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

数据科学和药物流行病学改善严重精神疾病的结果（DS-SMI）

• 批准号: MR/V023373/1
• 负责人: Joseph Hayes
• 金额: $ 155.74万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV023373%2F1
• 关键词: Data; science; pharmacoepidemiology; outcome; improvement

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.

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，他们还经历药物不良反应，发病率和死亡率增加。迫切需要改善SMI的药物治疗。解决这一问题的两种具有成本效益的方法是i）通过个性化改善对现有药物的反应，以及ii）识别已经存在的药物（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册、电子健康记录和移动的电话应用程序的力量，沿着现代统计和机器学习技术用于预测建模和因果推理。数据将来自英国、美国、瑞典、丹麦、香港和台湾。个性化药物咨询这个研究流将推进我目前的工作，即使用机器学习预测双相情感障碍个体的维持治疗反应。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，为特定患者匹配治疗往往是一个试验和错误的问题。通过预测建模，临床医生可以更精确地选择符合患者需求的治疗方法，从而改善他们的治疗效果。该研究流将侧重于：i）确定患者首次发病期间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不会得到充分治疗（治疗抵抗）iii）预测不良反应，包括体重增加，坐立不安（静坐不能）和过度镇静病历中包含的临床特征已被证明与反应，治疗抵抗和不良反应有关，但这些尚未以系统的方式结合起来。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，可以预测长期结果。这些变化不太可能在电子健康记录中记录，但可以通过患者的移动的电话获得。通过手机应用程序捕获被动数据现在很简单，可能包含精神状态变化的标记，例如睡眠，运动和手机使用。应用程序还有助于远程症状监测和认知任务的执行。这些信息将用于进一步增强预测模型。在这个奖学金的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。识别和测试药物再利用的目标这个转化研究流建立在我以前的工作，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住院和自我伤害率有影响。还有许多其他药物应通过类似的方法进行检查，以验证这些信号的潜在有效性，同时充分考虑潜在的混淆，这些药物包括一系列抗炎药。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，并为随机对照试验（RCT）提供了指导。在研究结束时，我将制定一个大型适应性随机对照试验所需的方案，并进行一个试点，以评估可行性和可接受性。

项目成果

Incidence and associations of hospital delirium diagnoses in 85,979 people with severe mental illness: A data linkage study.

85,979 名严重精神疾病患者的医院谵妄诊断的发生率和关联：一项数据关联研究。

• DOI: 10.1111/acps.13480
• 发表时间: 2023
• 期刊: Acta psychiatrica Scandinavica
• 影响因子: 6.7
• 作者: Bauernfreund Y

Gabapentinoid consumption in 65 countries and regions from 2008 to 2018: a longitudinal trend study.

• DOI: 10.1038/s41467-023-40637-8
• 发表时间: 2023-08-17
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Chan, Adrienne Y. L.;Yuen, Andrew S. C.;Tsai, Daniel H. T.;Lau, Wallis C. Y.;Jani, Yogini H.;Hsia, Yingfen;Osborn, David P. J.;Hayes, Joseph F.;Besag, Frank M. C.;Lai, Edward C. C.;Wei, Li;Taxis, Katja;Wong, Ian C. K.;Man, Kenneth K. C.
• 通讯作者: Man, Kenneth K. C.

Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform.

• DOI: 10.1016/j.eclinm.2023.102077
• 发表时间: 2023-07
• 期刊: ECLINICALMEDICINE
• 影响因子: 15.1
• 作者: Costello, Ruth E.;Tazare, John;Piehlmaier, Dominik;Herrett, Emily;Parker, Edward P. K.;Zheng, Bang;Mans, Kathryn E.;Henderson, Alasdair D.;Carreira, Helena;Bidulka, Patrick;Wong, Angel Y. S.;Warren-Gash, Charlotte;Hayes, Joseph F.;Quint, Jennifer K.;MacKenna, Brian;Mehrkar, Amir;Eggo, Rosalind M.;Katikireddi, Srinivasa Vittal;Tomlinson, Laurie;Langan, Sinead M.;Mathur, Rohini
• 通讯作者: Mathur, Rohini

Electronic screening for mental illness in patients with psoriasis.

• DOI: 10.1093/bjd/ljad141
• 发表时间: 2023-07-17
• 期刊: BRITISH JOURNAL OF DERMATOLOGY
• 影响因子: 10.3
• 作者: Bechman, Katie;Hayes, Joseph F.;Mathewman, Julian;Henderson, Alasdair D.;Adesanya, Elizabeth, I;Mansfield, Kathryn E.;Smith, Catherine H.;Galloway, James;Langan, Sinead M.
• 通讯作者: Langan, Sinead M.

Prescribing of antipsychotics among people with recorded personality disorder in primary care: a retrospective nationwide cohort study using The Health Improvement Network primary care database.

• DOI: 10.1136/bmjopen-2021-053943
• 发表时间: 2022-03-09
• 期刊: BMJ open
• 影响因子: 2.9
• 作者: Hardoon S;Hayes J;Viding E;McCrory E;Walters K;Osborn D
• 通讯作者: Osborn D