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

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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.