Measurement of psoas muscle size using existing computed tomography to predict patient outcomes in emergency departments

Sarcopenia is a major physical factor of frailty and can be predicted by existing patient computed tomography (CT) scans. The objective of this study was to investigate the relationships between prognosis of elderly patients visiting the emergency department and psoas muscle size measurements from CT scans that were acquired in other purpose. This was a retrospective study in a single center. The psoas muscle size (cross-section) and attenuation at the L3 vertebra were measured on CT scans. Logistic regression analysis was used to determine the association between mortality and muscle size and muscle attenuation after adjustment of basic characteristics. Also, we constructed receiver operating characteristic curves. A total of 279 patients were enrolled with diagnoses categorized from 13 chapters from on International Classification of Diseases-10th Revision (ICD-10). There were 56 patients (20.1%) admitted to the ICU, and 51 patients (18.3%) died in the hospital during the clinical process. The area under the receiver operating characteristic (AUROC) of muscle size for prediction of ICU admission was 0.706 (0.649–0.759), and the cut-off value was 390.18 with 51.8% sensitivity and 87.9% specificity. The AUROC of muscle size for prediction of death was 0.904 (0.864–0.936), and the cut-off value was 587.41 with 92.2% sensitivity and 78.5% specificity. Using existing CT can be an appropriate method for an early diagnosis of sarcopenia in older patients. In this study, measurement of muscle size using CT was shown to be a feasible modality for predicting poor prognoses for older patients.

Psoas muscle; Sarcopenia; Frail elderly; Emergency departments; Computed tomography

[1] Won CW. Frailty: its Scope and Implications for Geriatricians. Annals of Geriatric Medicine and Research. 2019; 23: 95–97.

[2] Artaza-Artabe I, Sáez-López P, Sánchez-Hernández N, Fernández-Gutierrez N, Malafarina V. The relationship between nutrition and frailty: Effects of protein intake, nutritional supplementation, vitamin D and exercise on muscle metabolism in the elderly. A systematic review. Maturitas. 2016; 93: 89–99.

[3] Hajek A, Bock JO, Saum KU, Matschinger H, Brenner H, Holleczek B, et al. Frailty and healthcare costs—longitudinal results of a prospective cohort study. Age and Ageing. 2018; 47: 233–241.

[4] Statistics Korea. Population projections for Korea. Daejeon: Statistics Korea. 2019.

[5] Abellan Van Kan G. Epidemiology and consequences of sarcopenia. The Journal of Nutrition, Health and Aging. 2009; 13: 708–712.

[6] Doherty TJ. Invited Review: Aging and sarcopenia. Journal of Applied Physiology. 2003; 95: 1717–1727.

[7] Santilli V, Bernetti A, Mangone M, Paoloni M. Clinical definition of sarcopenia. Clinical Cases in Mineral and Bone Metabolism. 2014; 11: 177–180.

[8] Hairi NN, Cumming RG, Naganathan V, Handelsman DJ, Le Couteur DG, Creasey H, et al. Loss of Muscle Strength, Mass (Sarcopenia), and Quality (Specific Force) and its Relationship with Functional Limitation and Physical Disability: The Concord Health and Ageing in Men Project. Journal of the American Geriatrics Society. 2010; 58: 2055–2062.

[9] Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age and Ageing. 2010; 39: 412–423.

[10] Tarantino U, Piccirilli E, Fantini M, Baldi J, Gasbarra E, Bei R. Sarcopenia and Fragility Fractures: Molecular and Clinical Evidence of the Bone-Muscle Interaction. Journal of Bone and Joint Surgery. 2015; 97: 429–437.

[11] Moisey LL, Mourtzakis M, Cotton BA, Premji T, Heyland DK, Wade CE, et al. Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients. Critical Care. 2013; 17: R206.

[12] Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, et al. The Obese without Cardiometabolic Risk Factor Clustering and the Normal Weight with Cardiometabolic Risk Factor Clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Archives of Internal Medicine. 2008; 168: 1617–1624.

