New approaches to safety monitoring of novel systemic treatments for atopic dermatitis in clinical practice and underrepresented populations

摘要： 重症治疗顽固性特应性皮炎(AD)是一种令人衰弱的疾病，人口众多 冲击力。皮肤科经历了靶向免疫调节药物(IMD)的出现，这些药物具有 治疗阿尔茨海默病的疗效前所未有。它们的最佳使用仍然是未知的，因为它们的安全性仍然 特征不充分的。 根据免疫途径，可以想象到一系列严重的副作用，尽管不太可能 它们都将在临床实践中发挥作用。量化或驳斥这些不良影响对于临床来说至关重要 效益-风险评估和个性化治疗决策。现有的试验还没有回答这些问题 这些问题不太可能在不久的将来得到解决。由此产生的不确定性导致了两个方面的过度 限制但也积极地开出高效的IMD处方，这项提案旨在结束这一明显的问题 知识鸿沟。 我们提出了一种基于人群的前瞻性药物安全监测系统，利用现有的数据源 这缩短了深入了解的时间，并通过高级因果推理提供了高有效性的结果 方法：研究方法。对纵向医疗保健数据库的分析覆盖了7800万美国人 并包括商业保险和医疗补助受益人。新的和迫切需要的安全见解将 反映临床实践，包括通常被排除在随机对照试验之外的人群，如儿童、妇女 育龄、复杂疾病患者、少数民族人群和存在危险因素的患者。 索赔数据源的大小增加了统计能力和与电子健康记录的链接 子集提高了临床深度。我们使用因果推理方法，在试点数据中证明了高有效性。 并用一种新的数据挖掘方法来识别未被怀疑的事件。进行了分析 具有最高的透明度和重复性，以支持临床决策。这个项目的发现是关于 临床实践中IMD的最佳使用将导致更有针对性的处方并使更大的患者群体受益， 包括在区域协调机制中代表性不足的人群：儿童、老年人、孕妇、少数族裔、 既有感染、癌症、静脉血栓形成和其他疾病的患者。 该项目具有很高的创新性，因为它将产生关于安全性和针对性的直接适用的临床见解 使用新型免疫调节药物治疗特应性皮炎。利用现有索赔数据 来源与增加的电子病历数据，它建立在新的因果推理方法，以减轻在真实的 皮肤科的世界数据分析。通过建议的前瞻性加速证据生成 监测系统结合我们在药物流行病学分析方面的记录，这项研究将 有效地弥合知识差距，以便在许多代表性不足和高危患者中最佳使用IMD。

Summary: Severe treatment recalcitrant atopic dermatitis (AD) is a debilitating condition with substantial population impact. Dermatology has experienced the emergence of targeted immuno-modulating drugs (IMDs) that have unprecedented efficacy in treating AD. Their optimal use is still unknown because their safety remains insufficiently characterized. A range of serious side effects are conceivable based on the immunologic pathways although it is unlikely that they will all play out in clinical practice. Quantifying or refuting these adverse effects is critical for a clinical benefit-risk assessment and personalized treatment decisions. Existing trials have not answered these questions and are unlikely to address them in the near future. The resulting uncertainty has led to both overly restrictive but also aggressive prescribing of highly efficacious IMDs and this proposal aims to close this glaring knowledge gap. We propose a population-based prospective drug safety monitoring system leveraging existing data sources that shortens the time to insights and provides high validity findings through advanced causal inference methods. Analyses of longitudinal healthcare databases cover a source population of >78 million Americans and include commercially insured and Medicaid beneficiaries. New and urgently needed safety insights will reflect clinical practice, including populations typically excluded from RCTs, like children, women in reproductive age, patients with complex diseases, minority populations, and patients with existing risk factors. The size of the claims data source increases statistical power and the linkage to electronic health records in subsets improves clinical depth. We use causal inference methods that demonstrated high validity in pilot data and complement them with a novel data mining approach to identify unsuspected events. Analyses are done with highest transparency and reproducibility to support clinical decision making. This project’s finding on the optimal use of IMDs in clinical practice will lead to more targeted prescribing and benefit large patient groups, including populations underrepresented in RCTs: children, older adults, pregnant women, racial minorities, patients with pre-existing infections, cancers, VTE and others. This project is highly innovative as it will generate directly applicable clinical insights on the safe and targeted use of new immuno-modulating drugs (IMDs) to treat atopic dermatitis. Leveraging existing claims data sources with added EHR data it builds on novel methods for causal inference to mitigate biases arising in real- world data analyses in dermatology. The expedited evidence generation via the proposed prospective monitoring system combined with our track record in pharmacoepidemiologic analyses, this research will efficiently close knowledge gaps for optimal IMD use in many underrepresented and high-risk patients.