Accuracy For Coronary Artery Disease Research Articles

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p= 9.17×10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p= 1.92×10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p= 7.58×10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p= 8.01×10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction= 2.6×10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Ultra-low radiation dose and contrast volume CT protocol and TAVI-CT score for TAVI planning and outcome.

To investigate the feasibility of an ultra-low radiation dose and contrast volume protocol using third-generation dual-source (DS) CT for transcatheter aortic valve implantation (TAVI) planning with coronary artery disease (CAD) assessment, coronary artery calcium score (CACS) and aortic valve calcium score (AVCS) quantification and to evaluate their relationship with TAVI outcome. In this retrospective study were selected 203 patients (131 males, 79.4 ± 5.4 years) underwent to TAVI and at 30- and 90-day follow-up. All patients had performed a third-generation 2 × 192-slices DSCT. The CT protocol included a non-contrast and a contrast high-pitch aortic acquisition for TAVI planning and CAD assessment. Semi-qualitative and quantitative image analysis were performed; the performance in CAD assessment was compared with ICA; the relationship between AVCS and CACS and paravalvular aortic regurgitation (PAR) and major cardiovascular events (MACEs) were evaluated. Mean radiation dose were calculated. Non-parametric tests were used. Semi-qualitative image analysis was good. Contrast enhancement >500 Hounsfield unit (HU) and contrast-to-noise ratio <20 were obtained in all segments. The diagnostic accuracy in CAD was 89.0%. AVCS was significantly higher in patients with 30-day severe PAR. AVCS and CACS were higher in patients with 90-day MACE complications, respectively, 1904.5 ± 621.3 HU (p < 0.0001) and 769.2 ± 365.5 HU (p < 0.0230). Mean radiation dose was 2.8 ± 0.3 mSv. A TAVI planning ultra-low radiation dose and contrast volume protocol using third-generation DSCT provides highly diagnostic images with CAD assessment, AVCS and CACS quantification and these latter were related with TAVI outcomes. The proposed protocol using third-generation 2 × 192-slices DSCT allows with an ultra-low radiation dose and contrast volume the TAVI planning and the coronary artery assessment.

Diagnostic accuracy for coronary artery disease by computed tomography angiography as compared to conventional invasive angiography

Background: Emerging as a major cause of death and disability in the developing countries is coronary artery disease (CAD). Traditional coronary angiography has traditionally been the preferred diagnostic procedure for CAD. Non-invasive evaluation of coronary artery disease (CAD) using computed tomography (CT) coronary angiography shows promise. This observational study's goal is to assess the relative efficacy of noninvasive multi-section computed tomography (MSCT) coronary angiography vs invasive selective coronary angiography in detecting coronary stenosis.Method and patient: This observational study carried out in the Azadi teaching Hospital/Kirkuk between November 2021 to March 2022. Patients who suspected to have coronary artery disease underwent both CTCA and invasive coronary angiography in different centers in our country comparing and analyzing their results.Result: In total, 92 participants were enrolled in the trial. Patients had a mean age of 57.207.3 years, and men made up the vast majority of those who appeared (67.3 percent). Chest pain was the most common presenting complaint (94 percent). Patients exhibited a prevalence of 84% for HTN, 55% for hyperlipidaemia, 58% for diabetes, and 4% for ischemic heart disease. In addition, 50% of the patients are either current or former smokers. Half of the patients received 64-slice CT scans, nearly half had 128-slice scans, and 3.2% got 256-slice scans. CCTA's sensitivity was 100%, and its specificity was 66.7%. Positive predictive value for CCTA was 98.8 percent (meaning it correctly identified positive cases in 98.8 percent of patients) and negative predictive value was 100 percent, for a total diagnostic accuracy of 98.9 percent (which mean CCTA can exclude all negative cases and did not miss any case). When looking at individual arteries, the LAD artery was shown to be the most accurate, while the LCX artery was the least accurate (95.6 percent and 78.2 percent respectively). In terms of sensitivity, LAD performed at a 99.1% rate, while LMS performed at a 0% rate (33.3 percent). RCA had the most specificity (95.2 percent), whereas LAD had the lowest (88.8 percent) (40 percent). In order to determine what factors, influence CCTA positivity, we performed a binary logistic regression analysis for positive CCTA (negative as reference) with all variables from the studies. The results showed that IHD, lesions in the LAD artery, lesions in the LCX artery, and CT slice thickness (128 and 256 slices) all have a negative effect on CCTA (p0.05).Conclusions: When compared to traditional angiography, multi-slice computed tomography has demonstrated superior sensitivity and specificity for the identification of coronary artery disease.

