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|Title||Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors.|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman ES, Smith M, Escobar GJ|
|Date Published||2011 Nov|
|Keywords||Adult, Age of Onset, Anti-Bacterial Agents, Bacteremia, Bayes Theorem, Case-Control Studies, Female, Follow-Up Studies, Gestational Age, Gram-Negative Bacteria, Gram-Positive Bacteria, Humans, Infant, Newborn, Infant, Newborn, Diseases, Infectious Disease Transmission, Vertical, Male, Multivariate Analysis, Pregnancy, Pregnancy Complications, Infectious, Prevalence, Probability, Reproducibility of Results, Retrospective Studies, Risk Assessment, Severity of Illness Index, Treatment Outcome, United States|
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.
|PubMed Central ID||PMC3208962|
|Grant List||R01-GM-80180-3 / GM / NIGMS NIH HHS / United States|