Dataset Information

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis.

ABSTRACT:

Aims

To evaluate population-based electronic health record (EHR) definitions of atrial fibrillation (AF) and valvular heart disease (VHD) subtypes, time trends in prevalence and prognosis.

Methods and results

A total of 76 019 individuals with AF were identified in England in 1998-2010 in the CALIBER resource, linking primary and secondary care EHR. An algorithm was created, implemented, and refined to identify 18 VHD subtypes using 406 diagnosis, procedure, and prescription codes. Cox models were used to investigate associations with a composite endpoint of incident stroke (ischaemic, haemorrhagic, and unspecified), systemic embolism (SSE), and all-cause mortality. Among individuals with AF, the prevalence of AF with concomitant VHD increased from 11.4% (527/4613) in 1998 to 17.6% (7014/39 868) in 2010 and also in individuals aged over 65?years. Those with mechanical valves, mitral stenosis (MS), or aortic stenosis had highest risk of clinical events compared to AF patients with no VHD, in relative [hazard ratio (95% confidence interval): 1.13 (1.02-1.24), 1.20 (1.05-1.36), and 1.27 (1.19-1.37), respectively] and absolute (excess risk: 2.04, 4.20, and 6.37 per 100 person-years, respectively) terms. Of the 95.2% of individuals with indication for warfarin (men and women with CHA2DS2-VASc ?1 and ?2, respectively), only 21.8% had a prescription 90?days prior to the study.

Conclusion

Prevalence of VHD among individuals with AF increased from 1998 to 2010. Atrial fibrillation associated with aortic stenosis, MS, or mechanical valves (compared to AF without VHD) was associated with an excess absolute risk of stroke, SSE, and mortality, but anticoagulation was underused in the pre-direct oral anticoagulant (DOAC) era, highlighting need for urgent clarity regarding DOACs in AF and concomitant VHD.

SUBMITTER: Banerjee A

PROVIDER: S-EPMC6888023 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis.

Banerjee Amitava A Allan Victoria V Denaxas Spiros S Shah Anoop A Kotecha Dipak D Lambiase Pier D PD Joseph Jacob J Lund Lars H LH Hemingway Harry H

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 20191201 12

<h4>Aims</h4>To evaluate population-based electronic health record (EHR) definitions of atrial fibrillation (AF) and valvular heart disease (VHD) subtypes, time trends in prevalence and prognosis.<h4>Methods and results</h4>A total of 76 019 individuals with AF were identified in England in 1998-2010 in the CALIBER resource, linking primary and secondary care EHR. An algorithm was created, implemented, and refined to identify 18 VHD subtypes using 406 diagnosis, procedure, and prescription codes ...[more]

PMID: 31408153

Similar Datasets

Project description: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.

Project description:ObjectivesThis study sought to determine whether the risk of atrial fibrillation AF can be estimated accurately by using routinely ascertained features in the electronic health record (EHR) and whether AF risk is associated with stroke.BackgroundEarly diagnosis of AF and treatment with anticoagulation may prevent strokes.MethodsUsing a multi-institutional EHR, this study identified 412,085 individuals 45 to 95 years of age without prevalent AF between 2000 and 2014. A prediction model was derived and validated for 5-year AF risk by using split-sample validation and model performance was compared with other methods of AF risk assessment.ResultsWithin 5 years, 14,334 individuals developed AF. In the derivation sample (7,216 AF events of 206,042 total), the optimal risk model included sex, age, race, smoking, height, weight, diastolic blood pressure, hypertension, hyperlipidemia, heart failure, coronary heart disease, valvular disease, prior stroke, peripheral arterial disease, chronic kidney disease, hypothyroidism, and quadratic terms for height, weight, and age. In the validation sample (7,118 AF events of 206,043 total) the AF risk model demonstrated good discrimination (C-statistic: 0.777; 95% confidence interval [CI:] 0.771 to 0.783) and calibration (0.99; 95% CI: 0.96 to 1.01). Model discrimination and calibration were superior to CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) (C-statistic: 0.753; 95% CI: 0.747 to 0.759; calibration slope: 0.72; 95% CI: 0.71 to 0.74), C2HEST (Coronary artery disease / chronic obstructive pulmonary disease; Hypertension; Elderly [age ≥75 years]; Systolic heart failure; Thyroid disease [hyperthyroidism]) (C-statistic: 0.754; 95% CI: 0.747 to 0.762; calibration slope: 0.44; 95% CI: 0.43 to 0.45), and CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ≥75 years, Diabetes mellitus, Prior stroke, transient ischemic attack [TIA], or thromboembolism, Vascular disease, Age 65-74 years, Sex category [female]) scores (C-statistic: 0.702; 95% CI: 0.693 to 0.710; calibration slope: 0.37; 95% CI: 0.36 to 0.38). AF risk discriminated incident stroke (n = 4,814; C-statistic: 0.684; 95% CI: 0.677 to 0.692) and stroke within 90 days of incident AF (n = 327; C-statistic: 0.789; 95% CI: 0.764 to 0.814).ConclusionsA model developed from a real-world EHR database predicted AF accurately and stratified stroke risk. Incorporating AF prediction into EHRs may enable risk-guided screening for AF.

Dataset Information

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis.

Aims

Methods and results

Conclusion

Publications

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis.

Similar Datasets

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