Cerebral palsy (CP) is the most frequent disability in children. The vast majority of these
patients are malnourished. In this population, there are practical difficulties to perform a
nutritional and growth assessment which makes it difficult to treat and follow up, because of
the lack of reference growth in Argentina, and the difficulty in taking anthropometric
measurements of weight and height because of their motor compromise, posture and muscle tone.
The main objective is to design and validate predictive models for the nutritional and growth
assessment of children and adolescents with CP and instruments for estimating weight and
height from body segments, in order to improve care, quality of life of these patients to
promote their social inclusion.
Material and method: It will be an observational, descriptive and cross-sectional study.
There will be two parts of the study, in the first part the population will be healthy
children from 2 to 18 years old from Cordoba, Argentina. The sample size was calculated based
on growth WHO standards data, for α=0.05 and 1-β=0.80, creating an stratified sampling
divided in 16 age groups for each age. This first part will help to establish which body
segments to use.
In the second part, the population will be children and adolescents from 2 to 18 years old
with diagnosis of CP from Córdoba, Argentina. A stratified sequential sampling shall be
performed. The sample size will be 192 patients, 12 per age stratum. The variables studied
will be: weight, height, body segments, sex, age, CP type, feeding path and type of feeding.
For the analysis of the data the normal continuous variables will be described in means with
their respective standard deviations and those of non-normal distribution in medians with
their ranges. For the development of the predictive equations using body segments measures, a
generalizable linear regression model will be used. The correlation coefficient r,
determination R2 and test of F will be calculated with p <0.05. To generate predictive growth
models, the percentiles from 3 to 97 will be calculated, using the LMS method and a q-q
graph.