Department of Newborn Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
Curr Opin Pediatr. 2013 Apr;25(2):161-6. doi: 10.1097/MOP.0b013e32835e1f96.
Neonatal early-onset sepsis (EOS) is a very low-incidence, but potentially fatal condition among term and late preterm newborns. EOS algorithms based on risk-factor threshold values result in evaluation and empiric antibiotic treatment of large numbers of uninfected newborns, leading to unnecessary antibiotic exposures and maternal/infant separation. Ideally, risk stratification should be quantitative, employ information conserving strategies, and be readily transferable to modern comprehensive electronic medical records.
We performed a case-control study of infants born at or above 34 weeks' gestation with blood culture-proven EOS. We defined the relationship of established predictors to the risk of EOS, then used multivariate analyses and split validation to develop a predictive model using objective data. The model provides an estimation of sepsis risk that can identify the same proportion of EOS cases by evaluating fewer infants, as compared with algorithms based on subjective diagnoses and cut-off values for continuous predictors.
An alternative approach to EOS risk assessment based only on objective data could decrease the number of infants evaluated and empirically treated for EOS, compared with currently recommended algorithms. Prospective evaluation is needed to determine the accuracy and safety of using the sepsis risk model to guide clinical decision-making.
新生儿早发性败血症（EOS）是足月和晚期早产儿中发病率非常低但潜在致命的疾病。基于危险因素阈值的 EOS 算法导致大量未感染新生儿接受评估和经验性抗生素治疗，导致不必要的抗生素暴露和母婴分离。理想情况下，风险分层应该是定量的，采用信息保守策略，并易于转移到现代综合电子病历中。
我们对胎龄在 34 周及以上且血培养证实为 EOS 的婴儿进行了病例对照研究。我们确定了既定预测因素与 EOS 风险的关系，然后使用多元分析和拆分验证，使用客观数据开发预测模型。与基于主观诊断和连续预测因素临界值的算法相比，该模型提供了一种可以通过评估较少的婴儿来识别相同比例 EOS 病例的败血症风险估计。
仅基于客观数据的 EOS 风险评估替代方法与目前推荐的算法相比，可能会减少评估和经验性治疗 EOS 的婴儿数量。需要前瞻性评估使用败血症风险模型指导临床决策的准确性和安全性。
