OBJECTIVE:P- wave dispersion (PD) is an indicator of inhomogeneous and discontinuous propagation of sinus impulses. In the present study we aimed to investigate the PD and its association with the severity of the disease. in patients with stable coronary artery disease.METHODS:We prospectively analyzed 60 subjects with coronary artery disease (CAD) and 25 subjects with nor-mal coronary angiograms (control group). The maximum and minimum P-wave duration and PD were measured from the 12-lead surface electrocardiograms. The CAD severity was assessed by the severity score (Gensini score) and the number of vessels involved (vessel score).RESULTS:P max was longer in CAD group compared with the control group (p<0.001). PD was greater in the CAD group, compared with the control group (p<0.001). However, P min did not differ between the two groups. In bi-variate correlation, increased PD was correlated with presence of diabetes mellitus (r=0.316, p=0.014), smoking (r=0.348, p=0.006), left ventricular ejection fraction (r=-0.372, p=0.003), vessel score (r=0.848, p=0.001), and Gensini score (r=0.825, p=0.001). Multiple linear regression analysis showed that PD was independently associ¬ated with vessel score ((3=0.139, p=0.002) and Gensini score ((3=0.132, p=0.007).CONCLUSION:PD was greater in patients with CAD than in controls and it was associated with CAD severity.
Aim: Confirmatory factor analysis (CFA) and Rasch Analysis are commonly used methods to examine the structure of the psychological scales. In this study, it is aimed to evaluate the factor structure Turkish version of the State-Trait Anxiety Inventory by using statistics based on the Rasch model and CFA. Methods: The State-Trait Anxiety Inventory (STAI) was used for the analysis. Of the study group, 186 (46.5%) were male and 214 (53.5%) were female. Unidimensionality was investigated using a Rasch-based principal component analysis (PCA) of the residuals, chi-square tests, item fit statistics, and other statistics. CFA has also been applied to test the hypothesis of a one-factor solution.Results: The item-trait interaction chi-square statistic was 342.344 for the state scale (p<0.001) and 381.247 for the trait scale (p<0.001). For the state scale, 16.00% of the t-tests for the PCA were significant at the 5% level, while 19.50% were significant for the trait scale. The fit residuals of items 4, 8, and 18 on the state scale were over the +2.5 threshold, while the fit residuals of items 23, 24, and 34 on the trait scale were above the +2.5 threshold. Similarly, the scale structure evaluated by CFA was conditioned to be inadequate goodness-of-fit. Conclusion: This study found that neither the trait nor the state scale of the STAI met the unidimensionality assumption. Consequently, both the Rasch analysis and CFA have been verified as succeeding tools in assessing the scale sub-dimensions and determining whether the response items can be utilized for a total scale score.
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