Diagnosis Of Coronary Artery Disease Research Articles

Artificial Intelligence in the Heart of Medicine: Transforming Arrhythmia Care with Intelligent Systems.

At a critical juncture in the ongoing fight against cardiovascular disease (CVD), healthcare professionals are striving for more informed and expedited decisionmaking. Artificial Intelligence (AI) promises to be a guiding light in this endeavor. The diagnosis of coronary artery disease has now become non-invasive and convenient, while wearable devices excel at promptly detecting life-threatening arrhythmias and treatments for heart failure. This study aimed to highlight the applications of AI in cardiology with a particular focus on arrhythmias and its potential impact on healthcare for all through careful implementation and constant research efforts. An extensive search strategy was implemented. The search was conducted in renowned electronic medical databases, including Medline, PubMed, Cochrane Library, and Google Scholar. Artificial Intelligence, cardiovascular diseases, arrhythmias, machine learning, and convolutional neural networks in cardiology were used as keywords for the search strategy. A total of 6876 records were retrieved from different electronic databases. Duplicates (N = 1356) were removed, resulting in 5520 records for screening. Based on predefined inclusion and exclusion criteria, 4683 articles were excluded. Following the full-text screening of the remaining 837 articles, a further 637 were excluded. Ultimately, 200 studies were included in this review. AI represents not just a development but a cutting-edge force propelling the next evolution of cardiology. With its capacity to make precise predictions, facilitate non-invasive diagnosis, and personalize therapies, AI holds the potential to save lives and enhance healthcare quality on a global scale.

Coronary computed tomography and intracoronary imaging for the diagnosis of coronary artery disease and the guidance of percutaneous revascularization

Coronary computed tomography and intracoronary imaging for the diagnosis of coronary artery disease and the guidance of percutaneous revascularization

Myocardial remodelling and left ventricular recovery after guideline-directed medical therapy in non-ischaemic dilated cardiomyopathy

Abstract Aims We aimed to identify the proportion of patients with presumed dilated cardiomyopathy (DCM) who undergo left ventricular (LV) reverse remodelling on initiation of guideline-directed medical therapy (GDMT). Our secondary aim was to identify if any clinical or echocardiographic parameters of presentation were predictive of reverse remodelling. Methods We prospectively recruited patients diagnosed by echocardiography with DCM as defined by the current criteria, who were referred for early CMR examination. Patients with an established diagnosis of coronary artery disease and known structural or congenital heart disease were excluded. LV reverse remodelling was defined as LV cavity dilatation on intial echocardiography with subsequent normalisation of LVEDVi measured by cardiovascular magnetic resonance (CMR) . Results A total of 395 patients were recruited including 166 (42%) who fulfilled the definition of LV reverse remodelling. DCM patients who did not achieve reverse remodelling exhibited larger LV (LVEDVi 111.8 ±31.7 vs 81 ± 16.5 mL/BSA, p<0.0001), worse LVEF (26.7 ± 9.24 vs 30.7 ±9.8%, p<0.0001), greater left atrium dilatation (LAVi 49.7 ± 16.5 vs 43 ± 9.7 mL/BSA, p=0.0008), higher incidence of mitral regurgitation (MR) of greater severity, more congestion requiring diuretic therapy and a higher probability of pulmonary hypertension as determined by echocardiography. These individuals also presented higher prevalence of ischaemic LGE (24.1 vs 15.7%, p= 0.04), LBBB (39.6 vs 18%, p=0.0003), and greater QRS duration (124.2 ±30.7 vs 105.7 ± 23.5 ms, p<0.0001), but a lower incidence of AF(26.6 vs 47%, p=0.0005). The multivariable regression analysis adjusted for NYHA class, number of days between echocardiography and CMR, and diuretic use showed that LVEDVi (OR 1.08, 95% CI 1.05 to 1.12, p <0.0001), LVEF (OR 0.97, 95%CI 0.94 to 0.99, p=0.01), more than mild mitral regurgitation (MR, OR 2.08, 95% CI 1.32 to 3.27, p = 0.0017 ), more than mild tricuspid regurgitation (TR, OR 1.99, 95% CI 1.07 to 3.69, p = 0.03), LBBB (OR 3.26 , 95% CI 1.64 to 6.45, p = 0.0007) and QRS duration (OR 1.03 , 95% CI 1.01 to 1.04, p < 0.0001) remained independent predictors of failure of LV reverse remodelling in patients with DCM. Conversely, the presence of AF was associated with a higher probability of LV reverse remodelling (OR 0.45, 95% CI 0.28 to 0.71, p = 0.0007). Ischaemic LGE was also independently associated with LV reverse remodelling (OR 1.82, 95%CI 1.03 to 3.21, p=0.04). Conclusion Contemporary GDMT leads to reverse remodelling in a significant proportion of patients presenting with DCM. Predictors of failure to reverse remodel included larger LV cavity, lower LVEF, greater severity of MR and TR, wider QRS complex, and lower incidence of AF. Incidental ischaemic LGE was also associated with LV reverse remodelling. This has potential therapeutic significance as multimodality imaging is a powerful predictor of cardiovascular outcome in heart failure. Univariable logistic regression Multivariable logistic regression

