assessment scheme for patients suspected of having COVID-19—definition and evaluation.Radiology. 2020; 296: E97-E104Crossref PubMed Scopus (528) Google Scholar The MUMC+ cohort consisted of both hospitalized patients and patients who presented at the ED, but were not admitted, whereas the VieCuri cohort consisted of only hospitalized patients. Mortality within 30 days was checked systematically for all patients, regardless of hospitalization status. Because of the retrospective nature of the study, the medical ethics committee of MUMC+ waived ethical approval for this study (Identifier: METC 2020-2230), and therefore, no informed consent was required. Additionally, on discharge from the hospital, patients were informed about the possible use of their (anonymized) data for research purposes. In case patients objected to this, they were excluded from the database. Skeletal muscle and adipose tissue parameters were retrieved from routinely obtained chest CT scans at ED presentation or admission. All CT scans were obtained without the application of IV contrast. Total cross-sectional area (CSA) of the pectoralis major and minor muscles was measured bilaterally at the level of the fourth thoracic vertebra. Additionally, CSA of skeletal muscle, VAT, and SAT was demarcated at the level of the first lumbar vertebra (L1). The muscles analyzed at the L1 level included the psoas, erector, spinae, quadratus lumborum, transversus abdominis, external and internal oblique, and rectus abdominis. At both levels, following previously described methods, a single transverse image at the most cranial slide with both vertebral transverse processes clearly visible was used (Fig 1).12Tolonen A. Pakarinen T. Sassi A. et al.Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review.Eur J Radiol. 2021; 145109943Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar,13Sanders K.J.C. Degens J. Dingemans A.C. Schols A. Cross-sectional and longitudinal assessment of muscle from regular chest computed tomography scans: L1 and pectoralis muscle compared to L3 as reference in non-small cell lung cancer.Int J Chron Obstruct Pulmon Dis. 2019; 14: 781-789Crossref PubMed Scopus (14) Google Scholar If the selected image was of poor quality, had artefacts, or did not fully depict tissue of interest, the specific slice or missing tissue was considered as a missing value and was not analyzed. CSA of these structures were quantified by one trained assessor, blinded to clinical outcomes, based on pre-established Hounsfield units (HU) thresholds (skeletal muscle, –29 to 150 HU; SAT, –190 to –30 HU; and VAT, –150 to –50 HU).20Irving B.A. Weltman J.Y. Brock D.W. Davis C.K. Gaesser G.A. Weltman A. NIH ImageJ and Slice-O-Matic computed tomography imaging software to quantify soft tissue.Obesity (Silver Spring). 2007; 15: 370-376Crossref PubMed Scopus (111) Google Scholar,21Mourtzakis M. Prado C.M. Lieffers J.R. Reiman T. McCargar L.J. Baracos V.E. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care.Appl Physiol Nutr Metab. 2008; 33: 997-1006Crossref PubMed Scopus (1441) Google Scholar Boundaries were corrected manually when necessary. All analyses were performed with Slice-O-Matic software version 5.0 (Tomovision). Patient demographics (age, sex, body height, and weight), clinical observations (number of comorbidities, respiratory rate, peripheral oxygen saturation on room air, and Glasgow coma scale score), blood parameters (urea and CRP levels), and information on disease course (ICU or medium care unit admission, mechanical ventilation) were collected retrospectively from the electronic medical records in both institutions. The eight parameters of the 4C Mortality Score were categorized and scored, resulting in a total 4C Mortality Score ranging from 0 to 21.17Knight S.R. Ho A. Pius R. