Impact of Lesion Length on Functional Significance in Intermediate Coronary Lesions
International Journal of Cardiovascular Practice,
Vol. 2 No. 3 (2017),
30 July 2017
Introduction: The present study aimed at assessing the role of lesion length in predicting Fractional Flow Reserve (FFR) value for physiological evaluation of intermediate coronary lesions.
Methods: In the current study, 68 patients with 83 coronary lesions were enrolled. All of the patients in this study underwent routine coronary angiography, according to appropriate indications. To evaluate physiologically significant intermediate coronary stenosis (defined between 40% and 70% on visual estimation), the Fractional Flow Reserve (FFR) study was performed and the Quantitative Coronary Angiography (QCA) data were also assessed for measurement of lesion length. The correlation between QCA data and FFR values was also examined.
Results: Eighty-three lesions were evaluated from 68 patients. Stenosis was considered physiologically significant when FFR was lower than 0.75. The FFR was significant in twelve lesions (14.5%). There was a negative correlation between FFR value and lesion length (r = -0.294 and P = 0.013). Moreover, lesion length in physiologically significant FFR group (21.07 ± 6.9) was greater than that of the non-significant FFR group (15.23 ± 6.5) (P value < 0.05). Furthermore, the correlation between QCA data and FFR values was also investigated, yet, there was only a positive correlation between FFR and Minimum Luminal Diameter (MLD) values (r = 0.248 and P value = 0.04). The Receiver Operating Characteristic (ROC) curve analysis for predicting the significant FFR value demonstrated that a lesion length greater than 17.5 mm was the best cut-off point for prediction of the significant FFR value with acceptable sensitivity and specificity of 83.3% and 68.8%, respectively.
Conclusions: There is a negative correlation between lesion length and FFR value in intermediate coronary lesions. In addition, a lesion length greater than 17.5 mm is the best cut- off point for prediction of significant FFR values.
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