The Risk of Stroke after Acute Myocardial Infarction in Patients with and without Atrial fibrillation: A Nationwide Cohort Study in Taiwan

Data source

In Taiwan, the National Health Insurance Program has financed the healthcare for more than 99% residents of Taiwan since 1995. The NHIRD includes detailed information from the medical records of patients admitted to hospitals, including age, sex, diagnosis, intervention procedures, medication prescription, and relevant survival data. The NHIRD provides researchers with deidentified data via encryption of the identification codes to preserve patient anonymity and has been extensively used in epidemiologic studies in Taiwan.^4–6 Our use of the NHIRD data and the informed consent wavier was approved by the Human Research Committee of Kaohsiung Veterans General Hospital and the reference number was VGHKS15-CT12-01. All methods were carried out in accordance with relevant guidelines and regulations.

Definition of AMI Population

All patients admitted to hospitals in Taiwan with the primary diagnosis of acute myocardial infarction (AMI) (ICD-9: 410 to 410.92) between January of 2000 and December of 2012 were enrolled. From this group, who were younger than 20 years old or older than 120 years old, who had doubtful data, whose ICD-9 codes consisted both ST-elevated MI (STEMI) and Non-ST elevated MI (NSTEMI), and who had any previous history of stroke (ICD-9: 430 to 437 or A290 to A294) were excluded, leaving 170,472 patients of AMI (Fig. 1). All ICD-9 codes used for the diagnosis in this study were shown in supplemental Table 1.

Study Population

Among the 170,472 identified patients of the first hospitalization for AMI, 8,530 patients (5%) with atrial fibrillation in discharge diagnosis were identified. Of the remaining 161,942 patients, patients with any diagnosis of AF prior to this admission were excluded, leaving 158,046 control patients for comparison. A propensity score matching technique was used to minimize baseline differences between the control group and the AF group. One-to-one matching was based on the variables of sex and age. The data from 8,530 AMI patients with AF and 8,530 matched controls were, therefore, included in our final analysis. Among 8,530 AMI patients with AF during admission, 6,641 patients who did not have any prior AF diagnosis, were defined as new-onset AF. The remaining 1,889 patients who had the prior AF diagnosis, were defined as preexisting AF (Fig. 1).

Outcome Analysis

All enrolled patients were followed until stroke or 31 December 2012, whichever occurred first. Stroke was defined as the following admission with the first diagnosis code of stroke.

Statistical Analyses

Extraction of the data and statistical analysis were performed by SAS version 9.4 (SAS Institute, Inc., Cary, NC). Descriptive statistics were calculated for all variables, with categorical data reported as percentile values and continuous variables as a mean. Between group differences were evaluated by paired t test for continuous variables and Chi-squared test for categorical variables, with a P-value < 0.05 considered statistically significant. Cox proportional hazard regression analysis was used to calculate the hazard ratio (HR), and associated 95% confidence intervals (95% CI), for significant variables. Kaplan–Meier cumulative survival curves were constructed to compare stroke between AMI patients with AF and those who had not, including subgroup analysis of different genders, ages, and with or without intervention. Log-rank tests with a P < 0.05 were considered statistically significant.

Descriptive Characteristics of Study Group

The descriptive characteristics of the 8,530 patients forming the AMI with AF group (AF group) and the 8,530 matched controls (control group), including types of AMI, with or without intervention therapies [coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI)], comorbidities, CHA₂DS₂-VASc scores, and types of medications used, were listed in Table 1 and Supplemental Table 2. Groups were comparable on the primary demographic variables of age and distribution of genders. The ratio of NSTEMI was higher in AF group (66.07% vs. 62.44%, P < 0.001). The ratio of intervention therapy was lower in AF group (40.47% vs. 42.39%, P = 0.0108). Comorbidities including hypertension (30.81% vs. 35.38%, P < 0.0001), diabetes mellitus (DM) (20.2% vs. 29.84%, P < 0.0001), dyslipidemia (8.84% vs. 14.87%, P < 0.0001), and end-stage renal disease (ESRD) (1.41% vs. 1.79%, P = 0.0441) were lower in AF group. On the other hands, comorbidities including heart failure (29.57% vs. 19.64%, P < 0.0001) and chronic obstructive pulmonary disease (COPD) (7.15% vs. 6.19%, P = 0.0119) were higher in AF group. About medications, the use of aspirin (82.91% vs. 77.95%, P < 0.0001), clopidogrel (64.33% vs. 59.34%, P < 0.0001), angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) (66.62% vs. 59.5%, P < 0.0001), beta blocker (53.22% vs. 48.36%, P < 0.0001), warfarin (12.26% vs. 2.06%, P < 0.001), Class III antiarrhythmic drugs(AAD) (37.55% vs. 8.56%, P < 0.0001), digoxin (27.21% vs. 8,19%, P < 0.001), and non-dihydropyridine calcium channel blocker(CCB)(21.47% vs. 11.84%, P < 0.0001) were higher in AF group. On the other hands, the use of statin was lower in AF group (30.6% vs. 32.4%, P = 0.0111).

