Twelve years ago, 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.

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