A predictive model for perinatal hypoxic ischemic encephalopathy using linked maternal and neonatal hospital data

PURPOSE: To build an evidence-based model to estimate case-specific risk of perinatal hypoxic ischemic encephalopathy. METHODS: A retrospective, cross-sectional study of all births in Hawaii, Michigan, and New Jersey between 2010 and 2015, using linked maternal labor/delivery and neonatal birth records. Stepwise logistic regression and competitive Akaike information criterion were used to identify the most parsimonious model. Predictive ability of the model was measured with bootstrapped optimism-adjusted area under the ROC curve. RESULTS: Among 836,216 births there were 376 (0.45 per 1,000) cases of hypoxic ischemic encephalopathy. The final model included 28 variables, 24 associated with increased risk, and 4 that were protective. The optimism-adjusted area under the ROC curve was 0.84. Estimated risk in the study population ranged from 1 in ~323,000 to 1 in 2.5. The final model confirmed known risk factors (e.g., sentinel events and shoulder dystocia) and identified novel risk factors, such as maternal race and insurance status. CONCLUSION: Our study shows that risk of perinatal hypoxic ischemic encephalopathy injury can be estimated with high confidence. Our model fills a notable gap in the study of hypoxic ischemic encephalopathy prevention: the estimation of risk, particularly in the United States population which is unique with respect to racial and socioeconomic disparities.