Using existing data to optimise adaptive intervention in epilepsy

利用现有数据优化癫痫的适应性干预

• 批准号: 2896498
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2896498
• 关键词: Using; existing; data; optimise; adaptive

Approximately half of patients with newly diagnosed epilepsy who start antiepileptic drug (AED) monotherapy fail on their first treatment, after which the clinician and patient will discuss the likely overall prognosis and decide on the next AED to start. Failure on the second AED may lead to further substitution of AED as monotherapy, or the addition of further AEDs as polytherapy, with the potential for a large number of alternative treatment sequences and possible adaptations. The probability of achieving seizure control reduces with every failed AED and approximately 30% of patients will have a drug-resistant form of epilepsy. Traditionally trial designs for epilepsy focus on randomising newly diagnosed patients or drug-resistant patients as separate populations, and there is a dearth of evidence to support adaptive treatment decisions between these two points in an individual patient's pathway.The Sequential Multiple Assignment Randomised Trial (SMART) is an innovative clinical trial design, to provide high-quality data that can be used to inform the development of adaptive interventions across the pathway. A SMART involves multiple intervention stages where each stage corresponds to one of the critical decisions involved in the adaptive intervention and randomises participants at each state. Although such a SMART trial would be the gold standard for causal inference, the approach would be expensive and lengthy. Utilising existing real-world data by applying analytic techniques to emulate a SMART target trial could be an efficient use of currently available data and evidence. Personalising optimal pathways may also be possible by incorporating data about patient characteristics where this is available.The aim is to establish how real-world evidence can be used to optimise treatment decisions for patients with epilepsy and inform the design of a future SMART trial. Specific aims include:1. Summarise the evidence for treatment decisions following treatment failure.2. Identify risk factors for outcome following treatment failure in patients with epilepsy 3. Evaluate how previous SMART trials have used existing evidence in their design and analysis4. Implement an analytic approach that emulates a target SMART trial of AED treatments using existing real-world data 5. Design a SMART trial to explore the most promising treatment sequences in epilepsy incorporating existing evidence

大约一半的新诊断癫痫患者在开始抗癫痫药物（AED）单药治疗后第一次治疗失败，之后临床医生和患者将讨论可能的总体预后并决定下一次开始使用AED。第二种AED治疗失败可能导致进一步替代AED作为单一治疗，或增加进一步的AED作为综合治疗，有可能出现大量替代治疗序列和可能的适应。每次AED失败，实现癫痫控制的可能性就会降低，大约30%的患者会出现耐药性癫痫。传统的癫痫试验设计侧重于将新诊断的患者或耐药患者作为单独的人群随机分配，并且缺乏证据支持个体患者途径中这两点之间的适应性治疗决策。顺序多任务随机试验（SMART）是一项创新的临床试验设计，提供高质量的数据，可用于通知整个途径的适应性干预措施的发展。SMART包括多个干预阶段，每个阶段对应于自适应干预中涉及的一个关键决策，并随机分配每个状态的参与者。尽管这样一个聪明的试验将是因果推理的黄金标准，但这种方法将是昂贵和漫长的。通过应用分析技术来模拟SMART目标试验，利用现有的真实世界数据可以有效地利用当前可用的数据和证据。个性化的最佳路径也可能通过合并患者特征的数据，这是可行的。目的是确定如何使用真实世界的证据来优化癫痫患者的治疗决策，并为未来SMART试验的设计提供信息。具体目标包括：1。总结治疗失败后治疗决策的证据。确定癫痫患者治疗失败后预后的危险因素评估以前的SMART试验在设计和分析中是如何使用现有证据的。实施一种分析方法，利用现有的现实世界数据模拟AED治疗的目标SMART试验5。设计一项SMART试验，结合现有证据，探索最有希望的癫痫治疗方案