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Dosage of statin, cardiovascular comorbidities, and risk of atrial fibrillation: A nationwide population-based cohort study
International Journal of Cardiology 168 (2013) 1131–1136
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Dosage of statin, cardiovascular comorbidities, and risk of atrial fibrillation: A nationwide population-based cohort study Chen-Ying Hung a, Ching-Heng Lin b,⁎, Kuo-Yang Wang a, d, Jin-Long Huang a, c, d, Yu-Cheng Hsieh a, c, El-Wui Loh e, Tsuo-Hung Lan b, Pesus Chou f, Chih-Tai Ting a, c, Tsu-Juey Wu a, c, d,⁎ a
Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan Department of Medical Research, 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 d School of Medicine, Chung Shan Medical University, Taichung, Taiwan e Institute of Population Health Sciences, National Health Research Institutes, Taiwan f Community Medicine Research Center & Institute of Public Health, National Yang-Ming University, Taipei, Taiwan b c
a r t i c l e
i n f o
Article history: Received 18 September 2012 Received in revised form 9 November 2012 Accepted 11 November 2012 Available online 2 December 2012 Keywords: Atrial fibrillation Statin CHADS2 score CHA2DS2VASc score
a b s t r a c t Background: Statin has potential protective effects against atrial fibrillation. Clinically, there is a need to predict the atrial fibrillation protective effects in statin-treated patients. The purpose of this study was to investigate if cardiovascular co-morbidities or cumulative defined daily doses (cDDDs) of statin use could predict statin efficacy in atrial fibrillation prevention. Methods: Patients aged ≥ 50 years were identified from the Taiwan National Health Insurance Research Database. Medical records of 171,885 patients were used in this study, and 40,001 (23.3%) of the patients received statin therapy (≥ 28 cDDDs). Risk of new-onset atrial fibrillation in statin users and non-users (b28 cDDDs) was estimated. Results: During the 9-year follow-up period, 6049 patients experienced new-onset atrial fibrillation. Overall, statin therapy reduced the risk of atrial fibrillation by 28% (adjusted hazard ratio [HR] 0.72; 95% CI 0.68 to 0.77). There was a dose–response relationship between statin use and the risk of atrial fibrillation. The adjusted HRs for atrial fibrillation were 1.04, 0.85, and 0.50 when cDDDs ranged from 28 to 90, 91 to 365, and more than 365, respectively. Subgroup analysis showed that statin use was more beneficial in patients with higher CHADS2 and CHA2DS2VASc scores than those with a score of 0 (P value for interaction b 0.001). The therapy provided no obvious beneficial effect in those with a CHADS2 score of 0, a CHA2DS2VASc score of 0, or cDDDs less than 91. Conclusions: Statin therapy reduces the risk of new-onset atrial fibrillation in a dose-dependent manner, and is beneficial in patients with cardiovascular co-morbidities. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Atrial fibrillation (AF) is the most common serious arrhythmia and is associated with increased stroke, heart failure, mortality, and economic burden [1–3]. Old age, male gender, heart failure, hypertension, diabetes mellitus, vascular disease, pulmonary disease, chronic renal disease, and valvular heart disease have been reported as risk factors for the development of AF [2–7]. This arrhythmia has become more prevalent with the increase of the elderly population in recent years. Therefore, a major focus on the disease management is to effectively prevent the occurrence of new-onset AF.
⁎ Corresponding authors at: Cardiovascular Center, Taichung Veterans General Hospital, 160, Section 3, Chung-Kang Road, Taichung 407, Taiwan. Tel.: +886 4 24614049; fax: +886 4 24618922. E-mail addresses: [email protected] (C.-H. Lin), [email protected] (T.-J. Wu). 0167-5273/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2012.11.087
Because classic antiarrhythmic drugs have limited long-term efficacy and several side effects, current focus of AF primary prevention has shifted to upstream therapies that target AF substrate, such as statins, angiotensin-converting enzyme inhibitors (ACEIs), angiotensinreceptor blockers (ARBs), aldosterone antagonists, and omega-3 polyunsaturated fatty acids [8]. A recent guideline suggests that statins could be used for AF prevention in patients undergoing cardiac surgery or those with heart failure [8]. However, the number of current studies focusing on other high-risk groups, especially those with multiple cardiovascular co-morbidities, is still insufficient. Meta-analyses of randomized controlled trials have consistently showed a protective effect of statin in the primary prevention of AF [9–11]. However, possibly because of the heterogeneity of study designs, specific patient groups that obtain more benefits from the treatment have not yet been identified. Furthermore, no clear dose– response relationship has been reported. The purpose of the present study was to determine if cardiovascular co-morbidities or cumulative
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defined daily doses (cDDDs) of statin use could predict the effectiveness of statin on primary AF prevention in a nationwide populationbased cohort. Established cardiovascular co-morbidity scoring systems (CHADS2 score [12] and CHA2DS2VASc score [8]) were also used to evaluate the efficacy of statin treatment for AF prevention.
Table 1 Baseline characteristics. Variables
2. Methods 2.1. Study population and end-point The National Health Insurance program in Taiwan has been operating since 1995, and covers about 99% of the island's population and all forms of health care services. The National Health Research Institute (NHRI) of Taiwan has established a National Health Insurance Research Database. In this study, we used a systemic sampling of patient data from 2000 to 2009 with a total of 1,000,000 subjects, which was released by the NHRI as the Longitudinal Health Insurance Database. These random samples have been confirmed by the NHRI to be representative of the general Taiwanese population. There were no statistically significant differences in age and gender between the sample and overall population. Patients' information and characteristics were included in the database. These files also contain information about prescriptions, including the names of drugs, prescribed dosage, and drug use duration. The information about diagnoses and prescriptions is of high quality, and has previously been used for epidemiological researches [13,14]. The NHRI made data at the individual level available to us in an anonymous format, in which specific individuals cannot be identified. The NHRI safeguards the privacy of individuals and provides the data to researchers after ethical approval has been obtained. This study was approved by the Institutional Review Board of Taichung Veterans General Hospital. The authors of this manuscript have certified that they comply with the principles of ethical publishing in the International Journal of Cardiology [15]. The present study was a population-based cohort study, in which all patients aged ≥50 years in 2001 were identified from the research databases. Patients were not eligible for enrollment if they had a history of cardiac dysrhythmias (including AF), thyrotoxicosis, or valvular heart disease in 2000. This study included 171,885 patients for analysis. The study endpoint was defined as new-onset AF (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 427.31) or death during the 9-year follow-up period (2001–2009). All occurrences of AF were confirmed by the claims data. To ensure the diagnostic validity, only patients with at least 3 consensus AF diagnoses at outpatient departments (to avoid misclassification by including patients with tentative diagnosis for exams and those retrieving exam reports) or at least 1 inpatient hospitalization AF diagnosis were identified. The date of the end-point event (AF or death) was defined as the index date. 2.2. Data collection Statin use records were retrieved from ambulatory and inpatient claims data. The prescription dates and the number of pills per prescription were collated. Patients were divided into statin user group and non-user group according to their statin use between January 1, 2001, and the index date (if end-point event occurred) or December 31, 2009. We collected information on simvastatin, lovastatin, atorvastatin, fluvastatin, pravastatin, and rosuvastatin, which are the currently available statins in Taiwan. According to the payment regulations of the National Health Insurance program in Taiwan, statins were used in patients 1) with manifest cardiovascular disease of atherosclerotic origin, and low-density lipoprotein [LDL]≧100 mg/dL or total cholesterol [TC]≧160 mg/dL; 2) without manifest cardiovascular disease of atherosclerotic origin, but with ≧2 cardiovascular risk factors (including hypertension, diabetes mellitus, family history of premature coronary artery disease, male aged ≧45 years, female aged ≧ 55 years, and smoking) and LDL ≧130 mg/dL or TC≧ 200 mg/dL; or 3) without manifest cardiovascular disease of atherosclerotic origin, but with hypercholesterolemia (LDL ≧160 mg/dL or TC≧ 240 mg/d). The regulations incorporated not only laboratory but also clinical criteria. Therefore, statins were prescribed more in patients with apparent atherosclerotic diseases and multiple cardiovascular risk factors in this study (see Table 1). The defined daily dose (DDD), recommended by the World Health Organization, is the assumed average maintenance dose per day of a drug. In this study, we used DDD for measuring the prescribed amount of statin, and compared individual statin based on the same standard by using the following formula: (total amount of drug) / (amount of drug in a DDD) = number of DDDs [13]. Cumulative DDDs (cDDDs), the sum of DDDs of any statin, were served as the exposed duration of statins. We classified statin use into four categories (b28, 28 to 90, 91 to 365, and >365 cDDDs) because the duration of the refill period for chronic disease is 3 months in Taiwan. Patients who used statins for less than 28 cDDDs were defined as non-users. We identified many cardiovascular co-morbidities as potential confounders by ICD-9-CM diagnostic code between January 1, 2000, and December, 31, 2000. Patients were defined as having hypertension only when they had a diagnosis of hypertension (ICD-9-CM codes 401–405) and had used at least 1 antihypertensive drug. According to the payment regulations of the National Health Insurance program and guidelines of the Taiwan Society of Cardiology [16], antihypertensive agents should be prescribed for those with systolic and diastolic blood pressure more than 140/90 mm Hg (or 130/ 80 mm Hg for high-risk patients). Other co-morbidities were confirmed by ICD-9-CM
Age at entry, years Mean ± SD 50–64 65–74 ≧75 Female Medical disease Heart failure Hypertension Diabetes mellitus Stroke or TIA Vascular disease COPD Chronic renal disease Upstream therapy ACEIs and ARBs Aldosterone antagonists Other medications Aspirin Warfarin Alpha blocking agents Beta blocking agents Calcium channel blockers Diuretics Antiarrhythmics Digoxin CHADS2 score Mean ± SD Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–6 CHA2DS2VASc score Mean ± SD Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–5 Score = 6–9 Statin use (≧28 cDDDs) 28–90 cDDDs 91–365 cDDDs >365 cDDDs Simvastatin Lovastatin Atorvastatin Fluvastatin Pravastatin Rosuvastatin
All patients
Statin users
(n = 171,885)
(≧28 cDDDs; (b28 cDDDs; n= 40,001) n = 131,884)
No.
