CADCare: Smart System for CHD Identification & Sensor Alerts

Improved Accuracy of the Exercise Electrocardiogram in Detection of Coronary Artery and Three-Vessel Coronary Disease

Global longitudinal strain (GLS) at rest on two-dimensional speckle tracking echocardiography (2D STE) was demonstrated to help detect coronary artery disease (CAD). However, the optimal cut-off point of GLS and its diagnostic power for detecting critical CAD in non-diabetes mellitus (DM) patients are unknown. In the present study, 211 patients with suspected CAD were prospectively included, with DM patients excluded. All patients underwent echocardiography and subsequently coronary angiography within 3 days. Left ventricular (LV) GLSs were quantified by 2D STE. Territorial peak systolic longitudinal strains (TLSs) were calculated based on the perfusion territories of the 3-epicardial coronary arteries in a 17-segment LV model. Critical CAD was defined as an area stenosis ≥70% in ≥1 epicardial coronary artery (≥50% in left main coronary artery). Totally 145 patients were diagnosed as having critical CAD by coronary angiography. Significant differences were observed in all strain parameters between patients with and without critical CAD. The area under the receiver operating charcteristic (ROC) curve (AUC) for GLS in the detection of left main (LM) or threevessel CAD was 0.875 at a cut-off value of -19.05% with sensitivity of 78.1% and specificity of 72.7%, which increased to 0.926 after exclusion of apical segments (cut-off value -18.66%; sensitivity 84.4% and specificity 81.8%). The values of TLSs were significantly lower in regions supplied by stenotic arteries than in those by non-stenotic arteries. The AUC for the TLSs to identify critical stenosis of left circumflex (LCX) artery, left anterior descending (LAD) artery and right coronary artery (RCA), in order of diagnostic accuracy, was 0.818 for LCX, 0.764 for LAD and 0.723 for RCA, respectively. In conclusion, in non-DM patients with suspected CAD, GLS assessed by 2D STE is an excellent predictor for LM or three-vessel CAD with high diagnostic accuracy, and a higher cut-off point than reported before should be used. Excluding apical segments in the calculation of GLS can further improve the predictive accuracy of GLS. It is unsatisfactory for TLSs to be used to identify stenotic coronary arteries.

Accurate and safe diagnostic testing provides the crucial link between detection and optimal management of coronary artery disease (CAD). Noninvasive diagnostic testing for CAD may be less accurate in women than in men. Many noninvasive diagnostic modalities are available for this purpose. An exercise tolerance test provides an assessment of functional capacity and has the advantages of wide availability and low initial cost. However, exercise echocardiography may be the most cost-effective method for the initial assessment of coronary artery disease in intermediate-risk women owing to its higher sensitivity and specificity. Recent studies with electron-beam computed tomography reveal that women with no coronary calcification are very unlikely to have obstructive CAD. In symptomatic women with an intermediate likelihood of CAD, either an exercise treadmill test or exercise echocardiography is appropriate for initial screening and can provide useful prognostic information. Alternatively, an electron-beam computed tomographic scan with a 0 calcium score may spare many women with atypical chest pain or equivocal findings on an exercise tolerance test from undergoing more expensive stress imaging studies or coronary angiography. For high-risk symptomatic women, a more aggressive approach involving coronary angiography appears to be the preferred initial diagnostic strategy.

Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene-gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD.

Periodontal disease, smoking, cardiovascular complications and mortality in type 1 diabetes

The precise clinical utility of lung 201Tl uptake in exercise SPECT myocardial perfusion imaging remains open to research. This study validates an optimal index for lung 201Tl uptake measurement and assesses its value in the prediction of higher-risk coronary artery disease (CAD). Three hundred and ninety-eight patients underwent exercise SPECT myocardial perfusion imaging. They were separated into derivation (n = 217) and validation (n = 186) groups, both including sub-populations of lower- and higher-risk CAD, according to coronary angiography. Another 56 individuals with a low probability of CAD comprised the control group. From a planar, anterior, post-exercise acquisition, the lung (L) to heart (H) maximal (L/H(max)), total (L/H(mean)) and background-subtracted total (L/H(net)) ratios were calculated. These were also adjusted for confounding variables, as assessed from the control group. ROC analysis was used to compare the L/H ratios and define thresholds of abnormality. The performance of the optimal index was assessed in the derivation group and was then tested in the validation population. Subsequently, it was compared with other scintigraphic, exercise electrocardiography and clinical variables. In the derivation group L/H(net) was a better discriminator for higher-risk CAD than both L/H(max) and L/H(mean). Similarly, the adjusted L/H(net) was a better discriminator than both the adjusted L/H(max) and the adjusted L/H(mean). No significant difference was attained between L/H(net) and the adjusted L/H(net). At the upper defined threshold of abnormality, sensitivity and specificity of L/H(net) in the detection of higher-risk CAD in the derivation and the validation cohorts were 52% and 92% versus 47% and 94%, respectively (p = ns). The results were similar at other defined thresholds. Moreover, L/H(net) was found to be a significant predictor of higher-risk CAD, superior to myocardial perfusion images, transient ventricular dilation measurements, and clinical and exercise testing variables (ROC analysis and logistic regression). By raising the threshold of abnormality of L/H(net), specificity and positive predictive value increased, whereas sensitivity and negative predictive value decreased. Lung 201Tl assessment assists substantially in the identification of higher-risk CAD in exercise SPECT myocardial perfusion imaging and this is best achieved by L/H(net). This index is a significant predictor of higher-risk CAD, superior to myocardial perfusion images, and its value is associated with the probability of a disease state.

