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

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.