Background: Various cutoffs have been used to diagnose computed tomography (CT)-defined low skeletal muscle mass; however, the impact of this variability on predicting physical functional limitations (PFL) remains unclear. In the present study we aimed to evaluate the diagnostic test metrics for predicting PFLs using a fixed cutoff value from previous reports and sought to create a prediction score that incorporated the skeletal muscle index (SMI) and other clinical factors.
Methods: In this cross-sectional study including 237 patients with lung cancer, the SMI was assessed using CT-determined skeletal muscle area at the third lumbar vertebra. Physical function was assessed using the short physical performance battery (SPPB) test, with PFL defined as an SPPB score ≤9. We analyzed the diagnostic metrics of the five previous cutoffs for CT-defined low skeletal muscle mass in predicting PFL.
Results: The mean age of participants was 66.0 ± 10.4 years. Out of 237 patients, 158 (66.7%) had PFLs. A significant difference was observed in SMI between individuals with and without PFLs (35.7 cm2/m2 ± 7.8 vs. 39.5 cm2/m2 ± 8.4, p < 0.001). Diagnostic metrics of previous cutoffs in predicting PFL showed suboptimal sensitivity (63.29%-91.77%), specificity (11.39%-50.63%), and area under the receiver operating characteristic curve (AUC) values (0.516-0.592). Age and the SMI were significant predictors of PFL; therefore, a score for predicting PFL (age - SMI + 21) was constructed, which achieved an AUC value of 0.748.
Conclusion: Fixed cutoffs for CT-defined low skeletal muscle mass may inadequately predict PFLs, potentially overlooking declining physical functions in patients with lung cancer.
Keywords: SPPB; lung cancer; physical functions; sarcopenia; skeletal muscle mass.
© 2024 The Authors. Thoracic Cancer published by John Wiley & Sons Australia, Ltd.