The Prevalence of Frank’s Sign and the Diagnostic Accuracy in Coronary Artery Disease Among Chest Pain Patients at Lampang Hospital, Thailand

Background: One of the leading cause of death in Thailand is coronary artery disease (CAD). There are several studies that demonstrated a significant correlation between diagonal earlobe creases (DELC) or Frank’s sign and CAD, but data in Thai populations still remains unclear. Because of Frank’s sign is easily to observe by health-care professional including medical students, so it was very interesting to use to identify risk of atherosclerosis of the patients in hospital which has limitation of resources. The purpose of the study was to assess the prevalence of Frank’s sign and evaluate the diagnostic accuracy in CAD among the angina chest pain patients at Lampang Hospital. Methods: This study was a cross-sectional, observational, study. Three hundred and nine patients with angina chest pain who underwent coronary angiography (CAG) and followed up at the coronary clinic at Lampang Hospital were enrolled in the study. Both ears of the patients were inspected for Frank’s sign. The accuracy of Frank’s sign was confirmed by three cardiologists and clinical data was collected in medical record form. We excluded the patients whose earlobe could not be clearly observed, including those with keloid/ulcer on the earlobe or those with no earlobe. The correlation of Frank’s sign and CAD were analyzed by using logistic regression and receiving operative curve (ROC) curve. Results: 64.4% of the angina chest pain patients (N=199) have demonstrated for Frank’s sign, unilateral Frank’s sign in 12.3% (N=38) and bilateral Frank’s sign in 52.1% (N=161). Twenty-one patients (6.8%) were normal CAG and 288 patients (93.2%) were significant CAD, single vessel disease CAD (N=86), double vessel disease CAD (N=83) and triple vessel disease CAD (N=119). The diagnostic accuracy of the Frank’s sign revealed that 64.6% for the sensitivity and 38.1% for the specificity. Besides, the positive predictive value (PPV) was 0.935 and for negative predictive value (NPV) showed 0.073. Nevertheless, the positive likelihood ratio (LR+) was 1.032 and 0.947 for the negative likelihood ratio (LR-). Moreover, the area under the curve (AuROC) of Frank’s sign with respect to detection of CAD was 0.513 (95% Confidence Interval was 0.385-0.642, P= 0.838). Conclusion: Frank’s sign was observed in two-third of angina chest pain patients at Lampang Hospital and mostly of the patients with Frank’s sign were significant CAD. The results of this study showed that Frank’s sign was a simple, non-expensive and non-invasive method for medical student and health-care professional to identify risk of CAD.

Exercise-induced change in circulating NT-proBNP could not distinguish between patients with and without coronary artery disease: the CADENCE study

Objective. In patients with chest pain, exercise stress test has a moderate accuracy for coronary artery disease (CAD). Adding a reliable cardiac biomarker to the exercise test could potentially improve the precision of the test. We investigated circulating NT-proBNP levels before and during exercise stress test in patients with and without angiographically verified CAD. We hypothesized that NT-proBNP would give an additive diagnostic value to the exercise stress test. Methods. In patients presenting with symptoms of stable CAD, venous blood samples were taken at rest and within 5 min of termination of a maximal stress test on a bicycle ergometer. All study participants underwent coronary angiography. Significant CAD was defined as ≥75% stenosis in one or more segments of the coronary arteries. Results. Of the 297 participants, significant CAD was found in 111 (37%) patients. Resting levels of NT-proBNP were significantly higher in patients with CAD compared with patients without CAD (74.18 vs. 56.03 ng/L), p = .005. During exercise, NT-proBNP levels increased in the total population (p < .001). The rise was, however, not significantly different between the two groups (8.24 vs. 8.51 ng/L), p = .700. Combining resting NT-proBNP with positive exercise stress test was superior to exercise test alone in predicting CAD, AUC = 0.68 vs. 0.64. Conclusion. Exercise-induced change in circulating NT-proBNP could not distinguish between patients with or without CAD. However, resting levels of NT-proBNP were significantly higher in patients with CAD than those without CAD.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6months (n = 332) or low likelihood of CAD (n = 179). Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Improvement of the Accuracy in the Identification of Coronary Artery Disease Combining Heart Sound Features.

