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Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation

BACKGROUND: National electronic health records (EHR) are increasingly used for research but identifying disease cases is challenging due to differences in information captured between sources (e.g. primary and secondary care). Our objective was to provide a transparent, reproducible model for integr...

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Autores principales: Morley, Katherine I., Wallace, Joshua, Denaxas, Spiros C., Hunter, Ross J., Patel, Riyaz S., Perel, Pablo, Shah, Anoop D., Timmis, Adam D., Schilling, Richard J., Hemingway, Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219705/
https://www.ncbi.nlm.nih.gov/pubmed/25369203
http://dx.doi.org/10.1371/journal.pone.0110900
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author Morley, Katherine I.
Wallace, Joshua
Denaxas, Spiros C.
Hunter, Ross J.
Patel, Riyaz S.
Perel, Pablo
Shah, Anoop D.
Timmis, Adam D.
Schilling, Richard J.
Hemingway, Harry
author_facet Morley, Katherine I.
Wallace, Joshua
Denaxas, Spiros C.
Hunter, Ross J.
Patel, Riyaz S.
Perel, Pablo
Shah, Anoop D.
Timmis, Adam D.
Schilling, Richard J.
Hemingway, Harry
author_sort Morley, Katherine I.
collection PubMed
description BACKGROUND: National electronic health records (EHR) are increasingly used for research but identifying disease cases is challenging due to differences in information captured between sources (e.g. primary and secondary care). Our objective was to provide a transparent, reproducible model for integrating these data using atrial fibrillation (AF), a chronic condition diagnosed and managed in multiple ways in different healthcare settings, as a case study. METHODS: Potentially relevant codes for AF screening, diagnosis, and management were identified in four coding systems: Read (primary care diagnoses and procedures), British National Formulary (BNF; primary care prescriptions), ICD-10 (secondary care diagnoses) and OPCS-4 (secondary care procedures). From these we developed a phenotype algorithm via expert review and analysis of linked EHR data from 1998 to 2010 for a cohort of 2.14 million UK patients aged ≥30 years. The cohort was also used to evaluate the phenotype by examining associations between incident AF and known risk factors. RESULTS: The phenotype algorithm incorporated 286 codes: 201 Read, 63 BNF, 18 ICD-10, and four OPCS-4. Incident AF diagnoses were recorded for 72,793 patients, but only 39.6% (N = 28,795) were recorded in primary care and secondary care. An additional 7,468 potential cases were inferred from data on treatment and pre-existing conditions. The proportion of cases identified from each source differed by diagnosis age; inferred diagnoses contributed a greater proportion of younger cases (≤60 years), while older patients (≥80 years) were mainly diagnosed in SC. Associations of risk factors (hypertension, myocardial infarction, heart failure) with incident AF defined using different EHR sources were comparable in magnitude to those from traditional consented cohorts. CONCLUSIONS: A single EHR source is not sufficient to identify all patients, nor will it provide a representative sample. Combining multiple data sources and integrating information on treatment and comorbid conditions can substantially improve case identification.
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spelling pubmed-42197052014-11-12 Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation Morley, Katherine I. Wallace, Joshua Denaxas, Spiros C. Hunter, Ross J. Patel, Riyaz S. Perel, Pablo Shah, Anoop D. Timmis, Adam D. Schilling, Richard J. Hemingway, Harry PLoS One Research Article BACKGROUND: National electronic health records (EHR) are increasingly used for research but identifying disease cases is challenging due to differences in information captured between sources (e.g. primary and secondary care). Our objective was to provide a transparent, reproducible model for integrating these data using atrial fibrillation (AF), a chronic condition diagnosed and managed in multiple ways in different healthcare settings, as a case study. METHODS: Potentially relevant codes for AF screening, diagnosis, and management were identified in four coding systems: Read (primary care diagnoses and procedures), British National Formulary (BNF; primary care prescriptions), ICD-10 (secondary care diagnoses) and OPCS-4 (secondary care procedures). From these we developed a phenotype algorithm via expert review and analysis of linked EHR data from 1998 to 2010 for a cohort of 2.14 million UK patients aged ≥30 years. The cohort was also used to evaluate the phenotype by examining associations between incident AF and known risk factors. RESULTS: The phenotype algorithm incorporated 286 codes: 201 Read, 63 BNF, 18 ICD-10, and four OPCS-4. Incident AF diagnoses were recorded for 72,793 patients, but only 39.6% (N = 28,795) were recorded in primary care and secondary care. An additional 7,468 potential cases were inferred from data on treatment and pre-existing conditions. The proportion of cases identified from each source differed by diagnosis age; inferred diagnoses contributed a greater proportion of younger cases (≤60 years), while older patients (≥80 years) were mainly diagnosed in SC. Associations of risk factors (hypertension, myocardial infarction, heart failure) with incident AF defined using different EHR sources were comparable in magnitude to those from traditional consented cohorts. CONCLUSIONS: A single EHR source is not sufficient to identify all patients, nor will it provide a representative sample. Combining multiple data sources and integrating information on treatment and comorbid conditions can substantially improve case identification. Public Library of Science 2014-11-04 /pmc/articles/PMC4219705/ /pubmed/25369203 http://dx.doi.org/10.1371/journal.pone.0110900 Text en © 2014 Morley et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Morley, Katherine I.
Wallace, Joshua
Denaxas, Spiros C.
Hunter, Ross J.
Patel, Riyaz S.
Perel, Pablo
Shah, Anoop D.
Timmis, Adam D.
Schilling, Richard J.
Hemingway, Harry
Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title_full Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title_fullStr Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title_full_unstemmed Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title_short Defining Disease Phenotypes Using National Linked Electronic Health Records: A Case Study of Atrial Fibrillation
title_sort defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219705/
https://www.ncbi.nlm.nih.gov/pubmed/25369203
http://dx.doi.org/10.1371/journal.pone.0110900
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