ArticlePloS one2014
Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed.
- Association of hysterectomy and invasive epithelial ovarian and tubal cancer: a cohort study within UKCTOCS.BJOG : an international journal of obstetrics and gynaecology · 2022Trial
- Major adverse cardiovascular events after liver transplantation: a population cohort analysis of English transplant centres.EClinicalMedicine · 2026Article
- Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.BMC pulmonary medicine · 2025Article
- Clustering of electronic health records in atrial fibrillation patients and impact on prognosis and patient trajectories: a UK linked-dataset study.European heart journal. Digital health · 2025Article
- Role of primary and secondary care data in atrial fibrillation ascertainment: impact on risk factor associations, patient management, and mortality in UK Biobank.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Causes of death in patients with atrial fibrillation in the UK: a nationwide electronic health record study.European heart journal open · 2025Article
- Big data research is everyone's research-Making epilepsy data science accessible to the global community: Report of the ILAE big data commission.Epileptic disorders : international epilepsy journal with videotape · 2024Review
- Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).Military Medical Research · 2024Article
- Optimising the use of electronic medical records for large scale research in psychiatry.Translational psychiatry · 2024Review
- The quality of vital signs measurements and value preferences in electronic medical records varies by hospital, specialty, and patient demographics.Scientific reports · 2023Article
- Prognostic model for atrial fibrillation after cardiac surgery: a UK cohort study.Clinical research in cardiology : official journal of the German Cardiac Society · 2023Article
- Translating and evaluating historic phenotyping algorithms using SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Evaluation of transitions from early hypertension to hypertensive chronic kidney disease, coronary artery disease, stroke and mortality: a Thai real-world data cohort.Frontiers in cardiovascular medicine · 2023Article
- Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity.PloS one · 2023Article
- Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.Journal of the American Medical Informatics Association : JAMIA · 2022Observational
- Identification of Incident Atrial Fibrillation From Electronic Medical Records.Journal of the American Heart Association · 2022Article
- Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users.JMIR human factors · 2022Article
- Phenotyping in distributed data networks: selecting the right codes for the right patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Development of a standards-based phenotype model for gross motor function to support learning health systems in pediatric rehabilitation.Learning health systems · 2022Article
- Atrial fibrillation epidemiology, disparity and healthcare contacts: a population-wide study of 5.6 million individuals.The Lancet regional health. Europe · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundNational 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.
methodsPotentially 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.
resultsThe 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.
conclusionsA 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.