Limits...
Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation.

Morley KI, Wallace J, Denaxas SC, Hunter RJ, Patel RS, Perel P, Shah AD, Timmis AD, Schilling RJ, Hemingway H - PLoS ONE (2014)

Bottom Line: 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.A single EHR source is not sufficient to identify all patients, nor will it provide a representative sample.

View Article: PubMed Central - PubMed

Affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Melbourne School of Global and Population Health, The University of Melbourne, Melbourne, Australia.

ABSTRACT

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.

Show MeSH

Related in: MedlinePlus

Euler diagram displaying the number of incident cases identified in the different sources, including overlap between multiple sources.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4219705&req=5

pone-0110900-g003: Euler diagram displaying the number of incident cases identified in the different sources, including overlap between multiple sources.

Mentions: Using the phenotype algorithm we identified 80,261 individuals with an incident coded or inferred AF diagnosis in the CALIBER cohort. Of these, 7,468 had no diagnosis code but met the inferred diagnosis criteria. Almost half the patients with a diagnosis code (39.6%; 28,795 individuals) had diagnoses recorded in both primary and secondary care (see Figure 3). All sources provided unique diagnoses, but substantially more were identified from secondary care, which provided almost three times the number of unique cases (32,930 cases compared to 11,068 from primary care). The proportion of AF cases identified in primary care or by inferred diagnosis decreased by year of diagnosis, whereas the proportion identified in secondary care increased, but no threshold effect was identified around the introduction of the QOF in 2004.


Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation.

Morley KI, Wallace J, Denaxas SC, Hunter RJ, Patel RS, Perel P, Shah AD, Timmis AD, Schilling RJ, Hemingway H - PLoS ONE (2014)

Euler diagram displaying the number of incident cases identified in the different sources, including overlap between multiple sources.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219705&req=5

pone-0110900-g003: Euler diagram displaying the number of incident cases identified in the different sources, including overlap between multiple sources.
Mentions: Using the phenotype algorithm we identified 80,261 individuals with an incident coded or inferred AF diagnosis in the CALIBER cohort. Of these, 7,468 had no diagnosis code but met the inferred diagnosis criteria. Almost half the patients with a diagnosis code (39.6%; 28,795 individuals) had diagnoses recorded in both primary and secondary care (see Figure 3). All sources provided unique diagnoses, but substantially more were identified from secondary care, which provided almost three times the number of unique cases (32,930 cases compared to 11,068 from primary care). The proportion of AF cases identified in primary care or by inferred diagnosis decreased by year of diagnosis, whereas the proportion identified in secondary care increased, but no threshold effect was identified around the introduction of the QOF in 2004.

Bottom Line: 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.A single EHR source is not sufficient to identify all patients, nor will it provide a representative sample.

View Article: PubMed Central - PubMed

Affiliation: Farr Institute of Health Informatics Research, University College London, London, United Kingdom, and Clinical Epidemiology, Department of Epidemiology and Public Health, University College London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Melbourne School of Global and Population Health, The University of Melbourne, Melbourne, Australia.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus