Egor Avrutin1, Lesley L Moisey1, Roselyn Zhang1, Jenna Khattab1, Emma Todd1, Tahira Premji1, Rosemary Kozar2, Daren K Heyland3,4, Marina Mourtzakis1. 1. Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada. 2. Department of Surgery, University of Maryland, Baltimore, Maryland, USA. 3. Clinical Evaluation Research Unit, Kingston General Hospital, Kingston, Ontario, Canada. 4. Department of Critical Care Medicine, Queen's University, Kingston, Ontario, Canada.
Abstract
BACKGROUND: Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass. METHODS: We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region. RESULTS: There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R2 = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively. CONCLUSIONS: A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically ill patients with low muscularity who are at risk for worse clinical outcomes.
BACKGROUND: Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass. METHODS: We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region. RESULTS: There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R2 = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively. CONCLUSIONS: A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically illpatients with low muscularity who are at risk for worse clinical outcomes.
Authors: Carla M Prado; Sarah A Purcell; Carolyn Alish; Suzette L Pereira; Nicolaas E Deutz; Daren K Heyland; Bret H Goodpaster; Kelly A Tappenden; Steven B Heymsfield Journal: Ann Med Date: 2018-09-12 Impact factor: 4.709