Assessment of body cell mass at bedside in critically ill patients

• Published in: American journal of physiology: endocrinology and metabolism 2012-08, Vol.303 (3), p.E389-E396
• Main Authors: Savalle, Magali, Gillaizeau, Florence, Maruani, Gérard, Puymirat, Etienne, Bellenfant, Florence, Houillier, Pascal, Fagon, Jean-Yves, Faisy, Christophe
• Format: Article
• Language: English
• Subjects: Absorptiometry, Photon; Adipose Tissue - pathology; Adiposity - physiology; Adult; Aged; Aged, 80 and over; Body Composition; Body Weight - physiology; Cells; Critical Illness - therapy; Electric Impedance; Female; Health Status Indicators; Human body; Humans; Male; Mathematical models; Middle Aged; Nutritional Status - physiology; Point-of-Care Systems; Polynomials; Regression Analysis; Tissues; Variables
• ISSN: 0193-1849; 1522-1555
• DOI: 10.1152/ajpendo.00502.2011

Critical illness affects body composition profoundly, especially body cell mass (BCM). BCM loss reflects lean tissue wasting and could be a nutritional marker in critically ill patients. However, BCM assessment with usual isotopic or tracer methods is impractical in intensive care units (ICUs). We aimed to modelize the BCM of critically ill patients using variables available at bedside. Fat-free mass (FFM), bone mineral (Mo), and extracellular water (ECW) of 49 critically ill patients were measured prospectively by dual-energy X-ray absorptiometry and multifrequency bioimpedance. BCM was estimated according to the four-compartment cellular level: BCM = FFM - (ECW/0.98) - (0.73 × Mo). Variables that might influence the BCM were assessed, and multivariable analysis using fractional polynomials was conducted to determine the relations between BCM and these data. Bootstrap resampling was then used to estimate the most stable model predicting BCM. BCM was 22.7 ± 5.4 kg. The most frequent model included height (cm), leg circumference (cm), weight shift (Δ) between ICU admission and body composition assessment (kg), and trunk length (cm) as a linear function: BCM (kg) = 0.266 × height + 0.287 × leg circumference + 0.305 × Δweight - 0.406 × trunk length - 13.52. The fraction of variance explained by this model (adjusted r(2)) was 46%. Including bioelectrical impedance analysis variables in the model did not improve BCM prediction. In summary, our results suggest that BCM can be estimated at bedside, with an error lower than ±20% in 90% subjects, on the basis of static (height, trunk length), less stable (leg circumference), and dynamic biometric variables (Δweight) for critically ill patients.