Development and validation of anthropometric equations for the prediction of skeletal muscle mass in hospitalized adults and older people using computer tomography.
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Objective: To develop and validate a prediction model for estimating skeletal muscle mass (SMM) in hospitalized adults and older people based on easily obtained demographic and anthropometric data and using computer tomography (CT) as the gold standard. Methods: A methodological study was conducted with male and female participants ³ 20 years of age hospitalized at a public tertiary hospital in Northeast Brazil. Patients with characteristics that could influence the body composition analysis by CT and/or anthropometric measures were excluded. Sociodemographic characteristics and clinical data were obtained from the electronic records of the patients. Nutritional status was assessed using the anthropometric method (weight, height, body mass index, weight loss, arm circumference and calf circumference) and a subjective assessment. Abdominal CT images were used to obtain the area of SMM determined in cm 2 , taking density values of -29 to 150 Hounsfield units. Statistical analysis was performed with the aid of IBM SPSS version 16.0. Results: The sample comprised 564 individuals 20 to 89 years of age. Two prediction models were created – one for adults (SMM = 66.28 + (-36.20*sex) + (3.04*CC) + (0.19*weight), R 2 = 73.9%) and one for older people (SMM = -56.34 + (-26.70*sex) + (1.71*CC) + (0.97*height), R 2 = 67.3%). The predictive capacity of these equations was 74.1% for adults and 67.7% for older people compared to SMM determined by CT (p < 0.001). Conclusions: The equations developed in the present study should be tested for clinical purposes and in the analysis of data from mixed hospitalized populations.