Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank.
Creators: name: Patel, Riyaz S
name: Denaxas, Spiros
name: Howe, Laurence J
name: Eggo, Rosalind M
name: Shah, Anoop D
name: Allen, Naomi E
name: Danesh, John
name: Hingorani, Aroon
name: Sudlow, Cathie
name: Hemingway, Harry
Publisher: name: Apollo - University of Cambridge Repository
Year: 2022
Subjects: subject: Research Article
subject: Medicine and health sciences
subject: Physical sciences
subject: FOS: Physical sciences
subject: Research and analysis methods
subject: Biology and life sciences
subject: Computer and information sciences
subject: FOS: Computer and information sciences

description: Funder: The Healthcare Improvement Studies Institute
description: IMPORTANCE: A lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility. OBJECTIVE: To develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB). DESIGN: Case-control and cohort study. SETTING: Prospective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping. PARTICIPANTS: We included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated. EXPOSURE: CAD Phenotypes. MAIN OUTCOMES AND MEASURES: Association with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD. RESULTS: The algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/). CONCLUSIONS: An algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.