Most researchers use features of diastolic murmurs to identify coronary artery disease. However, the diastolic murmurs of coronary artery disease are usually very weak and are easily contaminated by noise and valvular murmurs. Therefore, the diagnostic accuracy of coronary artery disease when only using diastolic murmurs is not well. An algorithm for improving the accuracy in the identification of coronary artery disease by combining the features of the first heart sound and diastolic murmurs was proposed. Firstly, a first heart sound feature extraction algorithm was used to identify coronary artery disease from noncoronary artery disease. Secondly, the Empirical Wavelet Transform algorithm was used to decompose the diastolic heart sound into three modes, and the spectral energy of each mode was calculated to distinguish coronary artery disease from noncoronary artery disease. Then, the features of the fist heart sound, the second diastolic spectral energy, and the parameter P3, which was used to discriminate the diastolic murmurs in coronary artery disease and in valvular disease, were combined together to improve the diagnostic accuracy of coronary artery disease. The comparison experiment results show that the accuracy of the proposed algorithm is superior to some state-of-the-art methods when they are used to diagnose coronary artery disease.

Diagnostic accuracy of SPECT and PET myocardial perfusion imaging in patients with left bundle branch block or ventricular-paced rhythm

BackgroundThe difference in diagnostic accuracy of coronary artery disease (CAD) between vasodilator SPECT and PET myocardial perfusion imaging (MPI) in patients with left bundle branch block (LBBB) or ventricular-paced rhythm (VPR) is unknown. MethodsWe identified patients with LBBB or VPR who underwent either vasodilator SPECT or PET MPI and subsequent coronary angiography. LBBB/VPR-related septal and anteroseptal defects were defined as perfusion defects involving those regions in the absence of obstructive CAD in the left anterior descending artery or left main coronary artery. ResultsOf the 55 patients who underwent coronary angiography, 38 (69%) underwent SPECT and 17 patients (31%) underwent PET. PET compared to SPECT demonstrated higher sensitivity (88% vs 60%), specificity (56% vs 14%), positive predictive value (64% vs 20%), negative predictive value (83% vs 50%), and overall superior diagnostic accuracy (AUC .72 (95% CI .50-.93) vs .37 (95% CI .20-.54), P = .01) to detect obstructive CAD. LBBB/VPR-related septal and anteroseptal defects were more common with SPECT compared to PET (septal: 72% vs 17%, P = .001; anteroseptal: 47% vs 8%, P = .02). ConclusionsPET has higher diagnostic accuracy when compared to SPECT for the detection of obstructive CAD in patients with LBBB or VPR.

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design and evaluate a deep learning (DL) approach to automatic diagnosis of myocardial perfusion abnormalities from stress-only MPI. The new DL approach developed for this study was compared to a conventional quantitative perfusion defect size (DS) method. A total of 37,243 patients (51.5% males) undergone stress 99mTc-Tetrofosmin or 99mTc-Sestamibi MPI were selected retrospectively from Yale New Haven Hospital. Patients were dichotomized as studies with normal (75.4%) or abnormal (24.6%) myocardial perfusion based on final diagnoses of clinical nuclear cardiologists. Stress myocardial perfusion defect size was calculated using Yale quantitative analytic software. A deep CNN was trained using the circumferential count profile maps derived from SPECT MPI and was evaluated for the diagnosis of perfusion abnormality with a 5-fold cross-validation approach. In each fold, 27,933, 1862 and 7448 patients were used as training, validation and testing datasets, respectively. The area under the receiver-operating characteristic curve (AUC) was calculated and analyzed for all patients as well as for the eight sub-groups classified based on patient genders, quantitative algorithms, radioactive tracers and SPECT cameras. The AUC value resulted from the DL method was significantly higher than that from the DS method (0.872 ± 0.002 vs. 0.838 ± 0.003, p < 0.01). Across the eight sub-groups, the DL method provided more consistent AUC values in terms of smaller standard deviation and higher diagnostic accuracy and specificity, but slightly lower sensitivity than the DS method (AUC: 0.865 ± 0.010 vs. 0.838 ± 0.019, Accuracy: 82.7% ± 2.5% vs. 78.5% ± 3.6%, Specificity: 84.9% ± 3.7% vs. 77.5% ± 6.5%, Sensitivity: 74.4% ± 4.2% vs. 79.8% ± 5.8%). The incorporation of deep learning for stress-only MPI has a considerable potential to improve the diagnostic accuracy and consistency in the detection of myocardial perfusion abnormalities.

