Authors
Abstract
Objective: the evaluation of the prognostic ability of an algorithm to predict low weight and underweight at birth based on maternal anthropometry. Materials and methods: the sample was taken from a longitudinal study in 175 pregnant Cuban participants in a program of nutritional surveillance in a health area in Havana, Cuba. The dependent variable was birth weight and maternal independent variables were: gradients of arm circumference, calf, thigh and chest, skin-fold fat and weight of the triceps, subscapularis muscle, suprailiac and calf. Two classification trees were adjusted by means of the CART algorithm, taking as the dependent variable the birth weight recoded as low weight or underweight. Results: the significant predictors for low weight were: the gradients of the arm circumference and calf between the 2nd and 3rd trimesters, and maternal weight between the 1st and 2nd trimesters. The predictors of underweight were gradients of maternal weight between the 1st and 2nd trimesters, and arm circumference between the 2nd and 3rd trimesters. Conclusions: the relative simplicity of the classification trees as a prediction tool, and the low cost of measurement of the identified variables in the model provide a valuable resource to implement nutritional monitoring in pregnant women.
Keywords:
References
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