Understanding hearing loss phenotypes, their progression and associations with otological and non-otological disease using hearing health big data

研究目的和背景听力损失是人类最常见的感觉障碍，有15亿人在一生中受到影响。随着年龄的增长，某些类型的听力损失更为常见，因此随着老龄化人口的增加，听力损失的负担预计将进一步上升。听力损失对生活质量有很大影响，为了准备和应对这场迅速加剧的公共卫生危机，我们需要更好地了解不同类型的听力损失，以确定哪些患者最有可能出现听力损失恶化和其他相关医疗状况，以及哪些患者最有可能出现听力损失恶化和其他相关医疗状况。最有可能受益于新的治疗方法。患者健康记录的数字化提供了一个令人兴奋的机会，可以利用计算机科学方法的最新进展来查看大量听力健康患者数据并回答这些问题。该项目的另一个关键部分将查看鼓膜的临床照片。在社区中，能够评估鼓膜外观的受过训练的专家的机会有限，并且经常需要长时间等待转诊到专业耳鼻喉科服务。在全球资源贫乏的国家，这种情况甚至更糟。为了解决这个问题，我们建议开发一个自动程序，分析鼓膜的照片。临床图像还将用于评估鼓膜的变化是否可以表明血管疾病和糖尿病的存在，就像在眼睛中进行视网膜筛查一样。耳朵是一个更容易访问的区域比眼睛，可以提供一个简单的和具有成本效益的网站screening.MethodsWe将创建一个存储的患者数据，已被收集的一部分，常规的标准NHS临床护理。这将包括人口统计学详细信息，测试结果，测量和医疗条件的详细信息。一个强大的计算机程序将被用来分析这些数据，并描述不同类型的听力损失以及这些听力损失类型如何随时间变化。我们将进行进一步的分析，以确定这些听力损失亚型与其他医疗条件之间的联系，包括痴呆症，糖尿病，中风和高血压。在本研究的第二部分，我们将使用一种新的医疗设备捕获的鼓膜图片来开发和训练一个计算机程序，该程序可以识别和分级耳鼻喉专家评估的鼓膜的关键组成部分。我们将使用该程序以及提供的患者详细信息来探索是否存在可以预测糖尿病和心脏病存在的鼓膜变化。预期结果这项研究的主要目的是更好地了解听力损失的自然史。识别重度听力损失风险较高的患者对于资源规划、患者咨询和识别最有可能从新兴治疗或临床试验中受益的人群非常重要。识别听力损失与其他疾病之间的新关联可以识别出“处于风险中”的患者，从而促进早期诊断，并作为早期干预和促进生活方式改变的机会，以通过行为改变转移或延迟此类疾病的发作。

Aims and Background to the researchHearing loss is the most common sensory disorder in humans with 1.5 billion people affected by this during their lifetime. Some types of hearing loss are more common as we get older and therefore the burden of hearing loss is predicted to rise further as our aging population increases. Hearing loss has a significant impact on quality of life, patient health and safety as well as placing a huge demand on increasingly stretched public health services.In order to prepare and respond to this rapidly accelerating public health crisis we need to better understand the different types of hearing loss to identify who is most at risk of both worsening hearing loss but also other associated medical conditions and also which patients are most likely to benefit from new treatments. The digitialisation of patient health records offers an exciting opportunity to use the latest advances in computer science methods to look at large amounts of hearing health patient data and answer these questions.Another key part of this project will look at clinical photographs of the ear drum. Access to trained specialists who can assess the appearance of ear drums is limited in the community and there are often long waits for referrals to specialty ENT services. This situation is even worse globally in resource-poor countries. To address this problem, we propose to develop an automated programme that analyse photographs of the ear drum. The clinical images will also be used to assess whether changes in the ear drum could signal the presence of vascular disease and diabetes, much like retinal screening is performed in the eye. The ear is a more readily accessible area than the eye and could provide an easy and cost-effective site for screening.MethodsWe will create a store of patient data that has been collected routinely as part of standard NHS clinical care. This will include demographic details, test results, measurements, and details of medical conditions. A powerful computer programme will be used to analyse this data and describe different types of hearing loss as well as how these hearing loss types change over time. We will perform further analysis to identify links between these hearing loss subtypes and other medical conditions including dementia, diabetes, stroke and high blood pressure.For the second part of this study, we will use pictures of ear drums captured by a new medical device to develop and train a computer programme that can identify and grade the key components of an ear drum that are assessed by ENT specialists. We will use this programme alongside supplied patient details to explore whether there are changes in the ear drum that can predict the presence of diabetes and heart disease.Anticipated OutcomesThe key aim of this research is to better understand the natural history of hearing loss. Identifying patients who are at higher risk of developing severe hearing loss is important for resource planning, patient counselling and identifying people who are most likely to benefit from emerging treatments or clinical trials. Identifying new associations between hearing loss and other conditions could identify patients who are "at risk" prompting earlier diagnosis and act as an opportunity for early intervention and the promotion of lifestyle modifications to divert or delay the onset of such conditions through behavioural change.