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

总结 以QT间期延长和尖端扭转型室性心动过速为形式的药物诱导的心脏毒性是一种不常见的， 目前市场上有超过一百种药物的副作用。药物诱导QT间期的普遍性 延长（diLQTS）跨医学专业和条件创造了一个挑战，为供应商寻求 处方已知的QT延长药物，特别是用于非心脏疾病。通过我们的团队， 开发自动化临床决策支持（CDS）工具，提醒提供者患者风险已显示出希望 减少对高危人群的处方数量。然而，这些工具依赖于 心电图（ECG）与QT间期延长，以确定有风险的患者，从而排除了大量的 在我们的系统中没有ECG的潜在风险个体。通过一个独特的机构 与Google合作，我们的整个电子健康记录（EHR）的副本存储在Google上。 云平台（GCP），我们已经开发了初步的深度学习模型来预测diLQTS的风险。我们 还验证了QT间期和diLQTS在几个现实世界中的遗传关联 使用多基因风险评分的总体人群。通过建立一个机构生物库， 对结果的临床应用进行认证，以及基于云的EHR数据与遗传数据的集成， 我们有能力利用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。本研究将联合收割机结合我们独特的研究和临床 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 由研究药物基因组学和医学信息学的专家组成， 一种端到端CDS工具，结合遗传学和深度学习来预测diLQTS的风险。的 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）使用 机构生物库样本和多种族外部人群;（3）开发和测试CDS工具， 这些先进的方法来降低dilQTS的风险。我们将使用公共数据模型（观察 医学成果伙伴关系），以及定制的DNA阵列（多民族 基因分型阵列），旨在填补各种非欧洲血统，以确保我们的 预测模型和这项研究的结果可以在其他机构和人口中复制。 未来通过这种方式，这项调查不仅可以深入了解机器学习的使用， 这一发现不仅为diLQTS的风险预测提供了遗传学基础，而且还将为未来先进的CDS开发创造蓝图。 其他条件。

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.