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Infant lower extremity long bone growth rates: comparison of contemporary with early 20th century data using mixed effect models

Tsai, Andy ; Stamoulis, Catherine ; Barber, Ignasi ; Kleinman, Paul K. ;
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    Abstract Objectives Modern reference standards for long bone growth are lacking for infants (≤1 year). This study develops a quantitative framework to characterize lower extremity bone shaft growth during infancy based on radiographic images, and compares it to data from the 1930s. Methods Femoral, tibial, and fibular shaft length measurements (diaphysis plus metaphysis) were collected retrospectively from 70 infants on initial and follow‐up skeletal surveys performed for suspected abuse (7/2005—2/2013). These serial skeletal survey data (SSSD) were compared to the Denver Child Research Council data (DCRCD), a 1930's longitudinal dataset from 80 infants. Mixed effects regression models were developed to estimate growth trajectories from these data. Growth trajectories and short‐term (≤2 months) growth rates were compared. Results Statistically distinct models described the contemporary (SSSD) and historic (DCRCD) datasets; however, there was substantial overlap (77—90%) between their confidence bands for the three measured bones. Based on developed models, the average long bone shafts of the DCRCD are shorter at birth than SSSD (femur: 77.0 vs. 82.3 mm; tibia: 64.4 vs. 68.2 mm; fibula: 61.0 vs. 64.4 mm), but the DCRCD long bone growth rates are faster than SSSD (femur: 0.21 vs. 0.17 mm/day; tibia: 0.16 vs. 0.14 mm/day; fibula: 0.15 vs. 0.14 mm/day). Short‐term growth rates of these bones decreased with age. The effect of sexual dimorphism on long bone growth during infancy was non‐significant. Conclusion This study provides reference standards for long bone growth rates during the dynamic period of infancy that may aid clinical assessment, and also inform research studies of disorders associated with altered skeletal growth.


  • 주제어

    Denver Child Research Council Data .   infant .   long bone growth .   mixed effects regression model.  

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