No.
%
%
Non-users
No.
P value
%
62.7 ± 9.1 111,134 64.7 41,784 24.3 18,967 11.0 86,714 50.5
62.3 ± 8.1 26,327 65.8 10,695 26.7 2979 7.5 23,206 58.0
62.8 ± 9.4 b0.001 84,807 64.3 b0.001 31,089 23.6 15,988 12.1 63,508 48.2 b0.001
1332 47,860 16,376 7404 1978 7907 3368
0.8 27.8 9.5 4.3 1.2 4.6 2.0
419 17,440 8429 2482 819 1994 1202
913 30,420 7947 4922 1159 5913 2166
24,025 1577
14.0 9490 0.9 443
23.7 14,535 11.0 b0.001 1.1 1134 0.9 b0.001
81,381 4150 50,267 93,302 103,384
47.4 2.4 29.2 54.3 60.2
70.1 3.7 34.3 73.1 79.7
93,159 8909 11,840
54.2 27,773 69.4 65,386 49.6 b0.001 5.2 2704 6.8 6205 4.7 b0.001 6.9 3189 8.0 8651 6.6 b0.001
0.6 ± 0.9 105,210 44,413 14,625 5206 2431
61.2 25.8 8.5 3.0 1.4
0.9 ± 1.0 17,707 44.3 14,010 35.0 5603 14.0 1817 4.5 864 2.2
0.5 ± 0.8 b0.001 87,503 66.4 b0.001 30,403 23.1 9022 6.8 3389 2.6 1567 1.2
1.4 ± 1.3 40,794 63,317 35,890 19,554 11,086 1244
23.7 36.8 20.9 11.4 6.5 0.7
1.8 ± 1.3 5873 14.7 13,141 32.9 10,485 26.2 6173 15.4 3915 9.8 414 1.0
1.3 ± 1.2 b0.001 34,921 26.5 b0.001 50,176 38.1 25,405 19.3 13,381 10.2 7171 5.4 830 0.6
10,750 15,671 13,580 13,780 8857 19,655 8329 5713 9435
6.3 9.1 7.9 8.0 5.2 11.4 4.9 3.3 5.5
10,750 15,671 13,580 13,780 8857 19,655 8329 5713 9435
– – – – – – – – –
28,052 1465 13,724 29,234 31,879
1.1 43.6 21.1 6.2 2.1 5.0 3.0
26.9 39.2 34.0 34.4 22.1 49.1 20.8 14.3 23.6
53,329 2685 36,543 64,068 71,505
0.7 23.1 6.0 3.7 0.9 4.5 1.6
40.4 2.0 27.7 48.6 54.2
b0.001 b0.001 b0.001 b0.001 b0.001 b0.001 b0.001
b0.001 b0.001 b0.001 b0.001 b0.001
– – – – – – – – –
cDDDs = cumulative defined daily doses; SD = standard deviation; TIA = transient ischemic attack; COPD = chronic obstructive pulmonary disease; ACEIs= angiotensinconverting enzyme inhibitors; ARBs = angiotensin-receptor blockers.
diagnostic code (with at least 3 consensus diagnoses at an outpatient department or at least 1 inpatient hospitalization diagnosis): heart failure, diabetes mellitus, stroke or transient ischemic attack (TIA), vascular disease (defined as myocardial infarction or peripheral vascular disease), chronic obstructive pulmonary disease (COPD), and chronic renal disease. The CHADS2 score was calculated for each patient by assigning 1 point each for the presence of heart failure, hypertension, age ≥ 75 years, and diabetes mellitus, and 2 points for a history of stroke or TIA [12]. The CHA2DS2VASc score was calculated for each patient by assigning 1 point each for the presence of heart failure, hypertension, age 65–74 years, diabetes mellitus, vascular disease, and female gender, and 2 points for a history of stroke or TIA, and age ≥75 years [8]. Medications (ACEIs, ARBs, and aldosterone antagonists), which potentially could be used for AF prevention, and other cardiovascular medications were also identified between January 1, 2000, and December 31, 2000 (see Table 1).
C.-Y. Hung et al. / International Journal of Cardiology 168 (2013) 1131–1136 2.3. Statistical analysis The data are presented as the mean values and standard deviations (SD) for continuous variables, and proportions for categorical variables. The differences between continuous values were analyzed by using t test for continuous variables, and chi-square test for categorical variables. Multivariate Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between the use of statin and the occurrence of AF. Propensity analysis was used for further confirming this association. The AF-free survival curves were plotted via the Kaplan– Meier method with statistical significance examined by the log-rank test. All statistical analyses were carried out by SAS software version 9.2 (SAS Institute, Inc., Cary, NC, USA). A p value of b0.05 was considered statistically significant.
3. Results 3.1. Participants A total of 171,885 patients aged ≥ 50 years were enrolled in this study, of which 40,001 (23.3%) had used statins (≥ 28 cDDDs). Table 1 summarizes the baseline characteristics of the statin users and non-users. The mean age of the study population was 62.7 ± 9.1 years, with 24.3% of them aged 65–74 years, and 11.0% of them aged ≥75 years. Females accounted for 50.5% of the population. Statin users were younger than non-users (62.3± 8.1 vs. 62.8 ± 9.4 years; p b 0.001) and there were more females in statin users (statin users vs. non-users: 58.0% vs. 48.2%; p b 0.001). Among the statin users, 26.9% of them used 28–90 cDDDs, 39.2% of them used 91–365 cDDDs, and 34.0% of them used >365 cDDDs. Atorvastatin, simvastatin, and rosuvastatin were the most commonly used statins. Among the study subjects, 0.8% had heart failure, 27.8% had hypertension, 9.5% had diabetes mellitus, 4.3% had stroke or TIA, 1.2% had vascular disease, 4.6% had chronic obstructive pulmonary disease (COPD), and 2.0% had chronic renal disease. Statin users had a higher prevalence of these cardiovascular co-morbidities than non-users (p b 0.001). The rate of ACEIs and ARBs, aldosterone antagonists, and other cardiovascular medication usage was higher among statin users than non-users (p b 0.001). Overall, the average CHADS2 score of the cohort was 0.6± 0.9, and the average CHA2DS2VASc score was 1.4 ± 1.3. Statin users had higher CHADS2 (0.9 ± 1.0 vs. 0.5 ± 0.8; p b 0.001) and CHA2 DS2VASc scores (1.8 ± 1.3 vs. 1.3± 1.2; p b 0.001) than non-users. 3.2. CHADS2 score, CHA2DS2VASc score and treatment outcome During the 9-year follow-up (1,473,463 person-years), 6049 patients (3.5% of the study population) developed new-onset AF; the overall incidence rate was 4.1 per 1000 person-years. AF occurred less frequently among statin users compared with non-users before and after adjustments for important clinical variables (age, gender, cardiovascular co-morbidities, and upstream therapies; adjusted HR 0.72; 95% CI 0.68 to 0.77; p b 0.001), and other cardiovascular medications (aspirin, warfarin, alpha blocking agents, beta blocking agents, calcium channel blockers, diuretics, antiarrhythmics, and digoxin; adjusted HR 0.53; 95% CI 0.50 to 0.57; p b 0.001). The incidence rate of AF decreased from 4.4 to 3.3 per 1000 person-years after statin use. Table 2 demonstrates the HRs for the development of new-onset AF in the cohort. In subgroup analyses, there was a universal statin protective effect across age categories, in women as well as men, among those with or without any cardiovascular co-morbidity or using upstream therapy (adjusted HRs ranged from 0.50 to 0.84 in these subgroups). Statin use was more beneficial in female than male (p value for interaction b 0.001), in patients with age ≧ 65 years (p value for interaction=0.021), hypertension (p value for interactionb 0.001) and diabetes mellitus (p value for interaction=0.013) than those without. By using established cardiovascular co-morbidity scoring systems, statin use was more beneficial in patients with higher CHADS2 and CHA2DS2VASc scores than those with a score of 0 (p value for interaction b 0.001). No significant benefit was found in patients with a CHADS2 score of 0 (adjusted HR 0.94; 95% CI 0.83 to 1.05) or a
