Percentile‐based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels. Issue 1 (2nd November 2021)

• Record Type: Journal Article
• Authors: Marquardt, J. Peter; Roeland, Eric J.; Van Seventer, Emily E.; Best, Till D.; Horick, Nora K.; Nipp, Ryan D.; Fintelmann, Florian J.
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 13:Issue 1(2022)
• Page Start: 190
• Page End: 202
• Publication Date: 2021-11-02
• Subjects: Body composition analysis -- Skeletal muscle -- Patient‐reported outcomes -- Sarcopenia -- Survival -- Skeletal muscle -- BMI -- Skeletal muscle gauge; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.12848
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: Background: Skeletal muscle metrics on computed tomography (CT) correlate with clinical and patient‐reported outcomes. We hypothesize that aggregating skeletal muscle measurements from multiple vertebral levels and skeletal muscle gauge (SMG) better predict outcomes than skeletal muscle radioattenuation (SMRA) or ‐index (SMI) at a single vertebral level. Methods: We performed a secondary analysis of prospectively collected clinical (overall survival, hospital readmission, time to unplanned hospital readmission or death, and readmission or death within 90 days) and patient‐reported outcomes (physical and psychological symptom burden captured as Edmonton Symptom Assessment Scale and Patient Health Questionnaire) of patients with advanced cancer who experienced an unplanned admission to Massachusetts General Hospital from 2014 to 2016. First, we assessed the correlation of skeletal muscle cross‐sectional area, SMRA, SMI, and SMG at one or more of the following thoracic (T) or lumbar (L) vertebral levels: T5, T8, T10, and L3 on CT scans obtained ≤50 days before index assessment. Second, we aggregated measurements across all available vertebral levels using percentile‐based averaging (PBA) to create the average percentile. Third, we constructed one regression model adjusted for age, sex, sociodemographic factors, cancer type, body mass index, and intravenous contrast for each combination of (i) vertebral level and average percentile, (ii) muscle metrics (SMRA, SMI, & SMG), and (iii) clinical and patient‐reported outcomes. Fourth, we compared the performance of vertebral levels and muscle metrics by ranking otherwise identical models by concordance statistic, number of included patients, coefficient of determination, and significance of muscle metric. Results: We included 846 patients (mean age: 63.5 ± 12.9 years, 50.5% males) with advanced cancer [predominantly gastrointestinal (32.9%) or lung (18.9%)]. The correlation of muscle measurements between vertebral levels ranged from 0.71 to 0.84 for SMRA and 0.67 to 0.81 for SMI. The correlation of individual levels with the average percentile was 0.90–0.93 for SMRA and 0.86–0.92 for SMI. The intrapatient correlation of SMRA with SMI was 0.21–0.40. PBA allowed for inclusion of 8–47% more patients than any single‐level analysis. PBA outperformed single‐level analyses across all comparisons with average ranks 2.6, 2.9, and 1.6 for concordance statistic, coefficient of determination, and significance (range 1–5, μ = 3), respectively. On average, SMG outperformed SMRA and SMI across outcomes and vertebral levels: the average rank of SMG was 1.4, 1.4, and 1.4 for concordance statistic, coefficient of determination, and significance (range 1–3, μ = 2), respectively. Conclusions: Multivertebral level skeletal muscle analyses using PBA and SMG independently and additively outperform analyses using individual levels and SMRA or SMI.