A new prediction model for total body water estimation by BIA in children with normal and excessive weight. (February 2023)

• Record Type: Journal Article
• Authors: Lu, Hong; Shinki, Kazuhiko; Mattoo, Tej K.
• Is Part Of: Clinical nutrition ESPEN. Volume 53(2023)
• Page Start: 53
• Page End: 59
• Publication Date: 2023-02
• Subjects: Total body water (TBW) -- Prediction -- Equation -- Formula -- Children -- Bioelectrical impedance analysis (BIA); Nutritionally induced diseases -- Periodicals; Metabolism -- Disorders -- Periodicals; 616.39005
• DOI: 10.1016/j.clnesp.2022.11.014
• Languages: English
• ISSNs: 2405-4577

Abstract

Summary: Background: Various methods, including bioelectrical impedance analysis (BIA), are used for total body water (TBW) estimation. The objective of our study by BIA was to develop a new predication model based on corrected TBW for normal adult BMI, a concept similar to the standardization of glomerular filtration rate by relating it to the average adult body surface area. Method: We measured TBW by BIA in 335 children 3–21 years old with normal or excessive body weight. Based on our data, we derived a new prediction model for TBW (L) for females {[(72.784 + 0.4093 × weight)∗Corrected TBW]/100} and males {[(57.944 + 0.6551 × weight)∗Corrected TBW]/100}. For validation, we compared our prediction model with three other models on TBW by BIA and dilution methods. Results: Our model's error size to predict TBW showed lower cross-validated root mean square error (CV-RMSE) as compared to three other models versus our dataset by BIA and two other datasets by dilution methods. Our model also showed a smaller error (2.059) in CV-RMSE as compared to other models by dilution methods (2.126, 2.873, and 4.384) for normal and excessive weight combined. This implies that our model is more robust when excessive weight individuals are included in the data.. Conclusion: Our prediction model for TBW estimation by BIA performs better as compared to some other models based on BIA and dilution method datasets. Furthermore, our prediction model is the only one that is devised to be applicable to children and young adults with both normal as well as excessive weight.