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CHA2DS2VASc score of 0 (adjusted HR 1.21; 95% CI 1.00 to 1.47). Fig. 1 displays the hazard ratio plot of the protective effect of statin against AF according to the CHADS2 score and the CHA2DS2VASc score. We found that statin therapy has better efficacy in patients with a CHADS2 score of 2 (adjusted HR 0.51; 95% CI 0.44 to 0.60) and a CHA2DS2VASc score≥3 (adjusted HRs ranged from 0.57 to 0.60) compared with other subgroups. 3.3. Statin dose and treatment outcome Table 3 shows the dose relation analysis for AF prevention with statin therapy. The incidence rates of AF were 4.4, 4.1, 3.5, and 2.3 per 1000 person-years among patients with statin use of less than 28, 28 to 90, 91 to 365, and more than 365 cDDDs, respectively. In patients with 28–90 cDDDs, no significant protective effect was found (adjusted HR 1.04; 95% CI 0.94 to 1.15). On the other hand, the occurrence of new-onset AF was significantly different between statin users and non-users among those with 91–365 (adjusted HR 0.85; 95% CI 0.77 to 0.93) and more than 365 cDDDs (adjusted HR 0.50; 95% CI 0.44 to 0.56). The dose–response relationship was also observed in patients with a different CHADS2 score (see eTable 1), and has no significant changes in the HRs by using propensity analysis. The Kaplan–Meier survival plot presented in Fig. 2 shows the protective effect of statin against AF according to cDDDs of statin use. The survival curves began to separate early and continued to separate throughout the course of the study. The adjusted HRs for individual statins were 0.62, 0.61, 0.75, 0.69, 0.78, and 0.51 for patients who used simvastatin, lovastatin, atorvastatin, fluvastatin, pravastatin, and rosuvastatin, respectively. All individual statin use showed a significant effect of AF risk reduction (p b 0.001). 4. Discussion This nationwide cohort study is the largest and longest follow-up study for the analysis of statin protective effects against AF in the general population aged ≥50 years. The main results of this study were that the risk of AF was lower in statin users than non-users, especially for those with higher CHADS2 and CHA2DS2VASc score. Even though statin users in this study had more cardiovascular co-morbidities than the comparison cohorts, statin therapy remained a protective factor against new-onset AF before and after adjustment for potential confounders. Furthermore, there was a dose–response relationship between the use of statins and the risk of AF. These findings support the anti-arrhythmic effects of statin. Studies focusing on the statin protective effects against AF have yielded mixed results in people with cardiovascular diseases [17–24] and in reducing the overall AF burden or AF recurrence in patients with heart failure and AF [25]. Meta-analyses of randomized controlled trials and observational studies revealed that statin therapy is useful for primary prevention of AF with a 24 to 46% risk reduction [9–11,26]. However, these studies did not focus on people without cardiovascular co-morbidities [11]. In the present study, we showed an overall 28% AF risk reduction in patients treated with statin in the general population, but a lack of protective effect in those without any cardiovascular co-morbidity (CHADS2 score of 0 or CHA2DS2VASc score of 0). This result suggests that statin therapy not only treats lipid abnormalities but also reduces the risk of new-onset AF in patients with cardiovascular diseases. Possibly because of the relatively small patient numbers and the study designs of previous clinical trials, dose–response relationships of statin therapy for AF prevention have not yet been identified [9,11]. A population based cohort study may provide a chance to confirm the dose–response of statin in a large population. In this cohort, we evaluated the dose–response relationship by using cDDDs of statin and found a statistically significant inverse trend between the cumulative doses and the incidences of AF. Of note, statin therapy provided no
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Table 2 Crude and adjusted hazard ratios for the development of new-onset AF. Variables
All patients Female Male Age at entry, years 50–64 65–74 ≧75 Medical disease Heart failure No heart failure Hypertension No hypertension Diabetes mellitus No diabetes mellitus Stroke or TIA No stroke or TIA Vascular disease No vascular disease COPD No COPD Chronic renal disease No chronic renal disease Upstream therapy ACEIs and ARBs No ACEIs and ARBs Aldosterone antagonists No aldosterone antagonists CHADS2 scoreb Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–6 CHA2DS2VASc scorec Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–5 Score = 6–9
Statin users
Non-users
Crude HR
95% CI
Adjusted HRa
95% CI
P value for interaction
3.7 3.6 3.9
0.75 0.68 0.85
0.70–0.80 0.62–0.74 0.78–0.93
0.72 0.63 0.82
0.68–0.77 0.58–0.70 0.75–0.91
– b0.001
1594/84,807 1679/31,089 1634/15,988
1.9 5.4 10.2
0.96 0.73 0.67
0.86–1.06 0.66–0.81 0.59–0.77
0.78 0.64 0.68
0.70–0.86 0.57–0.71 0.59–0.79
0.021
9.3 2.8 3.7 2.2 3.2 2.8 4.9 2.7 4.8 2.8 4.8 2.8 4.6 2.8
169/913 4738/130,971 2030/30,420 2877/101,464 496/7947 4411/123,937 414/4922 4493/126,962 97/1159 4810/130,725 458/5913 4449/125,971 173/2166 4734/129,718
18.5 3.6 6.7 2.8 6.2 3.6 8.4 3.5 8.4 3.7 7.7 3.5 8.0 3.6
0.44 0.75 0.52 0.77 0.48 0.75 0.54 0.75 0.53 0.74 0.58 0.76 0.52 0.75
0.31–0.63 0.70–0.80 0.48–0.57 0.70–0.84 0.41–0.55 0.70–0.81 0.44–0.66 0.70–0.80 0.36–0.77 0.70–0.80 0.46–0.72 0.71–0.81 0.38–0.71 0.70–0.80
0.50 0.73 0.61 0.84 0.55 0.76 0.65 0.73 0.60 0.73 0.68 0.72 0.55 0.73
0.35–0.71 0.68–0.78 0.56–0.67 0.76–0.93 0.47–0.64 0.70–0.82 0.53–0.80 0.68–0.78 0.41–0.89 0.68–0.78 0.55–0.86 0.67–0.77 0.40–0.75 0.68–0.78
0.161
379/9490 763/30,511 28/443 1114/39,558
4.0 2.5 6.3 2.8
1082/14,535 3825/117,349 109/1134 4798/130,750
7.4 3.3 9.6 3.7
0.50 0.75 0.60 0.75
0.45–0.57 0.69–0.81 0.39–0.91 0.70–0.80
0.62 0.76 0.65 0.72
0.55–0.70 0.70–0.82 0.42–0.99 0.68–0.78
0.023
341/17,707 414/14,010 224/5603 103/1817 60/864
1.9 3.0 4.0 5.7 6.9
1777/87,503 1803/30,403 837/9022 308/3389 182/1567
2.0 5.9 9.3 9.1 11.6
0.93 0.47 0.39 0.57 0.53
0.83–1.05 0.42–0.52 0.34–0.45 0.46–0.71 0.40–0.71
0.94 0.64 0.51 0.66 0.70
0.83–1.05 0.57–0.71 0.44–0.60 0.52–0.82 0.51–0.95
b0.001
125/5873 225/13,141 296/10,485 249/6173 213/3915 34/414
2.1 1.7 2.8 4.0 5.4 8.2
590/34,921 1074/50,176 1281/25,405 1105/13,381 749/7171 108/830
1.7 2.1 5.0 8.3 10.4 13.0
1.25 0.79 0.53 0.45 0.47 0.56
1.03–1.51 0.68–0.91 0.47–0.61 0.39–0.51 0.40–0.55 0.38–0.82
1.21 0.81 0.70 0.57 0.58 0.60
1.00–1.47 0.70–0.93 0.61–0.80 0.49–0.66 0.50–0.68 0.40–0.89
b0.001
AFs/patients
%
AFs/patients
1142/40,001 571/23,206 571/16,795
2.9 2.5 3.4
4907/131,884 2257/63,508 2650/68,376
478/26,327 438/10,695 226/2979
1.8 4.1 7.6
39/419 1103/39,582 641/17,440 501/22,561 271/8429 871/31,572 122/2482 1020/37,519 39/819 1103/39,182 96/1994 1046/38,007 55/1202 1087/38,799
%
b0.001 0.013 0.445 0.634 0.723 0.429
0.317
AF = atrial fibrillation; HR = hazard ratio; CI = confidence interval; other abbreviations as in Table 1. a Adjusted for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. b The adjusted HRs for subgroups of CHADS2 score adjusted for age, gender, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. c The adjusted HRs for subgroups of CHA2DS2VASc score adjusted for age, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists.
