Neal Samuel R, Musorowegomo David, Gannon Hannah, Cortina Borja Mario, Heys Michelle, Chimhini Gwen, Fitzgerald Felicity
Infection, Immunity and Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
Department of Paediatrics and Child Health, University of Zimbabwe College of Health Sciences, Harare, Zimbabwe.
BMJ Open. 2020 Aug 20;10(8):e039712. doi: 10.1136/bmjopen-2020-039712.
Neonatal sepsis is responsible for significant morbidity and mortality worldwide. Diagnosis is often difficult due to non-specific clinical features and the unavailability of laboratory tests in many low-income and middle-income countries (LMICs). Clinical prediction models have the potential to improve diagnostic accuracy and rationalise antibiotic usage in neonatal units, which may result in reduced antimicrobial resistance and improved neonatal outcomes. In this paper, we outline our scoping review protocol to map the literature concerning clinical prediction models to diagnose neonatal sepsis. We aim to provide an overview of existing models and evidence underlying their use and compare prediction models between high-income countries and LMICs.
The protocol was developed with reference to recommendations by the Joanna Briggs Institute. Searches will include six electronic databases (Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, Global Index Medicus and the Cochrane Library) supplemented by hand searching of reference lists and citation analysis on included studies. No time period restrictions will be applied but only studies published in English or Spanish will be included. Screening and data extraction will be performed independently by two reviewers, with a third reviewer used to resolve conflicts. The results will be reported by narrative synthesis in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.
The nature of the scoping review methodology means that this study does not require ethical approval. Results will be disseminated through peer-reviewed publications and conference presentations, as well as through engagement with peers and relevant stakeholders.
新生儿败血症在全球范围内导致了显著的发病率和死亡率。由于临床特征不具特异性,且许多低收入和中等收入国家(LMICs)无法进行实验室检测,诊断往往很困难。临床预测模型有潜力提高新生儿病房的诊断准确性并使抗生素使用合理化,这可能会降低抗菌药物耐药性并改善新生儿结局。在本文中,我们概述了我们的范围综述方案,以梳理有关诊断新生儿败血症的临床预测模型的文献。我们旨在概述现有模型及其使用依据的证据,并比较高收入国家和低收入和中等收入国家之间的预测模型。
该方案是参照乔安娜·布里格斯研究所的建议制定的。检索将包括六个电子数据库(Ovid MEDLINE、Ovid Embase、Scopus、Web of Science、全球医学索引和考科蓝图书馆),并辅以对参考文献列表的手工检索和对纳入研究的引文分析。不设时间限制,但仅纳入以英文或西班牙文发表的研究。筛选和数据提取将由两名评审员独立进行,第三名评审员用于解决分歧。结果将根据系统评价和Meta分析扩展的范围综述指南的首选报告项目,通过叙述性综合报告。
范围综述方法的性质意味着本研究无需伦理批准。结果将通过同行评审出版物和会议报告进行传播,以及通过与同行和相关利益攸关方的互动进行传播。