We further divided AF group into new-onset AF or preexisting AF subgroups (Table 2 and Supplemental Table 3). There were 6,641 patients in new-onset AF subgroup and 1,889 patients in preexisting AF subgroup. The ratio of female gender was higher in preexisting AF subgroup (41.03% vs. 38.01%, P = 0.0174). The age was older in preexisting AF subgroup (P < 0.0001). The ratio of NSTEMI was higher in preexisting AF subgroup (71.89% vs. 64.42%, P < 0.0001). The intervention therapy ratio was lower in preexisting AF subgroup (35.63% vs. 41.85%, P < 0.0001). About the comorbidities, the ratios of hypertension (32.87% vs. 30.22%, P = 0.0275) and heart failure (35.31% vs. 27.93%, P < 0.0001) were higher and the ratio of dyslipidemia (6.56% vs. 9.49%, P < 0.0001) was lower in preexisting AF subgroup. CHA₂DS₂-VASc score was higher in preexisting AF subgroup (P < 0.0001). The use of aspirin (78.82% vs. 84.07%, P < 0.0001), and class III AAD (27.37% vs. 40.45%, P < 0.0001) were lower in preexisting AF subgroup. On the other hand, the use of warfarin (19.64% vs. 10.16%, P < 0.0001), digoxin (34.09% vs. 25.25%, P < 0.0001), and non-dihydropyridine CCB (23.13% vs. 20.99%, P = 0.0453) were higher in preexisting AF subgroup.

Stroke Analysis

Overall, the 12-year stroke rate was significant higher in AF group (log rank P < 0.001; Fig. 2, panel A). Among both genders, the rate of stroke was significant higher in the AF group (log rank P < 0.001 in both genders; Fig. 2, panel B and C). Patients were further divided by different age subgroups (age < 65 years, age ≥ 65 and < 75 years, and age ≥ 75 years). The Kaplan–Meier cumulative curves for stroke were significant higher in AF group in these three age subgroups (all log rank P < 0.001) (Fig. 3, panel A, B and C). The rates of stroke were significant higher in AF group either with or without intervention therapy (log rank P < 0.001 in both groups; Fig. 3, panel D and E). Two different type of AF subgroup patients were further analyzed, including new-onset AF and preexisting AF subgroups. In the subgroups of male genders, age below 65 years, and with intervention therapies, patients with preexisting AF have significant higher stroke rates as compared to patients with new-onset AF (Table 3 and Figs. 2 and 3).

The Cox proportional hazard regression analysis showed an increased rate of stroke after AMI in patients with one of the following characteristics (Table 4): older age (age ≥ 65 years and < 75 years vs. age < 65 years: HR 1.49; 95% CI, 1.3–1.71) (Age ≥ 75 years vs. age < 65 years: HR 2; 95% CI, 1.76–2.28), diabetes mellitus (HR 1.26; 95% CI, 1.13–1.4), heart failure (HR 1.18; 95% CI, 1.05–1.32), and AF (HR 1.67; 95% CI, 1.5–1.87).

Forest plot of HRs was used for subgroup analysis (Fig. 4). In patients with AMI and AF, HRs for stroke were higher in both male and female genders, and different ages (age < 65 years, age ≥ 65 years and < 75 years, and age ≥ 75 years). In patients suffered from AMI, those with AF also had high risks for stroke either with or without hypertension, DM, heart failure, dyslipidemia, and intervention therapies. Furthermore, whether the use of aspirin, clopidogrel, ACEI/ARB, beta-blocker, statin or CCB or not, AF also had higher HRs for stroke events. However, AF did not influence stroke attack in patients with COPD (HR 1.12; 95% CI, 0.76–1.66) and ESRD (HR 2.62; 95% CI, 0.89–7.69), and in patients with the use of warfarin (HR 1.33; 95% CI, 0.82–2.15), Class III AAD (HR 1.15; 95% CI, 0.85–1.53), and Digoxin (HR 1.29; 95% CI, 0.98–1.71).