obvious beneficial effect in those with cDDDs less than 91. This finding provides an explanation why previous studies differed in conclusions— heterogeneity of cumulative doses of statins may confound effectiveness of statin on AF. There is very little evidence to support the potency of statin's effect on primary prevention of AF in other high-risk groups, especially in those with multiple cardiovascular co-morbidities. In a recent meta-analysis of studies including patients not undergoing invasive procedures, Bang et al. assumed that the effect of statins may differ in different clinical settings [27]. Our recent study has shown that statin is more beneficial in hypertensive patients with a CHADS2 score ≥ 2 than in those with a score of 1 [23]. This study showed that patients with scores ≥ 1 benefited from statin use, especially those with a CHADS2 score of 2 or a CHA2DS2VASc score ≥ 3. In contrast, those without any co-morbidities (with scores of 0) gained no significant benefits from statin therapy for AF prevention. From this point of view, CHADS2 score and CHA2DS2VASc score represent not only clinical predictors for stroke risk stratification, but are also useful scoring systems for predicting the effectiveness of statin in AF prevention. Several biological mechanisms for a role of statin in AF prevention have been proposed, such as anti-inflammatory and antioxidant properties, and modulation of endothelial and ion channel function [28]. The JUPITER trial found that patients with elevated high-sensitivity
C-reactive protein, a status indicator of systemic inflammation [29], had a 27% reduction in AF risk after rosuvastatin therapy [22]. Recent studies also showed that CHADS2 score was positively related to systemic inflammation [30,31]. Furthermore, female gender and vascular disease, the different factors between CHA2DS2VASc score and CHADS2 score, were also related to increasing systemic inflammation [32–34]. Therefore, it is reasonable to deduce that patients with higher CHADS2 and CHA2DS2VASc scores may have a more severe inflammation status, and the anti-inflammatory effect of statin may be more obvious in these patients. These support the statin anti-inflammatory hypothesis. Our study has a number of strengths. The study cohort was taken from a computerized population-based database, allowing evaluation of a large cohort in a 9-year follow-up period. In addition, because the data on statin use includes all prescription information before the diagnosis of AF, recall bias had been avoided. Furthermore, we conducted subgroup, multivariate and propensity analyses to eliminate the misclassifications, and the results revealed no significant changes in the HRs before and after these analyses. Finally, we found a dose–response relationship existing between the use of statins and the risk of AF. The relationship was found in every subgroup of patients with different CHADS2 scores. There were some limitations in the present study. First, the study population included mainly Taiwanese subjects. Racial difference,
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Fig. 2. AF-free survival according to cumulative defined daily doses (cDDDs) of statin use.
Fig. 1. The effectiveness of statin in prevention of new-onset AF according to (A) CHADS2 score, and (B) CHA2DS2VASc score.
as showed in the ALLHAT study [2], may exist between ethnic populations. Second, types of AF and duration of each AF episode were not available in our research database, and the AF event definition in our study may be under-reported. Although we suppose that underreporting of AF would occur with equal frequency in both statin user and non-user groups, it is possible that statins modify the disease in such a way that the number of diagnosis appears in the claims data is affected (i.e. if patients on statin therapy are more likely to have asymptomatic episodes during sleep, for example). However, this kind of effect had not yet been reported. Third, several confounding factors, including body mass index, blood pressure, and alcohol intake, were not included in our database. Moreover, due to the payment regulations of the National Health Insurance program, statins are more likely to be prescribed in patients with cardiovascular diseases. This may lead to certain selection bias. Therefore, we performed
multivariate regression and propensity analysis to control possible confounding factors (cardiovascular co-morbidities, upstream therapies, and use of cardiovascular medications) and our data showed consistent results. Fourth, we assumed that patients took all prescribed medications. This may overestimate the actual dosage used. Finally, although our data and previous studies [10,17,24] suggested that pravastatin seems to be less potent for AF prevention, we cannot conclude that other statins have better protective effect against AF than pravastatin. This study was not designed to compare the effect of individual statin– mixed statin usage and some confounders may exist. This possible type-dependent effect of statin was recently mentioned in a large real-world registry study performed by Bang et al. [24]. Further studies are needed to confirm this relation. 5. Conclusions Statin therapy reduces the risk of new-onset AF in a dosedependent manner, and is beneficial in patients with cardiovascular co-morbidities. Further studies focusing on possible biological mechanisms and comparing the efficacy of an individual statin are needed. Acknowledgments This study is based in part on data obtained from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health, Taiwan, and managed by the National Health Research Institutes. The interpretation and
Table 3 Dose-relation analysis for new-onset AF. Variables
No. of person-years
Incidence (per 1000 person-years)
Model 1 Crude HRa
95% CI
Model 1 Adjusted HRb
95% CI
Model 2 Crude HRc
95% CI
Model 2 Crude HRd
95% CI
Non-users (b28 cDDDs) Statin user (≧28 cDDDs) 28–90 cDDDs 91–365 cDDDs >365 cDDDs Simvastatin Lovastatin Atorvastatin Fluvastatin Pravastatin Rosuvastatin
1,123,758 349,705 93,107 136,591 120,007 121,014 77,655 171,943 72,867 49,955 84,080
4.4 3.3 4.1 3.5 2.3 2.7 2.6 3.3 3.0 3.5 2.0
1.00 0.75 0.95 0.80 0.53 0.63 0.61 0.78 0.73 0.84 0.47
– 0.70–0.80 0.86–1.05 0.73–0.88 0.47–0.60 0.57–0.71 0.53–0.70 0.71–0.85 0.64–0.83 0.73–0.98 0.41–0.55
1.00 0.72 1.04 0.85 0.50 0.62 0.61 0.75 0.69 0.78 0.51
– 0.68–0.77 0.94–1.15 0.77–0.93 0.44–0.56 0.55–0.69 0.53–0.70 0.68–0.81 0.60–0.79 0.67–0.91 0.44–0.59
1.00 0.70 1.02 0.83 0.53 0.62 0.59 0.76 0.73 0.86 0.45
– 0.65–0.75 0.92–1.13 0.75–0.91 0.46–0.60 0.55–0.70 0.51–0.69 0.69–0.83 0.63–0.83 0.74–1.01 0.39–0.53
1.00 0.72 1.08 0.86 0.50 0.63 0.61 0.76 0.70 0.82 0.50
– 0.67–0.77 0.97–1.20 0.78–0.95 0.44–0.57 0.56–0.70 0.53–0.71 0.69–0.83 0.61–0.81 0.70–0.95 0.43–0.59
Abbreviations as in Table 1 and Table 2. a Model 1: original cohort. b Model 1 with adjustment for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. c Model 2: propensity score matched cohort (matched with age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, and chronic renal disease). d Model 2 with adjustment for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, and chronic renal disease.
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conclusions contained herein do not represent those of the Bureau of National Health Insurance, Department of Health, or National Health Research Institutes, Taiwan. Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.ijcard.2012.11.087. References [1] Wolf PA, Mitchell JB, Baker CS, et al. Impact of atrial fibrillation on mortality, stroke, and medical costs. Arch Intern Med 1998;158:229-34. [2] Haywood LJ, Ford CE, Crow RS, et al. Atrial fibrillation at baseline and during follow-up in ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial). J Am Coll Cardiol 2009;54:2023-31. [3] Krahn AD, Manfreda J, Tate RB, et al. The natural history of atrial fibrillation: incidence, risk factors, and prognosis in the Manitoba Follow-Up Study. Am J Med 1995;98:476-84. [4] Macfarlane PW, Murray H, Sattar N, et al. The incidence and risk factors for new onset atrial fibrillation in the PROSPER study. Europace 2011;13:634-9. [5] Kannel WB, Wolf PA, Benjamin EJ, Levy D. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol 1998;82:2N-9N. [6] Sandhu RK, Kurth T, Conen D, et al. Relation of renal function to risk for incident atrial fibrillation in women. Am J Cardiol 2012;109:538-42. [7] Calkins H, Kuck KH, Cappato R, et al. 2012 HRS/EHRA/ECAS Expert Consensus Statement on Catheter and Surgical Ablation of Atrial Fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design. Europace 2012;14: 528-606. [8] Camm AJ, Kirchhof P, Lip GY, et al. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Europace 2010;12:1360-420. [9] Wang Z, Zhang Y, Gao M, et al. Statin therapy for the prevention of atrial fibrillation: a meta-analysis of randomized controlled trials. Pharmacotherapy 2011;31:1051-62. [10] Fang WT, Li HJ, Zhang H, Jiang S. The role of statin therapy in the prevention of atrial fibrillation: a meta-analysis of randomized controlled trials. Br J Clin Pharmacol 2012;74:744-56. [11] Fauchier L, Pierre B, de Labriolle A, et al. Antiarrhythmic effect of statin therapy and atrial fibrillation: a meta-analysis of randomized controlled trials. J Am Coll Cardiol 2008;51:828-35. [12] Gage BF, van Walraven C, Pearce L, et al. Selecting patients with atrial fibrillation for anticoagulation: stroke risk stratification in patients taking aspirin. Circulation 2004;110:2287-92. [13] Tsan YT, Lee CH, Wang JD, Chen PC. Statins and the risk of hepatocellular carcinoma in patients with hepatitis B virus infection. J Clin Oncol 2012;30:623-30. [14] Cheng CL, Kao YH, Lin SJ, et al. Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiol Drug Saf 2011;20:236-42. [15] SLaC A. Ethics in the authorship and publishing of scientific articles. Int J Cardiol 2010;144:1-2.