Determination of the quantitative thallium imaging variables that optimize detection of coronary artery disease

ObjectiveCardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.MethodologyFor the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.ResultsNumerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

Identification of extensive coronary artery disease: Incremental value of exercise Tl-201 SPECT to clinical and stress test variables

The aim of the study was to examine the ability to simultaneously assess left ventricular function and myocardial perfusion by using a single injection of technetium-99m sestamibi at rest and during submaximal exercise to identify high-risk patients with left main, proximal left anterior descending (LAD), or three-vessel coronary artery disease (CAD) after an uncomplicated acute myocardial infarction (AMI). Multiple studies have evaluated the separate value of the exercise ECG, myocardial perfusion scintigraphy, and radionuclide angiocardiography (RNA) for identifying patients with severe CAD. The availability of technetium-99m (Tc99m)-labeled myocardial imaging agents offers the opportunity to evaluate simultaneously ventricular function and myocardial perfusion during a single exercise session. Only limited data are available about the value of this combined technique in the workout of patients early after an uncomplicated AMI. Combined first-pass RNA and myocardial perfusion tomoscintigraphy (SPECT) at rest and during submaximal exercise were performed in 52 patients, less than six weeks after an uncomplicated AMI, with use of Tc99m sestamibi. Patients were classified in two subgroups according to the presence of left main, proximal LAD, or three-vessel CAD. Stepwise logistic regression analysis was used to determine the independent predictors of severe CAD. All patients underwent the exercise testing without any medical complication. On univariate analysis, the global left ventricular ejection fraction (LVEF), wall motion score, and myocardial perfusion score, both at rest and at submaximal exercise, were significantly associated with the presence of severe CAD. The response of LVEF to exercise, and the presence of exercise-induced wall motion or myocardial perfusion abnormalities, were not associated with the severity of CAD. On multivariant analysis only the wall motion score during exercise was an independent predictor for the presence of severe CAD (P < 0.001, r = 0.6). In analyzing patients with anterior AMI separately, LVEF at submaximal exercise was the most accurate predictive parameter. If a cutoff value of 40% was chosen, the LVEF at exercise had a sensitivity of 85% and a specificity of 78% for the detection of severe CAD. In patients with inferior AMI, neither LVEF nor wall motion or myocardial perfusion scores were useful for differentiating the two subgroups. In these patients the presence of an additional perfusion defect during exercise in one of the anterior wall segments yielded a sensitivity of 70% and a specificity of 75% for the presence of severe CAD. simultaneous evaluation of LV function and myocardial perfusion at submaximal exercise, using a single injection of Tc99m-sestamibi, is a safe and accurate technique for selecting patients with severe CAD after an uncomplicated AMI.

Dobutamine stress myocardial perfusion imaging

ABSTRACTBackground: Optimized management of hypertension and coronary artery disease (CAD) improves cardiovascular risk and outcomes, and prevents complications. This article reviews evidence for the fixed combination of the angiotensin-converting enzyme (ACE) inhibitor perindopril and the calcium channel blocker amlodipine.Methods: A literature search was performed in PubMed/MEDLINE to identify articles published in English between 1988 and March 2008 describing clinical trials, particularly outcome trials, or mechanisms of therapeutic action relevant to the use of combination therapy in patients with hypertension or stable coronary artery disease with an ACE inhibitor (perindopril) and a calcium channel blocker (amlodipine).Findings: Clinical trials indicate that this combination may have a positive impact on cardiovascular mortality and morbidity in hypertensive individuals. The two complementary mechanisms of action appear to work in synergy, leading to more efficient blood pressure lowering, improved fibrinolytic function, and reduction of secondary effects. This also represents a simplified management strategy for stable CAD. Perindopril has proven efficacy in the prevention of cardiovascular events and mortality in CAD patients, while amlodipine is widely used in the symptomatic management of CAD. Both aspects of guideline-recommended management of CAD are therefore addressed in a single tablet.Conclusions: The clinical evidence for fixed-combination perindopril/amlodipine indicates it as a credible option for the optimization of the management of hypertension and CAD.

Coronary Artery Disease (CAD) remains a leading cause of morbidity and mortality worldwide. To evaluate the diagnostic performance of AI models in the early detection and risk stratification of CAD using ECG and imaging data and to compare their accuracy with cardiologist interpretation. Methods: This cross-sectional analytical study was conducted in Saudia, included 185 patients with suspected CAD who underwent both ECG and imaging evaluation. AI-based models, including convolutional neural networks for ECG and deep learning algorithms for imaging, were applied to detect CAD and stratify patients into risk categories. Cardiologist-confirmed diagnosis served as the reference standard. Results: The AI-ECG model achieved a sensitivity of 88.5%, specificity of 82.0% and an AUC of 0.90 (95% CI: 0.86–0.94). When ECG and imaging data were combined, diagnostic accuracy improved, with sensitivity of 92.4%, specificity of 85.2% and an AUC of 0.93 (95% CI: 0.89–0.96). AI-based risk stratification categorized 54 patients (29.2%) as low risk, 78 (42.2%) as intermediate risk and 53 (28.6%) as high risk. Confirmed CAD prevalence correlated strongly with AI-predicted risk groups, with 22.2% in the low-risk group and 88.7% in the high-risk group. Conclusion: Artificial intelligence demonstrates high accuracy in the early detection and risk stratification of CAD using ECG and imaging data. AI models performed comparably to cardiologists and offered significant efficiency gains. Integration of AI into cardiovascular workflows may enable earlier intervention, optimized resource allocation and improved patient outcomes. Further validation across larger and more diverse populations is warranted.

Dynamic 3-Dimensional Stress Cardiac Magnetic Resonance Perfusion Imaging: Detection of Coronary Artery Disease and Volumetry of Myocardial Hypoenhancement Before and After Coronary Stenting

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.