Regional Neonatal Intensive Care Unit, University Hospital of Wales, Cardiff, UK
Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.
BMJ Open. 2021 Dec 30;11(12):e050100. doi: 10.1136/bmjopen-2021-050100.
Diagnosing neonatal sepsis is heavily dependent on clinical phenotyping as culture-positive body fluid has poor sensitivity, and existing blood biomarkers have poor specificity.A combination of machine learning, statistical and deep pathway biology analyses led to the identification of a tripartite panel of biologically connected immune and metabolic markers that showed greater than 99% accuracy for detecting bacterial infection with 100% sensitivity. The cohort study described here is designed as a large-scale clinical validation of this previous work.
This multicentre observational study will prospectively recruit a total of 1445 newborn infants (all gestations)-1084 with suspected early-or late-onset sepsis, and 361 controls-over 4 years. A small volume of whole blood will be collected from infants with suspected sepsis at the time of presentation. This sample will be used for integrated transcriptomic, lipidomic and targeted proteomics profiling. In addition, a subset of samples will be subjected to cellular phenotype and proteomic analyses. A second sample from the same patient will be collected at 24 hours, with an opportunistic sampling for stool culture. For control infants, only one set of blood and stool sample will be collected to coincide with clinical blood sampling. Along with detailed clinical information, blood and stool samples will be analysed and the information will be used to identify and validate the efficacy of immune-metabolic networks in the diagnosis of bacterial neonatal sepsis and to identify new host biomarkers for viral sepsis.
The study has received research ethics committee approval from the Wales Research Ethics Committee 2 (reference 19/WA/0008) and operational approval from Health and Care Research Wales. Submission of study results for publication will involve making available all anonymised primary and processed data on public repository sites.
NCT03777670.
新生儿败血症的诊断严重依赖临床表型，因为有培养阳性的体液的敏感性较差，而现有的血液生物标志物特异性较差。通过机器学习、统计和深度通路生物学分析的组合，确定了一组具有生物学相关性的免疫和代谢标志物的三联体，该标志物对检测细菌感染的准确性超过 99%，灵敏度为 100%。本研究描述的队列研究旨在对之前的工作进行大规模临床验证。
这项多中心观察性研究将前瞻性招募总共 1445 名新生儿（所有胎龄）-1084 名疑似早发性或晚发性败血症，361 名对照-在 4 年内。在出现疑似败血症的婴儿时，将从他们身上采集少量全血。该样本将用于综合转录组、脂质组和靶向蛋白质组学分析。此外，还将对一部分样本进行细胞表型和蛋白质组学分析。将从同一患者采集第二份样本，在 24 小时后采集粪便培养的机会性样本。对于对照婴儿，仅采集一组血液和粪便样本，与临床采血时间一致。除了详细的临床信息外，还将分析血液和粪便样本，并利用这些信息来确定和验证免疫代谢网络在诊断细菌性新生儿败血症中的有效性，并鉴定病毒败血症的新宿主生物标志物。
该研究已获得威尔士研究伦理委员会 2 号（参考号 19/WA/0008）的研究伦理委员会批准和威尔士健康和护理研究的运营批准。发表研究结果的提交将包括在公共存储库网站上提供所有匿名的原始和处理后的数据。
NCT03777670。
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