[13] Tomiyama AJ, Hunger JM, Nguyen-Cuu J, Wells C. Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005–2012. International Journal of Obesity. 2016; 40: 883–886.

[14] Tan CY, Vidal-Puig A. Adipose tissue expandability: the metabolic problems of obesity may arise from the inability to become more obese. Biochemical Society Transactions. 2008; 36: 935–940.

[15] Jones SE, Maddocks M, Kon SS, Canavan JL, Nolan CM, Clark AL, et al. Sarcopenia in COPD: prevalence, clinical correlates and response to pulmonary rehabilitation. Thorax. 2015; 70: 213–218.

[16] Liu P, Hao Q, Hai S, Wang H, Cao L, Dong B. Sarcopenia as a predictor of all-cause mortality among community-dwelling older people: a systematic review and meta-analysis. Maturitas. 2017; 103: 16–22.

[17] Wong RMY, Wong H, Zhang N, Chow SKH, Chau WW, Wang J, et al. The relationship between sarcopenia and fragility fracture—a systematic review. Osteoporosis International. 2019; 30: 541–553.

[18] Mettler FA Jr, Bhargavan M, Faulkner K, Gilley DB, Gray JE, Ibbott GS, et al. Radiologic and Nuclear Medicine Studies in the United States and Worldwide: Frequency, Radiation Dose, and Comparison with other Radiation Sources—1950–2007. Radiology. 2009; 253: 520–531.

[19] Kocher KE, Meurer WJ, Fazel R, Scott PA, Krumholz HM, Nallamothu BK. National Trends in Use of Computed Tomography in the Emergency Department. Annals of Emergency Medicine. 2011; 58: 452–462.e3.

[20] Chou SC, Nagurney JM, Schuur JD, Weiner SG. Advanced imaging and trends in hospitalizations from the emergency department. PLoS ONE. 2020; 15: e0239059.

[21] Huber TC, Keefe N, Patrie J, Tracci MC, Sheeran D, Angle JF, et al. Predictors of all-Cause Mortality after Endovascular Aneurysm Repair: Assessing the Role of Psoas Muscle Cross-Sectional Area. Journal of Vascular and Interventional Radiology. 2019; 30: 1972–1979.

[22] Yaguchi Y, Kumata Y, Horikawa M, Kiyokawa T, Iinuma H, Inaba T, et al. Clinical Significance of Area of Psoas Major Muscle on Computed Tomography after Gastrectomy in Gastric Cancer Patients. Annals of Nutrition and Metabolism. 2017; 71: 145–149.

[23] Wang T, Feng X, Zhou J, Gong H, Xia S, Wei Q, et al. Type 2 diabetes mellitus is associated with increased risks of sarcopenia and pre-sarcopenia in Chinese elderly. Scientific Reports. 2016; 6: 38937.

[24] Akkoc I, Toptas M, Yalcin M, Demir E, Toptas Y. Psoas Muscle Area Measured with Computed Tomography at Admission to Intensive Care Unit: Prediction of in-Hospital Mortality in Patients with Pulmonary Embolism. BioMed Research International. 2020; 2020: 1586707.

[25] Gu DH, Kim MY, Seo YS, Kim SG, Lee HA, Kim TH, et al. Clinical usefulness of psoas muscle thickness for the diagnosis of sarcopenia in patients with liver cirrhosis. Clinical and Molecular Hepatology. 2018; 24: 319–330.

[26] Mccusker A, Khan M, Kulvatunyou N, Zeeshan M, Sakran JV, Hayek H, et al. Sarcopenia defined by a computed tomography estimate of the psoas muscle area does not predict frailty in geriatric trauma patients. The American Journal of Surgery. 2019; 218: 261–265.

[27] Hajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Anal-ysis for Medical Diagnostic Test Evaluation. Caspian Journal of Internal Medicine. 2013; 4: 627–635.

[28] Perkins NJ, Schisterman EF. The Inconsistency of “Optimal” Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve. American Journal of Epidemiology. 2006; 163: 670–675.

