OBJECTIVE
To evaluate the performance of childhood obesity prediction models in four independent cohorts in the United States, using previously validated variables obtained easily from medical records as measured in different clinical settings.
STUDY DESIGN
Data from four prospective cohorts, Latinx, Eating, and Diabetes (LEAD); Stress in Pregnancy Study (SIPS); Project Viva; and Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) were used to test childhood obesity risk models and predict childhood obesity by ages 4 through 6, using five clinical variables (maternal age, maternal pre-pregnancy body mass index, birth weight Z-score, weight-for-age Z-score change, and breastfeeding), derived from a previously validated risk model and as measured in each cohort's clinical setting. Multivariable logistic regression was performed within each cohort, and performance of each model was assessed based on discrimination and predictive accuracy.
RESULTS
The risk models performed well across all four cohorts, achieving excellent discrimination. The area under the receiver operator curve (AUROC) was 0.79 for CHAMACOS and Project Viva, 0.83 for SIPS, and 0.86 for LEAD. At a 50 percentile threshold, the sensitivity of the models ranged from 12 to 53%, and specificity was greater than or equal to 90%. The negative predictive values (NPV) were ≥ 80% for all cohorts, and the positive predictive values (PPV) ranged from 62-86%.
CONCLUSION
All four risk models performed well in each independent and demographically diverse cohort, demonstrating the utility of these five variables for identifying children at high risk for developing early childhood obesity in the United States.