Clinical Informatics to Advance Epidemiology and Pharmacogenetics of Serious Cutaneous Adverse Drug Reactions

项目摘要 严重皮肤不良反应（SCAR）是对药物的病态免疫反应， 死亡率为10- 50%。在过去的十年里，预测和预防的重大承诺来自于 发现许多SCAR与HLA I类等位基因内的变异相关。对于HLA-B*15：02， 导致在许多东南亚国家对卡马西平进行常规处方前筛查， 卡马西平SJS/TEN病例减少。尽管取得了这些进展，但人们对遗传和 与常用药物（如抗生素）相关的SCAR的流行病学风险因素。也有有限的 在美国不同人群中，关于SCAR的HLA风险的信息。此外，委员会认为， 临床表型的不精确性和缺乏标准化编码导致了寻找SCAR病例的挑战 电子健康记录（EHR）我们拟议的研究旨在解决我们的关键挑战和差距， 了解抗生素SCARs。 在目标1中，我们将利用先进的信息学和纵向EHR数据，从1100多万患者， 自20世纪80年代以来，合作伙伴医疗保健系统一直致力于识别SCAR病例。我们将创造、优化和标准化 用于发现SCAR病例和验证SCAR患者队列的可重复方法。该迭代过程 将被用来完善和传播一个电子表型，以验证跨机构。 在目标2中，我们将分析SCAR的患病率，并进行病例对照研究，以确定药物特异性和 患者特异性的危险因素与肿瘤相关的SCAR。我们将比较临床后遗症、生活质量和 通过经验证的调查工具，将SCAR患者的依从性与对照组进行比较。 在目标3中，我们将从使用经验证的抗生素的患者中鉴定候选HLA和遗传关联。 相关的SCARs我们将研究少数民族和健康差异人群中遗传风险的差异， 预测我们将有能力建立万古霉素DRESS的HLA相关性（即，药物反应伴 嗜酸性粒细胞增多症和全身症状）和磺胺类抗菌剂和β-内酰胺类SCAR。单独HLA，或 结合临床危险因素，可以改善SCAR的预防和早期诊断。我们将 以在线电子表型和患者登记的形式建立数据共享平台， 用于扩大未来大规模基因组学研究的SCAR队列。 我们开发的路线图将转化为严重不良反应的电子表型开发 促进基因发现的药物反应。所获得的知识对遗传数据的翻译至关重要 临床决策。这与NIH的研究使命密切相关， 发现医源性和可预防的药物引起的疾病，将转化为预防， 诊断和增强的机制理解，可能导致有针对性的治疗方法。

Project Summary Severe cutaneous adverse reactions (SCARs) are morbid immunologic reactions to drugs that confer a mortality of 10-50%. Over the last decade, significant promise for prediction and prevention has come from the discovery that many SCARs are associated with variation within HLA class I alleles. For HLA-B*15:02, this has led to routine pre-prescription screening for carbamazepine in many Southeast Asian countries and a significant reduction in cases of carbamazepine SJS/TEN. Despite this progress, there is little known about genetic and epidemiological risk factors for SCARs related to commonly used drugs such as antibiotics. There is also limited information about HLA risk for SCARs across the diverse populations present in the United States. Furthermore, imprecision of clinical phenotyping and lack of standardized coding has led to challenges in finding SCAR cases in the electronic health record (EHR). Our proposed study aims to address critical challenges and gaps in our knowledge of antibiotic SCARs. In Aim 1, we will leverage advanced informatics and longitudinal EHR data for over 11 million patients from Partners HealthCare System since the 1980s to identify SCAR cases. We will create, optimize and standardize reproducible methods for finding SCAR cases and validating a cohort of SCAR patients. This iterative process will be used to refine and disseminate an electronic phenotype to be validated cross-institutionally. In Aim 2, we will analyze SCAR prevalence and conduct a case-control study to identify drug-specific and patient-specific risk factors for antibiotic-associated SCARs. We will compare clinical sequelae, quality of life and adherence of SCAR patients compared to controls through validated survey instruments. In Aim 3, we will identify candidate HLA and genetic associations from patients with validated antibiotic- associated SCARs. We will examine difference in genetic risk in minority and health disparity populations and predict that we will be powered to establish HLA associations for vancomycin DRESS (i.e., drug reaction with eosinophilia and systemic symptoms) and sulfonamide antimicrobial and beta-lactam SCAR. HLA alone, or in combination with clinical risk factors, can lead to improved SCAR prevention and early diagnosis. We will establish a data sharing platform, in the form of an online electronic phenotype and patient registry, that can be used to enlarge SCAR cohorts for future large-scale genomics studies. The roadmap we develop will translate into the development of electronic phenotypes for serious adverse drug reactions that facilitate genetic discovery. Knowledge gained will be crucial to the translation of genetic data into clinical decision making. This is in close alignment with NIH’s research mission to accelerate genetic discovery for iatrogenic and preventable drug-induced diseases that will translate into prevention, earlier diagnosis and an enhanced mechanistic understanding that may lead to targeted therapeutic approaches.