Prevalence and patterns of coronary artery anomalies in 28,800 adult patients undergoing angiography in a large tertiary care centre in India

Coronary artery anomalies (CAAs) are a diverse group of disorders with varied clinical presentation and pathophysiological mechanisms. A majority of these anomalies are asymptomatic and often an incidental finding on coronary angiogram or autopsy. This retrospective study included 28,800 patients who underwent coronary angiography from 2016 to 2020. The coronary angiograms were reviewed by two independent reviewers and CAAs were documented. CAAs were classified into (a) anomalies of coronary artery connection, (b) anomalies of intrinsic coronary arterial anatomy and (c) anomalies of myocardial/coronary artery interaction as proposed by the European Society of Cardiology. Of the 28,800 coronary angiograms, CAAs were present in 4.12% with anomalies in the left coronary artery (LCA) being most common. Anomalies of coronary artery connection were most common (48.48%) followed by anomalies of myocardial/coronary artery interaction (34.49%) and anomalies of intrinsic coronary artery anatomy (17.03%). Among anomalies of coronary artery connection, absent left main trunk or split LCA with separate origins of left anterior descending coronary artery and left circumflex coronary artery from the left coronary sinus of Valsalva (22.59%) was most common. An intramural course or “myocardial bridge” had an incidence of 1.16% while incidence of coronary artery fistulae (CAF) was 0.115%.

Segmentation and Automatic Identification of Vasculature in Coronary Angiograms

Coronary angiography is the “gold standard” for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method.

Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is O(249) computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of 0.89 and 0.88 and Jaccard Index of 0.80 and 0.79, respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ≈0.02 s, which made it highly suitable to be used in clinical practice.

Prospective evaluation of donor-derived cell-free DNA as a potential biomarker for cardiac allograft vasculopathy

Background Cardiac allograft vasculopathy (CAV) remains a major cause of morbidity and mortality among long-term heart transplant (HT) recipients. There is clearly an unmet need for a noninvasive biomarker of CAV that could obviate the need to perform surveillance coronary angiograms in these patients. Purpose Our aim was to evaluate the performance of Donor-derived Cell Free DNA (dd-cfDNA) as a biomarker of CAV. Methods We prospectively measured dd-cfDNA levels in all consecutive asymptomatic patients undergoing surveillance coronary angiography >1 year after HT at a single center, between Jan 2019 and Jan 2021. Endpoints included the association between dd-cfDNA levels and the presence CAV, according to ISHLT 2010 classification. Patients with history of acute cellular rejection ≥1R or antibody mediated rejection in the previous 6 months were excluded. Results We included 94 HT recipients, median age 57 years (IQR 50–67), 67% men, a median of 10.9 years after transplant. Coronary angiogram revealed CAV0, CAV1, CAV2 and CAV3 in 61%, 19%, 14% and 6% of patients, respectively. Median dd-cfDNA values for each CAV group were: CAV0 0.92% (IQR 0.46–2.0), CAV1 1.4% (0.38–2.8), CAV2 0.17% (0.07–0.52) and CAV3 0.24% (0.057–0.87); p=0.0535. Figure 1 summarizes baseline characteristics of the cohort and results. Comparison of dd-cfDNA levels in patients with CAV0 and CAV1–2-3 did not show significant differences (0.92%, IQR 0.46–2.0 vs 0.46%, IQR 0.075–1.5, p=0.059) (Figure 2A), nor did the comparison between patients with stable CAV (no new coronary lesions since previous angiogram, n=77) and progressive CAV (patients with new coronary stenoses, n=17); median dd-cfDNA values were 0.735% (IQR 0.195–2.0) vs 0.9% (IQR 0.12–1.8), p=0.76 (Figure 2B). A subanalysis according to time after HT was also found non-significant: less than 5 years (p=0.95), 5 to 10 years (p=0.14) and more than 10 years after HT (p=0.16) (Figure 2C). The AUC ROC curve for the diagnosis of CAV revealed the lack of ability to predict the presence of any degree of CAV (AUC ROC = 0.38). Conclusion In our experience, dd-cfDNA did not perform as a useful biomarker to avoid surveillance coronary angiograms for CAV diagnosis. FUNDunding Acknowledgement Type of funding sources: Other. Main funding source(s): Sociedad Madrileña de Trasplantes