• Kavitha Ranganathan, Michael Terjimanian, Jeffrey Lisiecki, Jacob Rinkinen, Anudeep Mukkamala, Cameron Brownley, Steven R Buchman, Stewart C Wang, and Benjamin Levi.
• Department of Surgery, Section of Plastic Surgery, University of Michigan, Ann Arbor, Michigan.
• J. Surg. Res. 2014 Jan 1;186(1):246-52.

BackgroundThe psoas muscle has been shown to predict patient outcomes based on the quantification of muscle area using computed tomography (CT) scans. The accuracy of morphomic analysis on other muscles has not been clearly delineated. In this study, we determine the correlation between temporalis muscle mass, psoas muscle area, age, body mass index (BMI), and gender.MethodsTemporalis and psoas muscle dimensions were determined on all trauma patients who had both abdominal and maxillofacial CT scans at the University of Michigan between 2004 and 2011. Age, BMI, and gender were obtained through chart review. Univariate and multivariate analyses were performed to determine the relative relationship between morphomic data of the temporalis and psoas muscles and the ability of such information to correspond with clinical variables, such as BMI, age, and gender.ResultsA total of 646 patients were included in the present study. Among the 249 (38.5%) women and 397 (61.5%) men, the average age was 49.2 y. Average BMI was 27.9 kg/m². Total psoas muscle area directly correlated with mean temporalis muscle thickness (r = 0.57, P < 0.001). There was an indirect correlation between age and psoas muscle area (r = -0.52, P < 0.001) and temporalis muscle thickness (r = -0.36, P < 0.001). Neither psoas nor temporalis measurements correlated strongly with BMI (r = 0.18, P < 0.001; r = 0.14, P = 0.002), although stronger correlations were found in a more "frail," subgroup as defined by a BMI of <20 (r = 0.59, P = 0.002).ConclusionsWe demonstrate that dimensions of the temporalis muscle can be quantified and may serve as a proxy for age. Going forward, we aim to assess the utility of temporalis and psoas morphomics in predicting complication rates among trauma patients admitted to the hospital to predict outcomes in the future.Copyright © 2014 Elsevier Inc. All rights reserved.

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