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The Value of Multi-slice Computed Tomography Compared to Conventional Coronary Angiography for Detecting In-stent Restenosis.

ABSTRACT: Diagnostic value of multi-slice computed tomography (MSCT) for detecting in-stent restenosis in comparison with conventional coronary angiography remained uncertain. The present study aimed to determine the value of MSCT for detecting in-stent restenosis.This historical cohort study was included 226 patients with the history of percutaneous coronary intervention from 2000 to 2014 that referred to MSCT Unit at Alzahra Heart Center in Isfahan. The subjects were followed-up by telephone with regard to performing coronary angiography up to 3 months after MSCT and their status about cardiac events.Among all participants, 63 stents (27.9%) underwent coronary angiography up to 3 months after MSCT that 2 stents in left circumflex artery (LCX) assessment, 2 in left anterior descending (LAD) assessments and none in right coronary artery (RCA) assessment were uninterpretable. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MSCT was 92.9%, 66.6%. 92.9%, 66.6%, and 88.2%, respectively for detection of occlusion in LCX stents, 100%, 100%, 100%, 100%, and 100%, respectively for detection of occlusion in LAD stents, and 80.0%, 0.0%, 80.0%, 0.0%, and 66.7%, respectively for detection of occlusion in RCA stents. Overall, MSCT had sensitivity of 93.8%, specificity of 70.0%, PPV of 93.8%, NPV of 70.0%, and accuracy 89.7% for detection of coronary stent restenosis.MSCT has high diagnostic value for detecting in-stent restenosis. Diagnostic accuracy of MSCT for detecting stent restenosis is considerably different between the coronary arteries with the highest diagnostic values for LAD and the lowest diagnostic values for RCA.

SUBMITTER: Karami M

PROVIDER: S-EPMC5549549 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:This study was designed to investigate the long non-coding RNA (lncRNA)-metastasis associated lung adenocarcinoma transcript 1 (MALAT1) expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis. Ninety-five patients with coronary heart disease who came to our hospital for treatment and underwent stent implantation were selected as a research group (RG), and 95 volunteers undergoing physical examination who did not suffer from coronary heart disease during the same period were selected as a control group (CG). MALAT1 of subjects in both groups before and after treatment were detected by RT-qPCR, and N-terminal pro-brain natriuretic peptide (NT-proBNP), high sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), and creatine kinase isoenzyme (CK-MB) of them in the RG before treatment were detected. The level was evaluated and detected, and its correlation with MALAT1 was analyzed. Then, the predictive value of MALAT1 for in-stent restenosis in patients with coronary heart disease was analyzed. MALAT1 expression in patients with coronary heart disease was higher than that of normal subjects (P<0.05); after treatment, the expression levels of MALAT1, NT-proBNP, hs-CRP, LDH, and CK-MB in the serum of patients were significantly lower than those before treatment (P<0.05); MALAT1 expression was positively correlated with the expression levels of NT-proBNP, hs-CRP, LDH, and CK-MB (P<0.05). Receiver operating characteristic of MALAT1 for predicting in-stent restenosis in patients with coronary heart disease was over 0.8; the number of lesions, MALAT1, diabetes, NT-proBNP and hs-CRP were independent risk factors for in-stent restenosis. MALAT1 is highly expressed in the serum of patients with coronary heart disease, and it has high value in its diagnosis and the prediction of in-stent restenosis. It is also an independent risk factor for in-stent restenosis in patients with coronary heart disease.

Project description:BackgroundAccurate interpretation of coronary computed tomography angiography (CCTA) is a labor-intensive and expertise-driven endeavor, as inexperienced readers may inadvertently overestimate stenosis severity. Recent artificial intelligence (AI) advances in medical imaging present compelling prospects for auxiliary diagnostic tools in CCTA. This study aimed to externally validate an AI-assisted analysis system capable of rapidly evaluating stenosis severity, exploring its potential integration into routine clinical workflows.MethodsThis multicenter study consisted of an internal and external cohort of patients who underwent CCTA scans between April 2017 and February 2023. CCTA scans were evaluated using Coronary Artery Disease Reporting and Data System (CAD-RADS) scores to determine stenosis severity, while ground-truth stents were manually annotated by expert readers. The InferRead CT Heart (version 1.6; Infervision Medical Technology Co., Ltd., Beijing, China), which incorporates AI-assisted coronary artery stenosis quantification and automatic stent segmentation, was employed for CCTA scan analysis. AI-based stenosis assessment performance was determined using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), while the AI-based stent segmentation overlap was assessed using the Dice similarity coefficient (DSC).ResultsFor ≥50% stenosis diagnoses, the AI system attained per-patient sensitivity, specificity, PPV, and NPV surpassing 90.0% for the internal dataset; for the external dataset, the per-patient values were 88.0% [95% confidence interval (CI): 81.0-94.4%], 94.5% (95% CI: 90.7-97.6%), 90.0% (95% CI: 83.3-95.6%), and 93.4% (95% CI: 89.2-96.8%), respectively. For ≥70% stenosis diagnoses, the per-patient values on the internal dataset were 94.2% (95% CI: 89.2-98.1%), 95.8% (95% CI: 94.1-97.4%), 80.8% (95% CI: 73.5-87.7%), and 98.9% (95% CI: 97.9-99.6%), respectively; for the external dataset, the per-patient values were 91.9% (95% CI: 82.6-100.0%), 97.3% (95% CI: 94.9-99.1%), 85.0% (95% CI: 72.5-94.6%), and 98.6% (95% CI: 96.8-100.0%), respectively. Regarding CAD-RADS categorization, the Cohen kappa was 0.75 and 0.81 for the internal per-patient and per-vessel basis, respectively, and 0.72 and 0.76 for the external per-patient and per-vessel basis, respectively. The DSC for stent segmentation was 0.96±0.06.ConclusionsThe AI-assisted analysis system for CCTA interpretation exhibited exceptional proficiency in stenosis quantification and stent segmentation, indicating that AI holds considerable potential in advancing CCTA postprocessing techniques.

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The Value of Multi-slice Computed Tomography Compared to Conventional Coronary Angiography for Detecting In-stent Restenosis.

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