BACKGROUND AND PURPOSE: In patients with atrial fibrillation (AF), stroke risk stratification schema do not consider AF parameters. The aim of the study is to assess the impact of combining risk factors with continuous AF burden monitoring. METHODS: In this retrospective study 568 patients implanted with a DDDR-P pacemaker (AT-500; Medtronic) and a history of AF were continuously monitored for 1 year. RESULTS: During follow-up, 14 patients (2.5%) had a thromboembolic event. Patients were divided into 3 groups: AF burden ≤5 minutes per day (AF-free; n=223 [39%]), AF burden >5 minutes but <24 hours per day (AF-5 minutes; n=179 [32%]), and AF burden ≥24 hours (AF-24 hours; n=166 [29%]). Patients were also classified according to CHADS2 and CHA2DS2-VASc risk scores. The discrimination ability of each risk score was evaluated performing a logistic regression analysis and calculating the corresponding C-statistic. The addition of AF burden improved C-statistics: for CHADS2 from 0.653 (P=0.051) to 0.713 (P=0.007); for CHA2DS2-VASc, from 0.898 (P<0.0001) to 0.910 (P<0.0001). CONCLUSIONS: The CHA2DS2-VASc score had a high sensitivity to predict thromboembolism. Implementation of device data on AF presence/duration/burden has the potential to contribute to improved clinical risk stratification and should be tested prospectively.
BACKGROUND AND PURPOSE: In patients with atrial fibrillation (AF), stroke risk stratification schema do not consider AF parameters. The aim of the study is to assess the impact of combining risk factors with continuous AF burden monitoring. METHODS: In this retrospective study 568 patients implanted with a DDDR-P pacemaker (AT-500; Medtronic) and a history of AF were continuously monitored for 1 year. RESULTS: During follow-up, 14 patients (2.5%) had a thromboembolic event. Patients were divided into 3 groups: AF burden ≤5 minutes per day (AF-free; n=223 [39%]), AF burden >5 minutes but <24 hours per day (AF-5 minutes; n=179 [32%]), and AF burden ≥24 hours (AF-24 hours; n=166 [29%]). Patients were also classified according to CHADS2 and CHA2DS2-VASc risk scores. The discrimination ability of each risk score was evaluated performing a logistic regression analysis and calculating the corresponding C-statistic. The addition of AF burden improved C-statistics: for CHADS2 from 0.653 (P=0.051) to 0.713 (P=0.007); for CHA2DS2-VASc, from 0.898 (P<0.0001) to 0.910 (P<0.0001). CONCLUSIONS: The CHA2DS2-VASc score had a high sensitivity to predict thromboembolism. Implementation of device data on AF presence/duration/burden has the potential to contribute to improved clinical risk stratification and should be tested prospectively.