Perinatal risk factors and outcome coding in clinical and administrative databases

Abstract

BACKGROUND AND OBJECTIVES: Administrative databases may allow true population-based studies and quality improvement endeavors, but the accuracy of billing codes for capturing key risk factors and outcomes needs to be assessed. We sought to describe the performance of a statewide administrative database and the clinical database from the California Perinatal Quality Care Collaborative (CPQCC). METHODS: This population-based retrospective cohort study linked key perinatal risk factors and outcomes from the 133-unit CPQCC database to relevant billing codes from administrative maternal and newborn inpatient discharge records, for 50631 infants born from 2006 to 2012. Using the CPQCC record as the gold standard, we calculated the positive predictive value, negative predictive value, and Matthews correlation coefficient for each item, then evaluated comparative performance across units. RESULTS: The Matthews correlation coefficient was highest (>0.7; strong positive correlation) for multiple delivery, Cesarean delivery, very low birth weight, maternal hypertension, maternal diabetes, patent ductus arteriosus, in-hospital death, patent ductus arteriosus and retinopathy of prematurity surgeries, extracorporeal life support, and intraventricular hemorrhage. Maternal chorioamnionitis, fetal distress, retinopathy of prematurity staging, chronic lung disease, and pneumothorax were the least reliably coded. Maternal factors and delivery details were more reliably coded in the maternal inpatient record than the newborn inpatient record. CONCLUSIONS: Several important perinatal risk factors and outcomes are highly congruent between these administrative and clinical databases. Several subjective risk factors and outcomes are appropriate targets for data improvement initiatives. The ability for timely extraction of administrative inpatient data will be key to their usefulness in quality metrics.