TITLE

Prediction models for short children born small for gestational age (SGA) covering the total growth phase. Analyses based on data from KIGS (Pfizer International Growth Database)

AUTHOR(S)
Ranke, Michael B.; Lindberg, Anders
PUB. DATE
January 2011
SOURCE
BMC Medical Informatics & Decision Making;2011, Vol. 11 Issue 1, p38
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Mathematical models can be developed to predict growth in short children treated with growth hormone (GH). These models can serve to optimize and individualize treatment in terms of height outcomes and costs. The aims of this study were to compile existing prediction models for short children born SGA (SGA), to develop new models and to validate the algorithms. Methods: Existing models to predict height velocity (HV) for the first two and the fourth prepubertal years and during total pubertal growth (TPG) on GH were applied to SGA children from the KIGS (Pfizer International Growth Database) - 1st year: N = 2340; 2nd year: N = 1358; 4th year: N = 182; TPG: N = 59. A new prediction model was developed for the 3rd prepubertal year based upon 317 children by means of the all-possible regression approach, using Mallow's C(p) criterion. Results: The comparison between the observed and predicted height velocity showed no significant difference when the existing prediction models were applied to new cohorts. A model for predicting HV during the 3rd year explained 33% of the variability with an error SD of 1.0 cm/year. The predictors were (in order of importance): HV previous year; chronological age; weight SDS; mid-parent height SDS and GH dose. Conclusions: Models to predict growth to GH from prepubertal years to adult height are available for short children born SGA. The models utilize easily accessible predictors and are accurate. The overall explained variability in SGA is relatively low, due to the heterogeneity of the disorder. The models can be used to provide patients with a realistic expectation of treatment, and may help to identify compliance problems or other underlying causes of treatment failure.
ACCESSION #
62662502

 

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