Living Evidence onFirst-EpiSodeof psychosis Treatments and Outcomes (EFESTO): a living individual participant data network meta-analysis and learning

关于精神病首次发作治疗和结果的活生生证据（EFESTO）：活生生的个体参与者数据网络荟萃分析和学习

• 批准号: 2444869
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444869
• 关键词: Living; Evidence; onFirst; EpiSodeof; psychosis

To overcome the limited power of current detection strategies for psychosis-risk, the EPIC lab at IoPPN has recently leveraged precision medicine methods and developed clinical prediction models, including the 'transdiagnostic risk calculator'. This model indexes the probability of transition to psychosis (i.e. the risk of the participant developing a first psychotic disorder over time) within secondary mental health care (1). This model was based on preselected clinically based variables which are widely available in electronic health records (EHRs): index ICD-10/At-Risk Mental State (ARMS) diagnosis, age, sex, age by sex, and race/ethnicity. The model was termed "transdiagnostic" because it predicts the onset of psychosis not only from a CHR-P stage but also from other non-psychotic mental disorders such as affective mental disorders. Indeed, there is accumulating evidence showing that the majority of CHR-P individuals would also meet criteria for a secondary diagnosis of co-morbid mental disorder, mostly depression and anxiety (2). Moreover, bipolar mood disorders can also predate the onset of psychotic disorders (1).The transdiagnostic risk calculator was externally validated in a large clinical registry cohort of adults in the SLaM boroughs of Lewisham and Croydon (n=54,716) and showed good external accuracy (Harrell's C=0.79). Further replications have demonstrated its transportability. Specifically, the transdiagnostic risk calculator has been externally validated in three independent EHR datasets of different population profiles, demonstrating adequate prognostic performance. Firstly, with the Camden and Islington NHS Foundation Trust (n=13,702, Harrell's C=0.73) (3), which comprised an older population relative to the one used to develop the calculator. Secondly, with the Oxford Health NHS Foundation Trust (n=33,710, Harrell's C=0.79), whichcontained a mix of both urban and rural backgrounds (4).Thirdly, the calculator was externally validated internationally in the IBM Commercial database (n=2,430,333, Harrell's C=0.68), retaining an accuracy above chance despite the lack of ethnicity data in this replication (5), which represents the largest external replication of clinical prediction models in psychiatry. In a further step, this clinical prediction model has been refined with Natural Language Processing applications to automatically screen the text of clinical notes and letters. This enables the inclusion of more fine-grained predictors such as symptom/substance misuse predictors (tearfulness, poorappetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia). NLP methods are based on data-mining artificial intelligence and represent bespoke analytic techniques available at the IoPPN. This refinement further improved the model significantly to Harrell's C=0.85 (95% CI 0.84-0.86), an increase of 0.06 from the original model. The above findings show that the risk calculator provides a potential tool that could improve detection of individuals at risk of psychosis. However there is a dearth of implementation science in psychiatry and only less than 1% of published clinical prediction models are eventually implemented in clinical practice (6). Despite this, the real-world implementation of the transdiagnostic risk calculator was tested in a pilot study conducted at the IoPPN (7), with about 77% of clinicians respondingto prompts issued by the risk calculator, indicating a good level of adherence.

为了克服目前精神病风险检测策略的有限能力，IoPPN的EPIC实验室最近利用精确医学方法并开发了临床预测模型，包括“跨诊断风险计算器”。这个模型索引了二级精神卫生保健中转变为精神病的可能性(即参与者随着时间的推移发展为第一次精神病的风险)(1)。该模型基于预先选定的临床变量，这些变量在电子健康记录(EHR)中广泛可用：ICD-10指数/高危精神状态(ARMS)诊断、年龄、性别、按性别划分的年龄以及种族/民族。该模型被称为“跨诊断”，因为它不仅可以预测精神障碍的发病，还可以预测其他非精神病性精神障碍的发病，如情感性精神障碍。事实上，越来越多的证据表明，大多数CHR-P患者也符合第二诊断标准，即共病精神障碍，主要是抑郁和焦虑(2)。此外，双相情感障碍也可以早于精神病的发病(1)。跨诊断风险计算器在路易斯汉姆和克罗伊登的一个大型临床登记队列中进行了外部验证(n=54,716)，并显示出良好的外部准确性(Harrell‘s C=0.79)。进一步的复制证明了它的可移植性。具体地说，跨诊断风险计算器已经在不同人群特征的三个独立的EHR数据集中进行了外部验证，证明了足够的预后性能。首先，与卡姆登和伊斯灵顿NHS基金会信托基金(n=13,702，哈雷尔的C=0.73)(3)，其中包括一个相对于用于开发计算器的老年人口。其次，与牛津健康NHS基金会信托基金(n=33,710，Harrell的C=0.79)，其中包含城市和农村背景的混合(4)。第三，该计算器在IBM商业数据库中进行了国际外部验证(n=2,430,333，Harrell的C=0.68)，尽管在这次复制中缺乏种族数据，但仍保持高于机会的准确性(5)，这是精神病学临床预测模型的最大外部复制。在进一步的步骤中，这个临床预测模型已经用自然语言处理应用程序进行了改进，以自动筛选临床笔记和信件的文本。这使得能够纳入更细粒度的预测指标，如症状/物质滥用预测指标(流泪、食欲不佳、体重减轻、失眠、大麻、可卡因、内疚、易怒、妄想、绝望、睡眠障碍、洞察力差、烦躁不安和偏执)。自然语言处理方法以数据挖掘人工智能为基础，代表了IoPPN提供的定制分析技术。这一改进进一步将模型显著改进为Harrell‘s C=0.85(95%CI 0.84-0.86)，比原始模型增加了0.06。上述发现表明，风险计算器提供了一种潜在的工具，可以改善对精神病风险个体的检测。然而，精神病学中缺乏实施科学，仅有不到1%的已发表的临床预测模型最终在临床实践中得到实施。尽管如此，跨诊断风险计算器的实际实施在IoPPN(7)进行的一项试点研究中进行了测试，约77%的临床医生对风险计算器发出的提示做出了反应，表明遵守程度良好。