AimsTo develop a risk score to quantify bleeding risk in outpatients with or at risk of atherothrombosis.Methods and resultsWe studied patients in the REACH Registry, a cohort of 68 236 patients with/at risk of atherothrombosis. The outcome of interest was serious bleeding (non-fatal haemorrhagic stroke or bleeding leading to hospitalization and transfusion) over 2 years. Risk factors for bleeding were assessed using modified regression analysis. Multiple potential scoring systems based on the least complex models were constructed. Competing scores were compared on their discriminative ability via logistic regression. The score was validated externally using the CHARISMA population. From a final cohort of 56 616 patients, 804 (1.42%, 95% confidence interval 1.32–1.52) experienced serious bleeding between baseline and 2 years. A nine-item bleeding risk score (0–23 points) was constructed (age, peripheral arterial disease, congestive heart failure, diabetes, hypertension, smoking, antiplatelets, oral anticoagulants, hypercholesterolaemia). Observed incidence of bleeding at 2 years was: 0.46% (score ≤6); 0.95% (7–8); 1.25% (9–10); 2.76% (≥11). The score's discriminative performance was consistent in CHARISMA and REACH (c-statistics 0.64 and 0.68, respectively); calibration in the CHARISMA population was very good (modified Hosmer-Lemeshow c2 = 4.74; P = 0.69).ConclusionBleeding risk increased substantially with a score >10. This score can assist clinicians in predicting the risk of serious bleeding and making decisions on antithrombotic therapy in outpatients.

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.

SummaryEstablished primary prevention strategies of cardiovascular diseases are based on understanding of risk factors, but whether the same risk factors are associated with atrial fibrillation (AF) remains unclear. We conducted a systematic review and field synopsis of the associations of 23 cardiovascular risk factors and incident AF, which included 84 reports based on 28 consented and four electronic health record cohorts of 20,420,175 participants and 576,602 AF events. We identified 3-19 reports per risk factor and heterogeneity in AF definition, quality of reporting, and adjustment. We extracted relative risks (RR) and 95 % confidence intervals [CI] and visualised the number of reports with inverse (RR [CI]<1.00), or direct (RR [CI]>1.00) associations. For hypertension (13/17 reports) and obesity (19/19 reports), there were direct associations with incident AF, as there are for coronary heart disease (CHD). There were inverse associations for non-White ethnicity (5/5 reports, with RR from 0.35 to 0.84 [0.82–0.85]), total cholesterol (4/13 reports from 0.76 [0.59–0.98] to 0.94 [0.90–0.97]; 8/13 reports with non-significant inverse associations), and diastolic blood pressure (2/11 reports from 0.87 [0.78–0.96] to 0.92 [0.85–0.99]; 5/11 reports with non-significant inverse associations), and direct associations for taller height (7/10 reports from 1.03 [1.02–1.05] to 1.92 [1.38–2.67]), which are in the opposite direction of known associations with CHD. A systematic evaluation of the available evidence suggests similarities as well as important differences in the risk factors for incidence of AF as compared with other cardiovascular diseases, which has implications for the primary prevention strategies for atrial fibrillation.

No abstract