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370: m3339Crossref PubMed Scopus (605) Google Scholar Additionally, date of presentation at the ED, date of CT scan imaging, date of discharge, and (if applicable) date of death also were retrieved from the medical records. Baseline clinical variables of individuals who died in hospital within 30 days and of survivors were compared using the χ2 test for categorical variables and the nonparametric Mann-Whitney U test for the continuous CT scan-derived variables with skewed distributions. Data are presented as numbers and percentages for categorical variables and medians and interquartile ranges (IQRs) for continuous variables. Missing values of individual components of the 4C Mortality Score were replaced using multiple imputation if values were missing in < 20% of patients. In case more than two components of the 4C Mortality Score were missing, no imputation was performed. Receiver operating characteristic curve analysis was performed to check the area under the receiver operating characteristic curve (AUC) of the 4C Mortality Score. Univariate Cox proportional hazard regression models were applied to assess the association of independent CT scan-derived skeletal muscle mass and adipose tissue CSA with 30-day in-hospital mortality. All parameters with a P value of ≤ .1 were considered for inclusion in a multivariate model, adjusted for the 4C Mortality Score, with a forward selection likelihood ratio approach. Multivariate Cox regression analyses then were applied to assess the association of the CT scan-derived parameters adjusted for the 4C mortality score with 30-day in-hospital mortality. Results of the Cox regressions are presented as hazard ratios (HRs) with 95% CIs. Because the 4C Mortality Score was validated specifically for hospitalized patients, sensitivity analyses were performed investigating the association between CT scan-derived parameters and 30-day in-hospital mortality in hospitalized patients as well as the association with 30-day overall mortality in all patients. Subsequently, potential interactions between CT scan-derived parameters and individual components of the 4C Mortality Score were evaluated using nonparametric Kruskal-Wallis tests. This allowed the construction of categorical variables using age- and sex-specific cutoffs based on the cohort's IQRs, which were added to an adjusted 4C Mortality Score. Finally, a comparative receiver operating characteristic curve analysis was performed to quantify the added value of CT scan-derived parameters to the 4C Mortality Score. All statistical analyses were performed using SPSS statistical software (SPSS Statistics for Windows version 27.0; IBM). A P value of ≤ .05 was considered statistically significant. Data from 587 patients were analyzed, including 374 patients from the MUMC+ cohort and 213 patients from the VieCuri cohort (Fig 2), with an admission rate of 82.5% (484 patients). Missing values for urea level (2.7%) and CRP level (0.3%) were imputed. Most patients were elderly men, with most being overweight or obese (Table 1). Within 30 days, 107 patients (18.2%) died in hospital with a median time to death of 6 days (IQR, 3-11 days). An additional 13 patients (2.2%) died outside of the hospital within 30 days. Deceased patients had significantly more comorbidities and scored significantly worse on all clinical and blood parameters of the 4C Mortality Score compared with survivors. In univariate Cox regression analysis, the 4C Mortality Score significantly predicted 30-day in-hospital mortality (HR, 4.6; 95% CI, 3.4-6.3; P < .001). The AUC for the 4C Mortality Score was 0.806 (95% CI, 0.765-0.848; P < .001).Table 1General CharacteristicsVariableIn-Hospital Survival (> 30 d; n = 480 [81.8%])In-Hospital Death (≤ 30 d; n = 107 [18.2%])P ValueAge, y< .001 < 5049 (10.2)0 (0) 50-6097 (20.2)5 (4.7) 60-70137 (28.5)7 (6.5) 70-80131 (27.3)48 (44.9) > 8066 (13.8)47 (43.9)Male sex302 (62.9)80 (74.8).020BMI, kg/m2.036 < 204 (1.0)5 (5.2) 20-25112 (28.4)26 (26.8) 26-30157 (39.8)42 (43.3) > 30121 (30.7)24 (24.7)No. of comorbidities< .001 0139 (29.0)9 (8.4) 1105 (21.9)17 (15.9) ≥ 2236 (49.2)81 (75.7)SpO2 < 92%145 (30.2)53 (49.5)< .001Respiratory rate, breaths/min.001 < 20216 (45.0)28 (26.2) 20-30202 (42.1)56 (52.3) > 3062 (12.9)23 (21.5)Glasgow coma scale < 1542 (8.8)22 (20.6)< .001Blood urea, mmol/L< .001 < 7306 (63.7)34 (31.8) 7-14132 (27.5)49 (45.8) > 