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Correlation of Computed Tomographic Angiography in Patients Undergoing Coronary Artery Bypass Grafting and Effect of Standardized Rehabilitation Nursing

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This study aimed to explore the clinical application of computed tomographic angiography (CTA) and standardized rehabilitation nursing in patients with coronary artery bypass grafting (CABG). CTA image was segmented by… Click to show full abstract

This study aimed to explore the clinical application of computed tomographic angiography (CTA) and standardized rehabilitation nursing in patients with coronary artery bypass grafting (CABG). CTA image was segmented by reconstruction algorithm and finally assembled into a whole image. Three-dimensional reconstruction of the coronary artery was then performed. 52 patients were selected as the research objects, and standardized rehabilitation nursing was carried out after surgery to analyze the vascular lesion rate of arterial bridge and venous bridge and compare their nursing satisfaction. The results showed that the CTA images were clearer after reconstruction. The number of male patients with venous and arterial lesions was significantly higher than that of the female patients, and the difference between the two groups was obvious (P < 0.05). The number of patients combining with risk factors and LIMA bridge vessels was 0 in grade 3 patients, accounted for the highest proportion (16.67%) in the grade 1 patients, and was 2 in the grade 2 patients (accounting for 4.17%). The satisfaction of patients who received standardized nursing was 97.25%, that of patients who received conventional nursing was 83.42%, and the difference was significant (P < 0.05). In conclusion, CTA images of patients' cardiac vessels can be clearly seen by using a block image reconstruction algorithm, which can realize clinical personalized treatment. In addition, patients were more satisfied with standardized rehabilitation nursing.

Keywords: coronary artery; standardized rehabilitation; computed tomographic; rehabilitation nursing

Journal Title: Computational Intelligence and Neuroscience
Year Published: 2022

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