Detection Of Coronary Artery Disease Research Articles

Diagnostic performance of high and ultra-high-resolution photon counting CT for detection of coronary artery disease in patients evaluated for transcatheter aortic valve implantation.

We assessed the diagnostic performance of both ultra-high-resolution (UHR) and high-resolution (HR) modes of photon-counting detector (PCD)-CT within the confines of standard pre-TAVI CT scans, as well as the performance of UHR mode adjusted specifically for coronary imaging, using quantitative coronary angiography (QCA) as the reference. We included 60 patients undergoing pre-TAVI planning CT scans. Patients were divided into 3 groups: 20 scanned in HR mode, 20 in UHR mode, and 20 in adjusted UHR mode, on a dual-source PCD-CT. The adjusted UHR mode employed a lower tube voltage (90kV vs. 120kV) and a higher image quality level (65 vs. 34) to enhance coronary artery visualization. Patients underwent invasive coronary angiography as part of clinical routine. CCTA and QCA were reviewed to assess CAD presence defined as stenosis ≥ 50% in proximal and middle coronary segments. We included 60 patients (mean age 79 ± 7 years; 39(65%) men). Mean heart rate during scanning was 72 ± 13bpm. Median coronary calcium score was 973[379-2007]. QCA identified significant CAD in 24 patients (40%): 9 patients scanned with HR mode, 10 patients with the UHR mode, and 5 patients with the UHR adjusted mode. Per-patient area under the curves were 0.57 for HR, 0.80 for UHR, and 0.80 for adjusted UHR, with no significant differences between the scan modes, and per-vessel the area under the curves were 0.73 for HR, 0.69 for UHR, and 0.87 for adjusted UHR, with significant differences between UHR and adjusted UHR (p = 0.04). UHR and adjusted UHR modes of dual source PCD-CT show potential for improved sensitivity and negative predictive value for detecting CAD in patients undergoing pre-TAVI scans, however, no statistically significant difference from HR mode was observed.

Use of ST/HR hysteresis decreases false positive rate in exercise electrocardiography test of middle-aged asymptomatic women

BackgroundExercise electrocardiography (ECG) is a common diagnostic and prognostic method for the detection of coronary artery disease (CAD). However, its accuracy in asymptomatic women has not been comprehensively investigated and the diagnostic criteria may require refinements. This study evaluated the performance of ECG-derived ST/HR-index, ST/HR hysteresis and ST-segment depression parameters among asymptomatic middle-aged women. Methods108 women (mean age 56 ± 4 years) performed exercise ECG test on treadmill until exhaustion three times within a nine-month period. False-positive rates of maximum ST/HR-index, ST/HR hysteresis, and ST-segment depression values measured from 12 leads at peak exercise and after one-minute recovery were evaluated with recommended diagnostic partition values. Repeatability was assessed with intraclass correlation (ICC) and Bland-Altman plot analysis. ResultsFalse-positive rate was lower for all variables when a two‑lead configuration was used instead of a single‑lead configuration. Using a two‑lead configuration, ST/HR hysteresis (0–1.9 %) and ST-segment depression after one-minute recovery (0–2.8 %) had lower false-positive rates compared to ST/HR index (3.7–20.4 %) and ST-segment depression at peak exercise (9.3–27.8 %). ICC values indicated moderate repeatability for ST/HR hysteresis while ST/HR index, ST-segment at peak exercise, and ST-segment after one-minute recovery had moderate-to-good repeatability. Bland-Altman analysis indicated poor repeatability for all evaluated ECG variables. ConclusionIn asymptomatic middle-aged women, practitioners should prefer the use of ST/HR hysteresis and ST-segment after one-minute recovery over the conventional ST-segment depression at peak exercise or ST/HR index, and evaluate the ECG data from a two‑lead configuration instead of single‑lead.

Enhanced CAD Detection Using Novel Multi-Modal Learning: Integration of ECG, PCG, and Coupling Signals

Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification.

Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.

