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.

项目摘要 酒精使用障碍（AUD）是一种高度流行、异质性、可遗传的疾病， 成果。加强我们对AUD遗传基础的理解，可以开发新的， 更有效的治疗。尽管AUD全基因组协会的研究已经确定并复制了 尽管许多基因中的位点存在关联，但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。澳元也经常未被发现， 诊断不足，潜在偏倚GWAS和随访分析。大型、纵向 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， 被动收集澳元数据，跨越性别和血统，与昂贵的劳动力形成鲜明对比， 对AUD的传统确认的密集过程。此外，基于EHR的表型分析是一种成本- 在遗传学和流行病学研究结果中显示出对其他精神疾病的强有效性的有效策略 条件这项研究将在范德比尔特大学医学中心（VUMC）进行，这是一个综合性的研究中心。 一个拥有EHR的卫生系统，包括320万与BioVU相关的患者，BioVU是一个基因组资源， 94，000名不同血统患者的广泛基因型数据。我们的第一个目标是开发和验证一个 算法来识别EHR中的AUD个体（目标1）。我们将结合使用结构化的EHR 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，临床笔记），以开发一种复杂的算法，用于更好地对 电子病历我们还将在男性和女性以及不同种族和民族中测试算法性能， 以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实用性（目的2）。我们将测试一个 仅基于计费代码的算法可以复制AUD相关的遗传发现，相比之下， 一种结合了结构化和非结构化数据的算法。此外，由 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 因此能够检测AUD的遗传基因座。我们的做法是对NIAAA最近宣布的 （NOT-AA-20-018），并提出了创新的分析与现有的酒精研究数据。验证AUD 在范德比尔特的EHR表型是一个重要的第一步，随后将允许我们进行系统的 研究遗传变异与其他AUD相关风险因素之间的相互作用。