Identification Of Coronary Artery Disease Research Articles

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

[This retracts the article DOI: 10.1155/2022/3058835.].

Consensus on the diagnosis and management of patients with type 2 myocardial infarction: an international Delphi study

Abstract Background Type 2 myocardial infarction occurs due to a reduction in myocardial oxygen supply or an increase in demand without evidence of atherosclerotic plaque rupture. It is common representing around 20% of myocardial infarctions (1-4). Outcomes are poor with up to one third alive at five years, with cardiovascular outcomes comparable to type 1 myocardial infarction (1,5,6). Despite this, there are no evidence-based guidelines to guide assessment and management for clinicians. Purpose To gain consensus on the assessment and management of type 2 myocardial infarction from an international group of experts. Methods A systematic review of type 2 myocardial infarction was undertaken to identify the corresponding or senior author of all original research studies. We identified 73 experts from multiple clinical specialties. A three-round international eDelphi study was circulated, with round one consisting of open questioning to generate statements for deductive analysis. Consensus was defined a priori as >70% agreement on a 5-point Likert scale. Statements for which no consensus could be obtained were adapted and recirculated in round three (Figure 1). Results Of the 68 experts contacted there was a 56% (38/68), 54% (37/68) and 72% (49/68) response rate in round one, two and three, respectively. Consensus was achieved on 64% (43/67) of statements. Consensus varied across the following domains i) definition and diagnosis 42% (5/12), ii) risk stratification 75% (3/4), iii) assessment of coronary artery disease and cardiac function 50% (9/18), iv) specialty management 60% (6/10), v) treatment and secondary prevention 100% (9/9) and vi) communication and rehabilitation 79% (11/15). The panel agreed that all patients should undergo echocardiography (mean [SD] 2.08 [1.23]) and CT coronary imaging or a functional test should be performed on those with intermediate probability of future cardiovascular events (mean [SD] 1.81 [0.52]). Over 90% of the panel agreed that both patient and clinician understanding of type 2 myocardial infarction was poor and that further randomised controlled trials are need to direct care. Conclusion There remains uncertainty regarding the current definition of type 2 myocardial infarction and overall patient and clinician understanding was thought to be poor. However, it is generally accepted that the mainstay of management should be on the identification of coronary artery disease and left ventricular impairment with optimisation of therapies with proven benefit. Management should involve an individualised assessment and the multidisciplinary team.

Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: A proof-of-concept study

BackgroundA proof-of-concept study assessing a novel approach to identify patients with coronary artery disease (CAD) using deep learning analysis of bilateral-site photoplethysmography (PPG) waveforms (“DL-PPG”). MethodologyDL-PPG was studied in 37 participants (with 21 having CAD). Scalogram ‘spectral’ images were derived from right and left index finger PPG measurements collected using a 3-phase protocol (baseline, unilateral arm pressure cuff occlusion, reactive hyperaemia flush). Artificial Intelligence (AI) analysis, namely deep learning, was employed for scalogram image classification using a Convolutional Neural Network (CNN, “GoogLeNet”), with classification performance obtained using 10-fold stratified cross validation (CV). A conventional machine learning (ML) classifier (K-nearest neighbour, K-NN, K = 9) was also evaluated for comparison with the CNN deep learning methodology. Blood samples were also collected giving 2 biochemical biomarkers of endothelial function. Test sensitivities, specificities, accuracies, and Kappa statistics were determined. ResultsDL-PPG sensitivity was 80.9 % (95% CI, 78.6–83.0), specificity 87.7% (85.5–89.7), accuracy 83.8 % (82.2–85.3), and Kappa 0.68 (0.65–0.71). Comparative K-NN ML performance was 69.4% (95% CI, 68.7–70.1), 37.5% (36.7–38.2), 53.9% (53.3–54.4), and 0.069 (0.058–0.079), respectively. No differences between patients and controls were found for the biochemical biomarkers of endothelial function. ConclusionSubstantial overall agreement was found between DL-PPG classification and CAD angiography, with DL-PPG performance clearly better than for a conventional ML technique. Our deep learning classification approach, using only basic pre-processing of the PPG pulse waveforms before classification, could offer significant benefits for the diagnosis of CAD in a variety of clinical settings needing low-cost portable and easy-to-use diagnostics.