1442 (8.8)24 (22.4)Blood CRP, mg/L.034 < 50170 (36.5)29 (27.1) 50-100137 (28.8)25 (23.4) > 100173 (34.8)53 (49.5)ICU admission74 (15.7)31 (29.0).001MC admission35 (7.1)10 (9.3).470Mechanical ventilation80 (18.8)39 (38.6)< .001Data are presented as No. (%), unless otherwise indicated. Boldface indicates a P value with statistical significance. CRP = C-reactive protein; MC; medium care, min; minute, SpO2 = peripheral oxygen saturation on room air. Open table in a new tab Data are presented as No. (%), unless otherwise indicated. Boldface indicates a P value with statistical significance. CRP = C-reactive protein; MC; medium care, min; minute, SpO2 = peripheral oxygen saturation on room air. Within the total cohort of 587 patients, pectoralis muscle CSA could be determined on 571 scans (97.3%), whereas at the L1 level, VAT CSA could be analyzed on 307 scans (52.3%), muscle CSA on 283 scans (48.2%), and SAT CSA on 201 scans (34.2%) (Fig 2). Patients who died within 30 days showed a significantly lower pectoralis muscle CSA (P = .002) and tended to have a lower L1 muscle CSA (P = .087) compared with survivors. Additionally, their VAT, but not SAT CSA was significantly higher (P = .013 and P = .983, respectively) (Table 2). Pectoralis muscle CSA was significantly higher in men (median, 38.8 cm2; IQR, 32.0-46.2) compared with that in women (median, 26.5 cm2; IQR, 22.1-31.7 cm2; P < .001) and decreased with increasing age (P < .001 for trend).Table 2CT Scan-Derived Body Composition ParametersVariableIn-Hospital Survival (> 30 d)In-Hospital Death (≤ 30 d)P ValuePectoralis muscle, cm2 No.467104 Median (IQR)35.4 (27.2-44.2)32.6 (24.3-38.8).002L1 muscle, cm2 No.23251 Median (IQR)88.1 (72.1-108.6)85.7 (66.4-103.2).087L1 VAT, cm2 No.25156 Median (IQR)112.9 (63.7-174.1)151.1 (93.6-219.7).013L1 SAT, cm2 No.15942 Median (IQR)98.1 (64.4-146.0)104.5 (71.1-130.4).983Data are presented as median (interquartile range), unless otherwise indicated. Boldface indicates a P value with statistical significance. IQR = interquartile range; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue. Open table in a new tab Data are presented as median (interquartile range), unless otherwise indicated. Boldface indicates a P value with statistical significance. IQR = interquartile range; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue. Univariate Cox proportional hazards regression analyses demonstrated that pectoralis muscle CSA, L1 muscle CSA, and L1 VAT CSA were associated with 30-day in-hospital mortality (pectoralis muscle CSA: HR, 0.97 [95% CI, 0.95-0.99]; L1 muscle CSA: HR, 0.99 [95% CI, 0.97-1.00]; and L1 VAT CSA: HR, 1.00 [95% CI, 1.00-1.01]) (Table 3, model 1). In the multivariate Cox regression model adjusted for the 4C Mortality Score, pectoralis muscle CSA remained associated significantly with 30-day in-hospital mortality (4C Mortality Score: HR, 1.4 [95% CI, 1.3-1.4]; pectoralis muscle CSA: HR, 0.98 [95% CI, 0.96-1.00]) (Table 3, model 2). The sensitivity analyses (e-Tables 1-4) similarly demonstrated that pectoralis muscle CSA was associated significantly with 30-day in-hospital mortality as well as with 30-day overall mortality.Table 3CT Scan-Derived Body Composition Values as Predictors for 30-Day In-Hospital MortalityVariableModel 1Model 2HR (95% CI)P ValueHR (95% CI)P ValuePectoralis muscle, cm20.97 (0.95-0.99).0020.98 (0.96-1.00).038L1 muscle, cm20.99 (0.97-1.00).0560.99 (0.98-1.00).080L1 VAT, cm21.00 (1.00-1.01).0031.00 (1.00-1.00).451L1 SAT, cm21.00 (0.99-1.00).643……Univariate Cox proportional hazards regression analysis (model 1) and multivariate analysis adjusted for 4C Mortality Score (model 2). Boldface indicates a P value with statistical significance. HR = hazard ratio; L1 = first lumbar vertebra; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue. Open table in a new tab Univariate Cox proportional hazards regression analysis (model 1) and multivariate analysis adjusted for 4C Mortality Score (model 2). Boldface indicates a P value with statistical significance. HR = hazard ratio; L1 = first lumbar vertebra; SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue. Explorative