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Pediatrics. 2011 Nov;128(5):e1155-63. doi: 10.1542/peds.2010-3464. Epub 2011 Oct 24.

Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors.

Author information

  • 1Department of Newborn Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA. kpuopolo@partners.org

Abstract

OBJECTIVE:

To develop a quantitative model to estimate the probability of neonatal early-onset bacterial infection on the basis of maternal intrapartum risk factors.

METHODS:

This was a nested case-control study of infants born at ≥34 weeks' gestation at 14 California and Massachusetts hospitals from 1993 to 2007. Case-subjects had culture-confirmed bacterial infection at <72 hours; controls were randomly selected, frequency-matched 3:1 according to year and birth hospital. We performed multivariate analyses and split validation to define a predictive model based only on information available in the immediate perinatal period.

RESULTS:

We identified 350 case-subjects from a cohort of 608,014 live births. Highest intrapartum maternal temperature revealed a linear relationship with risk of infection below 100.5°F, above which the risk rose rapidly. Duration of rupture of membranes revealed a steadily increasing relationship with infection risk. Increased risk was associated with both late-preterm and postterm delivery. Risk associated with maternal group B Streptococcus colonization is diminished in the era of group B Streptococcus prophylaxis. Any form of intrapartum antibiotic given >4 hours before delivery was associated with decreased risk. Our model showed good discrimination and calibration (c statistic = 0.800 and Hosmer-Lemeshow P = .142 in the entire data set).

CONCLUSIONS:

A predictive model based on information available in the immediate perinatal period performs better than algorithms based on risk-factor threshold values. This model establishes a prior probability for newborn sepsis, which could be combined with neonatal physical examination and laboratory values to establish a posterior probability to guide treatment decisions.

PMID:
22025590
[PubMed - indexed for MEDLINE]
PMCID:
PMC3208962
Free PMC Article

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