A Risk Score for Childhood Obesity in an Urban Latino Cohort.

Objective: To assess whether individual obesity risk factors, present during gestation, and the first 6 months of life, can be combined into a simple prognostic model that has the ability to accurately predict childhood obesity at age 5 years in a high-risk cohort.

Study Design: A total of 201 Latina women were recruited during pregnancy, and their infants followed longitudinally. Ten risk factors for childhood obesity were included in an initial logistic model; a second reduced model was created via stepwise deletion (confirmed with nonparametric conditional random forest classifier), after which 5 risk factors remained. From each model, an obesity risk equation was derived, and an obesity risk score was generated for each patient. Derived algorithms were assessed using discrimination, calibration, and via predictive statistics.

Results: Of the 166 children followed through age 5 years, 56 (32%) met criteria for childhood obesity. Discrimination accuracy for both derivation models was excellent, and after optimism-corrected bootstrapping, both models showed meaningful clinical performance. Both models were adequately calibrated, showed strong sensitivity and negative predictive value at conservatively set obesity risk thresholds, and displayed excellent specificity among those classified as highest risk. Birth weight z-score and change in weight-for-age z-score between birth and 6 months were the risk factors with the strongest contribution to the obesity risk score.

Conclusions: Obesity risk algorithms are reliable in their prediction of childhood obesity and have the potential to be integrated into the electronic medical record. These models could provide a filter for directing early prevention resources to children with high obesity risk but should be evaluated in a larger external dataset.