A Validated Calculator to Estimate Risk of Chorioamnionitis in Laboring and Induced Patients at Term.

IF 1.5 4区 医学 Q3 OBSTETRICS & GYNECOLOGY
Helen Gomez Slagle,Richard J Caplan,Tetsuya Kawakita,Anthony Sciscione,Matthew Hoffman
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Abstract

OBJECTIVE Chorioamnionitis is associated with neonatal morbidity and infection-related mortality, but our ability to predict intrapartum infection is limited. We sought to derive and validate a prediction model for chorioamnionitis among patients presenting to labor and delivery at term. STUDY DESIGN This was a planned secondary analysis of a large cohort study from 2014 through 2018 at an academic tertiary care center. To derive a prediction model for chorioamnionitis, we limited our analysis to full-term (≥37 weeks) patients with a singleton gestation undergoing labor induction and presenting in labor. Both nulliparous and multiparous patients were included. Patients with a planned cesarean delivery, fever on admission, or missing data were excluded. The model was derived using multivariable logistic regression. Refinement of the prediction model with internal calibration was performed. External validation was performed utilizing a publicly available database (Consortium on Safe Labor) and applying the same inclusion and exclusion criteria. The discriminative power of each model was assessed using a bootstrap, bias-corrected area under the curve. RESULTS The chorioamnionitis rates in the derivation and external validation groups were: 5% (1,005/19,966) and 5.8% (n = 3,005/52,171), respectively. In multivariable modeling, maternal age, nulliparity, gestational age, smoking status, group B streptococcus colonization, hours ruptured, number of cervical exams, length of labor, epidural use, internal monitoring, and meconium were significantly associated with infection. A calculator was created and externally validated with an area under the curve of 0.82 (95% confidence interval, 0.81-0.82). External validity was further confirmed with a calibration intercept of 0.81. CONCLUSION This is the first infection calculator created and validated for the prediction of developing chorioamnionitis in patients undergoing induction of labor at term. This calculator can be used to augment patient counseling and guide intrapartum infection surveillance in laboring patients.
估算临产和引产患者绒毛膜羊膜炎风险的有效计算器。
目的绒毛膜羊膜炎与新生儿发病率和感染相关死亡率有关,但我们预测产内感染的能力有限。我们试图推导并验证一个预测模型,用于预测临产患者的绒毛膜羊膜炎。研究设计这是对一家学术性三级护理中心从 2014 年到 2018 年进行的一项大型队列研究的二次分析。为了得出绒毛膜羊膜炎的预测模型,我们将分析对象限定为接受引产和分娩的单胎足月(≥37 周)患者。单胎和多胎患者均包括在内。排除了计划剖宫产、入院时发烧或数据缺失的患者。该模型采用多变量逻辑回归法得出。通过内部校准完善了预测模型。外部验证利用公开数据库(安全分娩联盟)进行,并采用相同的纳入和排除标准。结果推导组和外部验证组的绒毛膜羊膜炎发生率分别为分别为 5%(1,005/19,966)和 5.8%(n = 3,005/52,171)。在多变量建模中,产妇年龄、无胎儿、孕龄、吸烟状况、B 组链球菌定植、破裂时间、宫颈检查次数、产程长短、硬膜外麻醉使用情况、内部监测和胎粪与感染显著相关。我们创建了一个计算器并进行了外部验证,其曲线下面积为 0.82(95% 置信区间,0.81-0.82)。结论:这是首个用于预测足月引产患者绒毛膜羊膜炎的感染计算器。该计算器可用于加强对患者的咨询,并指导对临产患者进行产前感染监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
American journal of perinatology
American journal of perinatology 医学-妇产科学
CiteScore
5.90
自引率
0.00%
发文量
302
审稿时长
4-8 weeks
期刊介绍: The American Journal of Perinatology is an international, peer-reviewed, and indexed journal publishing 14 issues a year dealing with original research and topical reviews. It is the definitive forum for specialists in obstetrics, neonatology, perinatology, and maternal/fetal medicine, with emphasis on bridging the different fields. The focus is primarily on clinical and translational research, clinical and technical advances in diagnosis, monitoring, and treatment as well as evidence-based reviews. Topics of interest include epidemiology, diagnosis, prevention, and management of maternal, fetal, and neonatal diseases. Manuscripts on new technology, NICU set-ups, and nursing topics are published to provide a broad survey of important issues in this field. All articles undergo rigorous peer review, with web-based submission, expedited turn-around, and availability of electronic publication. The American Journal of Perinatology is accompanied by AJP Reports - an Open Access journal for case reports in neonatology and maternal/fetal medicine.
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