New onset and preexisting AF in AMI

Our data revealed that the incidence of AF during hospitalization was 5% (8,530 in 170,472 patients) and the majority was new-onset AF (78%, 6,641 in 8,530 patients). According to the previous study, the incidence of AF during hospitalization for AMI was reported as a incidence ranging from 4–19%.⁷ The increased rate of AF can be resulted from an increased left atrial pressure.⁸ This can either be a direct consequence of atrial ischemia or be indirectly caused by an enhanced left ventricular filling pressure or a restricted left ventricular function.^{9, 10}

In a previous study by Zusman et al., the new onset AF following myocardial infarction was associated with a nearly 35-fold increased risk of stroke during follow-up (mean 41 months; HR 34.6, 95% CI: 4.0-296.8).¹¹ However, the limited number of patients and events (14 events out of 300 patients) made their results seem to be less precise, as evidenced by such a wide 95% CI. In a study with the use of data from Danish National Patients Registry with a total of 89,703 patients with myocardial infarction being analyzed and at the end of 5-year follow-up, new onset AF complicating myocardial infarction was demonstrated as an independent predictor for fetal and non-fetal stroke (HR: 2.34; 95% CI: 2.12–2.57 and HR: 2.47; 95% CI: 2.24–2.73, respectively).¹² Additionally, Luo et al. reported a meta-analysis which showed that new-onset AF was associated with an increased risk of ischemic stroke (risk ratios: 2.84, 95% CI: 1.91–4.23; 6 studies).¹³ About preexisting AF, Tanne et al. reported that chronic AF was associated with significant appearance of stroke/TIA in hospital-discharged survivors of AMI (odds ratio: 5.71, 90% CI: 1.55–21.01).¹⁴ Our data included both patients with new-onset and preexisting AF, with a longer follow-up duration (up to 12 years) and a high amount of patients with AF (8,530 patients). The stroke risk after AMI in our study was significant higher in patients with AF than those without AF, with a HR of 1.67 (95% CI, 1.5–1.87).

AMI Patients with preexisting AF may have the presentation of previous diastolic dysfunction and cardiomyopathy. On the other hand, AMI patients with new-onset AF during is due to acute change at the time of AMI, including left atrial ischemia or overload, as well as neuroendocrine activation and tachycardia due to hemodynamic instability. Therefore, preexisting AF and new-onset AF may influence outcomes differently. To our knowledge, there was only one report directly compare preexisting and new-onset AF in the influence of stroke in patients with AMI by Gourronc el al. They have compared preexisting AF, new-onset AF and AF-free patients with AMI. The results showed that there was no significant difference in respect of stroke between preexisting AF, new-onset AF, and AF-free patients (2.2%, 0.5%, and 0.8%, respectively, p = 0.327).¹⁵ Our data showed that in AMI patients with AF, including new-onset and preexisting, have significant higher stroke rates as compared to those without AF. Our data was different from Gourronc et al. may be because that we have much more patients enrolled (8,530 AF patients in our study as compared to 436 patients with AF in their study) and much longer follow-up durations (up to 12 years in our study as compared to 1 year in their study).

Our study showed that in the subgroup of male, age younger than 65 years and with intervention therapies, AMI patients with prior AF have significant higher stroke rate in the subgroup than those with new-onset AF (Table 4). Besides, AMI patients with prior AF have higher ratios of female gender, older ages, NSTEMI, and heart failure and lower ratios of intervention therapies and dyslipidemia. The CHA₂DS₂-VASc scores were significant higher in prior AF group as compared to new-onset AF group. Lau et al. reported that in patients with acute coronary syndrome, those with prior AF had significant higher ratio of older ages, female gender and had significant lower ratio of intervention therapy.¹⁶ The results were compatible with our study. As a result, patients with prior AF tend to be more fragile and could explain the significant higher stroke rate in male, younger, and intervention therapy than patients with new onset AF.

Limitations

There are several limitations in this study. First, although previous study had confirmed the accuracy of NHIRD as a valid resource for research of cardiovascular disease¹⁷, the relevant clinical variables such as cardiac biomarkers, left ventricular ejection fraction, and Killip grade were unavailable and these variables had important influences on the occurrence of stroke. Second, the type of AF did not present in this study. However, according to the previous study, the type of AF did not significantly affect the risk of stroke and should not influence the decision of stroke prevention.¹⁸ Third, the anticoagulation therapy in this study period for AF patients was mainly warfarin. However, non-Vitamin K oral anticoagulants are now used widely in the prevention of stroke in patients with AF and percentage of anticoagulants use has been largely increased in recent years.¹⁹ The clinical practice nowadays may affect the rates of stroke in patients with AF. However, it remains challenging issue to use antiplatelet and anticoagulant therapy in AMI patients with AF, which need to be investigated in further large-scale trail.