Hyperbilirubinemia, Neonatal Clinical Trial
Official title:
An Evidence-based Strategy for Assessing the Risk of Significant Neonatal Hyperbilirubinemia
Objective: To develop an evidence-based strategy for assessing the risk of significant
hyperbilirubinemia in healthy term and near-term (late-preterm) neonates.
Hypothesis: A stepwise strategy which combines clinical parameters and serial non-invasive
transcutaneous bilirubin (TcB) values could reliably predict significant neonatal
hyperbilirubinemia.
Methods: Data from neonates >34 weeks' gestation included in the registry for neonatal
hyperbilirubinemia of the well-baby nursery of the University Hospital of Patras, from
January 2008 to December 2010 will be reviewed.
The registry includes prospectively collected data such as sex, gestational age, gestation
and perinatal information, mother's and infant's ABO group and Rh, G6PD deficiency, Coombs
test, type of delivery and complications, birthweight, postnatal medications and
interventions, type and volume of feeding (daily), extension of jaundice, TcB measurements
at intervals of 12+/-4 hours until discharge, total serum bilirubin values (if obtained),
TcB or TSB measurements at follow-up, weight at discharge, need of phototherapy (inpatient
or after discharge). TcB and TSB values are plotted on a hour-specific chart.
A novel predictive nomogram based on TcB measurements (Varvarigou et al. Pediatrics
2009;124:1052-9) will be used to classify TcB values as high, intermediate, and low risk.
Significant hyperbilirubinemia will be defined as a TSB value above the phototherapy
threshold level according to the AAP 2004 guidelines
Statistics: Independent and joint effects of various clinical factors on the development of
significant hyperbilirubinemia will be evaluated by logistic regression analysis Cluster
analysis and Chi-squared Automatic Interaction Detection (CHAID) tree method will be used to
develop the strategy. At each step, CHAID chooses the independent (predictor) variable that
has the strongest interaction with the dependent variable. Categories of each predictor are
merged if they are not significantly different with respect to the dependent variable.
n/a
Observational Model: Cohort
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