Neonatal Early-Onset Sepsis Calculator (Kaiser)

Kaiser Early-Onset Sepsis calculator (Kuzniewicz 2017): the probabilistic EOS risk per 1,000 live births from gestational age, maximum temperature, ROM duration, GBS status, intrapartum antibiotics, and the newborn exam.

Open the calculator → Runs in your browser. Data leaves only if you deliberately send a problem report from the interactive tool.

Example

Gestational age
39
Maximum maternal temperature
100.4
Duration of ROM
18
Maternal GBS status
Positive
Intrapartum antibiotics
None, or any antibiotic under 2 h before delivery
Newborn clinical exam
Well-appearing

Result: EOS risk 0.62 per 1,000 (risk at birth 1.50 per 1,000): below the 1 per 1,000 culture threshold, but the risk at birth is 1 per 1,000 or higher -- enhanced observation with vitals every 4 h for 24 h.

The tool opens with these values already filled in. Replace them with your own.

What the result means

Prior
a logistic model over gestational age, highest intrapartum temperature (°F), ROM duration, GBS status, and intrapartum antibiotics gives the EOS risk at birth.
Posterior
the prior is combined by Bayes with the exam likelihood ratio (well 0.41, equivocal 5.0, clinical illness 21.2) to give the EOS probability per 1,000.
Management
under 1/1,000 routine care; 1 to under 3 blood culture; 3 or more empiric antibiotics; enhanced observation when risk at birth is 1/1,000 or higher.

What you enter

  • EOS baseline incidence (per 1,000)
  • Gestational age (weeks)
  • Maximum maternal temperature (°F)
  • Duration of ROM (hours)
  • Maternal GBS status
  • Intrapartum antibiotics
  • Newborn clinical exam
How this is calculated

Kuzniewicz MW, Puopolo KM, Fischer A, et al. A quantitative, risk-based approach to the management of neonatal early-onset sepsis. JAMA Pediatr. 2017;171(4):365-371; on the model of Puopolo KM, et al. Pediatrics. 2011;128(5):e1155. Read the source ↗

A reference and educational tool. Not medical, legal, or financial advice, and not a substitute for clinician judgment.

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Built by Clay Good. Source on GitHub.