Kang et al. 
analyzed the NHI research database and reported a modestly increased risk of stroke after occurrence of ACS (adjusted HR
1.22, 95% CI, 1.06–1.40). In the present large-scale population-based follow-up study, we attempted to replicate the previously reported positive association between ACS and stroke. However, using the complete NHI database, we found that occurrence of ACS was not associated with an increased stroke risk after applying propensity score-matching to balance the baseline characteristics between subjects with and without ACS (HR
0.93, 95% CI, 0.83–1.04), and there was no significant difference in stroke-free survival rate between the ACS and non-ACS groups. We think this discrepancy is mainly attributed to the substantial imbalance in the distribution of age, diabetes, and hyperlipidemia in the study of Kang et al, which did not match these variables in the ACS and non-ACS subjects. As ACS has been associated with age, diabetes, and hyperlipidemia 
, it might be expected that, without matching, subjects with ACS would be older and have a higher prevalence of diabetes and hyperlipidemia than those without ACS, as seen in the study of Kang et al. Moreover, age, diabetes, and hyperlipidemia are known risk factors for stroke 
. Consequently, an imbalance in the baseline demographic and comorbidity variables would lead to a confounded association between ACS and stroke. Although multiple regression analysis has been applied to the adjustment for potential confounders in observational studies, the confounding may not be completely overcome by covariate adjustment in multiple regression analysis due to the following concerns. First, multiple regression analysis commonly assumes linearity of the effects of confounders. However, the true effect may be a non-linear form such as quadratic or exponential 
. Second, unless the confounders are measured without error, the confounding cannot be completely removed simply by adjusting for covariates in multiple regression 
. Matching is an alternative method to control for confounding. However, an important drawback is the difficulty in finding close matches as the number of variables for matching increases. Propensity scoring summarizes all measured potential confounders into a single composite score 
. Matching on propensity score will be similar to matching on all the included covariates used for computing the propensity score. As shown in , there was no significant difference in all demographic and comorbidities variables after matching. By applying propensity score matching, we minimized the potential confounding effects of these variables and found that occurrence of ACS was not related to an increased risk of subsequent stroke.
In our study, there was no significant difference in the distribution of stroke subtypes between the ACS and non-ACS groups (). This finding is different from that of Kang et al 
, who reported a significantly higher risk of ischemic stroke in subjects with ACS than those without ACS. It has been suggested that persons with advanced age and diabetes are prone to developing ischemic stroke, rather than hemorrhagic stroke 
, which may account for the predisposition of ACS subjects to develop ischemic stroke reported by Kang et al, as the ACS group in their study included more subjects with advanced age and diabetes. By matching demographic and comorbidities variables, we demonstrated that there was no predisposition of ACS patients to develop ischemic stroke. This result is consistent with our above finding that ACS is not associated with an increased risk of stroke.
Before matching on propensity scores (), the ACS group had higher prevalence of pre-existing major vascular risk factors including diabetes, hypertension, and hyperlipidemia than the non-ACS group. Although diabetes and hyperlipidemia have been associated with ACS 
, the mechanisms underlying such association are not clear. Diabetes is considered to be a chronic inflammatory condition with elevated inflammatory markers 
. In addition, increased expression of vascular endothelial growth factor in the synovium has been found in patients with diabetes 
. Therefore, the association between diabetes and ACS may be explained, at least in part, by a diabetes-related chronic inflammatory process with increased growth factor expression, which, in turn, leads to joint synovitis and subsequent capsular fibrosis.
Several strengths of this study should be addressed. First, this study used a longitudinal population-based database, which enabled us to identify all incident strokes and evaluate the temporal relationship between ACS and stroke. Second, we used propensity score matching to minimize the potential confounding effects of all the included covariates. Third, the large study population (22025 ACS subjects and 22025 matched non-ACS subjects) can provide adequate statistical power to minimize the chance of false-negative results on the association between ACS and stroke.
Nevertheless, this study is subject to several possible limitations. First, the diagnosis of diabetes, ACS, stroke, and medical comorbidities was entirely determined using the ICD codes from the NHI claim database and there may be concern about the diagnostic accuracy of the database. However, the Bureau of the NHI has formed different audit committees that randomly sample the claim data from every hospital and review charts on a regular basis to verify the diagnostic validity and quality of care. Accordingly, the NHI claim database is an established research database and has been used in various biomedical research fields 
. Second, in the ambulatory medical setting, the diagnosis of ACS is usually made on a clinical basis without using gold standard diagnostic tools, such as an arthrogram or arthroscopy. Nevertheless, it has been suggested that most cases of ACS can be effectively diagnosed through adequate history taking and physical examination 
. Third, due to the inherent limitation of the NHI database, information was lacking regarding lifestyle factors, such as smoking, alcohol consumption, physical inactivity, and obesity, which may affect the interpretation of our findings. However, since lifestyle factors have not been identified as risk factors for ACS and since no significant association between ACS and stroke was found in our study, we believe that lifestyle factors are not likely to confound the relationship between ACS and stroke.
In conclusion, the present large-scale population-based propensity score-matched study showed that occurrence of ACS was not linked to an increased risk of developing stroke. Our findings suggest that the previously reported positive association between ACS and stroke may result from the confounding effects of age and pre-existing comorbidities.