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

电子健康记录中酒精使用障碍诊断算法的开发和验证

• 批准号: 10430841
• 负责人: Maria Niarchou
• 金额: $ 9.35万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10430841
• 关键词: Academic Medical Centers; African American; Alcohol dehydrogenase; Alcohols; Algorithms; Award; Classification; Clinical; Code; Current Procedural Terminology Codes; Data; Data Collection; Development; Diagnosis; Dopamine D2 Receptor; Eating Disorders; Economics; Electronic Health Record; Ensure; Epidemiology; Ethnic Origin; European; Female; Funding; Future; Genes; Genetic; Genetic Variation; Genomics; Genotype; Gilles de la Tourette syndrome; Health Status; Heritability; Individual; Informatics; Integrated Health Care Systems; International Classification of Disease Codes; Investigation; Link; Liver Function Tests; Machine Learning; Medical Genetics; Mental disorders; Meta-Analysis; National Institute on Alcohol Abuse and Alcoholism; Natural Language Processing; Obsessive-Compulsive Disorder; Outcome; Patients; Performance; Phenotype; Positioning Attribute; Process; Psychology; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Running; Sample Size; Sampling; Secure; Substance Use Disorder; Testing; Validation; alcohol research; alcohol use disorder; aldehyde dehydrogenases; algorithm development; autism spectrum disorder; base; biobank; career; clinical biomarkers; cost; cost effective; diagnostic algorithm; effective therapy; electronic structure; experience; follow-up; genome wide association study; genome-wide; genomic locus; health data; improved; innovation; longitudinal dataset; male; precision medicine; prescription procedure; psychiatric genomics; recruit; risk variant; sex; statistics; structured data; substance use; trait; treatment strategy; unstructured data

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.

项目概要 酒精使用障碍 (AUD) 非常普遍、异质、具有遗传性，会导致一系列负面影响 增强我们对 AUD 遗传基础的理解可以促进新的和新的研究的发展。 尽管 AUD 全基因组协会研究已经确定并重复了更有效的治疗方法。 由于许多基因位点的关联，AUD GWAS 的样本量仍然相对较小，表明 可能还有更多与 AUD 相关的基因位点有待发现，而且 AUD 也经常未被发现。 诊断不足、可能存在偏差的 GWAS 和后续分析的可用性。 与电子健康记录 (EHR) 相关的数据集与临床和遗传数据相关联，使 被动收集澳元数据，跨性别和血统，这与昂贵且劳力的收集形成鲜明对比。 此外，基于 EHR 的表型分析是一种成本高昂的方法。 有效的策略，在其他精神病学的遗传和流行病学研究结果中显示出强大的有效性 该研究将在范德比尔特大学医学中心（VUMC）进行，这是一个综合性的中心。 拥有 EHR 的卫生系统，包括与 BioVU 相关的 320 万名患者，BioVU 是一种基因组资源，具有基因组- 我们的首要目标是开发和验证 94,000 名不同血统患者的广泛基因型数据。 算法在 EHR 中识别 AUD 个体（目标 1）。我们将使用结构化 EHR 的组合。 数据（例如，账单代码、电子处方、程序、实验室、生命体征的诊断）和非结构化 数据（例如临床记录），开发复杂的算法，以更好地对 AUD 进行表型分类 我们还将测试男性和女性以及不同种族和民族的算法性能， 确保我们在后续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效用（目标 2）。 与 AUD 相比，仅基于计费代码的算法可以复制与 AUD 相关的遗传发现 结合结构化和非结构化数据的算法此外，还创建了 GWAS 摘要统计数据。 然后我们的分析将与其他 GWAS 研究一起进行荟萃分析，有助于增加样本量 因此，我们的方法能够检测 AUD 的基因位点，以响应 NIAAA 最近的公告。 (NOT-AA-20-018) 并提出利用现有酒精研究数据验证 AUD 的创新分析。 范德比尔特电子病历中的表型是重要的第一步，随后我们将能够进行系统性分析 研究遗传变异与其他 AUD 相关风险因素之间的相互作用。