Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Hong, JC; Hauser, ER; Redding, TS; Sims, KJ; Gellad, ZF; O'Leary, MC; Hyslop, T; Madison, AN; Qin, X; Weiss, D; Bullard, AJ; Williams, CD; Sullivan, BA; Lieberman, D; Provenzale, D

Published Date

  • April 14, 2021

Published In

Volume / Issue

  • 11 / 1

Start / End Page

  • 8104 -

PubMed ID

  • 33854078

Pubmed Central ID

  • PMC8046765

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/s41598-021-85546-2

Language

  • eng

Conference Location

  • England