CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Abstract

Aims: To validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF's potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.

Methods: We included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.

Results: Among 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF's C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%-5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA₂DS₂-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.

Conclusion: In patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA₂DS₂-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

Keywords: atrial fibrillation; electronic health records; epidemiology; risk factors.

Figure 1

Panel A: calibration plot for CHARGE-AF. The points indicate intersects of observed and expected for each decile of baseline CHARGE-AF risk, with brackets indicating the 95% CI of observed AF probability during 5-year follow-up in each decile. The red line indicates the trend for CHARGE-AF calibration in the sample. When the intersect of observed and expected AF incidence exceeds the dotted line, this indicates underestimation of AF risk by CHARGE-AF for that decile. When the intersect of observed and expected AF incidence is below the dotted line, this indicates overestimation of AF risk by CHARGE-AF for that decile. The spikes on the x-axis indicate the distribution of AF-free survivors by CHARGE-AF risk; panel B: Kaplan-Meier plot of AF incidence stratified according to baseline CHARGE-AF predicted risk categories <2.5%, 2.5%–5% and >5%. AF, atrial fibrillation; CHARGE-AF, Cohorts for Heart and Aging Research in Genomic Epidemiology-atrial fibrillation.

Figure 2

Panel A: Kaplan-Meier (KM) plot of AF incidence dichotomised according to baseline CHARGE-AF predicted risk cut-off 2.5%; panel B: KM plot of AF incidence dichotomised according to baseline CHARGE-AF predicted risk cut-off 5%; panel C: KM plot of AF incidence dichotomised according to baseline CHARGE-AF predicted risk cut-off 10%; panel D: table of outcomes if CHARGE-AF risk cut-offs 2.5%, 5% and 10%, respectively, had been applied for patient selection. AF, atrial fibrillation; CHA₂DS₂-VASc, congestive heart failure, hypertension, age, diabetes and previous stroke or transient ischaemic attack, vascular disease and female sex category; CHARGE-AF, cohorts for Heart and Ageing Research in Genomic Epidemiology model for atrial fibrillation; PY, person years; Nivel-PCD, Netherlands Institute for Health Services Research Primary Care Database.