Identification of novel phenotypes of acute lung injury using multimodal longitudinal data

使用多模态纵向数据识别急性肺损伤的新表型

• 批准号: MR/Y000404/1
• 负责人: Dominic Marshall
• 金额: $ 34.55万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY000404%2F1
• 关键词: Identification; novel; phenotypes; acute; lung

Acute lung injury (ALI) and its severe form acute respiratory distress syndrome (ARDS) are life-threatening conditions commonly seen in the intensive care unit (ICU) where a patient's lungs become damaged comprimising their ability to take in oxygen. The condition is known as a 'syndrome' because it has a variety of causes and can progress in many ways. The condition can happen after a problem in the lung such as a chest infection or alternatively, can be due to inflammation from an illness in another part of the body. Before COVID-19 approximately 10% of critical care beds were occupied by patients with this condition. This has dramatically increased as this is the most common way that COVID-19 patients become critically unwell and die. Approximately 20,000 patients a year suffer with ARDS in the UK, and nearly half die. Patients with this condition require life support on a ventilator. Studies over 20 years ago demonstrated that careful management of ventilator settings is important to protect patients' lungs. The only other therapy that has shown significant benefit is to lay the patient on their front (proning). Except for patients with COVID-19, no medication has been shown to be of benefit in ARDS, despite decades of research.We think that the reason that it has been so difficult to find new treatments is that the ARDS represents a range of different diseases which have been grouped together. Therefore something that works for one subgroup may not work for another. This has been demonstrated with COVID-19: this is a form of ARDS with one specific cause. The focused research on this group has led to several new treatments.My research will focus on using clinical data generated routinely through admission to ICU, as well as the chest x-rays taken, to identify different subgroups within ALI. This has previously been shown possible using specific laboratory tests which are not easily performed. The advantage of this approach is to take data already collected and use a variety of computer-based methods to find patterns that become apparent when hundreds of different variables from thousands of patients are analysed. These methods are summarised as 'machine learning' where the computer learns patterns from the data. To build on previous work I will focus on how a patient changes over time (their 'trajectory') and this will define their subgroup. It is also novel to combine information from x-rays with clinical data. Once these subgroups have been identified, I will assess whether they make sense to doctors and how they compare with previously identified subgroups using biological data. This will improve our understanding of the illness and provide indications as to personalised therapies.I will then analyse whether different treatments are more beneficial in specific subgroups. These treatments will include different settings on a ventilator and medications. For example, I may identify a subgroup of patients that have more inflammation driving their illness. There is evidence for this from previous studies of biological data. This subgroup may benefit from anti-inflammatory medicines such as steroids. Steroids have side effects such as impairing the immune system and therefore giving them in a non-targeted way may be harmful.Ultimately this approach will advance the field of personalised medicine with the eventual goal of providing personalised treatment to every patient by selecting the right treatment for the right patient at the right time. The COVID-19 pandemic has demonstrated that by understanding one specific cause of respiratory illness we can rapidly identify effective treatments. Personalising our approach to this condition is a critical step towards improving survival of this common critical illness.

急性肺损伤（ALI）及其严重形式的急性呼吸窘迫综合征（ARDS）是危及生命的疾病，常见于重症监护病房（ICU），患者的肺部受损，危及其吸入氧气的能力。这种情况被称为“综合症”，因为它有多种原因，可以通过多种方式发展。这种情况可能发生在肺部出现问题（如胸部感染）之后，也可能是由于身体其他部位的疾病引起的炎症。在COVID-19之前，大约10%的重症监护床位被这种疾病的患者占用。这一数字急剧增加，因为这是COVID-19患者严重不适和死亡的最常见方式。在英国，每年大约有2万名患者患有急性呼吸窘迫综合征，其中近一半死亡。这种情况的患者需要呼吸机维持生命。20多年前的研究表明，仔细管理呼吸机设置对于保护患者的肺部非常重要。唯一显示出显著益处的其他疗法是让患者俯卧（俯卧）。尽管进行了数十年的研究，但除了COVID-19患者外，没有任何药物被证明对ARDS有益。我们认为找到新的治疗方法如此困难的原因是ARDS代表了一系列不同的疾病，这些疾病被归为一类。因此，对一个子群体有效的东西可能对另一个子群体无效。COVID-19已经证明了这一点：这是一种有一个特定原因的ARDS。对这一群体的重点研究已经导致了几种新的治疗方法。我的研究将集中在使用通过ICU入院常规生成的临床数据，以及胸部x光片，以确定ALI的不同亚组。以前已经证明，使用不容易进行的特定实验室测试是可能的。这种方法的优点是利用已经收集的数据，并使用各种基于计算机的方法，在分析数千名患者的数百个不同变量时，发现明显的模式。这些方法被概括为“机器学习”，即计算机从数据中学习模式。在之前工作的基础上，我将重点关注患者如何随时间变化（他们的“轨迹”），这将定义他们的亚组。将x光信息与临床数据相结合也是一种新颖的方法。一旦确定了这些亚群，我将评估它们对医生是否有意义，以及它们如何与先前使用生物学数据确定的亚群进行比较。这将提高我们对这种疾病的了解，并为个性化治疗提供指示。然后，我将分析不同的治疗方法是否对特定的亚组更有益。这些治疗包括对呼吸机和药物的不同设置。例如，我可能会确定一个亚组患者，他们有更多的炎症导致他们的疾病。之前的生物数据研究证明了这一点。这类人群可能受益于类固醇等抗炎药物。类固醇有副作用，比如损害免疫系统，因此以非靶向方式给药可能是有害的。最终，这种方法将推动个性化医疗领域的发展，最终目标是通过在正确的时间为正确的患者选择正确的治疗方法，为每位患者提供个性化治疗。COVID-19大流行表明，通过了解呼吸道疾病的一个特定原因，我们可以迅速确定有效的治疗方法。个性化我们对这种情况的治疗方法是提高这种常见危重疾病生存率的关键一步。