Comorbidity Characterization Among eMERGE Institutions: A Pilot Evaluation with the Johns Hopkins Adjusted Clinical Groups® System

Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts ofpatients for sequencing or genome wide association studies (GWAS). To date, the electronic medical records and genomics (eMERGE) network institutions have developed and applied such algorithms to identify cohorts with associated DNA samples used to discover new genetic associations. For complex diseases, there are benefits to stratifying cohorts using comorbidities in order to identify their genetic determinants. The objective of this study was to: (a) characterize comorbidities in a range of phenotype-selected cohorts using the Johns Hopkins Adjusted Clinical Groups® (ACG®) System, (b) assess the frequency of important comorbidities in three commonly studied GWAS phenotypes, and (c) compare the comorbidity characterization of cases and controls. Our analysis demonstrates a framework to characterize comorbidities using the ACG system and identified differences in mean chronic condition count among GWAS cases and controls. Thus, we believe there is great potential to use the ACG system to characterize comorbidities among genetic cohorts selected based on EHR phenotypes.

The Physiology Constant Database of Teen-Agers in Beijing

Physiology constants of adolescents are important to understand growing living systems and are a useful reference in clinical and epidemiological research. Until recently, physiology constants were not available in China and therefore most physiologists, physicians, and nutritionists had to use data from abroad for reference. However, the very difference between the Eastern and Western races casts doubt on the usefulness of overseas data. We have therefore created a database system to provide a repository for the storage of physiology constants of teen-agers in Beijing. The several thousands of pieces of data are now divided into hematological biochemistry, lung function, and cardiac function with all data manually checked before being transferred into the database. The database was accomplished through the development of a web interface, scripts, and a relational database. The physiology data were integrated into the relational database system to provide flexible facilities by using combinations of various terms and parameters. A web browser interface was designed for the users to facilitate their searching. The database is available on the web. The statistical table, scatter diagram, and histogram of the data are available for both anonym and user according to queries, while only the user can achieve detail, including download data and advanced search.