Studying pediatric health outcomes with electronic health records using Bayesian clustering and trajectory analysis

Abstract

Use of routinely collected data from electronic health records (EHR) can expedite longitudinal studies that investigate childhood exposures and rare pediatric health outcomes. For instance, characteristics of the body mass index (BMI) trajectory early in life may be associated with subsequent development of type 2 diabetes. Past studies investigating these relationships have used longitudinal cohort data collected over the course of many years to investigate the connection between BMI trajectory and subsequent development of diabetes. In contrast, EHR data from routine clinical care can provide longitudinal information on early-life BMI trajectories as well as subsequent health outcomes without requiring any additional data collection. In this study, we introduce a Bayesian joint phenotyping and BMI trajectory model to address data quality challenges in an EHR-based study of early-life BMI and type 2 diabetes in adolescence. We compared this joint modeling approach to traditional approaches using a computable phenotype for type 2 diabetes or separately estimated BMI trajectories and type 2 diabetes phenotypes. In a sample of 49,062 children derived from the PEDSnet consortium of pediatric healthcare systems, a median 8 (interquartile range [IQR] 5–13) BMI measurements were available to characterize the early-life BMI trajectory. The joint modeling and computable phenotype approaches found that age at adiposity rebound between 5 and 9 years was associated with higher odds of type 2 diabetes in adolescence compared to age at adiposity rebound between 2 and 5 years (joint model odds ratio [OR] = 1.77; computable phenotype OR = 1.88) and that BMI in excess of 140% of the 95th percentile for age and sex at age 9 years was associated with higher odds of type 2 diabetes in adolescence relative to children with BMI from 100 to 120% of the 95th percentile (joint model OR = 6.22; computable phenotype OR = 13.25). Estimates from the separate phenotyping and trajectory model were substantially attenuated towards the null. These results demonstrate that EHR data coupled with modern methodologic approaches can improve efficiency and timeliness of studies of childhood exposures and rare health outcomes.