[16] Chiang CE, Wang TD, Li YH, et al. 2010 guidelines of the Taiwan Society of Cardiology for the management of hypertension. J Formos Med Assoc 2010;109:740-73. [17] Komatsu T, Tachibana H, Sato Y, et al. Long-term efficacy of upstream therapy with lipophilic or hydrophilic statins on antiarrhythmic drugs in patients with paroxysmal atrial fibrillation: comparison between atorvastatin and pravastatin. Int Heart J 2011;52:359-65. [18] Negi S, Shukrullah I, Veledar E, et al. Statin therapy for the prevention of atrial fibrillation trial (SToP AF trial). J Cardiovasc Electrophysiol 2011;22:414-9. [19] Maggioni AP, Fabbri G, Lucci D, et al. Effects of rosuvastatin on atrial fibrillation occurrence: ancillary results of the GISSI-HF trial. Eur Heart J 2009;30:2327-36. [20] Schwartz GG, Chaitman BR, Goldberger JJ, Messig M. High-dose atorvastatin and risk of atrial fibrillation in patients with prior stroke or transient ischemic attack: analysis of the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial. Am Heart J 2011;161:993-9. [21] Pellegrini CN, Vittinghoff E, Lin F, et al. Statin use is associated with lower risk of atrial fibrillation in women with coronary disease: the HERS trial. Heart 2009;95: 704-8. [22] Pena JM, MacFadyen J, Glynn RJ, Ridker PM. High-sensitivity C-reactive protein, statin therapy, and risks of atrial fibrillation: an exploratory analysis of the JUPITER trial. Eur Heart J 2012;33:531-7. [23] Hung CY, Lin CH, Loh EW, et al. CHADS2 score, statin therapy, and risks of atrial fibrillation. Am J Med 2013 Feb;126(2):133-40. [24] Bang CN, Gislason GH, Greve AM et al. Statins reduce new-onset atrial fibrillation in a first-time myocardial infarction population: a nationwide propensity score-matched study. Eur J Prev Cardiol doi:10.1177/2047487312462804. [25] Koutbi L, Roy D, Talajic M, et al. Statins and atrial fibrillation in patients with left ventricular dysfunction: insights from the AF-CHF trial. Int J Cardiol 2013;165:575-7. [26] Liu T, Li L, Korantzopoulos P, et al. Statin use and development of atrial fibrillation: a systematic review and meta-analysis of randomized clinical trials and observational studies. Int J Cardiol 2008;126:160-70. [27] Bang CN, Greve AM, Abdulla J, et al. The preventive effect of statin therapy on new-onset and recurrent atrial fibrillation in patients not undergoing invasive cardiac interventions: A systematic review and meta-analysis. Int J Cardiol 2013;167:624-30. [28] Savelieva I, Kakouros N, Kourliouros A, Camm AJ. Upstream therapies for management of atrial fibrillation: review of clinical evidence and implications for European Society of Cardiology guidelines. Part I: primary prevention. Europace 2011;13: 308-28. [29] Rifai N, Ridker PM. High-sensitivity C-reactive protein: a novel and promising marker of coronary heart disease. Clin Chem 2001;47:403-11. [30] Maehama T, Okura H, Imai K, et al. Usefulness of CHADS2 score to predict C-reactive protein, left atrial blood stasis, and prognosis in patients with nonrheumatic atrial fibrillation. Am J Cardiol 2010;106:535-8. [31] Crandall MA, Horne BD, Day JD, et al. Atrial fibrillation and CHADS2 risk factors are associated with highly sensitive C-reactive protein incrementally and independently. Pacing Clin Electrophysiol 2009;32:648-52. [32] Ishii S, Karlamangla AS, Bote M, et al. Gender, obesity and repeated elevation of C-reactive protein: data from the CARDIA cohort. PLoS One 2012;7:e36062. [33] Amer MS, Elawam AE, Khater MS, et al. Association of high-sensitivity C-reactive protein with carotid artery intima-media thickness in hypertensive older adults. J Am Soc Hypertens 2011;5:395-400. [34] Wang TJ, Larson MG, Levy D, et al. C-reactive protein is associated with subclinical epicardial coronary calcification in men and women: the Framingham Heart Study. Circulation 2002;106:1189-91.
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Dosage of statin, cardiovascular comorbidities, and risk of atrial fibrillation: A nationwide population-based cohort study Chen-Ying Hung a, Ching-Heng Lin b,⁎, Kuo-Yang Wang a, d, Jin-Long Huang a, c, d, Yu-Cheng Hsieh a, c, El-Wui Loh e, Tsuo-Hung Lan b, Pesus Chou f, Chih-Tai Ting a, c, Tsu-Juey Wu a, c, d,⁎ a
Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan Department of Medical Research, 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 d School of Medicine, Chung Shan Medical University, Taichung, Taiwan e Institute of Population Health Sciences, National Health Research Institutes, Taiwan f Community Medicine Research Center & Institute of Public Health, National Yang-Ming University, Taipei, Taiwan b c
a r t i c l e
i n f o
Article history: Received 18 September 2012 Received in revised form 9 November 2012 Accepted 11 November 2012 Available online 2 December 2012 Keywords: Atrial fibrillation Statin CHADS2 score CHA2DS2VASc score
a b s t r a c t Background: Statin has potential protective effects against atrial fibrillation. Clinically, there is a need to predict the atrial fibrillation protective effects in statin-treated patients. The purpose of this study was to investigate if cardiovascular co-morbidities or cumulative defined daily doses (cDDDs) of statin use could predict statin efficacy in atrial fibrillation prevention. Methods: Patients aged ≥ 50 years were identified from the Taiwan National Health Insurance Research Database. Medical records of 171,885 patients were used in this study, and 40,001 (23.3%) of the patients received statin therapy (≥ 28 cDDDs). Risk of new-onset atrial fibrillation in statin users and non-users (b28 cDDDs) was estimated. Results: During the 9-year follow-up period, 6049 patients experienced new-onset atrial fibrillation. Overall, statin therapy reduced the risk of atrial fibrillation by 28% (adjusted hazard ratio [HR] 0.72; 95% CI 0.68 to 0.77). There was a dose–response relationship between statin use and the risk of atrial fibrillation. The adjusted HRs for atrial fibrillation were 1.04, 0.85, and 0.50 when cDDDs ranged from 28 to 90, 91 to 365, and more than 365, respectively. Subgroup analysis showed that statin use was more beneficial in patients with higher CHADS2 and CHA2DS2VASc scores than those with a score of 0 (P value for interaction b 0.001). The therapy provided no obvious beneficial effect in those with a CHADS2 score of 0, a CHA2DS2VASc score of 0, or cDDDs less than 91. Conclusions: Statin therapy reduces the risk of new-onset atrial fibrillation in a dose-dependent manner, and is beneficial in patients with cardiovascular co-morbidities. © 2012 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Atrial fibrillation (AF) is the most common serious arrhythmia and is associated with increased stroke, heart failure, mortality, and economic burden [1–3]. Old age, male gender, heart failure, hypertension, diabetes mellitus, vascular disease, pulmonary disease, chronic renal disease, and valvular heart disease have been reported as risk factors for the development of AF [2–7]. This arrhythmia has become more prevalent with the increase of the elderly population in recent years. Therefore, a major focus on the disease management is to effectively prevent the occurrence of new-onset AF.
⁎ Corresponding authors at: Cardiovascular Center, Taichung Veterans General Hospital, 160, Section 3, Chung-Kang Road, Taichung 407, Taiwan. Tel.: +886 4 24614049; fax: +886 4 24618922. E-mail addresses: [email protected] (C.-H. Lin), [email protected] (T.-J. Wu). 0167-5273/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2012.11.087
Because classic antiarrhythmic drugs have limited long-term efficacy and several side effects, current focus of AF primary prevention has shifted to upstream therapies that target AF substrate, such as statins, angiotensin-converting enzyme inhibitors (ACEIs), angiotensinreceptor blockers (ARBs), aldosterone antagonists, and omega-3 polyunsaturated fatty acids [8]. A recent guideline suggests that statins could be used for AF prevention in patients undergoing cardiac surgery or those with heart failure [8]. However, the number of current studies focusing on other high-risk groups, especially those with multiple cardiovascular co-morbidities, is still insufficient. Meta-analyses of randomized controlled trials have consistently showed a protective effect of statin in the primary prevention of AF [9–11]. However, possibly because of the heterogeneity of study designs, specific patient groups that obtain more benefits from the treatment have not yet been identified. Furthermore, no clear dose– response relationship has been reported. The purpose of the present study was to determine if cardiovascular co-morbidities or cumulative
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defined daily doses (cDDDs) of statin use could predict the effectiveness of statin on primary AF prevention in a nationwide populationbased cohort. Established cardiovascular co-morbidity scoring systems (CHADS2 score [12] and CHA2DS2VASc score [8]) were also used to evaluate the efficacy of statin treatment for AF prevention.
