egression).

that women experience more back pain, indigestion, palpitation, vomiting, fatigue, and numbness [4,5]. Both the present study and western studies show that women had more associated symptoms than did men. An understanding of gender difference in symptoms can provide clinicians with additional means to identify a cardiac event. The potential reasons for the differences between the findings of the present study and those of Western studies warrant further investigation.

The present study showed that, when symptoms occurred, 85% of participants experienced a state of anxiety and 68% had trait anxiety. The level of anxiety in our study was higher than that of the Koivula et al. (2001) [10], a study of patients waiting for cardiac surgery. In the present study, women experienced higher anxiety, a finding that is in keeping with previous research [15]. The anxiety may come from uncertainty and the “unknowns” associated with the disease [7]. Thus, increasing patients’ knowledge of their disease and educating them to self-manage or to seek help is very important.

Certain pathophysiology mechanisms were involved in anxiety responds which might affect one’s physical aspect of quality of life. Anxiety was associated with excessive of sympathetic nervous system (SNS) activation, which may result in poor cardiac outcomes. SNS activation contributes to platelet aggregation, volume contraction, recurrent thrombus formation, electric instability and endothelial dysfunction were reported [36]. Previous research also revealed compare to non-anxious patients, patients suffering from anxiety are generally more sensitive to physiologic changes which might have negative impact on recovery [37].

CART analysis uses binary recursive partitioning to split the original node into two nodes. Rood node was first split by trait anxiety, which indicating trait anxiety is the most significant independent variable and the second child node was split down by exercise habit. This process repeats until an optimal tree, for which each terminal node indicates a specific pattern of subgroups, is established. All of the information in the database was able to be analyzed in a decision tree model, rather than through linear regression, which usually selects certain independent variables based on the literature or clinical experience [26,27].

According to CART analysis, among ACS patients, less trait anxiety and a regular exercise habit predicted a better quality of life. The symptoms of ACS, however, were not selected, which indicated that trait anxiety and a regular exercise habit had a greater contribution to the quality of life than did the symptoms that ACS patient experienced. CART analysis is simple and clear, which are considered its greatest strength. Nevertheless, the results based on CART might be difficult to compare with the results of other studies because previous research that uses CART analysis to determine predictors of ACS is very limited. In such studies, linear regression is more commonly used.

In addition to trait anxiety and a regular exercise habit, sweating, new onset cough, self-perceived family support, and a state of anxiety were identified as significant predictors of better quality of life in linear regression model. These predictors were identified and their contribution to the dependent variable was determined through the value of the coefficient. Anxiety, exercise, and family support have been reported as important contributors to better quality of life. As such, the results of the present study support those of previous studies [19,23-25,38,39]. Our study’s result on the impact of sweating and new onset of coughing on the quality of life, is a new finding. It may be that patients’ experiencing a cough for the first time results in anxiety over the presence of a new symptom. The sweating, then, is a response to this anxiety. Such discomfort and anxiety could decrease quality of life.

In short, each approach presented had its advantages and disadvantages. CART analysis is appropriate for medical/clinical use as a means to determine the characteristics of good outcomes. However, the decision tree was developed to only four terminal nodes, and some factors, such demographics and symptoms of ACS were not selected to be part of the tree. Unlike CART, linear regression is a parametric analysis method and requires a normal distribution of the database. The benefits of linear regression are that it is widely used, accepted, and understood. In short, using both CART and linear regression for clinical data is a good way to analyze the results from different statistical approaches, yielding valuable information for clinical use.

5. LIMITATIONS AND RECOMMENDATIONS

The contribution of anxiety and ACS symptoms during a cardiac event to the quality of life one month later was examined in this study. Nevertheless, what occurred during one month later was not followed or recorded. Thus, related medical treatment could have influenced the outcomes. Therefore, conducting face-to-face interviews a month after the cardiac event to discover how the patients felt is recommended. In addition, NYHA (New York Heart Association) function status should be assessed and collected since large or recurrent infraction may lead to heart failure. Anxiety was measured only at an early stage, rather than a long-term follow up after discharge. Patients might have the same, decreased, or increased levels of anxiety, which could potentially affect their quality of life. Thus, the collection of data at different points of time after discharge is recommended to identify healthcare needs at specific times. Another limitation is the low reliability of the SACSI, which might have been due to the unfamiliar terms used to describe different characteristics of pain. Employing pilot testing and an expert panel discussion as a means to review or delete terms on the SACSI is recommended. In addition, samples need to contain a greater number of women and more participants from a variety of locations to allow for analysis of gender and for the effect of menopause on these variables.

6. CONCLUSION

Two important new findings were reported. First, anxiety is a most significant predictor as well as a stronger predictor than the symptoms of ACS for quality of life. Anxiety level that patient experienced at the time heart attack occurred can predict quality of life a month later. Second, the majority of ACS patients experienced a moderate to high level of anxiety during a heart attack. To reduce anxiety and increase the quality of life, education should focus on what could happen in a cardiac event and what to do when it occurs in ACS patients or those who are at risk of ACS. While it is important for healthcare professionals to understand these aspects of ACS, it is just as important for them to educate their patients. This would be a good first step toward patients’ having the knowledge needed to reduce their anxiety.

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NOTES

*Corresponding author.

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