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<p>Bioelectrical impedance analysis (BIA) requires validated equations tailored to specific populations and devices to estimate body composition. In this study, we aimed to develop a predictive equation for BIA to evaluate skeletal muscle area (SMA) using computed tomography (CT) as the reference method. This is bi-center cross-sectional study, involving 211 patients. BIA was conducted using a tetrapolar model, measuring resistance (R), and reactance (Xc) values. The equation was developed using a linear regression model, maintaining variables that best correlate to SMA<sub>CT</sub>. Validity was assessed using Bland-Altman plots and bootstrapping resampling method. Lins’ concordance correlation coefficient (CCC), root mean squared error (RMSE), and mean absolute error (MAE) were calculated before and after resampling. The proposed equation included sex, age, weight, height, resistance and reactance. This model accounted for more than 85% of the variability in SMA<sub>CT</sub> (R<sup>2</sup> <sub>adjusted</sub> = 0.86), with a RMSE of 10.37 cm<sup>2</sup> and MAE of 8.28 cm<sup>2</sup>. SMA<sub>BIA</sub> was highly correlated with SMA<sub>CT</sub> (ρ = 0.93, <i>P</i> &lt; .001). Bland-Altman plots and CCC (0.92) demonstrated a moderate agreement between SMA<sub>BIA</sub> and SMA<sub>CT</sub>. The newly proposed BIA equation demonstrated potential for predicting SMA<sub>CT</sub> as the reference standard. Our hypothesis requires further investigation in both healthy and clinical populations.</p>

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A potential bioelectrical impedance equation for estimating skeletal muscle area using computed tomography in colorectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Amanda de Sousa Rebouças,
  • Jarson P. Costa-Pereira,
  • Rodrigo Albert Baracho Ruegg,
  • Galtieri Otávio Cunha de Medeiros,
  • Nithaela Alves Bennemann,
  • Nilian Carla Souza,
  • Sílvia Fernandes Maurício,
  • Alcides da Silva Diniz,
  • Maria Cristina Gonzalez,
  • Carla M. Prado,
  • Ana Paula Trussardi Fayh

摘要

Bioelectrical impedance analysis (BIA) requires validated equations tailored to specific populations and devices to estimate body composition. In this study, we aimed to develop a predictive equation for BIA to evaluate skeletal muscle area (SMA) using computed tomography (CT) as the reference method. This is bi-center cross-sectional study, involving 211 patients. BIA was conducted using a tetrapolar model, measuring resistance (R), and reactance (Xc) values. The equation was developed using a linear regression model, maintaining variables that best correlate to SMACT. Validity was assessed using Bland-Altman plots and bootstrapping resampling method. Lins’ concordance correlation coefficient (CCC), root mean squared error (RMSE), and mean absolute error (MAE) were calculated before and after resampling. The proposed equation included sex, age, weight, height, resistance and reactance. This model accounted for more than 85% of the variability in SMACT (R2 adjusted = 0.86), with a RMSE of 10.37 cm2 and MAE of 8.28 cm2. SMABIA was highly correlated with SMACT (ρ = 0.93, P < .001). Bland-Altman plots and CCC (0.92) demonstrated a moderate agreement between SMABIA and SMACT. The newly proposed BIA equation demonstrated potential for predicting SMACT as the reference standard. Our hypothesis requires further investigation in both healthy and clinical populations.