Two-Stent Strategy for Bifurcation Lesions in Percutaneous Transluminal Coronary Angioplasty: Real-World Evidence

Background: Bifurcation lesions pose a high risk for adverse events after percutaneous coronary intervention (PCI). Evidence supporting the benefits of the two-stent strategy (2SS) for treating coronary bifurcation lesions in India is limited. This study aimed to evaluate the clinical outcomes of various 2SSs for percutaneous transluminal coronary angioplasty for bifurcation lesions in India. Materials and Methods: This retrospective, observational, multicentric, real-world study included 64 patients over 8 years. Data on demographics, medical history, PCI procedures, and outcomes were recorded. Descriptive statistics were computed using the SPSS software. Results: Patients (n = 64) had an average age of 65.3 ± 11.1 years, with 78.1% males. Acute coronary syndrome was reported in 18.8%, chronic stable angina in 40.6%, and unstable angina in 34.4% of participants. Two-vessel disease was observed in 98.4% of patients, and 99.4% had true bifurcation lesions. The commonly involved vessels were the left anterior descending artery (50%), left circumflex coronary artery (34.4%), and first diagonal artery (43.8%). Mean percent diameter stenosis was 87.2% ± 10.1%. The mean number of stents used was 2.00 ± 0.34. The 2SS techniques included the T and small protrusion (TAP) (39.1%), double kissing (DK) crush (18.8%), and the culotte techniques (14.1%). Procedural and angiographic success rate was 92.18%. Major adverse cardiovascular events at 1-year follow-up occurred in 7.8% of cases. Conclusion: The 2SS for bifurcation lesions showed favorable in-hospital and follow-up outcomes. Findings can serve as a resource for bifurcation angioplasty in India. Larger real-world studies with robust methodology are needed to validate these results.

Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor younger.There is a need to develop new technologies andmethods to deal with this disease.Many researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a challenge.Several different types of angiograms are adopted for CAD diagnosis.This paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art results.We have collected the 2D X-ray images from the hospital,and the proposed model has been applied to them.Image augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s size.Also,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high accuracy.As the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.