Development of End-To-End Clinical Decision Support Tools To Prevent Cardiotoxic Drug Response

摘要 药物引起的心脏毒性，以QT延长和尖端扭矩的形式出现，是一种罕见的 100多种目前上市的药物的毁灭性副作用。药物性QT的普遍存在 跨医疗专业和条件的延长(DiLQTS)为寻求以下目标的提供商带来了挑战 开出已知的延长QT的药物，特别是对非心脏疾病。我们小组致力于 开发自动化临床决策支持(CDS)工具，提醒提供商患者风险已显示出希望 减少高危人群的处方数量。然而，这些工具依赖于 有QT间期延长的心电图可以识别高危患者，从而排除大量 在我们的系统中没有心电图的潜在高危人群。通过一个独特的机构 与谷歌合作，我们的整个电子健康记录(EHR)的副本存储在谷歌的 在云平台(GCP)的基础上，我们开发了初步的深度学习模型来预测diLQTS的风险。我们 我还在几个真实世界中验证了QT间期和diLQTS之间的遗传关联 使用聚合多基因风险评分的人群。通过创建一个机构生物库 认证结果的临床应用，以及基于云的EHR数据与基因数据的集成， 我们有能力利用我们现有的基础设施来研究深度学习和遗传学的作用 降低diLQTS的风险。这项研究将把我们独特的研究和临床结合起来 科罗拉多大学安舒茨医学院的基础设施与我们的调查团队 由药物基因组学和医学信息学研究专家组成的开发和研究 结合遗传学和深度学习的端到端CDS工具，用于预测diLQTS的风险。这个 该应用程序的具体目标包括：(1)开发和测试基于云的深度学习模型 使用住院和门诊患者的EHR数据预测diLQTS的风险；(2)验证diLQTS的遗传预测因子 机构生物库样本和多种族外部人口；以及(3)开发和测试CDS工具 这些先进的方法降低了diLQTS的风险。我们将使用通用数据模型(观察性数据 医疗结果伙伴关系)，以及自定义DNA阵列(多种族 基因分型阵列)设计用于跨各种非欧洲祖先的归属，以确保我们的 这项研究的预测模型和研究结果可以在其他机构和人口中复制 未来。通过这种方式，这项调查不仅将提供对机器学习和 遗传学用于diLQTS的风险预测，但它也将为未来高级CDS的发展绘制蓝图 对于其他情况。

SUMMARY Drug-induced cardiac toxicity, in the form of QT prolongation and torsade de pointes, is an uncommon but devastating side effect of over one hundred currently marketed drugs. The ubiquity of drug-induced QT prolongation (diLQTS) across medical specialties and conditions creates a challenge for providers seeking to prescribe known QT-prolonging medications, particularly for non-cardiac conditions. Work by our group to develop automated clinical decision support (CDS) tools that alert providers of patient risk has shown promise towards reducing the number of prescriptions to at-risk individuals. However, these tools rely on a history of an electrocardiogram (ECG) with QT prolongation to identify at-risk patients, and thus exclude a large number of potentially at-risk individuals who have not had an ECG within our system. Through a unique institutional partnership with Google, in which a copy of our entire electronic health record (EHR) is stored on the Google Cloud Platform (GCP), we have developed preliminary deep-learning models to predict risk of diLQTS. We have also validated the genetic association with the QT interval and diLQTS across several real-world populations using an aggregate polygenic risk score. Through creation of an institutional biobank with certification for clinical application of results, as well as cloud-based integration of EHR data with genetic data, we have the capability to leverage our existing infrastructure to study the role of deep learning and genetics to reduce the risk of diLQTS. This investigation will combine our unique research and clinical infrastructure on the University of Colorado Anschutz Medical Campus with our investigative team composed of experts in the study of pharmacogenomics and medical informatics to develop and study an end-to-end CDS tool incorporating genetics and deep learning to predict risk of diLQTS. The specific aims of this application include the following: (1) develop and test a cloud-based, deep-learning model using EHR data on in- and outpatients to predict risk of diLQTS; (2) validate genetic predictors of diLQTS using institutional biobank samples, and a multi-ethnic external population; and (3) develop and test CDS tools using these advanced methods to reduce the risk of diLQTS. We will use a common data model (Observational Medical Outcomes Partnership) mapped from EHR data, as well as a custom DNA array (Multi-Ethnic Genotyping Array) designed for imputation across a variety of non-European ancestries, to ensure that the our prediction model and findings from this study can be replicated in other institutions and populations in the future. In such a way, this investigation will not only provide insight into the use of machine learning and genetics for risk prediction of diLQTS, but it will also create a blueprint for future advanced CDS development for other conditions.