Table 1 Baseline characteristics. Variables
2. Methods 2.1. Study population and end-point The National Health Insurance program in Taiwan has been operating since 1995, and covers about 99% of the island's population and all forms of health care services. The National Health Research Institute (NHRI) of Taiwan has established a National Health Insurance Research Database. In this study, we used a systemic sampling of patient data from 2000 to 2009 with a total of 1,000,000 subjects, which was released by the NHRI as the Longitudinal Health Insurance Database. These random samples have been confirmed by the NHRI to be representative of the general Taiwanese population. There were no statistically significant differences in age and gender between the sample and overall population. Patients' information and characteristics were included in the database. These files also contain information about prescriptions, including the names of drugs, prescribed dosage, and drug use duration. The information about diagnoses and prescriptions is of high quality, and has previously been used for epidemiological researches [13,14]. The NHRI made data at the individual level available to us in an anonymous format, in which specific individuals cannot be identified. The NHRI safeguards the privacy of individuals and provides the data to researchers after ethical approval has been obtained. This study was approved by the Institutional Review Board of Taichung Veterans General Hospital. The authors of this manuscript have certified that they comply with the principles of ethical publishing in the International Journal of Cardiology [15]. The present study was a population-based cohort study, in which all patients aged ≥50 years in 2001 were identified from the research databases. Patients were not eligible for enrollment if they had a history of cardiac dysrhythmias (including AF), thyrotoxicosis, or valvular heart disease in 2000. This study included 171,885 patients for analysis. The study endpoint was defined as new-onset AF (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 427.31) or death during the 9-year follow-up period (2001–2009). All occurrences of AF were confirmed by the claims data. To ensure the diagnostic validity, only patients with at least 3 consensus AF diagnoses at outpatient departments (to avoid misclassification by including patients with tentative diagnosis for exams and those retrieving exam reports) or at least 1 inpatient hospitalization AF diagnosis were identified. The date of the end-point event (AF or death) was defined as the index date. 2.2. Data collection Statin use records were retrieved from ambulatory and inpatient claims data. The prescription dates and the number of pills per prescription were collated. Patients were divided into statin user group and non-user group according to their statin use between January 1, 2001, and the index date (if end-point event occurred) or December 31, 2009. We collected information on simvastatin, lovastatin, atorvastatin, fluvastatin, pravastatin, and rosuvastatin, which are the currently available statins in Taiwan. According to the payment regulations of the National Health Insurance program in Taiwan, statins were used in patients 1) with manifest cardiovascular disease of atherosclerotic origin, and low-density lipoprotein [LDL]≧100 mg/dL or total cholesterol [TC]≧160 mg/dL; 2) without manifest cardiovascular disease of atherosclerotic origin, but with ≧2 cardiovascular risk factors (including hypertension, diabetes mellitus, family history of premature coronary artery disease, male aged ≧45 years, female aged ≧ 55 years, and smoking) and LDL ≧130 mg/dL or TC≧ 200 mg/dL; or 3) without manifest cardiovascular disease of atherosclerotic origin, but with hypercholesterolemia (LDL ≧160 mg/dL or TC≧ 240 mg/d). The regulations incorporated not only laboratory but also clinical criteria. Therefore, statins were prescribed more in patients with apparent atherosclerotic diseases and multiple cardiovascular risk factors in this study (see Table 1). The defined daily dose (DDD), recommended by the World Health Organization, is the assumed average maintenance dose per day of a drug. In this study, we used DDD for measuring the prescribed amount of statin, and compared individual statin based on the same standard by using the following formula: (total amount of drug) / (amount of drug in a DDD) = number of DDDs [13]. Cumulative DDDs (cDDDs), the sum of DDDs of any statin, were served as the exposed duration of statins. We classified statin use into four categories (b28, 28 to 90, 91 to 365, and >365 cDDDs) because the duration of the refill period for chronic disease is 3 months in Taiwan. Patients who used statins for less than 28 cDDDs were defined as non-users. We identified many cardiovascular co-morbidities as potential confounders by ICD-9-CM diagnostic code between January 1, 2000, and December, 31, 2000. Patients were defined as having hypertension only when they had a diagnosis of hypertension (ICD-9-CM codes 401–405) and had used at least 1 antihypertensive drug. According to the payment regulations of the National Health Insurance program and guidelines of the Taiwan Society of Cardiology [16], antihypertensive agents should be prescribed for those with systolic and diastolic blood pressure more than 140/90 mm Hg (or 130/ 80 mm Hg for high-risk patients). Other co-morbidities were confirmed by ICD-9-CM
Age at entry, years Mean ± SD 50–64 65–74 ≧75 Female Medical disease Heart failure Hypertension Diabetes mellitus Stroke or TIA Vascular disease COPD Chronic renal disease Upstream therapy ACEIs and ARBs Aldosterone antagonists Other medications Aspirin Warfarin Alpha blocking agents Beta blocking agents Calcium channel blockers Diuretics Antiarrhythmics Digoxin CHADS2 score Mean ± SD Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–6 CHA2DS2VASc score Mean ± SD Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–5 Score = 6–9 Statin use (≧28 cDDDs) 28–90 cDDDs 91–365 cDDDs >365 cDDDs Simvastatin Lovastatin Atorvastatin Fluvastatin Pravastatin Rosuvastatin
All patients
Statin users
(n = 171,885)
(≧28 cDDDs; (b28 cDDDs; n= 40,001) n = 131,884)
No.
No.
%
%
Non-users
No.
P value
%
62.7 ± 9.1 111,134 64.7 41,784 24.3 18,967 11.0 86,714 50.5
62.3 ± 8.1 26,327 65.8 10,695 26.7 2979 7.5 23,206 58.0
62.8 ± 9.4 b0.001 84,807 64.3 b0.001 31,089 23.6 15,988 12.1 63,508 48.2 b0.001
1332 47,860 16,376 7404 1978 7907 3368
0.8 27.8 9.5 4.3 1.2 4.6 2.0
419 17,440 8429 2482 819 1994 1202
913 30,420 7947 4922 1159 5913 2166
24,025 1577
14.0 9490 0.9 443
23.7 14,535 11.0 b0.001 1.1 1134 0.9 b0.001
81,381 4150 50,267 93,302 103,384
47.4 2.4 29.2 54.3 60.2
70.1 3.7 34.3 73.1 79.7
93,159 8909 11,840
54.2 27,773 69.4 65,386 49.6 b0.001 5.2 2704 6.8 6205 4.7 b0.001 6.9 3189 8.0 8651 6.6 b0.001
0.6 ± 0.9 105,210 44,413 14,625 5206 2431
61.2 25.8 8.5 3.0 1.4
0.9 ± 1.0 17,707 44.3 14,010 35.0 5603 14.0 1817 4.5 864 2.2
0.5 ± 0.8 b0.001 87,503 66.4 b0.001 30,403 23.1 9022 6.8 3389 2.6 1567 1.2
1.4 ± 1.3 40,794 63,317 35,890 19,554 11,086 1244
23.7 36.8 20.9 11.4 6.5 0.7
1.8 ± 1.3 5873 14.7 13,141 32.9 10,485 26.2 6173 15.4 3915 9.8 414 1.0
1.3 ± 1.2 b0.001 34,921 26.5 b0.001 50,176 38.1 25,405 19.3 13,381 10.2 7171 5.4 830 0.6
10,750 15,671 13,580 13,780 8857 19,655 8329 5713 9435
6.3 9.1 7.9 8.0 5.2 11.4 4.9 3.3 5.5
10,750 15,671 13,580 13,780 8857 19,655 8329 5713 9435
– – – – – – – – –
28,052 1465 13,724 29,234 31,879
1.1 43.6 21.1 6.2 2.1 5.0 3.0
26.9 39.2 34.0 34.4 22.1 49.1 20.8 14.3 23.6
53,329 2685 36,543 64,068 71,505
0.7 23.1 6.0 3.7 0.9 4.5 1.6
40.4 2.0 27.7 48.6 54.2
b0.001 b0.001 b0.001 b0.001 b0.001 b0.001 b0.001
b0.001 b0.001 b0.001 b0.001 b0.001
– – – – – – – – –
cDDDs = cumulative defined daily doses; SD = standard deviation; TIA = transient ischemic attack; COPD = chronic obstructive pulmonary disease; ACEIs= angiotensinconverting enzyme inhibitors; ARBs = angiotensin-receptor blockers.
diagnostic code (with at least 3 consensus diagnoses at an outpatient department or at least 1 inpatient hospitalization diagnosis): heart failure, diabetes mellitus, stroke or transient ischemic attack (TIA), vascular disease (defined as myocardial infarction or peripheral vascular disease), chronic obstructive pulmonary disease (COPD), and chronic renal disease. The CHADS2 score was calculated for each patient by assigning 1 point each for the presence of heart failure, hypertension, age ≥ 75 years, and diabetes mellitus, and 2 points for a history of stroke or TIA [12]. The CHA2DS2VASc score was calculated for each patient by assigning 1 point each for the presence of heart failure, hypertension, age 65–74 years, diabetes mellitus, vascular disease, and female gender, and 2 points for a history of stroke or TIA, and age ≥75 years [8]. Medications (ACEIs, ARBs, and aldosterone antagonists), which potentially could be used for AF prevention, and other cardiovascular medications were also identified between January 1, 2000, and December 31, 2000 (see Table 1).
C.-Y. Hung et al. / International Journal of Cardiology 168 (2013) 1131–1136 2.3. Statistical analysis The data are presented as the mean values and standard deviations (SD) for continuous variables, and proportions for categorical variables. The differences between continuous values were analyzed by using t test for continuous variables, and chi-square test for categorical variables. Multivariate Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the association between the use of statin and the occurrence of AF. Propensity analysis was used for further confirming this association. The AF-free survival curves were plotted via the Kaplan– Meier method with statistical significance examined by the log-rank test. All statistical analyses were carried out by SAS software version 9.2 (SAS Institute, Inc., Cary, NC, USA). A p value of b0.05 was considered statistically significant.