Influence of gender on the prognostic significance of the wall motion score index in Dobutama n stress echocardiography

Abstract Background/Aim The dobutamine stress echocardiographic test (DSE) plays a significant role in the diagnosis of coronary artery disease (CAD). The aims of our stady were to examine the influence of gender on the prognostic significance of the wall motion score index (WMSI) in the dobutamine stress echocardiographic test for ischemia. Methods It is a prospective, observational study that was conducted at the Clinic for Cardiovascular Diseases of the University-Clinical Center of the Republic of Srpska. The study included 143 patients who, due to suspicion of coronary disease, underwent a dobutamine stress echocardiographic test in the period from 01.01. 2021. until 01.02.2022. Results Adverse cardiovascular events observed one year after DSE test were more frequent in men (43%) than in women (19%), this difference was statistically significant (p<0.01). WMSI in men had sensitivity (96 %) and specificity (824%) in detection of adverse cardiovascular events. In women, the WMSI in detection of adverse cardiovascular events, sensitivity (93.3%) and specificity (90.6%). Conclusion Our results identified WMSI as a prognostic factor for adverse cardiovascular events in both sexes, with sensitivity being higher in men and specificity in women. Adverse cardiovascular events in women Adverse cardiovascular events in men

Zero calcium score: a potential pitfall in the diagnosis of coronary artery disease.

Zero calcium score: a potential pitfall in the diagnosis of coronary artery disease.

Bariatric Surgery and Cardiovascular Outcomes in Patients with Obesity, Metabolic Dysfunction-Associated Steatotic Liver Disease, and Coronary Artery Disease: A Population-Based Matched Cohort Study.

Bariatric surgery (BS) is a potential treatment option for patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and obesity. These patients are also at substantial risk of developing cardiovascular events and associated mortality. We aimed to assess if BS could reduce major adverse cardiovascular events (MACE) and mortality and improve long-term survival. Using the TriNetX data, adult patients (>18 years) with the diagnosis of MASLD, obesity (i.e. body mass index [BMI] ≥35kg/m2), and pre-existing coronary artery disease (CAD) between January 1, 2005, to December 31, 2022, were included. Patients with a BS were compared to those with no history of BS. Primary outcomes were the incidence of MACE, heart failure, cerebrovascular events, and coronary artery procedures or surgeries at years 1, 3, 5, 7, and 10. The secondary outcome was all-cause mortality at years 1, 3, 5, 7, and 10. We performed 1:1 propensity score matching (PSM), sensitivity analysis, and survival analysis. After PSM, both groups had a total of 1038 patients. At year 1, BS patients had a significantly lower incidence of MACE (HR=0.56, 95% CI, 0.39-0.80), cerebrovascular disease (HR=0.62, 95% CI, 0.46-0.82), and coronary artery procedures and surgeries (HR=0.65, 95% CI, 0.42-0.98). Similarly, at 3, 5, 7, and 10, BS patients had a significantly lower incidence of MACE, heart failure, cerebrovascular disease, and coronary artery procedures and surgeries. BS patients had significantly lower 3, 5, 7, 10-year all-cause mortality. Sensitivity analysis confirmed these findings. BS in patients with MASLD, obesity, and pre-existing CAD can considerably reduce the risk of recurring cardiovascular events and markedly improve survival immediately within the first year of BS and can persist long-term, even a decade after BS.

Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study.

Serum levels of silent information regulator 6 (SIRT6), a key biomarker of aging, were identified as a predictor of coronary artery disease (CAD), but whether SIRT6 can distinguish severity of coronary artery lesions in older adult patients is unknown. This study developed a nomogram to demonstrate the functionality of SIRT6 in assessing severity of coronary artery atherosclerosis. Patients aged 60 years and older with angina pectoris were screened for this single-center clinical study between October 1, 2022, and March 31, 2023. Serum specimens of eligible patients were collected for SIRT6 detection by enzyme-linked immunosorbent assay. Clinical data and putative predictors, including 29 physiological characteristics, biochemical parameters, carotid artery ultrasonographic results, and complete coronary angiography findings, were evaluated, with CAD diagnosis as the primary outcome. The nomogram was derived from the Extreme Gradient Boosting (XGBoost) model, with logistic regression for variable selection. Model performance was assessed by examining discrimination, calibration, and clinical use separately. A 10-fold cross-validation technique was used to compare all models. The models' performance was further evaluated on the internal validation set to ensure that the obtained results were not due to overoptimization. Eligible patients (n=222) were divided into 2 cohorts: the development cohort (n=178) and the validation cohort (n=44). Serum SIRT6 levels were identified as both an independent risk factor and a predictor for CAD in older adults. The area under the receiver operating characteristic curve (AUROC) was 0.725 (95% CI 0.653-0.797). The optimal cutoff value of SIRT6 for predicting CAD was 546.384 pg/mL. Predictors included in this nomogram were serum SIRT6 levels, triglyceride glucose (TyG) index, and apolipoprotein B. The model achieved an AUROC of 0.956 (95% CI 0.928-0.983) in the development cohort. Similarly, in the internal validation cohort, the AUROC was 0.913 (95% CI 0.828-0.999). All models demonstrated satisfactory calibration, with predicted outcomes closely aligning with actual results. SIRT6 shows promise in predicting CAD, with enhanced predictive abilities when combined with the TyG index. In clinical settings, monitoring fluctuations in SIRT6 and TyG may offer valuable insights for early CAD detection. The nomogram for CAD outcome prediction in older adult patients with angina pectoris may aid in clinical trial design and personalized clinical decision-making, particularly in institutions where SIRT6 is being explored as a biomarker for aging or cardiovascular health.

Electronic Patient-Reported Outcome System Implementation in Outpatient Cardiovascular Care

