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.

scholarly journals Accuracy of MSCT Coronary Angiography with 64 Row CT Scanner—Facing the Facts

2010 ◽  
Vol 4 ◽  
pp. CMC.S3864 ◽  
Author(s):  
M. Wehrschuetz ◽  
E. Wehrschuetz ◽  
H. Schuchlenz ◽  
G. Schaffler
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Angiography ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Diagnostic Potential ◽  
Coronary Artery Stenoses ◽  
Artery Disease ◽  
Msct Angiography

Improvements in multislice computed tomography (MSCT) angiography of the coronary vessels have enabled the minimally invasive detection of coronary artery stenoses, while quantitative coronary angiography (QCA) is the accepted reference standard for evaluation thereof. Sixteen-slice MSCT showed promising diagnostic accuracy in detecting coronary artery stenoses haemodynamically and the subsequent introduction of 64-slice scanners promised excellent and fast results for coronary artery studies. This prompted us to evaluate the diagnostic accuracy, sensitivity, specificity, and the negative und positive predictive value of 64-slice MSCT in the detection of haemodynamically significant coronary artery stenoses. Thirty-seven consecutive subjects with suspected coronary artery disease were evaluated with MSCT angiography and the results compared with QCA. All vessels were considered for the assessment of significant coronary artery stenosis (diameter reduction ≥ 50%). Thirteen patients (35%) were identified as having significant coronary artery stenoses on QCA with 6.3% (35/555) affected segments. None of the coronary segments were excluded from analysis. Overall sensitivity for classifying stenoses of 64-slice MSCT was 69%, specificity was 92%, positive predictive value was 38% and negative predictive value was 98%. The interobserver variability for detection of significant lesions had a κ-value of 0.43. Sixty-four-slice MSCT offers the diagnostic potential to detect coronary artery disease, to quantify haemodynamically significant coronary artery stenoses and to avoid unnecessary invasive coronary artery examinations.

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

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 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.

scholarly journals Diagnostic performance of an acoustic-based system for coronary artery disease risk stratification

Heart ◽  
2017 ◽  
Vol 104 (11) ◽  
pp. 928-935 ◽  
Author(s):  
Simon Winther ◽  
Louise Nissen ◽  
Samuel Emil Schmidt ◽  
Jelmer Sybren Westra ◽  
Laust Dupont Rasmussen ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Stratification ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Disease Risk ◽  
Fractional Flow ◽  
Clinical Risk ◽  
Flow Reserve ◽  
Artery Disease

ObjectiveDiagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intracoronary turbulence due to obstructive coronary disease could potentially change this. The aim of this study was thus to test the diagnostic accuracy of a new portable acoustic device for detection of CAD.MethodsWe included 1675 patients consecutively with low to intermediate likelihood of CAD who had been referred for cardiac CT angiography. If significant obstruction was suspected in any coronary segment, patients were referred to invasive angiography and fractional flow reserve (FFR) assessment. Heart sound analysis was performed in all patients. A predefined acoustic CAD-score algorithm was evaluated; subsequently, we developed and validated an updated CAD-score algorithm that included both acoustic features and clinical risk factors. Low risk is indicated by a CAD-score value ≤20.ResultsHaemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the predefined CAD-score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated CAD-score value ≤20. At this cut-off, sensitivity was 81% (95% CI 73% to 87%), specificity 53% (95% CI 50% to 56%), positive predictive value 16% (95% CI 13% to 18%) and negative predictive value 96% (95% CI 95% to 98%) for diagnosing haemodynamically significant CAD.ConclusionSound-based detection of CAD enables risk stratification superior to clinical risk scores. With a negative predictive value of 96%, this new acoustic rule-out system could potentially supplement clinical assessment to guide decisions on the need for further diagnostic investigation.Trial registration numberClinicalTrials.gov identifier NCT02264717; Results.

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