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.
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.
Entities:
Keywords:
early-onset sepsis; late preterm infant; predictive modeling; term newborn
Authors: Karen M Puopolo; David Draper; Soora Wi; Thomas B Newman; John Zupancic; Ellice Lieberman; Myesha Smith; Gabriel J Escobar Journal: Pediatrics Date: 2011-10-24 Impact factor: 7.124
Authors: G J Escobar; D K Li; M A Armstrong; M N Gardner; B F Folck; J E Verdi; B Xiong; R Bergen Journal: Pediatrics Date: 2000-08 Impact factor: 7.124
Authors: Halana V Whitehead; Christopher C McPherson; Zachary A Vesoulis; Barbara A Cohlan; Rakesh Rao; Barbara B Warner; F Sessions Cole Journal: J Pediatr Date: 2020-08 Impact factor: 4.406
Authors: James L Wynn; Hector R Wong; Thomas P Shanley; Matthew J Bizzarro; Lisa Saiman; Richard A Polin Journal: Pediatr Crit Care Med Date: 2014-07 Impact factor: 3.624
Authors: Paolo Montaldo; Roberto Rosso; Alfredo Santantonio; Giovanni Chello; Paolo Giliberti Journal: Pediatr Res Date: 2016-11-03 Impact factor: 3.756