Bone Abstracts

Objective: The treatment of many skeletal growth and/or developmental disorders often relies on a child’s biological maturity status, frequently determined by a skeletal maturity assessment. Rapid changes in the rate of skeletal maturation (i.e., the maturational spurt) during adolescence can significantly influence biological maturity status, affecting treatment type and timing as well as clinical outcomes. However, the chronological age at which peak maturational velocity (aPMV) is achieved is dependent on maturational trajectory form (i.e., curve form) – a characteristic that is difficult to predict. We aimed to determine how trajectory form influences aPMV for future application in pediatric clinical practice.

Methods: We used skeletal maturity assessments (Fels Method) of serial left hand-wrist radiographs from 214 participants (126 boys; 88 girls; 6,659 total observations) between the chronological ages of 3 and 20 years from the Fels Longitudinal Study. Maturational trajectories were modeled for each participant using both 4th and 5th order fixed effect polynomials; Akaike’s Information Criterion for Finite Sample Sizes (AICc) was used to determine the best model. Estimates of aPMV were calculated for each participant and compared between models (i.e., best fit vs. other) using a two-sided t-test with statistical significance set at ≤0.05.

Results: Estimates of aPMV from both 4th and 5th order fixed effect polynomials were not significantly different in either boys or girls. Nevertheless, aPMV was consistently earlier (boys, 8 months; girls, 1 month) in participants best fit with 5th order polynomials when modeled by 4th order polynomials. Similarly, aPMV was slightly later (boys, 2 months; girls, 1 month) in participants best fit with 4th order polynomials when modeled by 5th order polynomials.

Conclusion: In the present study, modeling maturational trajectories with 5th order polynomials appeared to have later estimates of aPMV, particularly in boys, than those derived from 4th order polynomials, despite non-significance. Because clinical treatment and outcomes could be negatively impacted by underestimating developmental milestones, such as aPMV, we advocate the use of 5th order polynomials to estimate aPMV.

Funding: This work was supported by funds from the National Institutes of Health (Award Numbers: F31 HD091939 and R01 AR055927).

Disclosure: The authors declared no competing interests.