analyses allowed for an adjusted 4C Mortality Score to be constructed using age- and sex-specific quartiles for pectoralis muscle CSA. After inspecting the HRs (e-Table 5), patients with pectoralis muscle CSA less than the 25th percentile were appointed two additional points. This adjusted 4C Mortality Score (range, 0-24 points) had an AUC of 0.808 (95% CI, 0.766-0.851), which was not significantly different from the initial 4C Mortality Score (AUC, 0.806; 95% CI, 0.765-0.848; P = .750). This large retrospective, multicenter cohort analysis demonstrated that CT scan-derived low pectoralis muscle CSA, high VAT CSA, and low muscle CSA at the L1 level were associated with higher in-hospital 30-day mortality in patients with COVID-19. Additionally, in a multivariate analysis, pectoralis muscle CSA was associated significantly with in-hospital 30-day mortality, independent of the 4C Mortality Score. These muscle-related findings are in line with existing literature, where multiple studies have demonstrated that low CT scan-derived muscle mass, assessed at different anatomic levels, significantly predict mortality and worse clinical outcome in patients with COVID-19.14Siahaan Y.M.T. Hartoyo V. Hariyanto T.I. Kurniawan A. Coronavirus disease 2019 (Covid-19) outcomes in patients with sarcopenia: a meta-analysis and meta-regression.Clin Nutr ESPEN. 2022; 48: 158-166Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar,22Damanti S. Cristel G. 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Kang W. et al.Association of paraspinal muscle measurements on chest computed tomography with clinical outcomes in patients with severe coronavirus disease 2019.J Gerontol A Biol Sci Med Sci. 2021; 76: e78-e84Crossref PubMed Scopus (13) Google Scholar Our data also demonstrated a significant association of high VAT with 30-day in-hospital mortality; however, with an HR of 1.00, the clinical relevance of this statistically significant association should be questioned. Additionally, when adjusted for the 4C Mortality Score, VAT was not associated significantly with mortality. This is in line with recent meta-analyses showing a significant positive association between VAT as well as obesity and COVID-19 severity, but not mortality.15Pranata R. Lim M.A. Huang I. et al.Visceral adiposity, subcutaneous adiposity, and severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis.Clin Nutr ESPEN. 2021; 43: 163-168Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar,27Abumweis S. Alrefai W. Alzoughool F. Association of obesity with COVID-19 diseases severity and mortality: a meta-analysis of studies.Obes Med. 2022; 33100431Google Scholar The predictive components of the 4C Mortality Score demonstrated that an older age, male sex, and having multiple comorbidities increased the risk of mortality in patients with COVID-19. The fact that low pectoralis CSA remains significantly associated with in-hospital 30-day mortality when adjusting for the 4C Mortality Score therefore is an important finding in this study. Pectoralis muscle CSA has been shown to be associated with third lumbar vertebra muscle CSA, which is linearly related to whole-body muscle mass assessed via MRI.11Shen W. Punyanitya M. Wang Z. et al.Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image.J Appl Physiol (1985). 2004; 97: 2333-2338Crossref PubMed Scopus (1155) Google Scholar,13Sanders K.J.C. Degens J. Dingemans A.C. Schols A. Cross-sectional and longitudinal assessment of muscle from regular chest computed tomography scans: L1 and pectoralis muscle compared to L3 as reference in non-small cell lung cancer.Int J Chron Obstruct Pulmon Dis. 2019; 14: 781-789Crossref PubMed Scopus (14) Google Scholar Low total muscle mass is indicative for sarcopenia and is well known to be associated with mortality.28Beaudart C. Zaaria M. Pasleau F. Reginster J.Y. Bruyere O. Health outcomes of sarcopenia: a systematic review and meta-analysis.PLoS One. 2017; 12e0169548Crossref PubMed Scopus (599) Google Scholar, 29Zhang X.M. Chen D. Xie X.H. Zhang J.E. Zeng Y. Cheng A.S. Sarcopenia as a predictor of mortality among the critically ill in an intensive care unit: a systematic review and meta-analysis.BMC Geriatr. 