Novel 3D printing technology for CT phantom coronary arteries with high geometrical accuracy for biomedical imaging applications

There is a growing interest in using Computed Tomography (CT) and Hounsfield Unit (HU) measurements in identifying and assessing non-calcified plaque; however, the complex geometry of the coronary arteries poses a challenge in achieving good image quality, which is crucial in providing patients with an accurate diagnosis. Minimizing artifacts associated with cardiac motion is also an important step in improving CT diagnostic accuracy of Coronary Artery Disease. Existing arterial phantoms are commonly straight, short tubular sections that are often rigid, and do not represent the geometry of the entire arterial network, with which image quality and Hounsfield Unit measurements vary and are dependent on. In this study, the process of manufacturing a plaque phantom with physiologically accurate geometry of the coronary arteries is demonstrated. A computer model is obtained by segmenting CTCA images, and several flexible commercially available materials are used to 3D print the model. The static and dynamic mechanical properties of the 3D printing materials are investigated under physiologically relevant loading and the CT numbers of contrast-enhanced tubular samples with 50%, 75%, and 90% concentric stenosis are characterized and compared with ranges for lipid-rich and fibrous plaque. The proposed plaque phantom design offers the possibility of investigating the effect of non-calcified plaque geometry and arterial motion on various parameters in CT optimization studies.

Comparison of diagnostic accuracy of dual-axis rotational versus standard coronary angiography

The data about the accuracy of dual-axis rotational coronary angiography (DARCA) in detecting coronary artery disease (CAD) is scare. This study aimed to compare the diagnostic accuracy of DARCA versus standard coronary angiography (SA). 70 patients with possible CAD underwent SA following by DARCA were prospectively enrolled. The primary endpoint was the non-inferiority comparison of the two modalities regarding diagnosis of CAD. Coronary lesion assessment, quantitative coronary angiography (QCA) analysis, and screening adequacy comparisons were performed. All images were analyzed by two independent reviewers except QCA analysis that was analyzed by the third independent reviewer. Radiation dose, contrast usage and procedural time were recorded. This trial is registered with ClinicalTrials.gov, Number NCT01776866. 63 of 70 patients were analyzed. DARCA was non-inferior to SA regarding the diagnosis of CAD (reviewer one-positive agreement: 100%, negative agreement: 100%, p = 1; p = 0.003 for non-inferiority; reviewer two-positive agreement: 96%, negative agreement: 95%, p = 1; p = 0.016 for non-inferiority). All reviewers showed good agreement between the two modalities for the diagnosis of CAD, coronary lesion assessment, QCA analysis, and screening adequacy, as reflected by kappa coefficients between 0.61 and 1.00. DARCA was associated with 41% reduction in radiation dose, 30% in contrast usage and 29% in procedure time (all p < 0.001). DARCA is clinically comparable to SA concerning the diagnostic accuracy for CAD, while markedly reduces radiation dose, contrast usage and procedure time.

P5252Can functional MR imaging supersede angiography? An audit of stress cardiac MRI outcomes compared those seen in the CE-MARC trials

Abstract Background Recent studies have compared the performance of cardiac MRI (CMR) with coronary angiography. The CE-MARC trial established CMR's high diagnostic accuracy for coronary artery disease (CAD). Following these results, and those of CE-MARC 2, which showed reduced unnecessary angiography rates with CMR-guided care, we increased our adoption of CMR as an investigation of choice for CAD at our centre. Purpose In patients who have a CMR for stable angina, what is the outcome after detection of CAD, how do findings compare with angiography, and do those without CAD identified go on to have a major adverse cardiovascular event (MACE)? Method We performed a retrospective audit of all stress CMR performed from August 2016 to March 2017 at our hospital in North England. All patients were followed up for a minimum of 12 months. NICE guideline care was used during the study period. The CE-MARC trial was used for quality standards and to compare results. Results 91 stress CMRs were performed. 13 were excluded as they were performed on out-of-area patients. Median follow up was 14.5 months. Of the remaining 78 patients, 34 (43%) had a positive CMR. 20/34 (59%) proceeded to angiogram. In 16/20 of patients, CMR findings correlated with angiogram findings. A PPV of 80%. The PPV in CE-MARC was 77.2% (72.1–81.6). Of those who did not proceed to angiography, 8/14 had non-viable myocardium, 3 continued with medical management, in two it was unclear. 3/34 (8.8%) with positive CMR had a MACE. 44 patients had a negative CMR. Three had an angiogram during follow up. All were negative. There was a MACE in 1/44 (2.3%). Conclusion The audit population has a similar PPV to that of CE-MARC. MACE rates at 12 months were similar to CE-MARC which suggests that the trial results are reproducible in our setting. The wider use of CMR can therefore improve investigation and management for patients with stable angina. The audit is limited by the small number of patients proceeding to angiogram and the ability to confirm negative CMR results.

