Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach
Abstract Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonosc...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-85546-2 |
| _version_ | 1818722846240669696 |
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| author | Julian C. Hong Elizabeth R. Hauser Thomas S. Redding Kellie J. Sims Ziad F. Gellad Meghan C. O’Leary Terry Hyslop Ashton N. Madison Xuejun Qin David Weiss A. Jasmine Bullard Christina D. Williams Brian A. Sullivan David Lieberman Dawn Provenzale |
| author_facet | Julian C. Hong Elizabeth R. Hauser Thomas S. Redding Kellie J. Sims Ziad F. Gellad Meghan C. O’Leary Terry Hyslop Ashton N. Madison Xuejun Qin David Weiss A. Jasmine Bullard Christina D. Williams Brian A. Sullivan David Lieberman Dawn Provenzale |
| author_sort | Julian C. Hong |
| collection | DOAJ |
| description | Abstract Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care. |
| first_indexed | 2024-12-17T21:01:07Z |
| format | Article |
| id | doaj.art-06ec3afaeed74231959160bb1fdf31d0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T21:01:07Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-06ec3afaeed74231959160bb1fdf31d02022-12-21T21:32:43ZengNature PortfolioScientific Reports2045-23222021-04-0111111110.1038/s41598-021-85546-2Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approachJulian C. Hong0Elizabeth R. Hauser1Thomas S. Redding2Kellie J. Sims3Ziad F. Gellad4Meghan C. O’Leary5Terry Hyslop6Ashton N. Madison7Xuejun Qin8David Weiss9A. Jasmine Bullard10Christina D. Williams11Brian A. Sullivan12David Lieberman13Dawn Provenzale14Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Coordinating Center, Perry Point VA Medical CenterCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemVA Portland Health Care SystemCooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care SystemAbstract Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed network graph to electronic health record (EHR) data and characterized comorbidities in a cohort of healthy veterans undergoing screening colonoscopy. The Veterans Affairs Cooperative Studies Program #380 was a prospective longitudinal study of screening and surveillance colonoscopy. We identified initial instances of three-digit ICD-9 diagnoses for participants with at least 5 years of linked EHR history (October 1999 to December 2015). For diagnoses affecting at least 10% of patients, we calculated pairwise chronological relative risk (RR). iGraph was used to produce directed graphs of comorbidities with RR > 1, as well as summary statistics, key diseases, and communities. A directed graph based on 2210 patients visualized longitudinal development of comorbidities. Top hub (preceding) diseases included ischemic heart disease, inflammatory and toxic neuropathy, and diabetes. Top authority (subsequent) diagnoses were acute kidney failure and hypertensive chronic kidney failure. Four communities of correlated comorbidities were identified. Close analysis of top hub and authority diagnoses demonstrated known relationships, correlated sequelae, and novel hypotheses. Directed network graphs portray chronologic comorbidity relationships. We identified relationships between comorbid diagnoses in this aging veteran cohort. This may direct healthcare prioritization and personalized care.https://doi.org/10.1038/s41598-021-85546-2 |
| spellingShingle | Julian C. Hong Elizabeth R. Hauser Thomas S. Redding Kellie J. Sims Ziad F. Gellad Meghan C. O’Leary Terry Hyslop Ashton N. Madison Xuejun Qin David Weiss A. Jasmine Bullard Christina D. Williams Brian A. Sullivan David Lieberman Dawn Provenzale Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach Scientific Reports |
| title | Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach |
| title_full | Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach |
| title_fullStr | Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach |
| title_full_unstemmed | Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach |
| title_short | Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach |
| title_sort | characterizing chronological accumulation of comorbidities in healthy veterans a computational approach |
| url | https://doi.org/10.1038/s41598-021-85546-2 |
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