Date Published: November 19, 2012
Publisher: Public Library of Science
Author(s): Chueh-Hung Wu, Yen-Ho Wang, Ya-Ping Huang, Shin-Liang Pan, German Malaga. http://doi.org/10.1371/journal.pone.0049343
A previous population-based study reported an increased risk of stroke after the occurrence of adhesive capsulitis of the shoulder (ACS), but there were substantial imbalances in the distribution of age and pre-existing vascular risk factors between subjects with ACS and without ACS, which might lead to a confounded association between ACS and stroke. The purpose of the present large-scale propensity score-matched population-based follow-up study was to clarify whether there is an increased stroke risk after ACS.
We used a logistic regression model that includes age, sex, pre-existing comorbidities and socioeconomic status as covariates to compute the propensity score. A total of 22025 subjects with at least two ambulatory visits with the principal diagnosis of ACS in 2001 was enrolled in the ACS group. The non-ACS group consisted of 22025, propensity score-matched subjects without ACS. The stroke-free survival curves for these 2 groups were compared using the Kaplan-Meier method. Stratified Cox proportional hazard regression with patients matched on propensity score was used to estimate the effect of ACS on the occurrence of stroke.
During the two-year follow-up period, 657 subjects in the ACS group (2.98%) and 687 in the non-ACS group (3.12%) developed stroke. The hazard ratio (HR) of stroke for the ACS group was 0.93 compared to the non-ACS group (95% confidence interval [CI], 0.83–1.04, P = 0.1778). There was no statistically significant difference in stroke subtype distribution between the two groups (P = 0.2114).
These findings indicate that ACS itself is not associated with an increased risk of subsequent stroke.
Adhesive capsulitis of the shoulder (ACS) is characterized by intense shoulder pain with progressive limitation of shoulder mobility in all planes . Although a minority of cases of ACS have been etiologically linked to previous trauma or surgery of the shoulder, most cases are considered to be idiopathic without a preceding trauma . The etiology and pathogenesis of ACS remains a mystery. Several systemic comorbid diseases, such as diabetes , thyroid diseases , and hyperlipidemia , , have been associated with ACS.
Table 1 shows the demographic and clinical characteristics of the ACS and non-ACS groups before propensity score matching. The ACS group had a higher prevalence of certain pre-existing medical comorbidities, including hypertension (P<0.0001), hyperlipidemia (P<0.0001), chronic rheumatic heart disease (P = 0.0260), coronary heart disease (P<0.0001), and other heart disease (P<0.0001) than the non-ACS group. There were also significant differences in the distribution of monthly income, urbanization level, and geographic region between the ACS and non-ACS groups. The ACS groups had higher propensity score than the non-ACS group (P<0.0001). After propensity score matching, the matched cohorts were well-balanced in terms of all observed covariates (Table 2). There was no statistically significant difference in all the baseline characteristics between the ACS group and matched non-ACS group (Table 2). 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 Table 2, 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. Source: http://doi.org/10.1371/journal.pone.0049343