title: Predicting weight using postmenstrual age--neonates to adults.
abstract: OBJECTIVE
  To describe the pattern and variability of body weight with postmenstrual age (PMA) using nonlinear mixed effect modeling and to create a single mathematical function that can be used from prematurity to adulthood.
  
  
  BACKGROUND
  PMA has been shown to predict functional properties of humans such as glomerular filtration rate and drug clearance. Widely used growth charts use postnatal age to predict weight in an idealized population and are not available as a mathematical function.
  
  
  METHODS
  We modeled 7164 body weight and PMA observations from a pooled database of 5031 premature neonates, infants, children, and adults. All subjects were participants in pharmacokinetic or renal function studies. PMA ranged from 23 weeks to 82 years. A mixed effect model was used to describe fixed (PMA, sex) and random between-subject variability.
  
  
  RESULTS
  A model based on the sum of three sigmoid hyperbolic and one exponential functions described the data. Females were typically 12% lighter in weight. Part of the between-subject variability in weight decreased exponentially with a half-life of 3.5 PMA years, while the remainder stayed a constant fraction of the weight asymptote for each of the four functions.
  
  
  CONCLUSIONS
  The change of weight with PMA and sex can be described with a simple equation. This is suitable for simulation of typical weight-age distributions and may be useful for evaluation of appropriate weight for age in children requiring medical treatment.
authors.affiliation: Department of Anaesthesiology, University of Auckland School of Medicine, Auckland, New Zealand.
authors.forename: Anita L
authors.initials: AL
authors.lastname: Sumpter
authors.forename: Nick H G
authors.initials: NH
authors.lastname: Holford
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001835:Body Weight / Q000502:physiology*; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D005865:Gestational Age; D006128:Growth / Q000502:physiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007234:Infant, Premature; D019102:Infant, Very Low Birth Weight; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011153:Population; D011237:Predictive Value of Tests; D011247:Pregnancy / Q000502:physiology*; D012015:Reference Standards; D014944:World Health Organization; D055815:Young Adult