A Hierarchical Bayesian approach to optimising hypertension management strategies

优化高血压管理策略的分层贝叶斯方法

• 批准号: ST/T002263/1
• 负责人: Francesco Shankar
• 金额: $ 14.35万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FT002263%2F1
• 关键词: Hierarchical; Bayesian; approach; optimising; hypertension

Many decisions in medicine are subject to measurement uncertainties and physiological variations which mean that treatment decisions may be made erroneously. These uncertainties are rarely explicitly considered in clinical management algorithms, limiting the efficacy and efficiency of clinical care. Management of raised blood pressure (hypertension) is a particularly important example, as hypertension is the single greatest cause of death and disability worldwide. In the UK approximately 1 in 3 adults require drug treatment for hypertension, imposing a huge burden on health care delivery. In an emergent collaboration between the Southampton Astronomy group and the Department of Clinical Pharmacology of St Thomas' Hospital at King's College London, we have adapted Monte Carlo simulations used in extra-galactic Astronomy to model the random effects of measurement uncertainty in a virtual population of hypertensive individuals. Our work showed that current treatment strategies for medication are too inefficient, with typically 40% of the population not optimally controlled, and thus at risk of adverse events. Our work obtained a Silver Award at the STEM for Britain competition 2019 at the House of Commons, which prizes "ground-breaking, frontier" projects in R&D. Building on the recent success of our collaboration, in this research proposal we aim to produce a tailored Hierarchical Bayesian Monte Carlo algorithm to develop the first smart blood pressure management algorithm. This algorithm will aim to combine patient-specific factors (for example starting blood pressure, sex, age and weight) with drug efficacy and measurement error, to predict the probability of an individual achieving blood pressure control for a given approach. The model will be validated using published data (from both clinical trials and observational cohorts) and real-world patient journeys from the St Thomas' Hospital Hypertension Clinic. More specifically, making use of anonymised data in the public domain, we will adopt the smart algorithm to conduct in silico clinical trials which aim to improve the proportion of hypertensive individuals achieving the desired blood pressure target with the minimal burden on both patient and healthcare system. This series of virtual clinical trials will aim to identify the most promising management approach(s) to take forward into real-world studies. Cardiovascular diseases have a huge cost of tens of millions pounds in the UK. Whilst the final evaluation of this work would require validation by means of a clinical trial comparing a final personalised treatment plan to standard care, the present approach has the potential to rapidly perform a large number of "in-silico" (i.e, virtual/simulated) comparisons to select a near-optimal treatment plan that can be tested in a clinical trial. Furthermore, it will provide a quantitative prediction of the degree of improvement expected, with the improved plan providing the necessary information to set up the clinical trial adequately. Our project has the potential to reduce cardiovascular events, improve efficiency of healthcare delivery, thus providing a substantial saving opportunity for the NHS. We will disseminate our work through the publication of peer-reviewed manuscripts and presentations at national/international conferences. We then envision a comprehensive research dissemination programme supported by in-house dissemination officers at the University of Southampton and at King's College London.

医学中的许多决策都受到测量不确定性和生理变化的影响，这意味着可能会错误地做出治疗决策。这些不确定性在临床管理算法中很少被明确考虑，限制了临床护理的功效和效率。血压升高（高血压）的管理是一个特别重要的例子，因为高血压是全世界死亡和残疾的最大原因。在英国，大约三分之一的成年人需要药物治疗高血压，这给医疗保健服务带来了巨大的负担。在南安普顿天文组和伦敦国王学院圣托马斯医院临床药理学系之间的紧急合作中，我们采用了银河系外天文学中使用的蒙特卡罗模拟来模拟高血压个体虚拟人群中测量不确定性的随机效应。我们的工作表明，目前的药物治疗策略效率太低，通常有40%的人群没有得到最佳控制，因此有发生不良事件的风险。我们的工作在下议院举行的2019年英国STEM竞赛中获得了银，该竞赛奖励研发中的“突破性，前沿”项目。基于我们最近合作的成功，在这项研究提案中，我们的目标是产生一个量身定制的分层贝叶斯蒙特卡罗算法，以开发第一个智能血压管理算法。该算法旨在将联合收割机患者特定因素（例如，起始血压、性别、年龄和体重）与药物疗效和测量误差相结合，以预测个体在给定方法下实现血压控制的概率。该模型将使用已发表的数据（来自临床试验和观察性队列）和来自St托马斯' Hospital Hypertension Clinic的真实患者旅程进行验证。更具体地说，我们将利用公共领域的匿名数据，采用智能算法进行计算机临床试验，旨在提高高血压患者达到理想血压目标的比例，同时对患者和医疗系统的负担最小。这一系列虚拟临床试验旨在确定最有前途的管理方法，以推进现实世界的研究。心血管疾病在英国有数千万英镑的巨额费用。虽然这项工作的最终评估将需要通过将最终个性化治疗计划与标准护理进行比较的临床试验进行验证，但本方法具有快速执行大量“计算机模拟”（即，虚拟/模拟）比较以选择可以在临床试验中测试的接近最佳的治疗计划的潜力。此外，它还将提供预期改善程度的定量预测，改进后的计划将提供必要的信息，以充分开展临床试验。我们的项目有可能减少心血管事件，提高医疗保健服务的效率，从而为NHS提供了大量的储蓄机会。我们将通过出版同行评审的手稿和在国家/国际会议上的演讲来传播我们的工作。然后，我们设想一个全面的研究传播方案，由内部传播人员在南安普顿大学和伦敦国王学院的支持。

项目成果

Impact of Therapeutic Inertia on Long-Term Blood Pressure Control: A Monte Carlo Simulation Study.

治疗惰性对长期血压控制的影响：蒙特卡罗模拟研究。

• DOI: 10.1161/hypertensionaha.120.15866
• 发表时间: 2021
• 期刊: 1979)
• 作者: Augustin A

Monte Carlo simulation of uncertainty to identify barriers to optimizing blood pressure control.

蒙特卡罗模拟不确定性，以确定优化血压控制的障碍。

• DOI: 10.1097/hjh.0000000000002546
• 发表时间: 2020
• 期刊: Journal of hypertension
• 影响因子: 4.9
• 作者: Zanisi L