2021; 21: 339Crossref PubMed Scopus (30) Google Scholar, 30Kelley G.A. Kelley K.S. Is sarcopenia associated with an increased risk of all-cause mortality and functional disability?.Exp Gerontol. 2017; 96: 100-103Crossref PubMed Scopus (49) Google Scholar Especially elderly people with comorbidities are more prone to sarcopenia, and as such have a higher risk for mortality.31Dionyssiotis Y. Sarcopenia in the elderly.Eur Endocrinol. 2019; 15: 13-14Crossref PubMed Google Scholar Although the exact underlying mechanisms for this association are not fully clear, it can be speculated that a higher muscle mass reflects an increased metabolic reserve, which during periods of acute catabolic disease, such as a COVID-19 infection, can protect whole-body functioning. Whereas previously the association of CT scan-derived muscle and adipose tissue CSA with disease severity and mortality in COVID-19 was demonstrated in research settings, determining these body composition parameters in an acute clinical setting is still not applied easily in daily practice. Clinical parameters as included in the highly predictive 4C Mortality Score are already part of standard patient assessment in daily practice, and therefore are readily available. However, some of these clinical parameters inherently also are associated with CT scan-derived muscle and adipose tissue parameters.32Janssen I. Heymsfield S.B. Wang Z.M. Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr.J Appl Physiol (1985). 2000; 89: 81-88Crossref PubMed Scopus (1929) Google Scholar Therefore, we investigated if CT scan-derived muscle and adipose tissue CSA are still associated with mortality when adjusted for a validated set of clinical parameters, eg, the 4C Mortality Score. Based on our data, we can conclude that only pectoralis muscle CSA remains associated with 30-day in-hospital mortality when adjusted for the 4C Mortality Score. Additional sensitivity analyses (e-Tables 1-5) demonstrated no difference in this outcome when using only hospitalized patients compared with the current population, which showed an admission rate of 82.5%. Whether pectoralis muscle CSA improves the predictability of the already highly predictive 4C Mortality Score and as such needs to be added to the 4C Mortality Score was not the main question of this study. However, we constructed an adjusted 4C Mortality Score including low pectoralis CSA. In comparative AUC analyses, addition of the pectoralis muscle to the 4C Mortality Score demonstrated a small, yet statistically insignificant, improvement to an already very well-performing score. This further underlines our findings that, despite the HR of 0.98, pectoralis muscle is associated significantly with 30-day in-hospital mortality. To investigate this further, a larger dataset as well as a validation dataset of comparable magnitude as the development and validation cohort of the initial 4C Mortality Score is warranted.17Knight S.R. Ho A. Pius R. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370: m3339Crossref PubMed Scopus (605) Google Scholar To add pectoralis muscle CSA to the 4C Mortality Score, cutoff values to identify patients with high or low muscle CSA are required. In line with other studies, our data demonstrated a significant association between muscle CSA and age and sex.11Shen W. Punyanitya M. Wang Z. et al.Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image.J Appl Physiol (1985). 2004; 97: 2333-2338Crossref PubMed Scopus (1155) Google Scholar,32Janssen I. Heymsfield S.B. Wang Z.M. Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr.J Appl Physiol (1985). 