Cargando…

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

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 pri...

Descripción completa

Detalles Bibliográficos
Autores principales: Fecho, Karamarie, Ahalt, Stanley C., Knowles, Michael, Krishnamurthy, Ashok, Leigh, Margaret, Morton, Kenneth, Pfaff, Emily, Wang, Max, Yi, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274244/
https://www.ncbi.nlm.nih.gov/pubmed/35837616
http://dx.doi.org/10.3389/frai.2022.918888
_version_ 1784745265391992832
author Fecho, Karamarie
Ahalt, Stanley C.
Knowles, Michael
Krishnamurthy, Ashok
Leigh, Margaret
Morton, Kenneth
Pfaff, Emily
Wang, Max
Yi, Hong
author_facet Fecho, Karamarie
Ahalt, Stanley C.
Knowles, Michael
Krishnamurthy, Ashok
Leigh, Margaret
Morton, Kenneth
Pfaff, Emily
Wang, Max
Yi, Hong
author_sort Fecho, Karamarie
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9274244
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92742442022-07-13 Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease Fecho, Karamarie Ahalt, Stanley C. Knowles, Michael Krishnamurthy, Ashok Leigh, Margaret Morton, Kenneth Pfaff, Emily Wang, Max Yi, Hong Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2022-06-28 /pmc/articles/PMC9274244/ /pubmed/35837616 http://dx.doi.org/10.3389/frai.2022.918888 Text en Copyright © 2022 Fecho, Ahalt, Knowles, Krishnamurthy, Leigh, Morton, Pfaff, Wang and Yi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Fecho, Karamarie
Ahalt, Stanley C.
Knowles, Michael
Krishnamurthy, Ashok
Leigh, Margaret
Morton, Kenneth
Pfaff, Emily
Wang, Max
Yi, Hong
Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title_full Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title_fullStr Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title_full_unstemmed Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title_short Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease
title_sort leveraging open electronic health record data and environmental exposures data to derive insights into rare pulmonary disease
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274244/
https://www.ncbi.nlm.nih.gov/pubmed/35837616
http://dx.doi.org/10.3389/frai.2022.918888
work_keys_str_mv AT fechokaramarie leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT ahaltstanleyc leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT knowlesmichael leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT krishnamurthyashok leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT leighmargaret leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT mortonkenneth leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT pfaffemily leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT wangmax leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease
AT yihong leveragingopenelectronichealthrecorddataandenvironmentalexposuresdatatoderiveinsightsintorarepulmonarydisease