Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease

Karamarie Fecho; Stanley C Ahalt; Michael Knowles; Ashok Krishnamurthy; Margaret Leigh; Kenneth Morton; Emily Pfaff; Max Wang; Hong Yi

doi:10.3389/frai.2022.918888

Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease

Front Artif Intell. 2022 Jun 28:5:918888. doi: 10.3389/frai.2022.918888. eCollection 2022.

Authors

Karamarie Fecho¹, Stanley C Ahalt¹, Michael Knowles², Ashok Krishnamurthy¹, Margaret Leigh², Kenneth Morton³, Emily Pfaff⁴, Max Wang³, Hong Yi¹

Affiliations

¹ Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
² School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
³ CoVar Applied Technologies, Durham, NC, United States.
⁴ North Carolina Clinical and Translational Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Abstract

Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data. We describe a proof-of-concept application of ICEES to examine demographics, clinical characteristics, environmental exposures, and health outcomes among a cohort of patients enriched for phenotypes associated with cystic fibrosis (CF), idiopathic bronchiectasis (IB), and primary ciliary dyskinesia (PCD). We then focus on a subset of patients with CF, leveraging the availability of a diagnostic code for CF and serving as a benchmark for our development work. We use ICEES to examine select demographics, co-diagnoses, and environmental exposures that may contribute to poor health outcomes among patients with CF, defined as emergency department or inpatient visits for respiratory issues. We replicate current understanding of the pathogenesis and clinical manifestations of CF by identifying co-diagnoses of asthma, chronic nasal congestion, cough, middle ear disease, and pneumonia as factors that differentiate patients with poor health outcomes from those with better health outcomes. We conclude by discussing our preliminary findings in relation to other published work, the strengths and limitations of our approach, and our future directions.

Keywords: application programming interface; biomedical informatics; environmental exposures; environmental health; open clinical data; rare disease.