Stratification of Patients using advanced Integrative modelling of Data Routinely acquired for diagnosing Rheumatic complaints

全球有17.1亿人患有肌肉骨骼症状，这是导致残疾的主要原因。早期疾病分层是重要的，以确保适当的护理（最适合的医疗保健提供者和最佳治疗选择）。目前，患者到诊断和有效治疗的路程漫长而低效，导致持续的疾病负担和经济损失。这是由于对病因了解不足、疾病之间症状相似、歧视性诊断测试不足以及在初始治疗中采用“试错”方法造成的。SPIDeRR旨在通过考虑影响患者症状的完整因素网络来解开风湿病早期诊断的现实复杂性。SPIDeRR的方法将在以下方面超越最先进的技术：-通过在症状相似的患者中识别不同的疾病组，需要不同的治疗方法，而不是传统的方法，旨在早期捕获一种疾病。- - - - - - ?通过整合来自每个医疗保健级别（初级和二级保健以及在线寻求建议的患者）的所有相关数据维度。- - - - - - ?通过将“组学”领域的机器学习技术翻译和应用于临床患者数据，SPIDERR将提供三种临床模型-患者症状检查器-为（初级）护理提供者提供决策支持工具，提供指导额外的检查和转诊决策-患者相似网络，以优化风湿病诊断组并支持治疗决策。为了实现这一目标，我们还提供数据集成和共享分析解决方案，通过符合GDPR的数字研究环境和联合学习管道。最后，我们将通过利益相关者研究来测试这些模型的可接受性，并提供一个适合欧洲当前医疗保健的实施场景。

Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economic loss. This is due to insufficiently understood disease causes, similarities in symptoms between diseases, insufficiently discriminatory diagnostic tests and a “ trial and error” approach in initial treatment. SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways: -By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early. -?By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online). -?By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science SPIDERR will deliver three clinical models -a symptom checker for patients -a decision support tool for (primary) care providers providing guiding additional examination and referral decisions -a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines. Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.