title: Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks' gestation.
abstract: OBJECTIVE
  To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  Using 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).
  
  
  CONCLUSIONS
  It 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.
authors.affiliation: Regional Director for Hospital Operations Research, Kaiser Permanente Northern California, Division of Research, 2000 Broadway Ave, Oakland, CA 94612. gabriel.escobar@kp.org.
authors.forename: Gabriel J
authors.initials: GJ
authors.lastname: Escobar
authors.forename: Karen M
authors.initials: KM
authors.lastname: Puopolo
authors.forename: Soora
authors.initials: S
authors.lastname: Wi
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Turk
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kuzniewicz
authors.forename: Eileen M
authors.initials: EM
authors.lastname: Walsh
authors.forename: Thomas B
authors.initials: TB
authors.lastname: Newman
authors.forename: John
authors.initials: J
authors.lastname: Zupancic
authors.forename: Ellice
authors.initials: E
authors.lastname: Lieberman
authors.forename: David
authors.initials: D
authors.lastname: Draper
mesh_terms: D017668:Age of Onset; D000465:Algorithms; D000900:Anti-Bacterial Agents; D016022:Case-Control Studies; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D007235:Infant, Premature, Diseases / Q000175:diagnosis* / Q000209:etiology / Q000628:therapy; D016015:Logistic Models; D008297:Male; D015999:Multivariate Analysis; D011379:Prognosis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D018805:Sepsis / Q000175:diagnosis* / Q000209:etiology / Q000628:therapy; D057832:Watchful Waiting
chemical_list: D000900:Anti-Bacterial Agents
keywords: early-onset sepsis; late preterm infant; predictive modeling; term newborn