Intermodel disagreement of myocardial blood flow estimation from dynamic CT perfusion imaging

PurposeTo assess the intermodel agreement of different tracer kinetic models to determine myocardial blood flow (MBF) and their diagnostic accuracy in coronary artery disease (CAD) at dynamic CT myocardial perfusion imaging (CTMPI). MethodsThree porcine hearts perfused in Langendorff mode and 15 patients with suspected CAD and perfusion single photon emission CT (SPECT) were included. Dynamic CTMPI was performed in shuttle-mode (70 kVp, 350mAs/rot) on 3rd generation dual-source CT. In porcine hearts and patients, myocardial segments (AHA-16-segment model) were drawn. Tissue attenuation curves were constructed per segment and arterial input functions were derived from the aorta. True MBF was calculated with input flow and weight of the porcine hearts. In patients, ischemic segments were based on SPECT results. MBF quantification was performed using the VPCT-software, Upslope, Extended Toft (ET), Two-compartment (TC) and Fermi models. ResultsIn porcine hearts, true MBF was 1.88 (interquartile range [IQR]:1.80-2.80)mL/g/min. Diagnostic accuracy was similar for all models: 0.96, 0.99, 0.92, 0.93 and 0.96 for VPCT software, Upslope method, Fermi, ET and TC model. The VPCT software and Upslope method resulted in lower MBF (median 1.44 [1.29–1.58] and 1.39 [1.25–1.59]mL/g/min) compared to the Fermi, ET, and TC model (median values of 1.76 mL/g/min [1.36–2.44], 2.55 mL/g/min [2.20–2.92], and 1.98 mL/g/min [1.60–2.60], respectively [p < 0.001]). In patients, all models showed a significant difference in MBF between the 34 ischemic and 206 non-ischemic segments (p-value<0.001). ConclusionAbsolute MBF values are significantly different between the models and a uniform threshold could not be determined; however, diagnostic accuracy for detecting ischemia is similar.

Positron Emission Tomography in the Diagnosis and Management of Coronary Artery Disease.

Cardiac positron emission tomography (PET) and positron emission tomography/computed tomography (PET/CT) are encouraging precise non-invasive imaging modalities that allow imaging of the cellular function of the heart, while other non-invasive cardiovascular imaging modalities are considered to be techniques for imaging the anatomy, morphology, structure, function and tissue characteristics. The role of cardiac PET has been growing rapidly and providing high diagnostic accuracy of coronary artery disease (CAD). Clinical cardiology has established PET as a criterion for the assessment of myocardial viability and is recommended for the proper management of reduced left ventricle (LV) function and ischemic cardiomyopathy. Hybrid PET/CT imaging has enabled simultaneous integration of the coronary anatomy with myocardial perfusion and metabolism and has improved characterization of dysfunctional areas in chronic CAD. Also, the availability of quantitative myocardial blood flow (MBF) evaluation with various PET perfusion tracers provides additional prognostic information and enhances the diagnostic performance of nuclear imaging.

Quantitative Myocardial Perfusion Imaging Versus Visual Analysis in Diagnosing Myocardial Ischemia: A CE-MARC Substudy

