Audit analysing the outcome of patients referred to the rapid-access chest pain clinic and assessing the positive predictive value of the rapid-access chest pain clinic of patients with coronary artery disease

Introduction The 2016 NICE clinical guideline 95 (CG95) demoted functional imaging to a second-line test following computed tomography coronary angiography (CTCA). Many cardiac CT services in the UK require substantial investment and growth to implement this. Chest pain services like ours are likely to continue to use stress testing for the foreseeable future. We share service evaluation data from our department to show that a negative stress echocardiogram can continue to be used for chest pain assessment. Methods 1815 patients were referred to rapid access chest pain clinic (RACPC) between June 2013 and March 2015. 802 patients had stress echocardiography as the initial investigation. 446 patients had normal resting left ventricular (LV) systolic function and a negative stress echocardiogram. At least 24 months after discharge, a survey was carried out to detect major adverse cardiovascular events (MACE) (cardiac death, myocardial infarction, admission to hospital for heart failure or angina, coronary artery disease at angiography, revascularisation by angioplasty or coronary artery bypass grafting) within 2 years. Results Overall, 351 patients were successfully followed up. The mean Diamond-Forrester (D-F) score and QRISK2 suggested a high pre-test probability (PTP) of coronary artery disease (CAD). There were nine deaths (eight non-cardiac deaths and one cardiac death). MACE occurred in four patients with a mean time of 17.5 months (11.6–23.7 months). The annual event rate was 0.6%. Conclusion A negative stress echocardiogram can reliably reassure patients and clinicians even in high PTP populations with suspected stable angina. It can continue to be used to assess stable chest pain post CG95.

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

Clinical Medicine Insights Cardiology ◽

10.4137/cmc.s3864 ◽

2010 ◽

Vol 4 ◽

pp. CMC.S3864 ◽

Cited By ~ 3

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.

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100 Predictive Value of Carotid Intima Media Thickness in the Diagnosis of Coronary Artery Disease in Emergency Department Patients With Chest Pain: A Preliminary Report

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2011.06.126 ◽

2011 ◽

Vol 58 (4) ◽

pp. S211

Author(s):

A.B. Cheng ◽

M. Huis in't Veld ◽

M.F. Boomkamp ◽

A.C. Matsuura ◽

F.M. Jason ◽

...

Keyword(s):

Coronary Artery Disease ◽

Emergency Department ◽

Chest Pain ◽

Coronary Artery ◽

Predictive Value ◽

Preliminary Report ◽

Intima Media Thickness ◽

Carotid Intima Media Thickness ◽

Artery Disease ◽

Emergency Department Patients

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

Circulation ◽

10.1161/circ.118.suppl_18.s_106-a ◽

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.

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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 ◽

10.1161/circ.132.suppl_3.15233 ◽

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

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

British Journal of Radiology ◽

10.1259/bjr.20191028 ◽

2020 ◽

Vol 93 (1113) ◽

pp. 20191028 ◽

Cited By ~ 2

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.

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

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.614204 ◽

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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