scholarly journals Accuracy and negative predictive value of adenosine contrast echocardiography are higher in patients with single vessel than multivessel coronary artery disease

2002 ◽  
Vol 39 ◽  
pp. 362-363
Author(s):  
Alvaro Moraes ◽  
Caio Medeiros ◽  
Fernando Morcerf ◽  
Marcia Carrinho ◽  
Marcia Castier ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Contrast Echocardiography ◽  
Multivessel Coronary Artery Disease ◽  
Single Vessel ◽  
Artery Disease

Abstract 5803: Optimal Threshold for Multi Detector Computer Tomography to Detect Physiologically Significant Stenosis in Patients with Ischemic Heart Disease

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Takashi Yamano ◽  
Atsushi Tanaka ◽  
Takashi Tanimoto ◽  
Shigeho Takarada ◽  
Hiroki Kitabata ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Angiography ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Optimal Threshold ◽  
Fractional Flow ◽  
Significant Stenosis ◽  
Artery Disease

PURPOSE: Sixty-four multi detector computed tomography angiography (64-MDCT) has emerged as a rapidly developing method for the noninvasive detection of coronary artery disease with high negative predictive value and relatively low positive predictive value, especially in patients with intermediate-severity coronary artery disease (ISCAD). There are, however, few studies regarding with optimal threshold for detection of physiologically significant stenosis in 64-MDCT. The purpose of this study was to investigate the optimal threshold for 64-MDCT to detect physiologically significant stenosis using fractional flow reserve of the myocardium (FFRmyo) in patients with ISCAD. METHODS: We enrolled single lesions detected by 64-MDCT of 64 ISCAD patients (age, 68.3 +/− 10.2 years; 78% male). FFRmyo </= 0.75 measured by a 0.014-inch pressure wire was used as the gold standard for presence of physiologically significant stenosis. The area stenosis (%AS) in 64-MDCT were compared with the results of FFRmyo and percent diameter stenosis (%DS) in quantitative coronary angiography (QCA) during elective coronary angiography. Using receiver operating characteristic (ROC) analysis, the optimum threshold for percent area stenosis (%AS) in 64-MDCT was determined in the prediction of FFRmyo </= 0.75. RESULTS: There was an inverse correlation between %AS in 64-MDCT and FFRmyo (65 +/− 20 % and 0.71 +/− 0.16, respectively; r = −0.67; p < 0.01). Furthermore, there was a positive correlation between %AS in 64-MDCT and %DS in QCA (65 +/− 20 % and 63 +/− 19 %, respectively; r = 0.69; p < 0.01). Using a cutoff of 62 %AS in 64-MDCT, ROC curve analysis shows 79 % sensitivity, 85 % specificity, 82% positive predictive value, 83% negative predictive value and 83% accuracy for detecting physiologically significant stenosis. CONCLUSION: > 62 %AS in 64-MDCT could predict the physiologically significant coronary stenosis in patients with ISCAD. Applying an alternative threshold to detect physiologically significant stenosis might contribute to improve the diagnostic accuracy for 64-MDCT in patients with ISCAD.

Abstract 15233: The Lesion Length Gives a Better Effect on the Fractional Flow Reserve Than the Minimal Luminal Area in the Intermediate Coronary Artery Lesion

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Yoon Juneyoung ◽  
Xiongjie Jin ◽  
Kyong-Woo Seo ◽  
Jin-sun Park ◽  
Hyoung-Mo Yang ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Lesion Length ◽  
Visual Estimation ◽  
Luminal Area ◽  
Artery Disease

