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This Article Full Text (Publish Ahead of Print[PDF]) All Versions of this Article: 89/2/491    most recent ajcn.2008.26629v1 Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Wells, J. C. Articles by Siervo, M. Search for Related Content PubMed PubMed Citation Articles by Wells, J. C. Articles by Siervo, M. Agricola Articles by Wells, J. C. Articles by Siervo, M.

Aggregate predictions improve accuracy when calculating metabolic variables used to guide treatment^1,2,3

¹ From the Childhood Nutrition Research Centre, UCL Institute of Child Health, London, United Kingdom (JCKW, JEW, DH, and MSF); the Department of Neuroscience, "Federico II" University, Medical School, Naples, Italy (AC); and MRC Human Nutrition Research, Elsie Widdowson Laboratory, Cambridge, United Kingdom (MS).

² Supported by the UK Medical Research Council (core funding for the body-composition measurements) and by the Medical School of "Federico II" University, Naples, Italy (core funding for the metabolism measurements).

³ Reprints not available. Address correspondence to JCK Wells, Childhood Nutrition Research Centre, UCL Institute of Child Health, 30 Guilford Street, London WC1N 1EH, United Kingdom. E-mail: j.wells{at}ich.ucl.ac.uk.

ABSTRACT

Background: Many components of clinical management are tailored to metabolic variables, such as fat-free mass, fat mass, resting metabolic rate (RMR), and body surface area. However, these traits are difficult to measure in routine care and are typically predicted from simple anthropometric or bedside body-composition measurements. Many prediction equations have been published, but validation studies have shown that these equations tend to have limited accuracy in individuals and many have significant average bias.

Objective: We tested a mathematical approach that assumes that the aggregate of many independent predictions is more accurate than the best single prediction.

Design: Body composition was measured in 196 children aged 4–16 y by using the 4-component model. RMR was measured in 142 adult women. Data on weight, height, age, skinfold thickness, and body impedance were used in published equations to predict body composition (12 equations) or RMR (13 equations). The accuracy of individual compared with aggregate predictions, relative to the reference measurements, was compared by using the Bland and Altman method.

Results: For children's body composition and adult RMR, the aggregate predictions had lower mean biases and lower limits of agreement than did the individual predictions, and the aggregate predictions performed better than did any individual prediction.

Conclusions: Aggregate predictions perform better than single predictions at predicting fat-free mass, fat mass, total body water, and RMR. Our findings indicate that the accuracy of calculating variables such as energy requirements and drug and dialysis dosages can be improved significantly with the use of our mathematical approach.

Received for publication July 1, 2008. Accepted for publication October 24, 2008.