Predictors of Clinical Anxiety Aggravation at the End of a Cardiac Rehabilitation Program

• Main Authors: Mozhgan Saeidi, Saeid Komasi, Behzad Heydarpour, Hossein Karim, Mehdi Nalini, Parvin Ezzati
• Format: Article
• Language: English
• Published: Wolters Kluwer Medknow Publications 2016-01-01
• Series: Research in Cardiovascular Medicine
• Subjects: Anxiety; Cardiac Patients; Cardiac Rehabilitation; Demographic Factors; Clinical Factors
• Online Access: http://www.rcvmonline.com/article.asp?issn=2251-9572;year=2016;volume=5;issue=1;spage=1;epage=1;aulast=Saeidi;type=0

Background: Anxiety is one of the most primary and common reactions to a cardiac event can lead to hypertension, tachycardia, and high cardiac output. Objectives: To investigate the predictors of clinical anxiety aggravation at the end of a cardiac rehabilitation (CR) program. Patients and Methods: This retrospective study used a database of a CR ward of a hospital in Iran. The demographic and clinical information of 574 patients participating in the CR program from April 2005 through April 2010 were analyzed. In order to determine the predictors of anxiety, binary logistic regression was performed. Results: After adjustment for gender, age and education, the results showed that 16.7% of the patients completed their CR program with increased levels of clinical anxiety. The following study variables were independently predictive of increased anxiety at the end of the CR program: male gender (OR = 2.04, 95% CI = 1.11 to 3.33, P = 0.048), no history of diabetes (OR = 4.24, 95% CI = 172 to 10.44, P = 0.002), family history of cardiac disease (OR = 2.63, 95% CI = 1.03 to 6.74, P = 0.043), and not quitting smoking (OR = 3.29, 95% CI = 1.38 to 7.85, P = 0.007). These variables could explain 9% - 15% of the variance in the dependent variable. Conclusions: It is possible to predict higher anxiety levels at the end of the CR program and implement preventive measures to control anxiety by considering certain demographic and clinical variables. Future studies should assess the predictive power of other variables .