Abstract
OBJECTIVES: To develop a data harmonization framework for neonatal hypoxic-ischemic encephalopathy (HIE) studies and demonstrate its suitability for prognostic biomarker development. MATERIALS AND METHODS: Variables were first categorized by chronological stages and then by medical topics. We created a dictionary to harmonize variable names and value coding. We began by merging comprehensive data from 2 landmark nationwide therapeutic hypothermia for HIE trials (2008-2016, 21 sites) in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Neonatal Research Network (NRN). The 2 datasets differ in available variables, variable naming and coding, necessitating harmonization. To demonstrate the utility of this data harmonization framework, we computed the distributions of variables and ranked them by the strength of associations with 18- to 22-month outcomes. Associations were measured using Pearson's correlation analysis. Outcomes were defined as (a) a 5-class variable: survivors with normal, mild, moderate, severe disability, or death and (b) the Bayley-III Scales. RESULTS: We created a dictionary of 1181 variables on 532 patients across 5 chronologic categories and 60 medical subcategories. The distribution of major predictive and outcome variables, and the variables strongly associated with neurodevelopmental outcomes at 18-22 months were presented. The modified Sarnat scores at the Post-intervention and NICU-discharge stage, and the NRN pattern of MRI injury score showed strong associations with outcome variables. CONCLUSION: We designed a data harmonization framework specifically for HIE. Our initial effort in merging 2 iconic clinical trials shows strong predictor-outcome associations, allowing subsequent development of advanced prognostic biomarkers of neonatal HIE.