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学术性妇产医院中脓毒症风险计算器的应用

Implementation of the Sepsis Risk Calculator at an Academic Birth Hospital.

作者信息

Dhudasia Miren B, Mukhopadhyay Sagori, Puopolo Karen M

机构信息

Section on Newborn Medicine, Pennsylvania Hospital, Philadelphia, Pennsylvania.

Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and.

出版信息

Hosp Pediatr. 2018 May;8(5):243-250. doi: 10.1542/hpeds.2017-0180.

Abstract

BACKGROUND

Multivariate predictive models for estimating the risk of neonatal early-onset sepsis (EOS) are available as a Web-based sepsis risk calculator (SRC) and may reduce the proportion of newborns empirically treated with antibiotics after birth. EOS risk assessment based on such models would require workflow changes at most birth hospitals.

METHODS

A multidisciplinary team of obstetric, neonatal, and information technology staff at a large, academic, birth hospital collaborated to implement the SRC. The obstetric electronic medical record was modified to provide a link to the SRC. Labor and delivery nurses calculated the sepsis risk at birth and alerted neonatal clinicians for risk estimates ≥0.7 cases per 1000 live births. Subsequent interventions were based on the risk estimate and newborn clinical examination. We compared the proportion of infants born at ≥36 weeks' gestation with laboratory testing and empirical antibiotics for risk of EOS during the 15-month periods before ( = 5692) and after ( = 6090) implementation. EOS cases were reviewed to assess for safety.

RESULTS

Empirical antibiotic use among newborns ≤72 hours old declined by 42% (6.3% to 3.7%; relative risk 0.58 [95% confidence interval, 0.50-0.69]), and laboratory testing declined by 82% (26.9% to 4.9%; relative risk 0.18 [95% confidence interval, 0.16-0.21]). The EOS incidence was not different between the study periods, and no safety concerns were identified.

CONCLUSIONS

The SRC was integrated into the workflow of a large, academic perinatal center, resulting in significant reductions in antibiotics and laboratory testing for EOS and demonstrating the potential for this approach to impact national practice.

摘要

背景

用于估计新生儿早发型败血症(EOS)风险的多变量预测模型可通过基于网络的败血症风险计算器(SRC)获取,这可能会减少出生后经验性使用抗生素的新生儿比例。基于此类模型的EOS风险评估将需要大多数产科医院改变工作流程。

方法

一家大型学术产科医院的产科、新生儿科和信息技术人员组成的多学科团队合作实施了SRC。对产科电子病历进行了修改,以提供与SRC的链接。分娩护士在出生时计算败血症风险,并在风险估计≥每1000例活产0.7例时提醒新生儿临床医生。随后的干预措施基于风险估计和新生儿临床检查。我们比较了在实施前(n = 5692)和实施后(n = 6090)的15个月期间,孕周≥36周的婴儿因EOS风险进行实验室检测和经验性使用抗生素的比例。对EOS病例进行了审查以评估安全性。

结果

72小时内新生儿的经验性抗生素使用减少了42%(6.3%降至3.7%;相对风险0.58[95%置信区间,0.50 - 0.69]),实验室检测减少了82%(26.9%降至4.9%;相对风险0.18[95%置信区间,0.16 - 0.21])。研究期间的EOS发病率没有差异,也未发现安全问题。

结论

SRC已整合到一家大型学术围产期中心的工作流程中,导致EOS的抗生素使用和实验室检测显著减少,并证明了这种方法影响全国实践的潜力。

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