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.

Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals.

Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing scalograms as vertical slices of the tensor. Each scalogram is computed from the considered time frame of a given HR signal. The derived scalogram represents the heterogeneity of data as a two-dimensional map. These two-dimensional maps are stacked one after the other horizontally along the z-axis to form a 3-way tensor for each HR signal. Each two-dimensional map is represented as a vertical slice in the xy - plane. Tensor factorization of such a fused tensor for every HR signal is performed using canonical polyadic (CP) decomposition. Only the core factor is retained later, excluding the three unitary matrices to provide the latent feature set for the detection task. The resultant latent features are then fed to machine learning classifiers for binary classification. Bayesian optimization is performed in a five-fold cross-validation strategy in search of the optimal machine learning classifier. The experimental results yielded the accuracy, sensitivity, and specificity of 96.62%, 96.53%, and 96.67%, respectively, with the bagged trees ensemble method. The proposed tensor decomposition deciphered higher-order interrelations among the considered time-frequency representations of HR signals.

Relation of neutrophil-to-lymphocyte ratio with coronary artery disease severity in patients undergoing coronary angiography

Background and Aims: Inflammation plays important role in atherosclerosis. Recently, neutrophil-to-lymphocyte ratio(NLR) has emerged as new inflammatory marker in identification of coronary artery disease(CAD). We aimed to evaluate correlation of NLR with CAD severity and propose suitable cut-off NLR for predicting CAD.
 Methods: This cross-sectional study included 147 patients undergoing coronary angiography for angina with ischemic ECG changes. CAD severity was assessed by SYNTAX score and categorized into low, intermediate and high SYNTAX group. Neutrophil and lymphocyte count determined by BeneSphera analyser and NLR was calculated. Statistical analysis was performed using chi-square, ANOVA, Pearson’s correlation test and logistic regression analysis.
 Results: In 147 patients [mean age 61.27±12.87year (25-86 years);68% male],NLR ranged from 1.0 to 9.66[median 2.53(1.96-3.73 IQR), mean 3.21±1.86].The NLR (mean ± SD) in low, intermediate and high SYNTAX group were 2.35±1.27, 3.04±1.54l, 4.72±2.07 respectively (p<0.0001).High SYNTAX group more frequently had diabetes mellitus, dyslipidemia, smoker, alcohol consumer, were older , and had significantly high neutrophil count, NLR values and low lymphocyte count. In univariate analysis, diabetes mellitus, NLR, neutrophil and lymphocyte count were predictors of high SYNTAX score. In the multiple logistic regression analysis, NLR >3[OR=17.36, 95% CI 6.7-44.5, p=0.001] was identified as independent predictor of high SYNTAX score. In ROC analysis, NLR ≥ 1.785 was best suitable cut-off to identify presence of CAD with sensitivity of 97.4% and specificity of 83.3%.
 Conclusion: NLR correlated positively with presence and severity of CAD as assessed by SYNTAX score

P343 VALIDATION OF ESC 2014 GUIDELINES PROTOCOL FOR CORONARY ARTERY DISEASE DETECTION IN PATIENTS CANDIDATES TO LIVER TRANSPLANTATION