Introduction: The pressure gradient of the circulation fluid in a stenosis area depends on minimal luminal area (MLA) of the stenosis, lesion length (LL), and the fluid velocity. However, the correlation of the LL and the MLA; the cutoff values are uncertain. Hypothesis: LL and MLA differently influences the FFR. Methods: We studied 117 patients with intermediate coronary artery disease who underwent FFR and IVUS measurement out of 302 patients in FAVOR study. This study was a prospective, 1:1 randomized, open label multicenter trial to demonstrate the clinical outcomes between FFR and IVUS-guided PCI. Inclusion criteria were as follows: 1)Angina or documented silent ischemia 2) De novo intermediate coronary artery disease (30-70% diameter stenosis) by visual estimation, 3) Reference vessel diameter ≥ 3.0mm by visual estimation. We excluded left main disease, MI, EF< 40%, and graft vessel. There were no significant differences in baseline clinical characteristics. The mean values are the QCA (54.3±14.0 %), MLA (3.6±1.4 mm2) and LL (20.6±1.4mm), respectively. We were performed the path analysis using AMOS 18, and estimated the ROC curve in SPSS 18. Results: Standardized estimates were the LL -0.47,QCA -0.28 and MLA -0.21 (R2=0.594, p<0.000) in path analysis. The model is recursive and statistically significant. The FFR was ≤0.80 in 47 lesions (31%). The optimal LL for an FFR of ≤0.80 was 15.8mm (90% sensitivity, 50% specificity, 44% positive predictive value, 87% negative predictive value, area under the curve: 0.75, 95% CI: 0.66 to 0.85; p < 0.001) and MLA 3.9mm (sensitivity 86%, specificity 59%, 35% positive predictive value , 94% negative predictive value, area under the curve: 0.78, 95% CI: 0.67 to 0.85; p < 0.001) Conclusions: The lesion length influenced more the FFR than MLA. The lesion length ≥ 15.8mm and MLA ≤ 3.9mm are risk zones, which need to be confirm the functional status with FFR because of the low positive predictive value

Abstract 587: Absence of Coronary Calcium on Non-contrast Enhanced, Non ECG-gated CT Correlates with Non-obstructive Coronary Artery Disease. A Retrospective Data Analysis of Liver Transplant Recipients

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Hilda M Gonzalez-Bonilla ◽  
Akanksha Thakkar ◽  
Antonio Duran ◽  
Alpana Senapati ◽  
Nakul Gupta ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Liver Transplant ◽  
Negative Predictive Value ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Chest Ct ◽  
Transplant Recipients ◽  
Artery Disease ◽  
Liver Transplant Recipients

Background: Coronary angiography (CAG) remains the gold standard to diagnose coronary artery disease (CAD). However, it is associated with multiple risks and its utility is not well defined in the liver transplant population. Alternatives to evaluate for CAD such as coronary artery calcium score (CACS) are being increasingly investigated. Hypothesis: To determine if the absence of coronary arterial calcium (CACS=0) on non-contrast, non-ECG gated chest CT scan can exclude obstructive CAD in liver transplant patients. Methods: We performed a retrospective analysis of data collected from liver transplant recipients. We included patients who had a CT chest without contrast and CAG less than one year apart. Agatston score was derived from non-IV contrast, non-ECG gated chest CT’s utilizing the syngo.via platform (Siemens Healthcare). CACS was compared against CAG. Patients with coronary stents were excluded. We determined NPV, PPV, sensitivity and specificity of using CACS = 0 as predictor of the absence of obstructive CAD. Results: Mean age at date of transplant was 59.03 and males accounted for 68.8% of our population. The negative predictive value for CACS=0 as a predictor of non-obstructive CAD was 100%. Positive predictive value for CACS≥1 was 6.8%. Sensitivity and specificity for the correlation between CACS and CAD were 100% and 33% respectively (Figure 1). CACS was stratified into four subgroups based severity, and we found that all patients with obstructive CAD had scores >400 (Figure 2). Conclusion: The absence of coronary arterial calcium (CACS=0) on non-contrast, non ECG gated chest CT has a high negative predictive value and can exclude the presence of obstructive CAD.

scholarly journals Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease

2020 ◽  
Vol 93 (1113) ◽  
pp. 20191028 ◽  
Author(s):  
Meng Chen ◽  
Ximing Wang ◽  
Guangyu Hao ◽  
Xujie Cheng ◽  
Chune Ma ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristics ◽  
Stenosis Detection ◽  
Artery Disease

Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

scholarly journals Focus on Stable Angina from the Perspective of Affected Coronary Artery Number and Structural and Functional Myocardial Changes

