Prediction models for short children born small for gestational age (SGA) covering the total growth phase. Analyses
Michael B Ranke1, Anders Lindberg,
1Paediatric Endocrinology Section, Children's Hospital, University of Tuebingen, D-72076 Tuebingen, Germany. Michael.Ranke@med.uni-tuebingen.de
Insights
Mathematical models accurately predict growth in short children treated with growth hormone (GH). These models help optimize treatment and manage expectations for children born small for gestational age (SGA).
Area of Science:
- Pediatric Endocrinology
- Growth Hormone Therapy
- Biometric Modeling
Background:
- Mathematical models can predict growth in children treated with growth hormone (GH).
- These models aid in optimizing and individualizing GH treatment for height outcomes and cost-effectiveness.
- This study focuses on children born small for gestational age (SGA).
Purpose of the Study:
- To compile existing prediction models for SGA children.
- To develop new prediction models for SGA children.
- To validate growth prediction algorithms for GH-treated SGA children.
Main Methods:
- Applied existing height velocity (HV) prediction models to SGA children from the KIGS database.
- Developed a new prediction model for the 3rd prepubertal year using the all-possible regression approach.
- Utilized Mallow's C(p) criterion for model development.
Main Results:
- Existing models showed no significant difference between observed and predicted height velocity in new cohorts.
- A new model for predicting 3rd-year HV explained 33% of variability with a 1.0 cm/year error SD.
- Key predictors for HV included previous year's HV, chronological age, weight SDS, mid-parent height SDS, and GH dose.
Conclusions:
- Accurate prediction models using accessible predictors are available for GH-treated short SGA children.
- Models can set realistic patient expectations and help identify treatment compliance issues.
- The heterogeneity of SGA contributes to relatively low overall explained variability.
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.
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