Bioengineering Department, University of California, San Diego, San Diego, California, USA.
Donald Danforth Plant Science Center, Saint Louis, Missouri, USA.
Clin Microbiol Rev. 2018 Feb 28;31(2). doi: 10.1128/CMR.00089-17. Print 2018 Apr.
Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.
快速准确地分析感染病原体仍然是现代医疗保健面临的重大挑战。尽管分子诊断技术取得了进步，但血液培养分析仍然是诊断败血症的金标准。然而，这种方法过于缓慢和繁琐，无法对患者的初始治疗产生重大影响。快速启动精确和靶向抗生素治疗取决于败血症诊断测试在 1 至 3 小时内捕获具有临床相关性的生物体以及抗菌药物耐药性的能力。适当的窄谱抗生素的给药要求该测试具有极高的阴性预测值和极高的灵敏度。此外，它应该使用小样本量并检测混合感染和污染物。所有这些都必须通过一个易于集成到临床工作流程中的平台来实现。在这篇综述中，我们概述了常规血液培养检测的局限性，并讨论了新兴的败血症技术如何融合理想的败血症诊断测试的特征。我们包括七种已经在临床血液标本或模拟样本上使用人血进行验证的分子技术。此外，我们还讨论了机器学习技术的进展，这些技术利用电子病历数据为临床决策提供上下文评估支持。
