Non-invasive assessment of atherosclerotic coronary lesion length using multidetector computed tomography angiography: comparison to quantitative coronary angiography

Multidetector computed tomography angiography (CTA) provides information on plaque extent and stenosis in the coronary wall. More accurate lesion assessment may be feasible with CTA as compared to invasive coronary angiography (ICA). Accordingly, lesion length assessment was compared between ICA and CTA in patients referred for CTA who underwent subsequent percutaneous coronary intervention (PCI). 89 patients clinically referred for CTA were subsequently referred for ICA and PCI. On CTA, lesion length was measured from the proximal to the distal shoulder of the plaque. Quantitative coronary angiography (QCA) was performed to analyze lesion length. Stent length was recorded for each lesion. In total, 119 lesions were retrospectively identified. Mean lesion length on CTA was 21.4 ± 8.4 mm and on QCA 12.6 ± 6.1 mm. Mean stent length deployed was 17.4 ± 5.3 mm. Lesion length on CTA was significantly longer than on QCA (difference 8.8 ± 6.7 mm, P < 0.001). Moreover, lesion length visualized on CTA was also significantly longer than mean stent length (CTA lesion length-stent length was 4.2 ± 8.7 mm, P < 0.001). Lesion length assessed by CTA is longer than that assessed by ICA. Possibly, CTA provides more accurate lesion length assessment than ICA and may facilitate improved guidance of percutaneous treatment of coronary lesions

The novel proteomic signature for cardiac allograft vasculopathy

Aims Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV. Methods and results We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75–0.91, P \u3c 0.001] and 0.71 (95% CI: 0.60–0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07–1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5–15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7–119.5, P \u3c 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility. Conclusions This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets