Data science and pharmacoepidemiology for outcome improvement in severe mental illness (DS-SMI)

患有严重精神疾病（SMI）的人，包括精神分裂症、双相情感障碍和其他精神疾病，通常对药物治疗只有部分反应。与一般人群相比，他们还经历了药物副作用，发病率和死亡率都有所增加。迫切需要改善重度精神分裂症的药物治疗。解决这一问题的两种具有成本效益的方法是：(1)通过个性化提高对现有药物的反应；(2)确定现有药物（具有不同的适应症），可以重新用于治疗精神症状。这些免费的转化研究流将利用大型常规健康登记、电子健康记录和移动电话应用程序的力量，以及用于预测建模和因果推理的现代统计和机器学习技术。数据将来自英国、美国、瑞典、丹麦、香港和台湾。个性化药物治疗这一研究流将推进我目前使用机器学习预测双相情感障碍患者维持治疗反应的工作。尽管最近在治疗个性化领域取得了进展，但精神病学仍落后于其他医学专业。目前，还没有经过验证的定制治疗选择系统，针对特定患者的匹配治疗往往是一个反复试验的问题。通过预测模型，临床医生可以更精确地选择治疗病人的需要，从而改善他们的结果。本研究将侧重于：i)确定患者首次发病时治疗反应的预测因素ii)预测哪些个体在两种药物试验后症状得不到充分治疗（治疗耐药性）iii)预测不良反应，包括体重增加、躁动（静坐症）和过度镇静医疗记录中包含的临床特征已被证明与反应相关。治疗耐药性和不良反应，但这些还没有系统地结合起来。电子健康记录在全球的规模和广泛使用现在允许使用多个数据集对生成的模型进行外部验证。在治疗过程的早期也可能有重要的变化，可以预测长期的结果。这些变化不太可能被记录在电子健康记录中，但可以通过患者的移动电话获得。通过手机应用获取被动数据现在很简单，而且可能包含精神状态变化的标记，比如睡眠、运动和手机使用情况。应用程序还可以促进远程症状监测和认知任务的执行。这些信息将用于进一步改进预测模型。在该项目早期阶段建立的模型将通过实施科学方法在临床人群中进行大规模测试。确定和测试药物再利用的目标这一转化研究流建立在我之前的工作基础上，该工作研究了一些被确定为具有再利用潜力的药物是否对精神病学住院治疗和重度精神分裂症患者的自残率有影响。还有许多其他药物应该通过类似的方法进行检查，以验证这些信号的潜在有效性，同时严格考虑潜在的混杂因素，其中包括一系列抗炎药。这项工作将在其他国际数据集中进行交叉验证。药物流行病学验证过程优化了成功的机会，并为哪些药物进行随机对照试验（RCT）提供了指导。在这个奖学金结束时，我将开发一个大型适应性随机对照试验所需的协议，并运行一个试点来评估可行性和可接受性。

People with severe mental illness (SMI), including schizophrenia, bipolar disorder and other psychotic illness often only partially respond to drug treatment. They also experience medication adverse effects, and increased morbidity and mortality compared to the general population. There is a desperate need to improve pharmacological treatment of SMI. Two cost effective approaches to addressing this problem are to i) improve response to existing medication via personalisation, and ii) identify drugs already in existence (with different indications) that can be repurposed to treat psychiatric symptoms.These complimentary translational research streams will harness the power of large routine health registers, electronic health records and mobile phone applications, along with modern statistical and machine learning techniques for prediction modelling and causal inference. Data will come from the United Kingdom, United States, Sweden, Denmark, Hong Kong and Taiwan.PERSONALISING DRUG TREATMENTThis research stream will advance my current work on prediction of maintenance treatment response in individuals with bipolar disorder using machine learning. Despite recent progress in the field of treatment personalisation, psychiatry lags behind other medical specialties. Currently, no validated system of tailoring treatment choices is available and matching treatment to specific patients is often a matter of trial and error. Via prediction modelling clinicians could more precisely select treatment for patients' needs and thus improve their outcomes. This research stream will focus on:i) Identifying predictors of treatment response in patients during their first illness episode ii) Predicting which individuals will not have their symptoms adequately treated after trials of two medications (treatment resistance)iii) Predicting adverse effects, including weight gain, restlessness (akathisia) and excess sedationClinical features contained in medical records have been shown to be associated with response, treatment resistance and adverse effects, but these have not been combined in a systematic way. The scale and widespread use of electronic health records globally now allows for use of multiple data sets for external validation of generated models. There may also be important changes early in the course of treatment that can predict long term outcomes. These changes are unlikely to be captured in electronic health records, but may be available via patients mobile phones. Capture of passive data via phone apps is now straightforward and potentially contains markers of changes in mental state, such as sleep, movement and phone usage. Apps also facilitate remote symptom monitoring and performance of cognitive tasks. This information will be used to further enhance prediction models. The models built during the early stage of this fellowship will be tested at scale in clinical populations via implementation science methods.IDENTIFYING AND TESTING TARGETS FOR DRUG REPURPOSINGThis translational research stream builds on my previous work which examined whether a number of drugs identified as having potential for repurposing had effects on psychiatric hospitalisation and self-harm rates in patients with SMI. There are a number of other drugs which should be examined via similar approaches to validate these signals for potential effectiveness, whilst robustly accounting for potential confounding, these include a range of anti-inflammatory agents. This work will be cross-validated in other international data sets.The process of pharmacoepidemiological validation optimises the chance of success and provides guidance on which drugs to take forward to randomised controlled trial (RCT). Towards the end of this fellowship I will develop the protocol necessary for a large adaptive RCT and run a pilot to assess feasibility and acceptability.