3. Results 3.1. Participants A total of 171,885 patients aged ≥ 50 years were enrolled in this study, of which 40,001 (23.3%) had used statins (≥ 28 cDDDs). Table 1 summarizes the baseline characteristics of the statin users and non-users. The mean age of the study population was 62.7 ± 9.1 years, with 24.3% of them aged 65–74 years, and 11.0% of them aged ≥75 years. Females accounted for 50.5% of the population. Statin users were younger than non-users (62.3± 8.1 vs. 62.8 ± 9.4 years; p b 0.001) and there were more females in statin users (statin users vs. non-users: 58.0% vs. 48.2%; p b 0.001). Among the statin users, 26.9% of them used 28–90 cDDDs, 39.2% of them used 91–365 cDDDs, and 34.0% of them used >365 cDDDs. Atorvastatin, simvastatin, and rosuvastatin were the most commonly used statins. Among the study subjects, 0.8% had heart failure, 27.8% had hypertension, 9.5% had diabetes mellitus, 4.3% had stroke or TIA, 1.2% had vascular disease, 4.6% had chronic obstructive pulmonary disease (COPD), and 2.0% had chronic renal disease. Statin users had a higher prevalence of these cardiovascular co-morbidities than non-users (p b 0.001). The rate of ACEIs and ARBs, aldosterone antagonists, and other cardiovascular medication usage was higher among statin users than non-users (p b 0.001). Overall, the average CHADS2 score of the cohort was 0.6± 0.9, and the average CHA2DS2VASc score was 1.4 ± 1.3. Statin users had higher CHADS2 (0.9 ± 1.0 vs. 0.5 ± 0.8; p b 0.001) and CHA2 DS2VASc scores (1.8 ± 1.3 vs. 1.3± 1.2; p b 0.001) than non-users. 3.2. CHADS2 score, CHA2DS2VASc score and treatment outcome During the 9-year follow-up (1,473,463 person-years), 6049 patients (3.5% of the study population) developed new-onset AF; the overall incidence rate was 4.1 per 1000 person-years. AF occurred less frequently among statin users compared with non-users before and after adjustments for important clinical variables (age, gender, cardiovascular co-morbidities, and upstream therapies; adjusted HR 0.72; 95% CI 0.68 to 0.77; p b 0.001), and other cardiovascular medications (aspirin, warfarin, alpha blocking agents, beta blocking agents, calcium channel blockers, diuretics, antiarrhythmics, and digoxin; adjusted HR 0.53; 95% CI 0.50 to 0.57; p b 0.001). The incidence rate of AF decreased from 4.4 to 3.3 per 1000 person-years after statin use. Table 2 demonstrates the HRs for the development of new-onset AF in the cohort. In subgroup analyses, there was a universal statin protective effect across age categories, in women as well as men, among those with or without any cardiovascular co-morbidity or using upstream therapy (adjusted HRs ranged from 0.50 to 0.84 in these subgroups). Statin use was more beneficial in female than male (p value for interaction b 0.001), in patients with age ≧ 65 years (p value for interaction=0.021), hypertension (p value for interactionb 0.001) and diabetes mellitus (p value for interaction=0.013) than those without. By using established cardiovascular co-morbidity scoring systems, statin use was more beneficial in patients with higher CHADS2 and CHA2DS2VASc scores than those with a score of 0 (p value for interaction b 0.001). No significant benefit was found in patients with a CHADS2 score of 0 (adjusted HR 0.94; 95% CI 0.83 to 1.05) or a
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CHA2DS2VASc score of 0 (adjusted HR 1.21; 95% CI 1.00 to 1.47). Fig. 1 displays the hazard ratio plot of the protective effect of statin against AF according to the CHADS2 score and the CHA2DS2VASc score. We found that statin therapy has better efficacy in patients with a CHADS2 score of 2 (adjusted HR 0.51; 95% CI 0.44 to 0.60) and a CHA2DS2VASc score≥3 (adjusted HRs ranged from 0.57 to 0.60) compared with other subgroups. 3.3. Statin dose and treatment outcome Table 3 shows the dose relation analysis for AF prevention with statin therapy. The incidence rates of AF were 4.4, 4.1, 3.5, and 2.3 per 1000 person-years among patients with statin use of less than 28, 28 to 90, 91 to 365, and more than 365 cDDDs, respectively. In patients with 28–90 cDDDs, no significant protective effect was found (adjusted HR 1.04; 95% CI 0.94 to 1.15). On the other hand, the occurrence of new-onset AF was significantly different between statin users and non-users among those with 91–365 (adjusted HR 0.85; 95% CI 0.77 to 0.93) and more than 365 cDDDs (adjusted HR 0.50; 95% CI 0.44 to 0.56). The dose–response relationship was also observed in patients with a different CHADS2 score (see eTable 1), and has no significant changes in the HRs by using propensity analysis. The Kaplan–Meier survival plot presented in Fig. 2 shows the protective effect of statin against AF according to cDDDs of statin use. The survival curves began to separate early and continued to separate throughout the course of the study. The adjusted HRs for individual statins were 0.62, 0.61, 0.75, 0.69, 0.78, and 0.51 for patients who used simvastatin, lovastatin, atorvastatin, fluvastatin, pravastatin, and rosuvastatin, respectively. All individual statin use showed a significant effect of AF risk reduction (p b 0.001). 4. Discussion This nationwide cohort study is the largest and longest follow-up study for the analysis of statin protective effects against AF in the general population aged ≥50 years. The main results of this study were that the risk of AF was lower in statin users than non-users, especially for those with higher CHADS2 and CHA2DS2VASc score. Even though statin users in this study had more cardiovascular co-morbidities than the comparison cohorts, statin therapy remained a protective factor against new-onset AF before and after adjustment for potential confounders. Furthermore, there was a dose–response relationship between the use of statins and the risk of AF. These findings support the anti-arrhythmic effects of statin. Studies focusing on the statin protective effects against AF have yielded mixed results in people with cardiovascular diseases [17–24] and in reducing the overall AF burden or AF recurrence in patients with heart failure and AF [25]. Meta-analyses of randomized controlled trials and observational studies revealed that statin therapy is useful for primary prevention of AF with a 24 to 46% risk reduction [9–11,26]. However, these studies did not focus on people without cardiovascular co-morbidities [11]. In the present study, we showed an overall 28% AF risk reduction in patients treated with statin in the general population, but a lack of protective effect in those without any cardiovascular co-morbidity (CHADS2 score of 0 or CHA2DS2VASc score of 0). This result suggests that statin therapy not only treats lipid abnormalities but also reduces the risk of new-onset AF in patients with cardiovascular diseases. Possibly because of the relatively small patient numbers and the study designs of previous clinical trials, dose–response relationships of statin therapy for AF prevention have not yet been identified [9,11]. A population based cohort study may provide a chance to confirm the dose–response of statin in a large population. In this cohort, we evaluated the dose–response relationship by using cDDDs of statin and found a statistically significant inverse trend between the cumulative doses and the incidences of AF. Of note, statin therapy provided no
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Table 2 Crude and adjusted hazard ratios for the development of new-onset AF. Variables
All patients Female Male Age at entry, years 50–64 65–74 ≧75 Medical disease Heart failure No heart failure Hypertension No hypertension Diabetes mellitus No diabetes mellitus Stroke or TIA No stroke or TIA Vascular disease No vascular disease COPD No COPD Chronic renal disease No chronic renal disease Upstream therapy ACEIs and ARBs No ACEIs and ARBs Aldosterone antagonists No aldosterone antagonists CHADS2 scoreb Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–6 CHA2DS2VASc scorec Score = 0 Score = 1 Score = 2 Score = 3 Score = 4–5 Score = 6–9
Statin users
Non-users
Crude HR
95% CI
Adjusted HRa
95% CI
P value for interaction
3.7 3.6 3.9
0.75 0.68 0.85
0.70–0.80 0.62–0.74 0.78–0.93
0.72 0.63 0.82
0.68–0.77 0.58–0.70 0.75–0.91
– b0.001
1594/84,807 1679/31,089 1634/15,988
1.9 5.4 10.2
0.96 0.73 0.67
0.86–1.06 0.66–0.81 0.59–0.77
0.78 0.64 0.68
0.70–0.86 0.57–0.71 0.59–0.79
0.021
9.3 2.8 3.7 2.2 3.2 2.8 4.9 2.7 4.8 2.8 4.8 2.8 4.6 2.8
169/913 4738/130,971 2030/30,420 2877/101,464 496/7947 4411/123,937 414/4922 4493/126,962 97/1159 4810/130,725 458/5913 4449/125,971 173/2166 4734/129,718
18.5 3.6 6.7 2.8 6.2 3.6 8.4 3.5 8.4 3.7 7.7 3.5 8.0 3.6
0.44 0.75 0.52 0.77 0.48 0.75 0.54 0.75 0.53 0.74 0.58 0.76 0.52 0.75
0.31–0.63 0.70–0.80 0.48–0.57 0.70–0.84 0.41–0.55 0.70–0.81 0.44–0.66 0.70–0.80 0.36–0.77 0.70–0.80 0.46–0.72 0.71–0.81 0.38–0.71 0.70–0.80
0.50 0.73 0.61 0.84 0.55 0.76 0.65 0.73 0.60 0.73 0.68 0.72 0.55 0.73
0.35–0.71 0.68–0.78 0.56–0.67 0.76–0.93 0.47–0.64 0.70–0.82 0.53–0.80 0.68–0.78 0.41–0.89 0.68–0.78 0.55–0.86 0.67–0.77 0.40–0.75 0.68–0.78
0.161
379/9490 763/30,511 28/443 1114/39,558
4.0 2.5 6.3 2.8
1082/14,535 3825/117,349 109/1134 4798/130,750
7.4 3.3 9.6 3.7
0.50 0.75 0.60 0.75
0.45–0.57 0.69–0.81 0.39–0.91 0.70–0.80
0.62 0.76 0.65 0.72
0.55–0.70 0.70–0.82 0.42–0.99 0.68–0.78
0.023
341/17,707 414/14,010 224/5603 103/1817 60/864
1.9 3.0 4.0 5.7 6.9
1777/87,503 1803/30,403 837/9022 308/3389 182/1567
2.0 5.9 9.3 9.1 11.6
0.93 0.47 0.39 0.57 0.53
0.83–1.05 0.42–0.52 0.34–0.45 0.46–0.71 0.40–0.71
0.94 0.64 0.51 0.66 0.70
0.83–1.05 0.57–0.71 0.44–0.60 0.52–0.82 0.51–0.95
b0.001
125/5873 225/13,141 296/10,485 249/6173 213/3915 34/414
2.1 1.7 2.8 4.0 5.4 8.2
590/34,921 1074/50,176 1281/25,405 1105/13,381 749/7171 108/830
1.7 2.1 5.0 8.3 10.4 13.0
1.25 0.79 0.53 0.45 0.47 0.56
1.03–1.51 0.68–0.91 0.47–0.61 0.39–0.51 0.40–0.55 0.38–0.82
1.21 0.81 0.70 0.57 0.58 0.60
1.00–1.47 0.70–0.93 0.61–0.80 0.49–0.66 0.50–0.68 0.40–0.89
b0.001
AFs/patients
%
AFs/patients
1142/40,001 571/23,206 571/16,795
2.9 2.5 3.4
4907/131,884 2257/63,508 2650/68,376
478/26,327 438/10,695 226/2979
1.8 4.1 7.6
39/419 1103/39,582 641/17,440 501/22,561 271/8429 871/31,572 122/2482 1020/37,519 39/819 1103/39,182 96/1994 1046/38,007 55/1202 1087/38,799
%
b0.001 0.013 0.445 0.634 0.723 0.429
0.317
AF = atrial fibrillation; HR = hazard ratio; CI = confidence interval; other abbreviations as in Table 1. a Adjusted for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. b The adjusted HRs for subgroups of CHADS2 score adjusted for age, gender, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. c The adjusted HRs for subgroups of CHA2DS2VASc score adjusted for age, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists.