2019 ◽  
Vol 26 (2) ◽  
Author(s):  
Iryna Kupnovytska ◽  
Nelia Romanyshyn
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricular Ejection Fraction ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Multivessel Coronary Artery Disease ◽  
Single Vessel ◽  
Vessel Coronary Artery Disease ◽  
Artery Disease ◽  
Vessel Coronary Artery

         The objective of the research was to study myocardial hemodynamics and contractility, as well as N-terminal pro-brain natriuretic peptide secretion in the patients with chronic coronary artery disease depending on affected coronary artery number according to coronary angiography.          Materials and methods. The study included 62 patients with chronic coronary artery disease, heart failure with preserved left ventricular ejection fraction. Among the examined patients, males prevailed – 52 (83.9%) individuals. The average age was 61.2±1.2 years. The control group included 15 apparently healthy individuals with preserved gender and age proportions. The patients were randomized by the number of the affected coronary arteries and divided into 2 subgroups according to the results of coronary angiography. Subgroup I included 16 (25.8%) patients with single-vessel coronary artery disease; subgroup II comprised 46 (74.2%) patients with multivessel coronary artery disease.           Results and discussion. According to Holter monitoring, average and maximum heart rate, extrasystoles and episodes of ST-segment depression/elevation were more often found in the patients with multivessel coronary artery disease (p<0.05). According to echocardioscopy, in the patients with coronary artery disease regardless of affected coronary artery number, hemodynamic indicators were higher as compared to healthy individuals (p<0.001), while left ventricular ejection fraction was lower in the patients with multivessel coronary artery disease (р<0.001). Serum level of N-terminal pro-brain natriuretic peptide exceeded reference value in both single-vessel coronary artery disease and multivessel coronary artery disease (р<0.001); however, the secretion of this peptide increased in multivessel coronary artery disease (р<0.05). There was observed a strong inverse correlation between left ventricular ejection fraction and N-terminal pro-brain natriuretic peptide in the patients with multivessel coronary artery disease and a moderate correlation in the patients with single-vessel coronary artery disease.          Conclusions. The nature and severity of coronary artery disease clinical course are associated with the number of the coronary arteries affected by atherosclerotic plaques. In multivessel coronary artery disease, according to the results of clinical, functional and laboratory studies, there was observed persistent progression of coronary artery disease and, consequently, chronic heart failure that is the reason for the improvement of schemata for successful treatment of the disease.

scholarly journals Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Wang ◽  
Yue Zhao ◽  
Bingyu Jin ◽  
Xuedong Gan ◽  
Bin Liang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Random Forest ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Validation Cohort ◽  
Random Forest Algorithm ◽  
Artery Disease

Early identification of coronary artery disease (CAD) can prevent the progress of CAD and effectually lower the mortality rate, so we intended to construct and validate a machine learning model to predict the risk of CAD based on conventional risk factors and lab test data. There were 3,112 CAD patients and 3,182 controls enrolled from three centers in China. We compared the baseline and clinical characteristics between two groups. Then, Random Forest algorithm was used to construct a model to predict CAD and the model was assessed by receiver operating characteristic (ROC) curve. In the development cohort, the Random Forest model showed a good AUC 0.948 (95%CI: 0.941–0.954) to identify CAD patients from controls, with a sensitivity of 90%, a specificity of 85.4%, a positive predictive value of 0.863 and a negative predictive value of 0.894. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value, and negative predictive value of 0.944 (95%CI: 0.934–0.955), 89.5%, 85.8%, 0.868, and 0.886 in the validation cohort 1, respectively, and 0.940 (95%CI: 0.922–0.960), 79.5%, 94.3%, 0.932, and 0.823 in the validation cohort 2, respectively. An easy-to-use tool that combined 15 indexes to assess the CAD risk was constructed and validated using Random Forest algorithm, which showed favorable predictive capability (http://45.32.120.149:3000/randomforest). Our model is extremely valuable for clinical practice, which will be helpful for the management and primary prevention of CAD patients.

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