CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction?

Abstract

Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice and is associated with increased morbidity and mortality. Various predictive scores for new-onset AF have been proposed, but so far none has been widely used in clinical practice. CHARGE-AF score was developed from a pooled diverse population from three large cohorts (Atherosclerosis Risk in Communities study, Cardiovascular Health Study and Framingham Heart Study). A simple 5-year predictive model includes the variables of age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction and heart failure. Recent studies report that CHARGE-AF score has good discrimination for incident AF and seems to be a quite promising prediction model for this arrhythmia. New screening tools (smartphone apps, smartwatches) are rapidly developing for AF detection. Therefore, the wide application of CHARGE-AF score in clinical practice along with the upcoming usage of mobile health technologies and smartwatches, may result in better AF prediction and adequate stroke prevention, especially in high-risk patients.

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

“…This would be a very minimal change (0.8%) knowing the average CHARGE-AF score in MESA AF cases was 12.8±0.9. Nonetheless, in the absence of an individualized metric with comparable predictive power, it serves as a useful tool for estimating risk and alerting high risk populations to reduce future AF risk 21,25 .…”

Protocol for a multicentre and prospective follow-up cohort study of early detection of atrial fibrillation, silent stroke and cognitive impairment in high-risk primary care patients: the PREFA-TE study

“…Cardiac monitoring for early detection of AF Various risk scales have been proposed to identify patients at high risk, for whom monitoring would facilitate early detection of AF. [15][16][17][18][19][20][21] The electrocardiogram (ECG) and machine learning classifiers provide valuable insights that can significantly enhance the identification of AF in patients with embolic stroke of undetermined source. 22 23 Prolonged monitoring using devices such as Holter monitors, implantable loop recorders or mobile cardiac telemetry can capture intermittent AF episodes that might go unnoticed during a brief in-office ECG.…”

“…Various risk scales have been proposed to identify patients at high risk, for whom monitoring would facilitate early detection of AF 15–21. The electrocardiogram (ECG) and machine learning classifiers provide valuable insights that can significantly enhance the identification of AF in patients with embolic stroke of undetermined source 22 23.…”