ObjectivesThis study sought to compare the diagnostic accuracy of visual and quantitative analyses of myocardial perfusion cardiovascular magnetic resonance against a reference standard of quantitative coronary angiography. BackgroundVisual analysis of perfusion cardiovascular magnetic resonance studies for assessing myocardial perfusion has been shown to have high diagnostic accuracy for coronary artery disease. However, only a few small studies have assessed the diagnostic accuracy of quantitative myocardial perfusion. MethodsThis retrospective study included 128 patients randomly selected from the CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) study population such that the distribution of risk factors and disease status was proportionate to the full population. Visual analysis results of cardiovascular magnetic resonance perfusion images, by consensus of 2 expert readers, were taken from the original study reports. Quantitative myocardial blood flow estimates were obtained using Fermi-constrained deconvolution. The reference standard for myocardial ischemia was a quantitative coronary x-ray angiogram stenosis severity of ≥70% diameter in any coronary artery of >2 mm diameter, or ≥50% in the left main stem. Diagnostic performance was calculated using receiver-operating characteristic curve analysis. ResultsThe area under the curve for visual analysis was 0.88 (95% confidence interval: 0.81 to 0.95) with a sensitivity of 81.0% (95% confidence interval: 69.1% to 92.8%) and specificity of 86.0% (95% confidence interval: 78.7% to 93.4%). For quantitative stress myocardial blood flow the area under the curve was 0.89 (95% confidence interval: 0.83 to 0.96) with a sensitivity of 87.5% (95% confidence interval: 77.3% to 97.7%) and specificity of 84.5% (95% confidence interval: 76.8% to 92.3%). There was no statistically significant difference between the diagnostic performance of quantitative and visual analyses (p = 0.72). Incorporating rest myocardial blood flow values to generate a myocardial perfusion reserve did not significantly increase the quantitative analysis area under the curve (p = 0.79). ConclusionsQuantitative perfusion has a high diagnostic accuracy for detecting coronary artery disease but is not superior to visual analysis. The incorporation of rest perfusion imaging does not improve diagnostic accuracy in quantitative perfusion analysis.

Fully quantitative pixel-wise analysis of cardiovascular magnetic resonance perfusion improves discrimination of dark rim artifact from perfusion defects associated with epicardial coronary stenosis

BackgroundDark rim artifacts in first-pass cardiovascular magnetic resonance (CMR) perfusion images can mimic perfusion defects and affect diagnostic accuracy for coronary artery disease (CAD). We evaluated whether quantitative myocardial blood flow (MBF) can differentiate dark rim artifacts from true perfusion defects in CMR perfusion.MethodsRegadenoson perfusion CMR was performed at 1.5 T in 76 patients. Significant CAD was defined by quantitative invasive coronary angiography (QCA) ≥ 50% diameter stenosis. Non-significant CAD (NonCAD) was defined as stenosis by QCA < 50% diameter stenosis or computed tomographic coronary angiography (CTA) < 30% in all major epicardial arteries. Dark rim artifacts had study specific and guideline-based definitions for comparison purposes. MBF was quantified at the pixel-level and sector-level.ResultsIn a NonCAD subgroup with dark rim artifacts, stress MBF was lower in the subendocardial than midmyocardial and epicardial layers (2.17 ± 0.61 vs. 3.06 ± 0.75 vs. 3.24 ± 0.80 mL/min/g, both p < 0.001) and was also 30% lower than in remote regions (2.17 ± 0.61 vs. 2.83 ± 0.67 mL/min/g, p < 0.001). However, subendocardial stress MBF in dark rim artifacts was 37–56% higher than in true perfusion defects (2.17 ± 0.61 vs. 0.95 ± 0.43 mL/min/g, p < 0.001). Absolute stress MBF differentiated CAD from NonCAD with an accuracy ranging from 86 to 89% (all p < 0.001) using pixel-level analyses. Similar results were seen at a sector level.ConclusionQuantitative stress MBF is lower in dark rim artifacts than remote myocardium but significantly higher than in true perfusion defects. If confirmed in larger series, this approach may aid the interpretation of clinical stress perfusion exams.Trial registrationClinicalTrials.gov Identifier: NCT00027170; first posted 11/28/2001; updated 11/27/2017.

