Developing and implementing tools to advance precision psychiatry using electronic health records

开发和实施工具，利用电子健康记录推进精准精神病学

• 批准号: 2886557
• 金额: --
• 依托单位: King's College London
• 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-2886557
• 关键词: Developing; implementing; tools; advance; precision

In recent years, precision medicine has emerged as an avenue for risk stratification and the provision of highly individualised care, through careful consideration of differences across individuals' characteristics, genes, environments, and lifestyles. Clinical prediction modelling is the backbone of precision medicine, and can be utilised to predict the probability of a condition being present (diagnostic models), its future course (prognostic models), and the response to associated interventions (predictive models). In several branches of medicine (e.g., cardiovascular disease, and cancer), a wide range of these models have been implemented for routine usage in clinical practice. However, within psychiatry, whilst such models have been developed, they have largely not been considered for implementation in clinical practice (Meehan et al., 2022; Salazar de Pablo et al., 2021). This is largely due to the relative lack of testing the performance of models using wholly independent data in new, real-life settings to assess their generalisability to related, but different settings (external validation). External validation, however, is costly and time-consuming. Thus, novel approaches are required to assess the robustness of models across different settings in order to improve the likelihood of models being implemented in clinical practice and at lower costs, and consequently progress precision psychiatry. Monte Carlo simulations using information of existing prediction models assessed in different settings would help to assess the expected robustness of a model in external settings. This would allow an assessment of the likelihood that a model will exhibit good external validation without requiring the collection of data and aids in decision-making regarding whether further costly and time-consuming external validation studies are warranted.Further, one of the most frequently investigated conditions of existing individualised prediction models are psychotic disorders (Meehan et al., 2022; Salazar de Pablo et al., 2021). Amongst other influences, this focus has been driven by a need to tackle the sizeable health disparity and burden associated with psychosis, as well as to advance the detection of individuals at clinical high risk state for psychosis (CHR-P) and prognostication of their outcomes (Fusar-Poli et al., 2016, 2020). An individualised prognostic model for psychosis onset has previously been developed and extensively externally validated (Fusar-Poli et al., 2017, 2019; Oliver et al., 2020; Puntis et al., 2021) and is ideal for testing novel approaches of simulating performance across multiple settings. Similar to psychotic disorders, personality disorders are associated with significant comorbidity, premature mortality, and high societal and personal costs (Tyrer et al, 2015). Further, they predict poor adherence to and engagement in the treatment of other comorbidities, and consequently can moderate transdiagnostic mental health and physical health outcomes. Despite this evident disease burden, a recent review (Meehan et al., 2022) identified only two individualised prediction models focussed on borderline personality disorder (BPD) and both were diagnostic, rather than prognostic. Given the extensive research into subthreshold personality disorder pathology and the malleability of BPD traits in youth/early adolescence (Sharp & Fonagy, 2015; Chanen & McCutcheon, 2018), the development of a prognostic model for personality disorder would be well-placed to help improve our understanding of emerging personality disorders; promote their prevention and early intervention; and improve long-term outcomes.

近年来，精准医疗已成为风险分层和提供高度个性化护理的途径，通过仔细考虑个体特征，基因，环境和生活方式的差异。临床预测模型是精准医学的支柱，可用于预测疾病存在的概率（诊断模型），其未来进程（预后模型）以及对相关干预措施的反应（预测模型）。在医学的几个分支中（例如，心血管疾病和癌症），这些模型中的大范围已经被实现用于临床实践中的常规使用。然而，在精神病学中，虽然已经开发了这样的模型，但是它们在很大程度上没有被考虑用于在临床实践中实施（Meehan等人，2022; Salazar de巴勃罗等人，2021年）。这在很大程度上是由于在新的现实生活环境中使用完全独立的数据来测试模型的性能，以评估其对相关但不同环境的可推广性（外部验证）相对缺乏。然而，外部验证既昂贵又耗时。因此，需要新的方法来评估模型在不同环境中的鲁棒性，以提高模型在临床实践中以较低成本实施的可能性，从而提高精确精神病学的进展。使用在不同环境中评估的现有预测模型的信息进行蒙特卡罗模拟将有助于评估模型在外部环境中的预期稳健性。这将允许评估模型将表现出良好外部验证的可能性，而不需要收集数据，并有助于决策是否需要进一步昂贵和耗时的外部验证研究。2022; Salazar de巴勃罗等人，2021年）。在其他影响中，这种关注是由解决与精神病相关的相当大的健康差异和负担的需要驱动的，以及推进对处于精神病临床高风险状态（CHR-P）的个体的检测和对其结果的描述（Fusar-Poli等人，2016年、2020年）。精神病发作的个体化预后模型先前已经被开发并被广泛地外部验证（Fusar-Poli等人，2017，2019;奥利弗等人，2020; Puntis等人，2021），是测试跨多个设置模拟性能的新方法的理想选择。与精神病性障碍相似，人格障碍与显著的合并症、过早死亡以及较高的社会和个人成本相关（Tyrer et al，2015）。此外，它们预测了对其他合并症治疗的依从性和参与度较差，因此可以缓和跨诊断的心理健康和身体健康结果。尽管有这种明显的疾病负担，最近的综述（Meehan et al.，2022）仅确定了两种侧重于边缘型人格障碍（BPD）的个性化预测模型，并且两者都是诊断性的，而不是预后性的。鉴于对阈下人格障碍病理学和青年/青春期早期BPD特征的可塑性的广泛研究（Sharp & Fonagy，2015; Chanen & McCutcheon，2018），人格障碍预后模型的开发将有助于提高我们对新兴人格障碍的理解;促进其预防和早期干预;并改善长期结果。