• Gabriel J Escobar, Karen M Puopolo, Soora Wi, Benjamin J Turk, Michael W Kuzniewicz, Eileen M Walsh, Thomas B Newman, John Zupancic, Ellice Lieberman, and David Draper.
• Regional Director for Hospital Operations Research, Kaiser Permanente Northern California, Division of Research, 2000 Broadway Ave, Oakland, CA 94612. gabriel.escobar@kp.org.
• Pediatrics. 2014 Jan 1; 133 (1): 30-6.

ObjectiveTo define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation.MethodsWe conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset.ResultsUsing a base population of 608,014 live births ≥ 34 weeks' gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000).ConclusionsIt is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80,000 to 240,000 US newborns each year.

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