Chi-Jen Weng1, Cheng-Hung Li2, Ying-Chieh Liao2, Che-Chen Lin3, Jiunn-Cherng Lin4, Shih-Lin Chang5, Chu-Pin Lo6, Kuo-Ching Huang7, Jin-Long Huang8, Ching-Heng Lin3, Yu-Cheng Hsieh9, Tsu-Juey Wu8. 1. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan. 2. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Internal Medicine, Faculty of Medicine, Institute of Clinical Medicine, Cardiovascular Research Center, National Yang-Ming University, School of Medicine, Taipei, Taiwan; Department of Data Science and Big Data Analytics, Providence University, Taichung, Taiwan; Department of Financial Engineering, Providence University, Taichung, Taiwan. 3. Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan. 4. Department of Internal Medicine, Faculty of Medicine, Institute of Clinical Medicine, Cardiovascular Research Center, National Yang-Ming University, School of Medicine, Taipei, Taiwan; Department of Data Science and Big Data Analytics, Providence University, Taichung, Taiwan; Department of Financial Engineering, Providence University, Taichung, Taiwan; Department of Internal Medicine, Taichung Veterans General Hospital, Chiayi Branch, Chiayi, Taiwan. 5. Department of Internal Medicine, Faculty of Medicine, Institute of Clinical Medicine, Cardiovascular Research Center, National Yang-Ming University, School of Medicine, Taipei, Taiwan; Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan. 6. Department of Data Science and Big Data Analytics, Providence University, Taichung, Taiwan. 7. Department of Financial Engineering, Providence University, Taichung, Taiwan. 8. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Internal Medicine, Faculty of Medicine, Institute of Clinical Medicine, Cardiovascular Research Center, National Yang-Ming University, School of Medicine, Taipei, Taiwan. 9. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Internal Medicine, Faculty of Medicine, Institute of Clinical Medicine, Cardiovascular Research Center, National Yang-Ming University, School of Medicine, Taipei, Taiwan; Department of Data Science and Big Data Analytics, Providence University, Taichung, Taiwan; Department of Financial Engineering, Providence University, Taichung, Taiwan. Electronic address: ychsieh@vghtc.gov.tw.
Abstract
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke and mortality. However, rhythm control strategy did not reduce cardiovascular risks in short-term studies. We hypothesize that rhythm control better prevents stroke and mortality than rate control in AF patients over a long-term period. METHODS: AF patients aged ≥18 years were identified from Taiwan National Insurance Database. Patients using anti-arrhythmia drugs to control rhythm at a >30 defined daily dose (DDD) were defined as the rhythm control group. Patients who used rate control medications for >30 DDDs constituted the rate control group. Multivariate Cox hazards regression model was used to evaluate the hazard ratio (HR) for major adverse cardiovascular events (MACE), including ischemic/hemorrhagic stroke and mortality. RESULTS: A total of 11,968 AF patients were enrolled, and 2850 of them (654 in rhythm control group; 2196 in rate control group) were analyzed. During a 6.3 ± 3.7 year's follow-up, a total of 1101 MACE occurred. Compared to rate control group, rhythm control group displayed a lower rate of ischemic stroke (adjusted HR: 0.65, p = 0.002) and mortality (adjusted HR: 0.81, p = 0.009). The rhythm control group showed a lower incidence of MACE than that of the rate control group (adjusted HR: 0.82, p = 0.009). The reduction of stroke (p = 0.004), mortality (p = 0.006), and MACE (p = 0.007) risk was observed particularly in rhythm control patients with a CHA2DS2-VASc score of ≥3. CONCLUSIONS: In patients with AF, rhythm control better prevents MACE risk than rate control over a long-term period, particularly in those at high risk (CHA2DS2-VASc score ≥3) for stroke.
BACKGROUND:Atrial fibrillation (AF) increases the risk of stroke and mortality. However, rhythm control strategy did not reduce cardiovascular risks in short-term studies. We hypothesize that rhythm control better prevents stroke and mortality than rate control in AFpatients over a long-term period. METHODS:AFpatients aged ≥18 years were identified from Taiwan National Insurance Database. Patients using anti-arrhythmia drugs to control rhythm at a >30 defined daily dose (DDD) were defined as the rhythm control group. Patients who used rate control medications for >30 DDDs constituted the rate control group. Multivariate Cox hazards regression model was used to evaluate the hazard ratio (HR) for major adverse cardiovascular events (MACE), including ischemic/hemorrhagic stroke and mortality. RESULTS: A total of 11,968 AFpatients were enrolled, and 2850 of them (654 in rhythm control group; 2196 in rate control group) were analyzed. During a 6.3 ± 3.7 year's follow-up, a total of 1101 MACE occurred. Compared to rate control group, rhythm control group displayed a lower rate of ischemic stroke (adjusted HR: 0.65, p = 0.002) and mortality (adjusted HR: 0.81, p = 0.009). The rhythm control group showed a lower incidence of MACE than that of the rate control group (adjusted HR: 0.82, p = 0.009). The reduction of stroke (p = 0.004), mortality (p = 0.006), and MACE (p = 0.007) risk was observed particularly in rhythm control patients with a CHA2DS2-VASc score of ≥3. CONCLUSIONS: In patients with AF, rhythm control better prevents MACE risk than rate control over a long-term period, particularly in those at high risk (CHA2DS2-VASc score ≥3) for stroke.