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基于新生儿早发性败血症计算器对培养证实的早发性败血症病例进行分层:一项个体患者数据荟萃分析。

Stratification of Culture-Proven Early-Onset Sepsis Cases by the Neonatal Early-Onset Sepsis Calculator: An Individual Patient Data Meta-Analysis.

机构信息

Department of Pediatrics, Tergooi Hospital, Blaricum, The Netherlands; Faculty of Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands; Department of Pediatrics, Erasmus University Medical Centre-Sophia Children's Hospital, Rotterdam, The Netherlands.

Department of Pediatrics, Tergooi Hospital, Blaricum, The Netherlands; Faculty of Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands.

出版信息

J Pediatr. 2021 Jul;234:77-84.e8. doi: 10.1016/j.jpeds.2021.01.065. Epub 2021 Feb 3.

Abstract

OBJECTIVES

To provide a comprehensive assessment of case stratification by the Neonatal Early-Onset Sepsis (EOS) Calculator, a novel tool for reducing unnecessary antibiotic treatment.

STUDY DESIGN

A systematic review with individual patient data meta-analysis was conducted, extending PROSPERO record CRD42018116188. Cochrane, PubMed/MEDLINE, EMBASE, Web of Science, Google Scholar, and major conference proceedings were searched from 2011 through May 1, 2020. Original data studies including culture-proven EOS case(s) with EOS Calculator application, independent from EOS Calculator development, and including representative birth cohorts were included. Relevant (individual patient) data were extracted from full-text and data queries. The main outcomes were the proportions of EOS cases assigned to risk categories by the EOS Calculator at initial assessment and within 12 hours. Evidence quality was assessed using Newcastle-Ottawa scale, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies, and GRADE tools.

RESULTS

Among 543 unique search results, 18 were included, totaling more than 459 000 newborns. Among 234 EOS cases, EOS Calculator application resulted in initial assignments to (strong consideration of) empiric antibiotic administration for 95 (40.6%; 95% CI, 34.2%-47.2%), more frequent vital signs for 36 (15.4%; 95% CI, 11.0%-20.7%), and routine care for 103 (44.0%; 95% CI, 37.6%-50.6%). By 12 hours of age, these proportions changed to 143 (61.1%; 95% CI, 54.5%-67.4%), 26 (11.1%; 95% CI, 7.4%-15.9%), and 65 (27.8%; 95% CI, 22.1%-34.0%) of 234 EOS cases, respectively.

CONCLUSIONS

EOS Calculator application assigns frequent vital signs or routine care to a substantial proportion of EOS cases. Clinical vigilance remains essential for all newborns.

摘要

目的

通过新生儿早发性败血症(EOS)计算器对病例进行全面分层评估,该计算器是一种减少不必要抗生素治疗的新工具。

研究设计

系统评价结合个体患者数据荟萃分析,扩展 PROSPERO 记录 CRD42018116188。从 2011 年至 2020 年 5 月 1 日,检索了 Cochrane、PubMed/MEDLINE、EMBASE、Web of Science、Google Scholar 和主要会议记录。包括培养证实的 EOS 病例(s)和 EOS 计算器应用程序的原始数据研究,与 EOS 计算器的开发无关,并且包括代表性的出生队列,均被纳入研究。从全文和数据查询中提取相关(个体患者)数据。主要结局为 EOS 计算器在初始评估和 12 小时内对 EOS 病例进行风险分层的比例。使用纽卡斯尔-渥太华量表、系统评价预测模型研究的批判性评价和数据提取以及 GRADE 工具评估证据质量。

结果

在 543 个独特的搜索结果中,有 18 个被纳入,总计超过 459000 名新生儿。在 234 例 EOS 病例中,EOS 计算器的应用导致初始分配(强烈考虑)经验性抗生素治疗 95 例(40.6%;95%CI,34.2%-47.2%),更频繁的生命体征 36 例(15.4%;95%CI,11.0%-20.7%)和常规护理 103 例(44.0%;95%CI,37.6%-50.6%)。到 12 小时龄时,这些比例分别变为 234 例 EOS 病例中的 143 例(61.1%;95%CI,54.5%-67.4%)、26 例(11.1%;95%CI,7.4%-15.9%)和 65 例(27.8%;95%CI,22.1%-34.0%)。

结论

EOS 计算器的应用将频繁的生命体征或常规护理分配给相当比例的 EOS 病例。对所有新生儿都必须保持临床警惕。

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