重症监护病房患者菌血症的替代血清生物标志物

Rúben Araújo, L. Ramalhete, Tiago A H Fonseca, Cristiana P Von Rekowski, Luís Bento, C. Calado
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引用次数: 0

摘要

在医院或临床环境中,感染的诊断通常涉及一系列耗时的步骤,包括生物样本收集、培养生长的生物分离和随后的表征。为此,有多种基于血液分析的感染生物标志物,然而,这些在重症监护病房出现混淆过程如炎症过程的患者中使用有限。本初步研究对102例ICU危重患者应用FTIR光谱血清分析预测菌血症进行了评价。分析了光谱预处理方法和光谱子区域对t分布随机邻域嵌入的影响。通过优化支持向量机(SVM)模型,基于较小子区域的归一化二阶导数光谱,可以获得良好的菌血症预测模型,灵敏度和特异性为76%。由于血清的FTIR光谱是通过一种简单、经济和快速的模式获得的,因此该技术具有成为一种具有成本效益的菌血症鉴定方法的潜力,对危重患者具有特殊意义,在危重患者中,快速感染诊断将允许避免不必要的抗生素使用,最终将减轻已经脆弱的患者的代谢负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alternative Serum Biomarkers of Bacteraemia for Intensive Care Unit Patients
The diagnosis of infections in hospital or clinical settings usually involves a series of time-consuming steps, including biological sample collection, culture growth of the organism isolation and subsequent characterization. For this, there are diverse infection biomarkers based on blood analysis, however, these are of limited use in patients presenting confound processes as inflammatory process as occurring at intensive care units. In this preliminary study, the application of serum analysis by FTIR spectroscopy, to predict bacteraemia in 102 critically ill patients in an ICU was evaluated. It was analysed the effect of spectra pre-processing methods and spectral sub-regions on t-distributed stochastic neighbour embedding. By optimizing Support Vector Machine (SVM) models, based on normalised second derivative spectra of a smaller subregion, it was possible to achieve a good bacteraemia predictive model with a sensitivity and specificity of 76%. Since FTIR spectra of serum is acquired in a simple, economic and rapid mode, the technique presents the potential to be a cost-effective methodology of bacteraemia identification, with special relevance in critically ill patients, where a rapid infection diagnostic will allow to avoid the unnecessary use of antibiotics, which ultimately will ease the load on already fragile patients' metabolism.
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