The integration of patient-reported outcome (PRO) assessments in cardiovascular care has encountered considerable obstacles despite their established clinical relevance. To assess the impact of a physician- and patient-friendly electronic PRO (ePRO) monitoring system on the quality of cardiovascular care in clinical practice. This open-label, multicenter, pilot randomized clinical trial was phase 2 of a multiphase study that was conducted from October 2022 to October 2023 and focused on the implementation and evaluation of an ePRO monitoring system in outpatient clinics in Japan. During phase 1 (conducted from December 2021 to March 2022), this ePRO monitoring system was developed at a single outpatient center using feedback from patients and physicians. Eligible patients were older than 18 years with a clinical diagnosis of heart failure (HF), atrial fibrillation (AF), or coronary artery disease (CAD). Participants were randomly assigned 1:1 to either the ePRO or control group. Statistical analysis was based on the intention-to-treat approach. Patients in the ePRO group were asked to complete the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12), Atrial Fibrillation Effect on Quality-of-Life Questionnaire (AFEQT), or Seattle Angina Questionnaire (SAQ) on smart tablets before their clinical examination. Results were reviewed by physicians at each outpatient visit. The control group received usual care consisting of medication management, lifestyle modification, and invasive procedures in accordance with clinical guidelines. Both groups had 5 monthly outpatient visits. Patient satisfaction, quality of information (QOI) provided by physicians, and disease knowledge were assessed using questionnaires (eg, Patient Satisfaction Questionnaire [PSQ]; score range: 5-25, with higher scores indicating greater patient satisfaction) at baseline and the fifth visit, over approximately 4 months. Of the 50 patients included, 48 (median [SD] age, 71.0 [62.3-75.0] years; 28 males [58.3%]) completed follow-up. The prevalence was 56.2% (n = 27) for HF, 75.0% (n = 36) for AF, and 10.4% (n = 5) for CAD. The distribution of PRO measures used was 21 patients (43.8%) for KCCQ-12, 24 (50.0%) for AFEQT, and 3 (6.2%) for SAQ. Compared with the control group, the ePRO group showed significant improvements in mean (SD) PSQ score (0.16 [2.06] vs 1.61 [1.75]; P = .01) and QOI score regarding treatment (-0.12 [0.53] vs 0.35 [0.71]; P = .01). The ePRO group exhibited a greater increase in the PSQ score regarding communication (-0.12 [0.53] vs 0.43 [0.90]; P = .01). In this randomized clinical trial, implementation of the ePRO monitoring system significantly enhanced patient-physician communication and the clarity of physicians' explanations about treatment. These findings suggest that the ePRO monitoring system is capable of supporting patient-centered cardiovascular care. University Hospital Medical Information Network Identifier: UMIN000049251.

Predicting Coronary Artery Disease using Explainable Machine Learning

Coronary artery disease (CAD) has been one of the leading causes of death worldwide; it is a result of narrowing or blocking of the coronary arteries due to the buildup of plaque, which reduces the flow of blood into the heart. Accurate and early diagnosis of CAD is especially important for proper treatment in pursuit of better outcomes for patients. However, traditional diagnostic methods, such as angiography, are invasive, time-consuming, and resource-intensive, which calls for non-invasive alternatives. This study applies machine learning in the analysis of the dataset Z-Alizadeh Sani, which encompasses 303 patient records grouped into 53 features like demographic, symptom and examination, ECG, laboratory, and echo data. Machine learning can predict with greater accuracy, speed, and efficiency CAD. It could also reveal important patterns and features, supporting clinicians in decision-making and personalized care. The presented research has shown the potential of machine learning in revolutionizing CAD diagnosis, thereby providing non-invasive, cost-effective, and reliable diagnostic tools. That can improve outcomes in health care but also take some burden off the healthcare systems. Key Words: Machine Learning, CAD, Medical, Explainability, Disease Prediction, Gradient Boosting

Advancing coronary artery disease diagnosis with 2D-Speckle Tracking Echocardiography

Advancing coronary artery disease diagnosis with 2D-Speckle Tracking Echocardiography

Preoperative Cardiac Assessment for Coronary Artery Disease: From Symptoms to Angiography.

Although cardiovascular anesthesiologists play a role in the multidisciplinary heart team, they are generally not involved prior to the diagnosis of coronary artery disease. This work was designed to broadly cover the factors influencing decision-making when proceeding with coronary angiography in a patient with suspected coronary artery disease. The authors cover the principles, rationale, benefits, and downsides of common tests involved.

Minimally Invasive Versus Invasive Proteomics: Urine and Blood Biomarkers in Coronary Artery Disease.

Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. This underlines the urgent need for effective biomarkers for early diagnosis, risk stratification, and therapeutic counseling. Proteomic signatures from plasma and urine have emerged as promising tools for these efforts, each offering unique advantages and challenges. This review provides a detailed comparison of urine and blood proteomic analyzes in the context of CAD and explores their respective advantages and limitations. Urine proteomics offers a minimally invasive, easily repeatable, and temporally stable sampling method, but faces challenges such as lower protein concentrations and potential contamination. Despite its invasive nature, blood proteomics captures high protein concentration and directly reflects systemic physiological changes, making it valuable for acute assessments. Advances in artificial intelligence (AI) have significantly improved the analysis and interpretation of proteomic data, enabling greater accuracy in diagnosis and predictive modeling. AI algorithms, particularly in pattern recognition and data integration, are helping to uncover subtle relationships between biomarkers and disease progression and supporting the discovery of plasma- and urine-based CAD biomarkers. This review demonstrates the potential of combining urine and blood proteomic data using AI to advance personalized approaches in CAD diagnosis and treatment. Future research should focus on standardization of collection protocols, validation of biomarkers in different populations, and the complexity of integrating data from different sources to maximize the potential of proteomics in the treatment of CAD.

Stress Cardiac Magnetic Resonance Imaging in Intermediate-Risk Emergency Department Patients with Abnormal High-Sensitivity Troponin.

Patients presenting to the emergency department (ED) with chest pain often have abnormal high-sensitivity troponin (hsTn). However, only about 5% have an acute coronary syndrome. We aimed to assess the safety, feasibility and utility of a clinical disposition protocol including outpatient observation with stress cardiac-magnetic-resonance (CMR) in intermediate-risk patients with abnormal hsTn of unclear etiology. Patients with abnormal hsTn and modified HEART-score≤6 underwent CMR to inform diagnosis, risk-stratification, and ED-disposition. Patients were followed at 30 and 90 days for all-cause mortality, readmission for myocardial infarction, and unplanned coronary revascularization. CMR was completed in 50 patients (64 years, 56% male) at a median of 23.2hours after presentation to the ED. CMR findings of coronary-artery-disease (CAD) were present in 19 (38%) of patients, of which 13 had known CAD and 6 received a new diagnosis of CAD. In 12 (24%) patients, cardiac noncoronary-artery-disease was diagnosed [cardiomyopathy (8), valvular disease (3), myocarditis/pericarditis (1)], of which the majority (83%) were new diagnoses. CMR was normal in 19 (38%) patients. After CMR results were reported, the decision to admit was made in 10 (20%) patients, while 40 (80%) were discharged from the ED without further cardiac testing. Follow-up was completed in 96% of patients, of which no patients experienced an adverse event. A disposition protocol with outpatient observation and stress-CMR is feasible and useful for determining the etiology of myocardial injury and risk-stratification in patients presenting to the ED with chest pain, abnormal hsTn, and intermediate risk.

Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.

This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction.

The clinical promise of 18F-flurpiridaz PET imaging heralds a new frontier in the diagnosis and management of coronary artery disease.

The clinical promise of 18F-flurpiridaz PET imaging heralds a new frontier in the diagnosis and management of coronary artery disease.

Automatic Vessel Segmentation and Reformation of Non-contrast Coronary Magnetic Resonance Angiography Using Transfer Learning-based 3D U-net with Attention Mechanism

Coronary Magnetic Resonance Angiography (CMRA) presents distinct advantages, but its reliance on manual image post-processing is labor-intensive and requires specialized knowledge. This study aims to design and test an efficient artificial intelligence (AI) model capable of automating coronary artery segmentation and reformation from CMRA images for coronary artery disease (CAD) diagnosis. By leveraging transfer learning from a pre-existing coronary CT angiography model, a 3D attention-aware U-Net was established, trained, and validated on a dataset of 104 subjects' CMRA. Furthermore, an independent clinical evaluation was conducted on an additional cohort of 70 patients. The AI model's performance in segmenting coronary arteries was assessed using the Dice Similarity Coefficient (DSC) and Recall. The comparison between the AI model and manual processing by experienced radiologists on vessel reformation was based on reformatted image quality (rIQ) scoring, post-processing time, and the number of necessary user interactions. The diagnostic performance of AI-segmented CMRA for significant stenosis (≥50% diameter reduction) was evaluated using conventional coronary angiography (CAG) as a reference in sub-set data. The DSC of the AI model achieved on the training and validation sets were 0.952 and 0.944, with recalls of 0.936 and 0.923, respectively. In the clinical evaluation, the model outperformed manual processes by reducing vessel post-processing time, from 632.6±17.0seconds to 77.4±8.9seconds, and the number of user interactions from 221±59 to 8±2. The AI postprocessed images maintained high rIQ scores comparable to those processed manually (2.7±0.8 vs 2.7±0.6; P=0.4806). In subjects with CAG, the prevalence of CAD was 71%. The sensitivity, specificity, and accuracy at patient-based analysis were 94%, 71%, and 88%, respectively, by AI post-processed whole-heart CMRA. The AI auto-segmentation system can effectively facilitate CMRA vessel reformation and reduce the time consumption for radiologists. It has the potential to become a standard component of daily workflows, optimizing the clinical application of CMRA in the future.