Longitudinal Strain Versus Exercise Treadmill Testing in Chronic Stable Angina

Background: Longitudinal strain analysis has been shown to be useful in patients with acute coronary syndromes and in stress echocardiography. The objective of this study is to identify the incremental diagnostic value of longitudinal strain analysis at rest, when added to routine treadmill testing (TMT), in identifying the presence of coronary artery disease in patients presenting with chronic stable angina (CSA). Methodology: We conducted a prospective study in which adult patients presenting with CSA and normal systolic function were enrolled. All patients were examined by 2-D echocardiography, longitudinal strain analysis, TMT, and either conventional coronary angiography and/ or coronary computed tomography angiography. Results: We enrolled a total of 150 subjects (54±7.8 years, 63% male). The sensitivity and specificity of TMT was 60.2% and 65.5% respectively. In comparison, global longitudinal strain (GLS) alone had better sensitivity (72.7%) and specificity (74.2%). Post-systolic shortening (PSS) had similar sensitivity for early detection of significant CAD. The combination of TMT and GLS provided the best overall diagnostic accuracy with a sensitivity of 85.4% and specificity of 87.5%, respectively. The optimal cut off for GLS for identification of coronary artery disease was −17.75%. Conclusions: In patients presenting with chronic stable angina and normal systolic function, global longitudinal strain and post systolic shortening assessed at rest have excellent diagnostic accuracy for coronary artery disease. Longitudinal strain analysis is superior to conventional TMT and adds to its diagnostic performance.

The impacts on healthcare when coronary angiography as the reference method for diagnostic accuracy of coronary artery disease is replaced by fractional flow reserve!

This editorial refers to ‘Diagnostic performance of cardiac imaging methods to diagnose ischaemia-causing coronary artery disease when directly compared with fractional flow reserve as a reference standard: a meta-analysis’, by I. Danad et al . doi:10.1093/eurheartj/ehw095. Diagnostic imaging methods for patients with angina and coronary artery disease (CAD) are frequently used in the healthcare system and recommended in guidelines for diagnosis and guidance of revascularization.1,2 Studies showing the diagnostic accuracy of different methods are plentiful. However, most studies have been performed using invasive coronary angiography (ICA) as the reference standard. More precisely, the coronary stenosis is graded on a scale from 0% to 100%, and the non-invasive imaging techniques, e.g. single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), stress-echocardiography (SE), and coronary computed tomography angiography (CCTA), are compared with the degree of stenosis. The fact that the degree of stenosis is not related to the physiological significance has been known for a long time3 and therefore a measure of perfusion with SPECT, PET, or MRI is expected to have limited performance in such a comparison. However, lacking a better reference method, ICA has prevailed as the method of choice in most studies. The FAME studies showed that using measurement of fractional flow resrve (FFR) to guide treatment to revascularization or optimal medication therapy only, improved outcome.4 In FFR, a pressure catheter is used during ICA to measure the pressure drop of a stenosis during pharmacological vasodilation. Thus, FFR, being a measure of the physiological significance of a stenosis, is a better reference method when determining the diagnostic accuracy of …

Decreased Diagnostic Accuracy of Multislice Coronary Computed Tomographic Angiography in Women with Atypical Angina Symptoms.

Background:Multislice computed tomography (MSCT) coronary angiography (CAG) is a noninvasive technique with a reported high diagnostic accuracy for coronary artery disease (CAD). Women, more frequently than men, are known to develop atypical angina symptoms. The purpose of this study was to investigate whether the diagnostic accuracy of MSCT in women with atypical presentation differs from that in men.Methods:We enrolled 396 in-hospital patients (141 women and 255 men) with suspected or proven CAD who successively underwent both MSCT and invasive CAG. CAD was defined as any coronary stenosis of ≥50% on conventional invasive CAG, which was used as the reference standard. The patients were divided into typical and atypical groups based on their symptoms of angina pectoris. The diagnostic accuracy of MSCT, including its sensitivity, specificity, negative predictive value, and positive predictive value (PPV), was calculated to determine the usefulness of MSCT in assessing stenoses. The diagnostic performance of MSCT was also assessed by constructing receiver operating characteristic (ROC) curves.Results:The PPV (91% vs. 97%, χ2 = 5.705, P < 0.05) and diagnostic accuracy (87% vs. 93%, χ2 = 5.093, P < 0.05) of MSCT in detecting CAD were lower in women than in men. Atypical presentation was an independent influencing factor on the diagnostic accuracy of MSCT in women (odds ratio = 4.94, 95% confidence intervals: 1.16–20.92, Walds = 4.69, P < 0.05). Compared with those in the atypical group, women with typical angina pectoris had higher PPV (98% vs. 74%, χ2 = 17.283. P < 0.001), diagnostic accuracy (93% vs. 72%, χ2 = 9.571, P < 0.001), and area under the ROC curve (0.91 vs. 0.64, Z = 2.690, P < 0.01) in MSCT diagnosis.Conclusions:Although MSCT is a reliable diagnostic modality for the exclusion of significant coronary artery stenoses in all patients, gender and atypical symptoms might have some influence on its diagnostic accuracy.