Abstract Introduction Orthotopic liver transplantation (OLT) is the only effective treatment in patients with end–stage liver disease. Coronary artery disease (CAD) is associated with increased peri–operative OLT mortality. Thus, identification of CAD is crucial in pre–OLT evaluation. There is not a specific pre–OLT accepted diagnostic algorithm for CAD detection. 2014 ESC guidelines for non–cardiac surgery management consider OLT as a high–risk abdominal surgery and only patients with CAD history, suspected symptoms and low exercise capacity are invited to undergo to specific functional exams such as stress–echo. Purpose of the present study was to compare the ESC protocol with our cardiologic pre–OLT protocol, in which tests of ischemia and coronary CT are largely administered on based on presence of risk factors. Methods. From 2014 to 2021 all OLT candidates have been evaluated by visit, electrocardiography, rest and, when possible, stress echocardiography. In subject aged > 50 years with diabetes and/or two or more of the following risk factors: smoking, CAD family history, peripheral vascular disease, CT was performed. CGF was scheduled when CAD history and typical symptoms of angina. We theoretically re–analyse our database in order to consider the potential destiny of each patient in case of application of ECS 2014 protocol. We were able to calculate the differences in the number of potentially loss CAD diagnosis. Results. 367 patients (58.2%, age 57±5.7 years, males 74%) underwent OLT. Stress Echo was performed in 239 (65.1%) and positivity was found in 24 pz (6,5%). Coronary CT was performed in 95 (25,8%). CGF was performed in 75 patients (20.1%), 38 (9,2%) directly for known CAD and/or symptoms. Critical coronary lesions were found in 12 patients (3,2 %): 7 revascularized by PTCA, 1 by CABG and 4 medically managed, 25 patients had non critical lesions (7.8%), 31 patients (9.6%) referred to CGF were subsequently removed by transplant list, whereas OLT was successfully performed in PTCA patients (n = 8). During follow–up (35±20 months) 28 patients died (14.2%), 5 with symptoms of congestive heart failure, but no ischemic events were observed. Conclusions CAD detectionis remains a clinical challenge in candidates to OLT. The large number of normal stress echoes and normal CGF, even in presence of cardiovascular risk factors, demonstrates that CAD prevalence in ESLD is no higher than in general population, but a gold method to detect CAD is still unclear.

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.

Cardiovascular risk stratification in the noncardiac solid organ transplant candidate.

Solid organ transplantation (SOT) has become a widely accepted therapy for end-stage disease across the spectrum of thoracic and abdominal organs. With contemporary advances in medical and surgical therapies in transplantation, candidates for SOT are increasingly older with a larger burden of comorbidities, including cardiovascular disease (CVD). CVD, in particular, is a leading cause of morbidity and mortality in SOT candidates with end-stage disease of noncardiac organs [1]. Identification of coronary artery disease (CAD), heart failure, and valvular disease are important in noncardiac SOT to ensure both appropriate peri-transplant management and equitable organ allocation. Although the American College of Cardiology (ACC) and the American Heart Association (AHA) have published guidelines and recommendations for the perioperative cardiovascular evaluation of patients undergoing noncardiac surgery, the implications of both symptomatic and asymptomatic CVD differ in patients with end-stage organ failure being considered for SOT when compared to the general population. Herein, we review the epidemiology, diagnosis, and evidence for the management of CVD in kidney and liver transplantation, combining current guidelines from the 2012 ACC/AHA scientific statement on cardiac disease evaluation in SOT with more contemporary evidenced-based algorithms.

QT variability unrelated to RR variability during stress testing for identification of coronary artery disease.

