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Editorial
September 3, 2019

Neonatal Sepsis Evaluation: Facing the Certainty of Uncertainty

Author Affiliations
  • 1Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
  • 2Perelman School of Medicine, Department of Pediatrics, University of Pennsylvania, Philadelphia
  • 3The Permanente Medical Group Inc, Oakland, California
  • 4Division of Research, Kaiser Permanente Northern California, Oakland
JAMA Pediatr. Published online September 3, 2019. doi:10.1001/jamapediatrics.2019.2832

In 2007, we began a National Institutes of Health–funded study of neonatal early-onset sepsis (EOS) whose goal was to develop multivariate predictive models that could be used by clinicians to evaluate a newborn’s risk of EOS. We approached this study with 3 objectives. First, we would evaluate the statistical association of individual, established risk factors for neonatal EOS with the outcome of culture-confirmed infection. Second, we would use routinely captured, objective data that could be found in an electronic medical record. Third, we would determine whether we could develop accurate multivariate predictive models without using the clinical diagnosis of chorioamnionitis. We took a Bayesian perspective that quantifies the value of incremental information explicitly. This approach begins with a prior probability (the incidence of EOS in the population as baseline risk). This prior probability is then modified as more information becomes available. We originally intended to modify this initial risk estimate using the likelihood ratios from 3 models: 1 model based on risk factors known at the moment of birth, 1 model based on the infant’s clinical condition, and 1 model based on the complete blood cell count. Performance of the complete blood cell count components was poor,1 so we ultimately decided not to include it in our final approach. We first published the model based on risk factors at birth,2 and later published the model based on the evolving clinical condition along with a proposed clinical management algorithm.3 The management algorithm was developed during a series of discussions with clinicians who practiced in the birth hospitals of Kaiser-Permanente Northern California (KPNC) that focused on the number-needed-to-treat associated with different levels of estimated risk. To facilitate clinical use of the models, we developed the web-based neonatal EOS calculator (https://neonatalsepsiscalculator.kaiserpermanente.org). We did not recommend use of this calculator in a vacuum; its deployment at KPNC hospitals was part of a carefully designed implementation program that included reassessment of the calculator with more recent KPNC data.4 We performed a large prospective validation study at the KPNC birth hospitals5 and a smaller study in Philadelphia, Pennsylvania.6 Although these studies used slightly different clinical management algorithms, both demonstrated significant declines in the use of empirical antibiotics among infants born at 35 or more weeks’ gestation. Neither study identified any short-term safety issues; most important, the KPNC study demonstrated no change in the very low incidence (approximately 1 in 20 000 live births) of readmission to the hospital with EOS after initial discharge from the birth hospital. Recently, as reviewed by Achten et al7 in this issue of JAMA Pediatrics, several other investigators have performed retrospective medical record analyses or prospective implementation studies to address the clinical performance of the calculator models. Achten and colleagues7 concluded that the use of this quantitative approach to neonatal EOS risk is associated with decreased antibiotic use compared with prior approaches, without concern that the approach fails to identify significant numbers of newborns with EOS.

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