R Reiter1, B Iglseder2, W Treschnitzer3, R Alzner4, B Mayr-Pirker5, M Kreutzer6, C Pirich7, H Kässmann8, P Dovjak9, J Reiss10. 1. Department of Geriatric Medicine, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: r.reiter@salk.at. 2. Department of Geriatric Medicine, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: B.Iglseder@salk.at. 3. Department of Clinical Psychology, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020 Salzburg, Austria. Electronic address: w.treschnitzer@salk.at. 4. Department of Geriatric Medicine, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: r.alzner@salk.at. 5. Department of Geriatric Medicine, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: b.mayr-pirker@salk.at. 6. Department of Clinical Nutrition, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: M.Kreutzer@salk.at. 7. Department of Endocrinology and Nuclear Medicine, Salzburger Landeskliniken, Paracelsus Medical University Salzburg, Müllner Hauptstraße 48, 5020, Salzburg, Austria. Electronic address: c.pirich@salk.at. 8. Department of Endocrinology and Nuclear Medicine, Salzburger Landeskliniken, Paracelsus Medical University Salzburg, Müllner Hauptstraße 48, 5020, Salzburg, Austria. Electronic address: H.Kaessmann@salk.at. 9. Department of Geriatric Medicine, Salzkammergut-Klinikum Gmunden, Miller-von-Aichholz-Straße 49, 4810 Gmunden, Austria. Electronic address: Peter.Dovjak@gespag.at. 10. Department of Geriatric Medicine, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Ignaz-Harrer-Straße 79, 5020, Salzburg, Austria. Electronic address: j.reiss@salk.at.
Abstract
BACKGROUND: Quantification of skeletal muscle mass is mandatory for diagnosing sarcopenia, a highly prevalent geriatric syndrome. While dual energy X-ray absorptiometry (DXA) is the reference method in a clinical context, bioimpedance analysis (BIA) is more readily applicable on a broad scale. Recently BIA equations for the prediction of appendicular skeletal muscle mass in higher age groups have been published, but data on their performance in geriatric inpatients are lacking. METHODS: In 144 geriatric inpatients (86 women and 58 men, mean age 80.7 ± 5.6 years) appendicular skeletal muscle mass was predicted by 4 different BIA equations and measured by DXA. Results were compared by linear regression analysis and Bland Altmann plots. The agreement with DXA in classifying subjects to have normal or reduced muscle mass was calculated for the BIA based approaches. RESULTS: The 4 BIA equations showed only minor differences in regression analysis, but major differences in mean error (range -0.98 kg to + 0.19 kg in women and -2.47 kg to -0.58 kg in men). Considering regression parameters and mean error, the equation of Scafoglieri et al. performed best, resulting in an agreement with DXA of more than 83%. Sensitivity to detect subjects with reduced muscle mass was <70% in the whole group for all BIA equations. CONCLUSION: The BIA equation of Scafoglieri et al. performs best in geriatric inpatients, with more than 83% of subjects classified correctly as having normal or reduced muscle mass compared to DXA. Low sensitivity to detect subjects with reduced muscle mass in geriatric inpatients remains a limitation of BIA.
BACKGROUND: Quantification of skeletal muscle mass is mandatory for diagnosing sarcopenia, a highly prevalent geriatric syndrome. While dual energy X-ray absorptiometry (DXA) is the reference method in a clinical context, bioimpedance analysis (BIA) is more readily applicable on a broad scale. Recently BIA equations for the prediction of appendicular skeletal muscle mass in higher age groups have been published, but data on their performance in geriatric inpatients are lacking. METHODS: In 144 geriatric inpatients (86 women and 58 men, mean age 80.7 ± 5.6 years) appendicular skeletal muscle mass was predicted by 4 different BIA equations and measured by DXA. Results were compared by linear regression analysis and Bland Altmann plots. The agreement with DXA in classifying subjects to have normal or reduced muscle mass was calculated for the BIA based approaches. RESULTS: The 4 BIA equations showed only minor differences in regression analysis, but major differences in mean error (range -0.98 kg to + 0.19 kg in women and -2.47 kg to -0.58 kg in men). Considering regression parameters and mean error, the equation of Scafoglieri et al. performed best, resulting in an agreement with DXA of more than 83%. Sensitivity to detect subjects with reduced muscle mass was <70% in the whole group for all BIA equations. CONCLUSION: The BIA equation of Scafoglieri et al. performs best in geriatric inpatients, with more than 83% of subjects classified correctly as having normal or reduced muscle mass compared to DXA. Low sensitivity to detect subjects with reduced muscle mass in geriatric inpatients remains a limitation of BIA.
Authors: Kwon Chan Jeon; So-Young Kim; Fang Lin Jiang; Sochung Chung; Jatin P Ambegaonkar; Jae-Hyeon Park; Young-Joo Kim; Chul-Hyun Kim Journal: Int J Environ Res Public Health Date: 2020-08-12 Impact factor: 3.390