Stress test electrocardiogram (ECG) analysis is widely used for coronary artery disease (CAD) diagnosis despite its limited accuracy. Alterations in autonomic modulation of cardiac electrical activity have been reported in CAD patients during acute ischemia. We hypothesized that those alterations could be reflected in changes in ventricular repolarization dynamics during stress testing that could be measured through QT interval variability (QTV). However, QTV is largely dependent on RR interval variability (RRV), which might hinder intrinsic ventricular repolarization dynamics. In this study, we investigated whether different markers accounting for low-frequency (LF) oscillations of QTV unrelated to RRV during stress testing could be used to separate patients with and without CAD. Power spectral density of QTV unrelated to RRV was obtained based on time-frequency coherence estimation. Instantaneous LF power of QTV and QTV unrelated to RRV were obtained. LF power of QTV unrelated to RRV normalized by LF power of QTV was also studied. Stress test ECG of 100 patients were analysed. Patients referred to coronary angiography were classified into non-CAD or CAD group. LF oscillations in QTV did not show significant differences between CAD and non-CAD groups. However, LF oscillations in QTV unrelated to RRV were significantly higher in the CAD group as compared with the non-CAD group when measured during the first phases of exercise and last phases of recovery. ROC analysis of these indices revealed area under the curve values ranging from 61 to 73%. Binomial logistic regression analysis revealed LF power of QTV unrelated to RRV, both during the first phase of exercise and last phase of recovery, as independent predictors of CAD. In conclusion, this study highlights the importance of removing the influence of RRV when measuring QTV during stress testing for CAD identification and supports the added value of LF oscillations of QTV unrelated to RRV to diagnose CAD from the first minutes of exercise. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Identification of Coronary Artery Disease using Artificial Neural Network and Case-Based Reasoning

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

Clinical and angiographical features of first episode of acute coronary syndrome in patients with human immunodeficiency virus infection

Background Patients affected by the human immunodeficiency virus (HIV) show an increased risk of myocardial infarction. Clinical and angiographic features of HIV positive (HIV+) patients presenting with the first episode of an acute coronary syndrome (ACS) are not well defined in previous studies. Objective To demonstrate that HIV + patients with acute coronary syndrome had different features than non-HIV patients. Methods We identified 48 HIV + patients without previous cardiovascular events admitted to our Emergency Department with ACS diagnosis between 2012 and 2020. Clinical and angiographic characteristics were compared with a control group of 48 non-HIV consecutive patients affected by ACS as first episode. Results HIV + patients were most frequently men (87.5% vs 62.5%, p = 0.009) and younger about a decade (mean age 53.8 ± 8.2 vs 63.7 ± 11.9 years old, p < 0.0001); statistically significant hypertriglyceridemia has been found in the HIV group (178,6 ± 59,8 mg/dl vs 142,7 ± 63,7 mg/dl, p = 0.005). HIV(+) patients had a higher rate of anterior ST-elevation myocardial infarction (STEMI) (65% vs 33%, p = 0.03) and significant lesions on left anterior descending (LAD) coronary artery (83% vs 58% p = 0.01). Conclusions HIV + patients with the first episode of ACS are generally young men with higher triglycerides and most frequently presenting with anterior STEMI and LAD involvement. The strict control of risk factors and a program for the early identification of coronary artery disease are strongly recommended in this subset of patients.

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

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.

WITHDRAWN: Survey on - Identification of Coronary Artery Disease using Deep Learning

Abstract Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic which is a study of whole genomes of organisms and protein sequences. Learning from these data facilitates understanding and identification of human health and disease is quite easy. The artificial neural networks, deep learning-based algorithms shows great promise in guiding, enhancing and extracting features and learning patterns from complex data within a span of time. In this paper we tried to present the basic idea about Coronary Artery Disease (CAD), Angiogram and literature on angiogram technique used to detect and predict Coronary Artery Disease using deep learning techniques.

Coronary artery disease in dialysis patients: evidence synthesis, controversies and proposed management strategies.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality among patients with end-stage renal disease (ESRD). Clustering of traditional atherosclerotic and non-traditional risk factors drive the excess rates of coronary and non-coronary CVD in this population. The incidence, severity and mortality of coronary artery disease (CAD) as well as the number of complications of its therapy is higher in dialysis patients than in non-chronic kidney disease patients. Given the lack of randomized clinical trial evidence in this population, current practice is informed by observational data with a significant potential for bias. Furthermore, guidelines lack any recommendation for these patients or extrapolate them from trials performed in non-dialysis patients. Patients with ESRD are more likely to be asymptomatic, posing a challenge to the correct identification of CAD, which is essential for appropriate risk stratification and management. This may lead to "therapeutic nihilism", which has been associated with worse outcomes. Here, the ERA-EDTA EUDIAL Working Group reviews the diagnostic work-up and therapy of chronic coronary syndromes, unstable angina/non-ST elevation and ST-elevation myocardial infarction in dialysis patients, outlining unclear issues and controversies, discussing recent evidence, and proposing management strategies. Indications of antiplatelet and anticoagulant therapies, percutaneous coronary intervention and coronary artery bypass grafting are discussed. The issue of the interaction between dialysis session and myocardial damage is also addressed.

