Gender differences in comorbidities and risk factors in ischemic stroke patients with a history of atrial fibrillation

Abstract

Background: Atrial Fibrillation (AF) is a common cardiac arrhythmia and has been identified as a major risk factor for acute ischemic stroke (AIS). Gender differences in the disease process, causative mechanisms and outcomes of AF have been investigated. In the current study, we determined whether there is a gender-based disparity in AIS patients with baseline AF, and whether such a discrepancy is associated with specific risk factors and comorbidities.

Methods: Baseline factors including comorbidities, risk and demographic factors associated with a gender difference were examined using retrospective data collected from a registry from January 2010 to June 2016 in a regional stroke center. Univariate analysis was used to differentiate between genders in terms of clinical risk factors and demographics. Variables in the univariate analysis were further analyzed using logistic regression. The adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for each factor were used to predict the increasing odds of an association of a specific comorbidity and risk factor with the male or female AIS with AF.

Results: In the population of AIS patients with AF, a history of drug and alcohol use (OR = 0.250, 95% CI, 0.497-1.006, P = 0.016), sleep apnea (OR = 0.321, 95% CI, 0.133-0.777, P = 0.012), and higher serum creatinine (OR = 0.693, 95% CI, 0.542-0.886 P = 0.003) levels were found to be significantly associated with the male gender. Higher levels of HDL-cholesterol (OR = 1.035, 95% CI, 1.020-1.050, P < 0.001), LDL-cholesterol (OR = 1.006, 95% CI, 1.001-1.011, P = 0.012), and the inability to ambulate on admission to hospital (OR = 2.258, 95% CI, 1.368-3.727, P = 0.001) were associated with females.

Conclusion: Our findings reveal that in the AIS patients with atrial fibrillation, migraines, HDL, LDL and poor ambulation were associated with females, while drugs and alcohol, sleep apnea, and serum creatinine level were associated with male AIS patients with AF. Further studies are necessary to determine whether gender differences in risk factor profiles and commodities require consideration in clinical practice when it comes to AF as a risk factor management in AIS patients.

Keywords: Acute ischemic stroke; Atrial fibrillation; Demographics; Gender; Risk factors.

Fig. 1

Forest Plot representation for clinical and demographic factors associated with ischemic stroke patients with and without atrial fibrillation. Adjusted OR < 1 denotes factors that are associated with male while OR > 1 denote factors that are associated with females. Hosmer-Lemeshow test (P = 0.546), Cox & Snell (R² = 0.149) were analyzed. The overall classified percentage of 67.1% was applied to check for fitness of the logistic regression model. *Indicates statistical significance (P < 0.05) with a 95% confidence interval. ^Indicates that data were modified by taking the 5th square root for graphing purposes

Fig. 2

ROC curve associated with acute ischemic stroke patients with and without atrial fibrillation. Elevated area under the curve (AUC) values in ROC analysis indicate stronger discrimination of the score for being female. ROC curve (AUC = 0.729, 0.712–0.746) was used to analyze sensitivity and specificity of the model

Fig. 3

Forest Plot representation for clinical factors associated with ischemic stroke patients without atrial fibrillation. Adjusted OR < 1 denote factors that are associated with females while OR > 1 denote factors that are associated with males. Hosmer-Lemeshow test (P = 0.866), Cox & Snell (R² = 0.189) were analyzed. The overall classified percentage of 70.0% was applied to check for fitness of the logistic regression model. *Indicates statistical significance (P < 0.05) with a 95% confidence interval. ^Indicates that data were modified by taking the 5th square root for graphing purposes

Fig. 4

ROC curve associated with acute ischemic stroke patients without atrial fibrillation. Area under the curve (AUC) values in ROC analysis indicate better discrimination of the score for being male. ROC curve (AUC = 0.757, 0.740–0.774) was used to analyze sensitivity and specificity of the model

Fig. 5

Forest Plot representation for clinical factors associated with ischemic stroke patients with atrial fibrillation. Adjusted OR < 1 denote factors that are associated with males while OR > 1 denote factors that are associated with females. Hosmer-Lemeshow test (P = 0.866), Cox & Snell (R² = 0.189) were analyzed. The overall classified percentage of 70.0% was applied to check for fitness of the logistic regression model. *Indicates statistical significance (P < 0.05) with a 95% confidence interval. ^Indicates that data were modified by taking the 5th square root for graphing purposes

Fig. 6

ROC curve associated with acute ischemic stroke patients with atrial fibrillation. Area under the curve (AUC) values in ROC analysis indicate better discrimination of the score for being female. ROC curve (AUC = 0.757, 0.721–0.793) was used to analyze sensitivity and specificity of the model