Untargeted metabolomics reveals biomarkers for the diagnosis of coronary artery plaques as observed by coronary cardiac computed tomography.

Atherosclerosis is a major cause of morbidity and mortality worldwide; in Israel, ischemic heart disease is the second leading cause of death for both genders aged 45 and above. Atherosclerosis involves stiffening of the arteries due to the accumulation of lipids and oxidized lipids on the blood vessel walls, triggering the development of artery plaque. Coronary artery disease (CAD) is the most common manifestation of atherosclerosis. The prevalence of CAD in the general population remains high, despite efforts to improve the identification of risk factors and preventive treatments. The discovery of new biomarkers is vital to improving the diagnosis of CAD and its risk factors. We aimed to identify novel biomarkers that could provide an early diagnosis of coronary artery atherosclerotic plaques, their type, and the percentage of stenosis. We used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAD detection. The study consisted of 109 patients who underwent cardiac computed tomography angiography at the Cardiology Department of Ziv Medical Center. Fifty-four patients were diagnosed with coronary atherosclerotic plaques (CAD group), and 55 without plaques used control. Untargeted metabolomics using LC-MS/MS revealed 2560 metabolites in the patients' serum: 106 showed statistically significant upregulation in the serum of the CAD group compared with the healthy control group (p < 0.05). These metabolites belonged to the following chemical families: acyl-carnitines, cyclodipeptides, lysophosphatidylcholine, and primary bile acids. In contrast, 98 metabolites displayed statistically significant downregulation in the serum of the CAD group compared with the control group, belonging to the following chemical families: GABA amino acids and derivatives (inhibitory neurotransmitters), lipids, and secondary bile acids. Our comprehensive untargeted serum metabolomic analysis revealed biomarkers that can be used for the diagnosis of patients with CAD. Further cohort studies with a larger number of participants are needed to validate the detected biomarkers.

Coronary artery profile on coronary angiography in TMT positive female patients presenting in BPKIHS, Nepal

BACKGROUND Diagnosis of Coronary artery disease by Coronary artery angiography is limited by its availability, cost and as an invasive procedure. Treadmill test is a noninvasive and easily available test for diagnosing as well as prognosis of coronary artery disease and is most commonly performed stress test for diagnosis of coronary artery disease. Women tend to have a greater release of catecholamines during exercise, which could potentiate coronary vasospasm and augment the incidence of abnormal exercise results. TMT in women have a relatively low diagnostic yield for CAD compared to men, especially when symptoms are atypical or non-specific. AIM AND OBJECTIVE To analyze the coronary artery profile on Coronary Angiography (CAG) in TMT positive female patients. METHODS This was a cross sectional observational study carried out over a period from 15th July 2024 to 15th October 2024 in Department of Cardiology. A total of 64 female patients with positive Treadmill test and underwent CAG after pre-test probability Results: A total of 64 patients were enrolled in the study. The mean age of the patient was 53.38±16.7 years. Hypertension was present in (43.8%) study female as must common risk factors. DM female patient was 35.9% study patients. Dyslipidemia was seen in 34.4% patients. CAD was seen in 29.7% of cases with significant CAD in 18.97% study patients. The most common coronary artery involved having significant disease during coronary angiogram was LAD in 38.89% of cases with CAD. Two third of patient who had CAD was at high pre-test probability categories. CONCLUSIONS TMT has a low predictive value of CAD in female patients except those female TMT positive patients with a high pre-test probability score.

