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

AimsTo 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.MethodsWe 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.ResultsAmong 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.ConclusionIn 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.

Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk Assessment

“…Discrimination consistently decreased with increased age (FIGURE 2A); for example, discrimination declined with increasing age from concordance index of 0.721 [95% CI, 0.716-0.726] for the youngest (45-54y) subgroup to 0.566 [0.556-0.577], for the oldest (85-90y) subgroup in Explorys. Discrimination was higher for females than for males, consistent with prior findings 15,18,6,30 , whereas differences across White versus Black race were minor. Discrimination was substantially lower among individuals with prevalent HF and stroke.…”

“…For these analyses, the CHARGE-AF and PCE scores were converted to event probabilities using their published equations 14,12 . Where fairness metrics required application of binary risk cutoffs (i.e., true positive rate difference and false positive rate difference), we defined high AF risk as estimated 5-year AF risk  5.0% using CHARGE-AF 30,18 and high ASCVD risk as estimated 10-year ASCVD risk  7.5% 31,1,2,6 .…”

Validating risk models versus age alone for atrial fibrillation in a young Dutch population cohort: should atrial fibrillation risk prediction be expanded to younger community members?

“…4 An important finding from the systematic review 4 was that validation cohorts of AF prediction models largely constituted older populations, while there was evidence for higher model discrimination among subgroups of younger patients. [13][14][15] Although younger AF patients would be less likely to reach the threshold for an anticoagulation indication, it can be argued that early AF detection is still important in these patient to increase long-term health and quality of life through, for example, lifestyle interventions and rate/rhythm control. 2 And while the number needed to screen for AF using single time-point measurements rise steeply with lower age, 16 it remains unanswered whether multivariable models could have increased this efficiency among younger individuals over a primarily age-based cut-off.…”

Prediction of Atrial Fibrillation Using Machine Learning: A Review

“…Validated clinical risk scores to predict AF, such as the FHS, ARIC, CHARGE-AF, C2HEST, and HATCH score, utilize readily obtainable clinical variables, such as age, ethnicity, height, weight, blood pressure, smoking status, antihypertensive medication use, history of diabetes, heart failure myocardial infarction, etc. Based on these readily available variables from the patient history, these risk scores have shown adequate model discrimination for the prediction of incident AF (area under the receiver operator curve, AUCs, generally around 0.70) (Schnabel et al, 2009;Chamberlain et al, 2011;Alonso et al, 2013;Suenari et al, 2017;Li et al, 2019;Hu and Lin, 2020;Lip et al, 2020;Himmelreich et al, 2021). AUCs, or c-statistic, are commonly used in studies of diagnostic test performance as an overall indicator of test performance (Bradley, 1997).…”

“…Beyond detection, there has been immense interest in prediction of AF using both clinical risk factors as well as objective testing. Numerous clinical risk scores have been proposed, incorporating readily available variables from the patient's medical history, such as age, ethnicity, height, weight, blood pressure, smoking status, medication use, and comorbidities (Schnabel et al, 2009;Chamberlain et al, 2011;Alonso et al, 2013;Suenari et al, 2017;Li et al, 2019;Hu and Lin, 2020;Lip et al, 2020;Himmelreich et al, 2021). Abnormalities in both cardiac and inflammatory biomarkers have been shown to augment the predictive ability of clinical prediction scores (O'Neal et al, 2016).…”