Technical assessment of the neonatal early-onset sepsis risk calculator

William E. Benitz*, Niek B. Achten

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

31 Citations (Scopus)


The use of the neonatal early-onset sepsis risk calculator, developed by Kaiser Permanente Northern California (CA, USA), is increasing for the management of late preterm and full term newborn babies at risk for early-onset sepsis. The calculator is based on a robust logistic regression model that provides quantitative individualised estimates of early-onset sepsis risk. Low sensitivity for prediction of sepsis at birth shows that standard perinatal risk factors alone are insufficient for ascertainment of neonatal early-onset sepsis. Performance is improved by the addition of physical examination findings at birth, but the sensitivity of combined findings remains limited. The present implementation of the calculator integrates risk factors and examination findings. A methodological error in adapting the regression for application in the population (rather than the development sample) and several subsequent modifications compromise the accuracy of quantitative predictions of the absolute risk of sepsis, but these factors are not expected to seriously undermine the use of the calculator for risk stratification. The calculator has served as an instrument of change away from previously recommended categorical risk ascertainment strategies, and its implementation reduces the need for diagnostic testing and empirical antibiotic treatment without apparent ill effects. However, the calculator should not be relied on to provide accurate estimates for individuals with regard to absolute risk of early-onset sepsis in newborn babies.

Original languageEnglish
Pages (from-to)e134-e140
JournalThe Lancet Infectious Diseases
Issue number5
Early online date29 Oct 2020
Publication statusPublished - 1 May 2021

Bibliographical note

Funding Information:
We thank Maija Benitz and Gordon Stewart (Roger Williams University, Bristol, RI, USA) for discussions of offsetting errors in estimation, Kristin Sainani (Stanford University, Stanford, CA, USA) for discussion of logistic regression modelling in case-control studies, and Frans Pl?tz (Tergooi Hospital, Blaricum, Netherlands) for encouragement and support.

Publisher Copyright:
© 2021 Elsevier Ltd


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