[29] Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977; 33: 159–174.

[30] Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in Older Adults: Evidence for a Phenotype. Journals of Gerontology - Series A Biological Sciences and Medical Sciences. 2001; 56: M146–M156.

[31] Steihaug OM, Gjesdal CG, Bogen B, Kristoffersen MH, Lien G, Ranhoff AH. Sarcopenia in patients with hip fracture: A multicenter cross-sectional study. PLoS ONE. 2017; 12: e0184780.

[32] Aubertin-Leheudre M, Lord C, Labonté M, Khalil A, Dionne IJ. Relation-ship between Sarcopenia and Fracture Risks in Obese Postmenopausal Women. Journal of Women and Aging. 2008; 20: 297–308.

[33] Cawthon PM, Blackwell TL, Cauley J, Kado DM, Barrett-Connor E, Lee CG, et al. Evaluation of the Usefulness of Consensus Definitions of Sarcopenia in Older Men: Results from the Observational Osteoporotic Fractures in Men Cohort Study. Journal of the American Geriatrics Society. 2015; 63: 2247–2259.

[34] Kershaw EE, Flier JS. Adipose Tissue as an Endocrine Organ. The Journal of Clinical Endocrinology and Metabolism. 2004; 89: 2548–2556.

[35] Johannsen DL, Conley KE, Bajpeyi S, Punyanitya M, Gallagher D, Zhang Z, et al. Ectopic Lipid Accumulation and Reduced Glucose Tolerance in Elderly Adults Are Accompanied by Altered Skeletal Muscle Mitochondrial Activity. The Journal of Clinical Endocrinology and Metabolism. 2012; 97: 242–250.

[36] Marzetti E, Calvani R, Cesari M, Buford TW, Lorenzi M, Behnke BJ, et al. Mitochondrial dysfunction and sarcopenia of aging: from signaling pathways to clinical trials. The International Journal of Biochemistry and Cell Biology. 2013; 45: 2288–2301.

[37] Kistorp CN, Svendsen OL. Body composition analysis by dual energy X- ray absorptiometry in female diabetics differ between manufacturers. European Journal of Clinical Nutrition. 1997; 51: 449–454.

[38] Scafoglieri A, Clarys JP, Bauer JM, Verlaan S, Van Malderen L, Vantieghem S, et al. Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline – the PROVIDE study. Clinical Nutrition. 2017; 36: 869–875.

[39] Rösler A, Lehmann F, Krause T, Wirth R, von Renteln-Kruse W. Nutritional and hydration status in elderly subjects: Clinical rating versus bioimpedance analysis. Archives of Gerontology and Geriatrics. 2010; 50: e81–e85.

[40] Paris MT, Tandon P, Heyland DK, Furberg H, Premji T, Low G, et al. Automated body composition analysis of clinically acquired computed tomography scans using neural networks. Clinical Nutrition. 2020; 39: 3049–3055.

[41] Hashimoto F, Kakimoto A, Ota N, Ito S, Nishizawa S. Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks. Radiological Physics and Technology. 2019; 12: 210–215.

[42] Wang NC, Zhang P, Tapper EB, Saini S, Wang SC, Su GL. Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients with Liver Disease. American Journal of Gastroenterology. 2020; 115: 1210–1216.

[43] Boutin RD, Bamrungchart S, Bateni CP, Beavers DP, Beavers KM, Meehan JP, et al. CT of Patients with Hip Fracture: Muscle Size and Attenuation Help Predict Mortality. American Journal of Roentgenology. 2017; 208: W208–W215.

[44] Yoo T, Lo WD, Evans DC. Computed tomography measured psoas density predicts outcomes in trauma. Surgery. 2017; 162: 377–384.

[45] Bae SJ, Lee SH. Computed tomographic measurements of the psoas muscle as a predictor of mortality in hip fracture patients: Muscle attenuation helps predict mortality in hip fracture patients. Injury. 2021; 52: 1456–1461.