Usefulness of the addition of two-dimensional speckle tracking during dobutamine stress echocardiography for the detection of coronary artery disease

Background Coronary artery disease (CAD) is the most frequent type of cardiac disorder. Initial evaluation for ischaemia using dobutamine stress echocardiography (DSE) is based on a visual interpretation of the regional wall motion abnormality. Aims To assess the usefulness of speckle tracking during DSE and evaluate the diagnostic accuracy of 2D global longitudinal strain (GLS) along with its additive role over wall motion abnormalities interpretation for the diagnosis of CAD. Materials and methods One hundred and twenty-five patients are included in the study. Full history, physical examination, ECG, echocardiography, DSE, speckle tracking echocardiography (STE) with global and regional post-systolic strain index (PSI) during rest, peak dobutamine stress and recovery phase, and coronary angiography are done. Results In the rest study, there is a statistically significant difference in the global and regional PSIs, both are higher in patients with CAD (group 1) than in patients without CAD (group 2) (PSI 5.1 ± 3 in group 1 and 2.7 ± 2.4 in group 2, p value .02) (regional PSI 5.4 ± 4.1 in group 1 and 3 ± 3.3 in group 2, p value .02). At peak stress, the global PSI is higher in group 1 (7 ± 4.2, p value .01) and regional PSI is higher in group 1 (7.9 ± 8.5, p value .03). In recovery period, the global PSI is higher in group 1 (8 ± 6.1, p value .01), the regional PSI is higher in group 1 (9.8 ± 8.5, p value .001) and the GLS is lower in group 1 than in group 2 (−15.5 ± 4 vs. −19.1 ± 3.1, p value .03). The most sensitive and specific parameters are the global and regional PSI in the recovery period, where both showed sensitivities of 95% and 98%, respectively, and specificities of 93% and 95%, respectively. Conclusions Speckle tracking during rest and DSE seems feasible in detecting the stenosis of the coronary arteries in patients with chronic CADs through the evaluation of GLS, and global and regional PSIs. The combination of DSE and STE increases accuracy, sensitivity, and specificity of diagnosing CADs.

Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data

Coronary artery disease (CAD) is the main cause of death. It is a complex heart disease that is linked with many risk factors and a variety of symptoms. In the past few years, CAD has experienced a remarkable growth. Prompt risk prediction of CAD would be capable of decreasing the death rate by permitting timely and targeted treatments. Angiography is the most precise CAD diagnosis technique; however, it has several side effects and is expensive. Multi-criteria decision-making approaches can well perceive CAD by analysing main clinical indicators like ChestPain type, ST_Slope, and HeartDisease presence. By assessing and evaluating these factors, the model improves diagnostic accuracy and aids informed clinical decisions for quick CAD detection. Mainly machine learning (ML) and deep learning (DL) use plentiful models and algorithms, which are commonly employed and very useful in exactly detecting the CAD within a short time. Current studies have employed numerous features in gathering data from patients while using dissimilar ML and DL models to attain results with high accuracy and lesser side effects and costs. This study presents a Leveraging Fuzzy Wavelet Neural Network with Decision Making Approach for Coronary Artery Disease Prediction (LFWNNDMA-CADP) technique. The presented LFWNNDMA-CADP technique focuses on the multi-criteria decision-making model for predicting CAD using biomedical data. In the LFWNNDMA-CADP method, the data pre-processing stage utilizes Z-score normalization to convert an input data into a uniform format. Furthermore, the improved ant colony optimization (IACO) method is used for electing an optimum sub-set of features. Furthermore, the classification of CAD is accomplished by utilizing the fuzzy wavelet neural network (FWNN) technique. Finally, the hyperparameter tuning of the FWNN model is accomplished by employing the hybrid crayfish optimization algorithm with the self-adaptive differential evolution (COASaDE) technique. The simulation outcomes of the LFWNNDMA-CADP approach are investigated under a benchmark database. The experimental validation of the LFWNNDMA-CADP approach portrayed a superior accuracy value of 99.49% over existing techniques.