obvious beneficial effect in those with cDDDs less than 91. This finding provides an explanation why previous studies differed in conclusions— heterogeneity of cumulative doses of statins may confound effectiveness of statin on AF. There is very little evidence to support the potency of statin's effect on primary prevention of AF in other high-risk groups, especially in those with multiple cardiovascular co-morbidities. In a recent meta-analysis of studies including patients not undergoing invasive procedures, Bang et al. assumed that the effect of statins may differ in different clinical settings [27]. Our recent study has shown that statin is more beneficial in hypertensive patients with a CHADS2 score ≥ 2 than in those with a score of 1 [23]. This study showed that patients with scores ≥ 1 benefited from statin use, especially those with a CHADS2 score of 2 or a CHA2DS2VASc score ≥ 3. In contrast, those without any co-morbidities (with scores of 0) gained no significant benefits from statin therapy for AF prevention. From this point of view, CHADS2 score and CHA2DS2VASc score represent not only clinical predictors for stroke risk stratification, but are also useful scoring systems for predicting the effectiveness of statin in AF prevention. Several biological mechanisms for a role of statin in AF prevention have been proposed, such as anti-inflammatory and antioxidant properties, and modulation of endothelial and ion channel function [28]. The JUPITER trial found that patients with elevated high-sensitivity
C-reactive protein, a status indicator of systemic inflammation [29], had a 27% reduction in AF risk after rosuvastatin therapy [22]. Recent studies also showed that CHADS2 score was positively related to systemic inflammation [30,31]. Furthermore, female gender and vascular disease, the different factors between CHA2DS2VASc score and CHADS2 score, were also related to increasing systemic inflammation [32–34]. Therefore, it is reasonable to deduce that patients with higher CHADS2 and CHA2DS2VASc scores may have a more severe inflammation status, and the anti-inflammatory effect of statin may be more obvious in these patients. These support the statin anti-inflammatory hypothesis. Our study has a number of strengths. The study cohort was taken from a computerized population-based database, allowing evaluation of a large cohort in a 9-year follow-up period. In addition, because the data on statin use includes all prescription information before the diagnosis of AF, recall bias had been avoided. Furthermore, we conducted subgroup, multivariate and propensity analyses to eliminate the misclassifications, and the results revealed no significant changes in the HRs before and after these analyses. Finally, we found a dose–response relationship existing between the use of statins and the risk of AF. The relationship was found in every subgroup of patients with different CHADS2 scores. There were some limitations in the present study. First, the study population included mainly Taiwanese subjects. Racial difference,
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Fig. 2. AF-free survival according to cumulative defined daily doses (cDDDs) of statin use.
Fig. 1. The effectiveness of statin in prevention of new-onset AF according to (A) CHADS2 score, and (B) CHA2DS2VASc score.
as showed in the ALLHAT study [2], may exist between ethnic populations. Second, types of AF and duration of each AF episode were not available in our research database, and the AF event definition in our study may be under-reported. Although we suppose that underreporting of AF would occur with equal frequency in both statin user and non-user groups, it is possible that statins modify the disease in such a way that the number of diagnosis appears in the claims data is affected (i.e. if patients on statin therapy are more likely to have asymptomatic episodes during sleep, for example). However, this kind of effect had not yet been reported. Third, several confounding factors, including body mass index, blood pressure, and alcohol intake, were not included in our database. Moreover, due to the payment regulations of the National Health Insurance program, statins are more likely to be prescribed in patients with cardiovascular diseases. This may lead to certain selection bias. Therefore, we performed
multivariate regression and propensity analysis to control possible confounding factors (cardiovascular co-morbidities, upstream therapies, and use of cardiovascular medications) and our data showed consistent results. Fourth, we assumed that patients took all prescribed medications. This may overestimate the actual dosage used. Finally, although our data and previous studies [10,17,24] suggested that pravastatin seems to be less potent for AF prevention, we cannot conclude that other statins have better protective effect against AF than pravastatin. This study was not designed to compare the effect of individual statin– mixed statin usage and some confounders may exist. This possible type-dependent effect of statin was recently mentioned in a large real-world registry study performed by Bang et al. [24]. Further studies are needed to confirm this relation. 5. Conclusions Statin therapy reduces the risk of new-onset AF in a dosedependent manner, and is beneficial in patients with cardiovascular co-morbidities. Further studies focusing on possible biological mechanisms and comparing the efficacy of an individual statin are needed. Acknowledgments This study is based in part on data obtained from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health, Taiwan, and managed by the National Health Research Institutes. The interpretation and
Table 3 Dose-relation analysis for new-onset AF. Variables
No. of person-years
Incidence (per 1000 person-years)
Model 1 Crude HRa
95% CI
Model 1 Adjusted HRb
95% CI
Model 2 Crude HRc
95% CI
Model 2 Crude HRd
95% CI
Non-users (b28 cDDDs) Statin user (≧28 cDDDs) 28–90 cDDDs 91–365 cDDDs >365 cDDDs Simvastatin Lovastatin Atorvastatin Fluvastatin Pravastatin Rosuvastatin
1,123,758 349,705 93,107 136,591 120,007 121,014 77,655 171,943 72,867 49,955 84,080
4.4 3.3 4.1 3.5 2.3 2.7 2.6 3.3 3.0 3.5 2.0
1.00 0.75 0.95 0.80 0.53 0.63 0.61 0.78 0.73 0.84 0.47
– 0.70–0.80 0.86–1.05 0.73–0.88 0.47–0.60 0.57–0.71 0.53–0.70 0.71–0.85 0.64–0.83 0.73–0.98 0.41–0.55
1.00 0.72 1.04 0.85 0.50 0.62 0.61 0.75 0.69 0.78 0.51
– 0.68–0.77 0.94–1.15 0.77–0.93 0.44–0.56 0.55–0.69 0.53–0.70 0.68–0.81 0.60–0.79 0.67–0.91 0.44–0.59
1.00 0.70 1.02 0.83 0.53 0.62 0.59 0.76 0.73 0.86 0.45
– 0.65–0.75 0.92–1.13 0.75–0.91 0.46–0.60 0.55–0.70 0.51–0.69 0.69–0.83 0.63–0.83 0.74–1.01 0.39–0.53
1.00 0.72 1.08 0.86 0.50 0.63 0.61 0.76 0.70 0.82 0.50
– 0.67–0.77 0.97–1.20 0.78–0.95 0.44–0.57 0.56–0.70 0.53–0.71 0.69–0.83 0.61–0.81 0.70–0.95 0.43–0.59
Abbreviations as in Table 1 and Table 2. a Model 1: original cohort. b Model 1 with adjustment for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, chronic renal disease, ACEIs and ARBs, and aldosterone antagonists. c Model 2: propensity score matched cohort (matched with age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, and chronic renal disease). d Model 2 with adjustment for age, gender, heart failure, hypertension, diabetes mellitus, stroke or TIA, vascular disease, COPD, and chronic renal disease.
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