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

Published: May 6, 2019
Authors: Adelaide Arruda-Olson MD PhD, Casey Overby Taylor PhD, Christopher G. Chute MD DrPH, David Carrell PhD, Eric B. Larson MD MPH, George Hripcsak MD MS, Iftikhar Kullo MD, Jonathan P. Weiner; DrPH, Joshua C. Denny MD MS, Kenneth D. Roe PhD, Klaus W. Lemke; PhD, Krzysztof Kiryluk MD, Nephi A. Walton MD MS, Peggy Peissig PhD MBA, Thomas M. Richards MSc, Ting He BS, Wei Wei-Qi MD PhD, Zi Ye MD PhD
Countries: USA
Language: null
Types: Population Health
Settings: Academic


Electronic health records (EHR) are valuable to define phenotype selection algorithms used to identify cohorts of patients 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.

EHR, comorbidities, phenotype selection algorithms

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