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.

<p><strong>OBJECTIVES: </strong>To determine if NICU teams participating in a multicenter quality improvement (QI) collaborative achieve increased compliance with the Centers for Disease Control and Prevention (CDC) core elements for antibiotic stewardship and demonstrate reductions in antibiotic use (AU) among newborns.</p>

<p><strong>METHODS: </strong>From January 2016 to December 2017, multidisciplinary teams from 146 NICUs participated in Choosing Antibiotics Wisely, an Internet-based national QI collaborative conducted by the Vermont Oxford Network consisting of interactive Web sessions, a series of 4 point-prevalence audits, and expert coaching designed to help teams test and implement the CDC core elements of antibiotic stewardship. The audits assessed unit-level adherence to the CDC core elements and collected patient-level data about AU. The AU rate was defined as the percentage of infants in the NICU receiving 1 or more antibiotics on the day of the audit.</p>

<p><strong>RESULTS: </strong>The percentage of NICUs implementing the CDC core elements increased in each of the 7 domains (leadership: 15.4%-68.8%; accountability: 54.5%-95%; drug expertise: 61.5%-85.1%; actions: 21.7%-72.3%; tracking: 14.7%-78%; reporting: 6.3%-17.7%; education: 32.9%-87.2%; &lt; .005 for all measures). The median AU rate decreased from 16.7% to 12.1% ( for trend &lt; .0013), a 34% relative risk reduction.</p>

<p><strong>CONCLUSIONS: </strong>NICU teams participating in this QI collaborative increased adherence to the CDC core elements of antibiotic stewardship and achieved significant reductions in AU.</p>

<p><strong>OBJECTIVE: </strong>To develop a quantitative model to estimate the probability of neonatal early-onset bacterial infection on the basis of maternal intrapartum risk factors.</p>

<p><strong>METHODS: </strong>This was a nested case-control study of infants born at ≥34 weeks' gestation at 14 California and Massachusetts hospitals from 1993 to 2007. Case-subjects had culture-confirmed bacterial infection at &lt;72 hours; controls were randomly selected, frequency-matched 3:1 according to year and birth hospital. We performed multivariate analyses and split validation to define a predictive model based only on information available in the immediate perinatal period.</p>

<p><strong>RESULTS: </strong>We identified 350 case-subjects from a cohort of 608,014 live births. Highest intrapartum maternal temperature revealed a linear relationship with risk of infection below 100.5°F, above which the risk rose rapidly. Duration of rupture of membranes revealed a steadily increasing relationship with infection risk. Increased risk was associated with both late-preterm and postterm delivery. Risk associated with maternal group B Streptococcus colonization is diminished in the era of group B Streptococcus prophylaxis. Any form of intrapartum antibiotic given &gt;4 hours before delivery was associated with decreased risk. Our model showed good discrimination and calibration (c statistic = 0.800 and Hosmer-Lemeshow P = .142 in the entire data set).</p>

<p><strong>CONCLUSIONS: </strong>A predictive model based on information available in the immediate perinatal period performs better than algorithms based on risk-factor threshold values. This model establishes a prior probability for newborn sepsis, which could be combined with neonatal physical examination and laboratory values to establish a posterior probability to guide treatment decisions.</p>