IMPORTANCE Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn's clinical status.OBJECTIVES To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. DESIGN, SETTING, AND PARTICIPANTS The study cohort includes 204 485 infants born at 35 weeks' gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period [January 1, 2010, through November 31, 2012]), (2) multivariable estimates of sepsis risk at birth (learning period [December 1, 2012, through June 30, 2014]), and (3) the multivariable risk estimate combined with the infant's clinical condition in the first 24 hours after birth (EOS calculator period [July 1, 2014, through December 31, 2015]). MAIN OUTCOMES AND MEASURES The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. RESULTS The study cohort included 204 485 infants born at 35 weeks' gestation or later: 95 343 in the baseline period (mean [SD] age, 39.4 [1.3] weeks; 46 651 male [51.0%]; 37 007 white, non-Hispanic [38.8%]), 52 881 in the learning period (mean [SD] age, 39.3 [1.3] weeks; 27 067 male [51.2%]; 20 175 white, non-Hispanic [38.2%]), and 56 261 in the EOS calculator period (mean [SD] age, 39.4 [1.3] weeks; 28 575 male [50.8%]; 20 484 white, non-Hispanic [36.4%]). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.03% in the baseline period, 0.03% in the learning period, and 0.02% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator.CONCLUSIONS AND RELEVANCE Clinical care algorithms based on individual infant ...

WHAT'S KNOWN ON THIS SUBJECT:The management of term and near-term newborns suspected of early-onset sepsis, particularly when they are not clearly symptomatic, remains controversial. Methods for quantifying risk that combine maternal factors with a newborn' s evolving clinical examination have been lacking. WHAT THIS STUDY ADDS:This study provides a method for predicting risk of early-onset sepsis. It combines maternal risk factors with objective measures of a newborn' s clinical examination and places newborns into 3 risk groups (treat empirically, observe and evaluate, and continued observation). 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.

BACKGROUND: The majority of newborns are exclusively breastfed during the birth hospitalization, and weight loss is nearly universal for these neonates. The amount of weight lost varies substantially among newborns with higher amounts of weight loss increasing risk for morbidity. No hour-by-hour newborn weight loss nomogram exists to assist in early identification of those on a trajectory for adverse outcomes.METHODS: For 161 471 term, singleton neonates born at $36 weeks' gestation at Northern California Kaiser Permanente hospitals in 2009-2013, data were extracted from the birth hospitalization regarding delivery mode, race/ethnicity, feeding type, and weights from electronic records. Quantile regression was used to create nomograms stratified by delivery mode that estimated percentiles of weight loss as a function of time among exclusively breastfed neonates. Weights measured subsequent to any nonbreastmilk feeding were excluded.RESULTS: Among this sample, 108 907 newborns had weights recorded while exclusively breastfeeding with 83 433 delivered vaginally and 25 474 delivered by cesarean. Differential weight loss by delivery mode was evident 6 hours after delivery and persisted over time. Almost 5% of vaginally delivered newborns and .10% of those delivered by cesarean had lost $10% of their birth weight 48 hours after delivery. By 72 hours, .25% of newborns delivered by cesarean had lost $10% of their birth weight.CONCLUSIONS: These newborn weight loss nomograms demonstrate percentiles for weight loss by delivery mode for those who are exclusively breastfed. The nomograms can be used for early identification of neonates on a trajectory for greater weight loss and related morbidities. WHAT'S KNOWN ON THIS SUBJECT:Exclusively breastfed newborns lose weight daily in the first few days after birth. The amount of weight lost varies substantially between newborns, with higher amounts of weight loss increasing risk for morbidity. WHAT THIS STUDY ADDS:This study presents nomograms demonstrating percentiles for weight loss by delivery mode for those who are exclusively breastfed. The nomograms have potential to be used for early identification of neonates on a trajectory for greater weight loss and related morbidities.

OBJECTIVE: De novo mutations of the gene sodium channel 1α (SCN1A) are the major cause of Dravet syndrome, an infantile epileptic encephalopathy. US incidence of DS has been estimated at 1 in 40 000, but no US epidemiologic studies have been performed since the advent of genetic testing. METHODS: In a retrospective, population-based cohort of all infants born at Kaiser Permanente Northern California during 2007–2010, we electronically identified patients who received ≥2 seizure diagnoses before age 12 months and who were also prescribed anticonvulsants at 24 months. A child neurologist reviewed records to identify infants who met 4 of 5 criteria for clinical Dravet syndrome: normal development before seizure onset; ≥2 seizures before age 12 months; myoclonic, hemiclonic, or generalized tonic-clonic seizures; ≥2 seizures lasting >10 minutes; and refractory seizures after age 2 years. SCN1A gene sequencing was performed as part of routine clinical care. RESULTS: Eight infants met the study criteria for clinical Dravet syndrome, yielding an incidence of 1 per 15 700. Six of these infants (incidence of 1 per 20 900) had a de novo SCN1A missense mutation that is likely to be pathogenic. One infant had an inherited SCN1A variant that is unlikely to be pathogenic. All 8 experienced febrile seizures, and 6 had prolonged seizures lasting >10 minutes by age 1 year. CONCLUSIONS: Dravet syndrome due to an SCN1A mutation is twice as common in the United States as previously thought. Genetic testing should be considered in children with ≥2 prolonged febrile seizures by 1 year of age.

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