2000; 89: 81-88Crossref PubMed Scopus (1929) Google Scholar However, age- and sex-specific cutoffs for pectoralis muscle CSA are still lacking. Therefore, future studies in large cohorts should focus on developing age- and sex-specific cutoff values for muscle CSA. The current analyses were performed retrospectively on chest CT scans that were obtained with the sole purpose of assessing intrapulmonary abnormalities in an acute clinical setting. This is an important limitation that needs to be addressed, because it might have caused bias resulting from missing data, specifically regarding the extrapulmonary tissue at the L1 level (missing in 52.3% of scans). A focus on ensuring that chest CT scans include the L1 level in the future will allow for more precise (retrospective) comparison of the prognostic value of pectoralis and L1 muscle CSA. Furthermore, the current method of semi-automated analysis of CT scan-derived muscle and adipose tissue CSA allows for retrospective analysis of CT scans at admission. This provides opportunities for long-term, longitudinal follow-up of patients with COVID-19 using the CT scans that are part of regular care. Relevant changes in muscle mass (and quality) during admission and 3 months after recovery from COVID-19 already have been described in recent studies.33van Gassel RJJ Bels J. Remij L. et al.Functional outcomes and their association with physical performance in mechanically ventilated coronavirus disease 2019 survivors at 3 months following hospital discharge: a cohort study.Crit Care Med. 2021; 49: 1726-1738Crossref PubMed Scopus (29) Google Scholar,34Attaway A. Welch N. Dasarathy D. et al.Acute skeletal muscle loss in SARS-CoV-2 infection contributes to poor clinical outcomes in COVID-19 patients.J Cachexia Sarcopenia Muscle. 2022; 13: 2436-2446Crossref PubMed Scopus (7) Google Scholar However, the current method requires trained analysts and is very labor intensive. This makes its application in the dynamic, fast-paced daily clinical practice and prognostic studies nearly impossible. Therefore, fully automated segmentation and analysis of CT scan-derived body composition through artificial intelligence algorithms has sparked attention. Both Goehler et al35Goehler A. Hsu T.H. Seiglie J.A. et al.Visceral adiposity and severe COVID-19 disease: application of an artificial intelligence algorithm to improve clinical risk prediction.Open Forum Infect Dis. 2021; 8: ofab275Crossref PubMed Scopus (14) Google Scholar and Hosch et al36Hosch R. Kattner S. Berger M.M. et al.Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity.Sci Rep. 2022; 1216411Crossref Scopus (3) Google Scholar demonstrated the potential of its use in COVID-19 by using an artificial intelligence algorithm to investigate the association of different CT scan-derived muscle and adipose tissue parameters on disease severity and mortality. Additionally, in acute trauma settings, the use of a deep learning algorithm to assess CT scan-derived muscle and adipose tissue recently was validated.37Ackermans L. Volmer L. Timmermans Q. et al.Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.Injury. 2022; 53: S30-S41Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar,38Ackermans L. Volmer L. Wee L. et al.Deep learning automated segmentation for muscle and adipose tissue from abdominal computed tomography in polytrauma patients.Sensors (Basel). 2021; 21: 2083Crossref PubMed Scopus (14) Google Scholar Complete automation of this process provides the potential to move these analyses from research settings to daily clinical practice in different areas of both acute and chronic care.39Bates D.D.B. Pickhardt P.J. CT-derived body composition assessment as a prognostic tool in oncologic patients: from opportunistic research to artificial intelligence-based clinical implementation.AJR Am J Roentgenol. 2022; : 1-10Google Scholar Low CT scan-derived pectoralis muscle, high VAT, and low muscle CSA at L1 are statistically significantly associated with higher 30-day in-hospital mortality in patients with COVID-19. Additionally, CT scan-derived pectoralis muscle CSA remains associated with 30-day in-hospital mortality in patients with COVID-19 independent of the clinical 4C Mortality Score. A research fellowship awarded to R. J. H. C. G. B. by the European Society of Clinical Nutrition and Metabolism and a grant from ZonMw [Project no. 100430040211004] awarded to H. A. G. and A. M. W. J. S. funded this work.
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