Is the achievement of 85% of age-predicted heart ratemax at exercise test sufficient to make diagnosis of myocardial ischemia in athletes?

Exercise test (ET) is a validated tool for the identification of coronary artery disease (CAD) even among athletes. A "cut-off" of 85% HRmax is often chosen as an "end point" although it has been shown that this percentage is far from a maximal effort and might be insufficient to evaluate athletes who often reach HRs close to 100% of the theoretical HRmax during sport activity. The aim of this study was to identify the percentage of theoretical HRmax (% HRmax) at which ST segment depression due to a coronary insufficiency on ECG at ET became significant and how much this value would be different from the "cut-off" of 85% of the theoretical HRmax. Forty-two male competitive athletes of three different sport disciplines (58±7.9 years) tested positive during ET at HRmax greater than 85% with ST depression ≥2 mm from V1 to V6 and / or ≥1 mm from D1 to aVf at J point + 80 ms, were included. ECG traces recorded at 85% of HRmax, were compared with those recorded at the percentage of HRmax corresponding to the significant ST depression (%HRmaxST). % HRmax at which the ST depression become significant was 93.4±3.4% with a Δ% of + 8.4% compared to 85%. ST segment depression at peak exercise on precordial leads was significantly different from that at 85% HRmax (P<0.0001). TC coronary scan confirmed CAD. The cut off of 85% HRmax is insufficient to identify signs of reduced coronary reserve in competitive athletes.

An improved method to detect coronary artery disease using phonocardiogram signals in noisy environment

Identification of coronary artery disease (CAD) from phonocardiogram (PCG) signal is a low signal to noise ratio (SNR) problem. This study proposes a PCG based CAD detection system robust against the environmental noise that does not require additional reference signals for noise acquisition and PCG segmentation. Here, the experiments are conducted on 40 CAD and 40 normal subjects. PCG signals are recorded from a multichannel data acquisition system from four auscultation sites on the left anterior chest. While heart sounds are propagated to different auscultation sites with a certain delay, the ambient noise appearing at microphone array are not mutually time-lagged. Thus, we propose to use the imaginary part of cross power spectral density (ICPSD) to capture the spectrum of heart sounds as it is unresponsive to zero time-lagged signals. Subband based spectral features obtained from ICPSD are classified in a machine learning framework. The performance of the system is studied in the presence of babble, vehicle and white noise in which useful information were extracted from both systolic and diastolic phases of cardiac cycle. The proposed method achieves accuracy, sensitivity and specificity of 74.98%, 76.50% and 73.46%, respectively in absence of ambient noise for k-fold (k=5) cross-validation. The accuracy for 0 dB SNR in presence of white, babble and vehicle noise were 71.13%,66.47% and 69.60%, respectively. The proposed method was found to be superior in CAD classification when compared with existing noise removal based approach. The present work shows the potential of developing a PCG-based multichannel CAD detection system as an affordable point of care device for real-life use, where a certain amount of ambient noise is expected.

