Skeletal Muscle Area on CT: Determination of an Optimal Height Scaling Power and Testing for Mortality Risk Prediction.

Abstract

BACKGROUND. Sarcopenia is commonly assessed on CT by use of the skeletal muscle index (SMI), which is calculated as the skeletal muscle area (SMA) at L3 divided by patient height squared (i.e., a height scaling power of 2). OBJECTIVE. The purpose of this study was to determine the optimal height scaling power for SMA measurements on CT and to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. METHODS. This retrospective study included 16,575 patients (6985 men, 9590 women; mean age, 56.4 years) who underwent abdominal CT from December 2012 through October 2018. The SMA at L3 was determined using automated software. The sample was stratified into two groups: 5459 patients without major medical conditions (based on ICD-9 and ICD-10 codes) who were included in the analysis for determining the optimal height scaling power and 11,116 patients with major medical conditions who were included for the purpose of testing this power. The optimal scaling power was determined by allometric analysis (whereby regression coefficients were fitted to log-linear sex-specific models relating height to SMA) and by analysis of statistical independence of SMI from height across scaling powers. Cox proportional hazards models were used to test the influence of the derived optimal scaling power on the utility of SMI in predicting all-cause mortality. RESULTS. In allometric analysis, the regression coefficient of log(height) in patients 40 years old and younger was 1.02 in men and 1.08 in women, and in patients older than 40 years old, it was 1.07 in men and 1.10 in women (all p < .05 vs regression coefficient of 2). In analyses for statistical independence of SMI from height, the optimal height scaling power (i.e., those yielding correlations closest to 0) was, in patients 40 years old and younger, 0.97 in men and 1.08 in women, whereas in patients older than 40 years old, it was 1.03 in men and 1.09 in women. In the Cox model used for testing, SMI predicted all-cause mortality with a higher concordance index using of a height scaling power of 1 rather than 2 in men (0.675 vs 0.663, p < .001) and in women (0.664 vs 0.653, p < .001). CONCLUSION. The findings support a height scaling power of 1, rather than a conventional power of 2, for SMI computation. CLINICAL IMPACT. A revised height scaling power for SMI could impact the utility of CT-based sarcopenia diagnoses in risk assessment.