Software and equations using segmental measures to estimate height in children and adolescents with cerebral palsy considering the level of gross motor function

Background & aims: In children with Cerebral palsy (CP) bone deformities create a difficulty in the collection of height measures by direct methods. Body segments are an alternative to study for anthropometric evaluation in children with CP. Motor compromise affects growth in these children. To our knowledge, no equations have been developed to estimate height that consider the level of involvement of children with CP. The aim was to develop equations to estimate height using segmental measures for children with cerebral palsy (CP). Methods: This was a cross-sectional study. The sample consisted of children and adolescents with CP of both sexes from 2 to 19 years old from five cities in Argentina. Children whose height and knee-heel height (KH) could be measured were included. Height, KH, and clinical covariables were collected. Linear regression models with height as the dependent variable and KH as predictors adjusted for significant covariates were developed and compared for R2, adjusted R2, and the root mean square of the error. Results: 242 children and adolescents (mean age 9 +/- 4 years) with a confirmed diagnosis of CP were included. The interaction between height and other variables such KH, sex, GMFCS, and age was analyzed. Two equations were developed to estimate height according to GMFCS level (GMFCS Level I -III: H = 1.5 x KH(cm) + 2.28 x age(years) + 51; GMFCS Level IV-V: H = 2.13 x KH (cm)+ 0.91 x age(years) + 37). The concordance correlation coefficient between estimated and observed height was 0.95 (95%CI [0.94; 0.96]). Conclusion: Height in children and adolescents with CP can be predicted using KH, GMFCS, and age. The equations and software can estimate height when this cannot be obtained directly. (c) 2024 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.