Left Ventricular Mid-Diastolic Wall Thickness: Normal Values for Coronary CT Angiography

To generate normal reference values for left ventricular mid-diastolic wall thickness (LV-MDWT) measured by using CT angiography. LV-MDWT was measured in 2383 consecutive patients, without structural heart disease, undergoing prospective electrocardiographically (ECG) triggered mid-diastolic coronary CT angiography. LV-MDWT was manually measured on automatically segmented short-axis images according to the American Heart Association's 17-segment model. Commercially available automatic software was used to calculate the left ventricular (LV) mass. Among the 2383 patients, average LV-MDWT was 7.24 mm ± 1.86 (standard deviation [SD]), with the basal anteroseptal segment being the thickest wall (8.71 mm ± 2.19) and the apical inferior segment being the thinnest wall (5.9 mm ± 1.58; P < .001). Over all LV segments, the maximum upper limit, as defined as 2 SD above the mean, was 13.6 mm for men (LV1) and 11.2 mm for women. For men, only the basal anterior segment was above 13 mm. There was a significant difference in average LV-MDWT between women and men with 6.47 mm ± 1.07 and 7.90 mm ± 1.24, respectively (P < .001). Significant differences in LV-MDWT were found in the subgroups aged less than 65 years and greater than or equal to 65 years (P < .001). There was a strong correlation between LV-MDWT and LV mass (P < .001). Normal sex- and age-specific reference ranges for LV-MDWT in prospective ECG-triggered mid-diastolic coronary CT angiography have been provided. These benchmarks may expand the diagnostic and prognostic roles of CT angiography, beyond its role in the identification of coronary artery disease.© RSNA, 2019.

Changes in ventricular depolarisation vectors during exercise caused by regional myocardial ischaemia

Research at the Aga Khan University for several years has been directed to find a reliable, low-cost, portable, non-invasive method for identification of coronary artery disease, its location and extent. A new method has been devised to measure the magnitude and direction of cardiac electrical vectors in three perpendicular planes during physical exercise to identify reduction in myocardial excitability as the electrophysiological marker of hypoxia. This report shows that changes in electrical forces due to exercise-induced regional hypoxia serve as indicators of reversible myocardial ischaemia. Changes in the magnitude and direction of vectors at stages of the Bruce protocol were measured in healthy volunteers, and patients undergoing the same exercise protocol for distribution of a radioactive tracer injected intravenously at peak exercise and after recovery (myocardial perfusion scan). Alterations in the magnitude and direction of resultant vectors during exercise were scored to enable analysis. Analysis identified slow progression of myocardial depolarisation as the electrophysiological marker of regional hypoxia relative to physical work. Compared with myocardial perfusion scan the sensitivity and specificity of electrical vectors for identification of ischaemia were 88% and 71%, respectively. Accuracy of ischaemia shown by electrical vectors is being assessed in patients undergoing elective coronary angiography.

Value of Duke Treadmill Score in Setting Priority of Patients for Coronary Angiography

Background: exercise treadmill test remains a good test in identification of coronary artery disease(CAD). The Duke Treadmill Score (DTS), shown to predict CAD better than ST segment deviation alone. Aim of the work: To determine the potential of DTS in setting priority of patients for coronary angiography. Patients and methods: This was a prospective study that was done from July 2017 to October 2018 and included 150 patients referred to Damannhour Teaching Hospital for evaluation of chest pain. Patients underwent: history, clinical examination, ECGs, Echocardiography, Treadmill exercise ECG with calculation of DTS then coronary angiography that was correlated with DTS and other variables. Results: There were 59 patients(39.3%)with high DTS and 91patients (60.7%) with intermediate DTS. For the intermediate Duke's risk group the result of coronary angiography distributed on normal (26.4%), single vessel disease (33%), two vessel disease (22%), and three vessels disease or left main disease (18.7%). For high Duke's risk group the result of coronary angiography showed mainly three vessels disease or left main disease (54.2%), two vessels disease (27.1%), single vessel disease (6.8%) and normal vessel (11.9%), The P value was<0.001.Adding patient's risk factors to intermediate DTS group were statistically significant in the diagnosis of CAD ,the P value was<0.001. Conclusion: The DTS is a good predictor for CAD. Intermediate risk DTS group exercise test results should be managed individually according to clinical data.