Leveraging Electronic Health Records and Genomic Biobanks for Kidney Stone Disease

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PROJECT SUMMARY Kidney stones are highly prevalent and recurrent. Our current understanding of kidney stone disease risk factors and disease associations has relied primarily on data from chart review, nonspecific administrative datasets, and secondary analyses of observation studies. Current study designs suffer from small sample sizes, heterogenous patient groups, and lack of standardized accuracy data and outcome definitions. The widespread adoption of electronic health records (EHRs) provides novel research opportunities for kidney stone disease. EHRs contain a robust clinical repository of data collected over time from clinical care. However, there are currently limited tools to identify and characterize kidney stone patients in the EHR. The objective of this study is to establish feasibility of utilizing EHR data to investigate kidney stone disease. To structure EHR data in an efficient and cost-effective manner, natural language processing and deep learning methods can be designed for identifying and phenotyping kidney stone patients and clinical outcomes. Our de-identified EHR is linked to a DNA biobank that can enable investigation of genetic associations with disease. This project has two specific aims. In Aim 1, we will perform genetic association studies in our EHR and linked DNA biobank. We will replicate previously described associations with genetic variants and kidney stone disease. We will then perform a genome-wide association study to discover novel associations. In Aim 2, our goal is to develop and validate a computable framework to extract clinical outcomes of kidney stone disease from the EHR. Clinically meaningful outcomes include symptomatic stone passage and radiographic stone characterization. We will develop and test natural language processing and deep learning algorithms to extract keywords and context-based information in clinical notes and reports. We will train and test these algorithms using manual annotation as the gold standard. This aim will enable rigorous phenotyping of each kidney stone patient using structured and unstructured EHR data. Successful completion of this project will lay the groundwork towards advancing genomic medicine and precision health to support clinical decision-making in kidney stone patients.