Aggregate predictions improve accuracy when calculating metabolic variables used to guide treatment.

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.