Evaluation of skeletal muscle mass using prediction formulas at the level of the 12th thoracic vertebra.
Nutrition
; 93: 111475, 2022 Jan.
Article
in En
| MEDLINE
| ID: mdl-34638102
OBJECTIVES: People with cancer have a high risk of cachexia and sarcopenia, which are associated with worse clinical outcomes. We evaluated the prediction accuracy of the Matsuyama et al. and Ishida et al. formulas using computed tomography (CT) slices from the twelfth thoracic vertebra (Th12) level in people with cancer. METHODS: This retrospective study included patients with advanced cancer who underwent thoracic and abdominal CT scans (n = 173). The cross-sectional area (CSA) on CT images was measured at the levels of Th12 and the third lumbar vertebra (L3). The Matsuyama et al. formula used the Th12 CSA, whereas the Ishida et al. formula used only the Th12 CSA of the spinal erectors; thus, the measurements were performed separately. The correlation between predicted and actual L3 CSA was assessed using r and the intraclass correlation coefficient. A prediction-accuracy analysis of the predicted values was also performed. RESULTS: The mean participant age was 66.2 ± 12.8 y; 50.3% of participants were women and 49.7% were men. Strong correlations were observed between the predicted and measured L3 values calculated from the two prediction formulas. The prediction-accuracy analysis using previously reported cutoff values showed that the Ishida et al. method had high sensitivity and the Matsuyama et al. method had high specificity for low skeletal muscle index determined by the predicted and measured L3 skeletal muscle index. CONCLUSIONS: Both the Matsuyama et al. and Ishida et al. formulas had good reliability on CT slices at the Th12 level in people with advanced cancer, indicating that these formulas can be applied in clinical practice.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sarcopenia
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Nutrition
Journal subject:
CIENCIAS DA NUTRICAO
Year:
2022
Document type:
Article
Affiliation country:
Country of publication: