Piloting a training program in computed tomography skeletal muscle assessment for Registered Dietitians

JOURNAL OF PARENTERAL AND ENTERAL NUTRITION(2022)

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摘要
Background Consensus definitions for disease-associated malnutrition and sarcopenia include reduced skeletal muscle mass as a diagnostic criterion. There is a need to develop and validate techniques to assess skeletal muscle in clinical practice. Skeletal muscle mass can be precisely quantified from computed tomography (CT) images. This pilot study aimed to train Registered Dietitians (RDs) to complete precise skeletal muscle measurements using CT. Methods Purposive sampling identified RDs employed in clinical areas in which CT scans are routinely performed. CT training included (1) a 3-Day training session focused on manual segmentation of skeletal muscle cross-sectional areas (cm(2), centimeter squared) from abdominal CT images at the third lumbar vertebra (L3), using sliceOmatic (R) software, and (2) a precision assessment to quantify the intraobserver and interobserver precision error of repeated skeletal muscle measurements (30 images in duplicate). Precision error is reported as the root mean standard deviation (cm(2)) and percent coefficient of variation (%CV), our primary performance indicator, was defined as a precision error of Five RDs completed CT training. RDs were from three clinical areas: cancer care (N = 1), surgery (N = 2), and critical care (N = 1). RDs' precision error was low and below the minimal acceptable error of <2%; intraobserver error was <= 1.8 cm(2) (range, 0.8-1.8 cm(2)) or <= 1.5% (range, 0.8%-1.5%) and interobserver error was 1.2 cm(2) or 1.1%. Conclusion RDs can be trained to perform precise CT skeletal muscle measurements. Increasing capacity to assess skeletal muscle is a first step toward developing this technique for use in clinical practice.
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关键词
body composition, computed tomography, malnutrition, nutrition assessment, precision error, registered dietitian, sarcopenia, skeletal muscle mass, training program, variability
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