Identification of therapeutically-relevant patient subgroups from clinical and biological data

从临床和生物学数据中识别治疗相关的患者亚组

• 批准号: 1805059
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1805059
• 关键词: Identification; therapeutically; relevant; patient; subgroups

As a disease, sepsis is important for a multitude of reasons. Severe sepsis is responsible for approximately 50% of admission in the Intensive Care Unit (ICU) and significant annual hospitalisation costs ($14 billion/year) (Cawcutt and Peters, 2014). In addition, sepsis can lead to septic shock, which is sepsis with hypotension or hyperlactatemia, a condition which can be fatal (24-32% mortality rate) (Daviaud et al, 2015). Even though mortality rates have decreased over the past years due to improved clinical care (Daviaud et al, 2015), there is still much room for improvement in terms of accurate and timely diagnosis.The aim of the project is to develop a method by which clinicians might identify patients with greater predisposition to develop sepsis, or septic shock, as a result of infection by specific invading pathogens. This test will enable clinicians to identify a patient as belonging to a risk group for a particular pathogen, thus allowing them to administer the proper course of antibiotics to treat this pathogen and save both time (since the most effective treatment will be given as soon as possible) and resources (as initial use of broad spectrum antibiotics will be limited, reducing costs).This will be done by means of bioinformatic analysis of patient's expression data using graph theory. The associations between patients will be discerned, allowing for their sub-categorisation into groups of different susceptibilities. The python programming language will be used to conduct this analysis and the work on this area will be based on previous research conducted by the Baillie lab.

作为一种疾病，败血症的重要性有多种。大约 50% 的重症监护病房 (ICU) 患者因严重败血症而入院，每年的住院费用高达 140 亿美元（Cawcutt 和 Peters，2014 年）。此外，脓毒症可导致脓毒症休克，即伴有低血压或高乳酸血症的脓毒症，这种情况可能致命（死亡率为 24-32%）（Daviaud 等，2015）。尽管由于临床护理的改善，死亡率在过去几年有所下降（Daviaud 等，2015），但在准确和及时的诊断方面仍有很大的改进空间。该项目的目的是开发一种方法，临床医生可以通过该方法识别因特定入侵病原体感染而更容易患败血症或败血性休克的患者。该测试将使临床医生能够识别患者是否属于特定病原体的风险组，从而使他们能够使用适当的抗生素疗程来治疗这种病原体，并节省时间（因为将尽快给予最有效的治疗）和资源（因为广谱抗生素的初始使用将受到限制，从而降低成本）。这将通过使用图论对患者表达数据进行生物信息分析来完成。患者之间的关联将被识别，从而将他们细分为不同易感性的组。 Python 编程语言将用于进行此分析，该领域的工作将基于 Baillie 实验室之前进行的研究。