Development and validation of a diagnostic algorithm for Alcohol Use Disorder in the Electronic Health Records

项目总结 酒精使用障碍(AUD)是一种非常普遍的、异质性的、可遗传的，并导致一系列负面结果 结果。加强我们对AUD的遗传基础的了解，可以使新的和 更有效的治疗方法。尽管，澳大利亚全基因组协会的研究已经确定并复制了 对于一些基因中的基因座关联，AUD GWAS的样本量仍然相对较小，这表明 可能有更多与AUD相关的遗传基因座有待发现。AUD也经常未被发现和 未得到充分诊断，可能会对全球气候变化分析和后续分析产生偏见。大的、纵向的 与链接到临床和遗传数据的电子健康记录(EHR)相关联的数据集使 被动地收集不同性别和祖先的澳元数据，这与昂贵和劳力- 对澳元进行密集的传统确认过程。此外，基于电子病历的表型分析是一种成本- 有效的策略，在遗传学和流行病学研究中显示出对其他精神病患者的强大有效性 条件。这项研究将在范德比尔特大学医学中心(VUMC)进行，该中心是一家综合性的 拥有EHR的卫生系统包括320万名患者与BioVU相关联，BioVU是一种具有基因组的基因组资源- 不同血统的94,000名患者的广泛基因数据。我们的首要目标是开发和验证 在EHR中识别患有AUD的个体的算法(目标1)。我们将使用结构化EHR的组合 数据(例如，帐单代码、电子处方、程序、实验室、生命体征的诊断)和非结构化 数据(例如，临床笔记)，以开发复杂的算法，以更好地对AUD进行表型分类 啊哈。我们还将在男性和女性以及不同的种族和民族中测试算法的性能， 以确保我们在随后的研究中避免对人口群体产生偏见。我们的第二个目标是确定 基于EHR的AUD诊断在基因组学研究中的应用(目标2)。我们将测试一个 仅基于账单代码的算法可以复制与AUD相关的基因发现，而不是 合并结构化和非结构化数据的算法。此外，由以下人员创建的GWAS汇总统计数据 然后，我们的分析将与其他GWAS研究一起进行荟萃分析，以帮助增加样本量 因此有能力检测AUD的遗传位点。我们的方法响应了NIAAA最近的声明 (非-AA-20-018)，并利用现有的酒精研究数据提出了创新的分析。正在验证澳元 范德比尔特的EHR的表型是重要的第一步，随后将使我们能够进行系统的 研究遗传变异与其他AUD相关危险因素之间的相互作用。

PROJECT SUMMARY Alcohol Use Disorder (AUD) is highly prevalent, heterogeneous, heritable and results in an array of negative outcomes. Enhancing our understanding of the genetic basis of AUD can enable the development of new and more effective treatments. Although, AUD Genome Wide Association studies have identified and replicated associations for loci in a number of genes, the sample sizes for AUD GWAS are still relatively small, indicating that there are likely more AUD related genetic loci to be discovered. AUD is also frequently undetected and under-diagnosed, potentially biasing GWAS and follow up analyses. The availability of large, longitudinal datasets associated with Electronic Health Records (EHR) that are linked to clinical and genetic data enables passive collection of data on AUD, across sexes and ancestries, in stark contrast to the costly and labor- intensive processes of traditional ascertainment for AUD. Furthermore, EHR-based phenotyping is a cost- effective strategy that shows strong validity in genetic and epidemiologic findings for other psychiatric conditions. The research will be conducted at Vanderbilt University Medical Center (VUMC), an integrated health system with an EHR including 3.2 million patients linked to BioVU, a genomic resource with genome- wide genotype data for 94,000 patients of diverse ancestry. Our first aim is to develop and validate an algorithm to identify individuals with AUD in the EHR (Aim 1). We will use a combination of structured EHR data (e.g., diagnosis of billing codes, electronic prescriptions, procedures, labs, vital signs) and unstructured data (e.g., clinical notes), to develop a sophisticated algorithm for better phenotypic classification of AUD in the EHR. We will also test the algorithm performance in males and females, and in different races and ethnicities, to ensure that we avoid biasing demographic groups in subsequent research. Our second aim is to determine the utility of EHR-based AUD diagnoses for genomics research (Aim 2). We will test the extent to which an algorithm based solely on billing codes can replicate the AUD related genetic findings, compared to an algorithm that incorporates structured and unstructured data. Also, the GWAS summary statistics created by our analyses will then be meta-analyzed together with other GWAS studies, helping increase the sample sizes and hence the power to detect genetic loci for AUD. Our approach responds to NIAAA’s recent announcement (NOT-AA-20-018) and proposes innovative analyses with existing alcohol research data. Validating the AUD phenotype in Vanderbilt’s EHR is an important first step that will subsequently allow us to perform systematic investigations into the interactions between genetic variation and other AUD-related risk factors.