Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks' gestation

Gabriel J Escobar, Karen M Puopolo, Soora Wi, Benjamin J Turk, Michael W Kuzniewicz, Eileen M Walsh, Thomas B Newman, John Zupancic, Ellice Lieberman, David Draper, Gabriel J Escobar, Karen M Puopolo, Soora Wi, Benjamin J Turk, Michael W Kuzniewicz, Eileen M Walsh, Thomas B Newman, John Zupancic, Ellice Lieberman, David Draper

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.

Keywords: early-onset sepsis; late preterm infant; predictive modeling; term newborn.

Figures

FIGURE 1
FIGURE 1
Sepsis risk at birth ranges identified via recursive partitioning. The boxes are drawn to scale and show the percentage distribution of patients (shaded boxes) and controls (clear boxes), with the sepsis risk at birth per thousand live births from the maternal model (see citation 2) at the top. The figure shows the highly uneven distribution of EOS cases in the study population of infants born at ≥34 weeks’ gestation.
FIGURE 2
FIGURE 2
Quantitative Risk Stratification for EOS. Quantitative risk stratification schema for newborns >34 weeks’ gestation developed in this study. Stratification is based on clinical evolution in the first 12 hours of age (rows) and sepsis risk at birth estimated from maternal risk factors (columns). Infants who have a sepsis risk at birth of >1.54/1000 live births, or who have a sepsis risk at birth >0.65/1000 and an equivocal presentation fall into the “Treat Empirically” group, which has an NNT of 118 and accounts for 4% of all live births. Infants with an equivocal presentation (middle cell, far left column) or who are well appearing but whose sepsis risk at birth is 0.65 to 1.54/1000 (top cell, middle column) fall into the “Observe and Evaluate” group, which has an NNT of 823 and accounts for 11% of all live births. Last, the largest group, well-appearing infants with a sepsis risk at birth

Source: PubMed

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