Diagnosis of Coronary In-Stent Restenosis Using 256 Slice Multidetector Row Spiral Computed Tomography

author.DisplayName 1 author.DisplayName 1 author.DisplayName 2 author.DisplayName 1 author.DisplayName 2 author.DisplayName 1 author.DisplayName 2 author.DisplayName 1
1Cardiovascular Medicine, Lady Davis Carmel Medical Center, Haifa, Israel
2Radiology, Lady Davis Carmel Medical Center, Haifa, Israel

Background: 256-slice multi detector coronary CT angiography (MDCT) has high diagnostic accuracy for detection of native coronary artery stenosis. However, whether new generation CT scanners allow more accurate diagnosis of coronary in-stent restenosis (ISR) is unknown. We evaluated the performance characteristics of 256 slice MDCT for diagnosing ISR.

Methods: The study included 72 symptomatic patients (age: 63±11 years, 74% males) with 111 coronary stents, who underwent both 256-slice MDCT and invasive coronary angiography (ICA) within 3 months. Performance characteristics of MDCT for diagnosing ISR (>50% stenosis) were evaluated using ICA as the reference standard.

Results: ISR was found on ICA in 24 stents (22% of 111) (in 23 patients). 23/24 restenotic stents were correctly identified by MDCT. Two additional stents were misclassified as stenosed by MDCT (false positive). Hence, on a stent based analysis, the sensitivity and specificity of MDCT to diagnose ISR was 96% and 98%, respectively. Both sensitivity and specificity on patient based analysis were 96%. Overall, the negative predictive value of MDCT for ISR was very high (table).

Conclusions: New generation 256-slice MDCT showed high accuracy for diagnosing ISR. Although MDCT may not replace ICA, the high diagnostic accuracy and especially the high negative predictive value imply clinical usefulness for exclusion of ISR.

Table: Performance characteristics of 256-slice MDCT for diagnosing in-stent restenosis

Parameter

Patient-based analysis (N=72) (%, 95% CI)

Stent-based analysis (N=111)(%, 95% CI)
Sensitivity 96 (88-100) 96 (88-100)
Specificity 96 (90-100) 98 (94-100)
Positive predictive value 92 (81-100) 92 (82-100)
Negative predictive value 98 (94-100) 99 (97-100)
Predictive accuracy 96 (91-100) 97 (94-100)








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