The prognostic value of contrast stress echocardiography in a real-world population of symptomatic patients without known coronary artery disease

Abstract Background Coronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. In addition to diagnosis, non-invasive tests may also provide prognostic information useful in the risk stratification and management of CAD patients. Stress echocardiography (SE) is an accurate, non-invasive method for the detection of obstructive CAD. However, in real-world clinical practice the test performance may vary among different centres, depending on availability, local protocols, and operator expertise. Purpose To evaluate the prognostic value of contrast SE in a real-world population of symptomatic patients without known CAD. Methods This was a cohort follow-up study of patients without known CAD and symptoms of atypical chest pain, unexplained dyspnoea or palpitations who were referred to the echocardiography lab of a tertiary hospital for SE between January 2020 and December 2021. Patients with resting regional wall motion abnormalities were excluded. All patients underwent treadmill exercise stress echocardiography (ESE) or dobutamine stress echocardiography (DSE) if unable to exercise. Contrast agents were used in almost all patients (99%). The study population was followed-up for at least 2 years after SE by phone and/or electronic hospital records. Cardiac outcome was defined as anatomically or functionally significant stenosis on angiography, revascularization, acute coronary syndrome (ACS), or cardiac-related death. Results The study comprised 266 patients (mean age 58±12 years, 63% male) who underwent either ESE (58%) or DSE (42%). The patients' details are reported in Table 1. The most common indication for the exam was atypical chest pain, with a prevalence of 73%. SE exams were positive for inducible ischaemia in 14 (5%) patients. Symptoms during the exam were present in 3 (21%) patients with positive SE and 4 patients (2%) with negative SE (p=0.004). No serious complications (sustained ventricular tachycardia, myocardial infarction, or death) were recorded. During the follow-up (median 30, IQR 23–38 months), 264 patients (99%) were successfully contacted. Cardiac outcome occurred in 10 patients. Among 14 patients with positive SE, 8 underwent revascularization due to significant coronary artery stenosis. Among 250 patients with negative SE, 1 underwent revascularization due to significant coronary artery stenosis and 1 presented with ACS 13 months after the test (both patients underwent DSE). Negative predictive value was 99% over a median follow-up of 30 months, corresponding to an annualized event rate of 0.32% per year. A positive SE was significantly associated with cardiac outcome (log-rank test p<0.0001, Figure 1). Conclusion Contrast SE carries significant prognostic implications in symptomatic patients without known CAD. A positive test is strongly associated with the need for revascularization, while a normal test result has an excellent negative predictive value regarding cardiovascular morbidity and mortality.Figure 1

Coronary Computed Tomographic Angiography (CCTA) in non-ST elevation acute coronary syndromes

Abstract Background In patients presenting with an acute coronary syndrome (ACS), a diverse range of coronary artery disease is observed from structurally normal vessels to non-obstructive atherosclerosis and severe/obstructive coronary artery disease (CAD). In current clinical practice guidelines, the main diagnostic route for optimal management of patients with non-ST segment elevation acute coronary syndrome is invasive coronary angiography. However, in 30-40% of cases, no significant coronary lesions are evident. The purpose of this study is to describe the role of coronary computed tomographic angiography (CCTA) in non-ST elevation acute coronary syndromes in low and intermediate risk patients. Methods We describe the results of 504 patients evaluated with a protocol that includes the use of CCTA in patients with ACS. Patients over 18 years of age with a diagnosis of ACS without ST elevation who did not meet high-risk criteria (hemodynamic instability, dynamic changes in the ST segment or T wave, ventricular arrhythmia, GRACE score >140, refractory angina, heart failure or cardiorespiratory arrest) were included. They underwent CCTA as the initial coronary evaluation. Data collection was carried out between May 11, 2022 and February 20, 2024. Results 504 patients were included, with a mean age of 58 years, of which 44% were women and 56% men. Among the admission diagnoses, 26% were NSTEMI and 74% were unstable angina according to the 4th universal definition of acute myocardial infarction (AMI). The median high-sensitivity cardiac troponin T value was 7 nanograms per liter (interquartile range, 7 to 15) for the first troponin measurement. CCTA was performed in 93% of the patients. It was observed that 28% did not have any coronary lesions or plaques. 36% had non-obstructive coronary artery disease (plaques <50%). The rest had significant lesions in at least one vessel (27%), predominantly in the left anterior descending artery, or were nondiagnostic (9%). 31% had myocardial bridges, while in 13% of the patients a high-risk plaque was found. 38% of the patients included underwent invasive coronary angiography and 23% required revascularisation. Conclusion This study shows that the prevalence of ACS with coronary arteries without significant lesions by CCTA was 28%, which suggests CCTA could have a predominant role in the diagnostic algorithm of acute coronary syndromes without high-risk criteria, implying a decrease in the need of invasive studies in these patients. Only 38% required invasive coronary angiography, while revascularisation was performed in 23% of the patients. Furthermore, CCTA allowed a better detection of non-obstructive coronary artery disease (NOCAD) in 36% of the patients, and a more complete characterization of the atherosclerotic plaque, which could prompt to an initiation of preventive therapies or the optimization of medical treatment in these patients.Table 1CCTA findings

HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling

Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic segmentation for coronary arteries through deep learning-based models presents challenges due to the morphological similarity among different types of coronary arteries, making it difficult to maintain high accuracy while keeping low computational complexity. To address this challenge, we propose an innovative approach using the hyper association graph-matching neural network with uncertainty quantification (HAGMN-UQ) for coronary artery semantic labeling on ICAs. The graph-matching procedure maps the arterial branches between two individual graphs, so that the unlabeled arterial segments are classified by the labeled segments, and the coronary artery semantic labeling is achieved. Leveraging hypergraphs not only extends representation capabilities beyond pairwise relationships, but also improves the robustness and accuracy of the graph matching by enabling the modeling of higher-order associations. In addition, employing the uncertainty quantification to determine the trustworthiness of graph matching reduces the required number of comparisons, so as to accelerate the inference speed. Consequently, our model achieved an accuracy of 0.9211 for coronary artery semantic labeling with a fast inference speed, leading to an effective and efficient prediction in real-time clinical decision-making scenarios.

Circulating miR-133a-3p and miR-451a as potential biomarkers for diagnosis of coronary artery disease.

Coronary artery disease (CAD) remains the leading cause of mortality and morbidity around the world. Despite significant progress in the diagnosis and treatment of cardiovascular diseases, still there is a clinical need to identify novel biomarkers for early diagnosis and treatment of CAD. The aim of the study is to investigate circulating miRNAs in CAD patients to identify potential biomarkers for early detection and therapeutic management of CAD. We assessed the expression of different candidate miRNAs (miR-21-5p, miR-133a-3p, miR-221-3p, miR-451a and miR-584-5p) in plasma from 50 CAD patients and 50 controls by qRT-PCR analysis. The expression levels of miR-133a-3p (fold change (FC): 28.05, p < 0.0001), miR-451a (FC: 27.47, p < 0.0001), miR-584-5p (FC: 7.89, p < 0.0001), miR-21-5p (FC: 5.35, p < 0.0001) and miR-221-3p (FC: 5.03, p < 0.0001) were significantly up-regulated in CAD patients compared to controls. Receiver operating characteristic curve analysis showed that miR-133a-3p and miR-451a were powerful biomarkers for detecting CAD. Our results suggested that miR-21-5p, miR-133a-3p, miR-221-3p, miR-451a and miR-584-5p may serve as independent biomarkers for CAD. Further, the combination of miR-133a-3p and miR-451a could be used as a specific signature in CAD diagnosis.

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners. We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives. AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.

Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography

PurposeThe purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). Materials and methodsConsecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. ResultsA total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83–0.94) at the patient level and 0.92 (95 % CI: 0.89–0.94) at the vessel level. ConclusionAutomated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.

Stress CMR Perfusion Imaging in the Medicare-Eligible Population: Insights From the SPINS Study.

Patients aged ≥65 years account for a disproportionately large portion of cardiovascular (CV) events and pose a challenge for noninvasive detection of coronary artery disease. This study sought to determine the prognostic value of stress cardiac magnetic resonance (CMR) in a Medicare-eligible group of patients in a multicenter setting in the United States. From a multicenter U.S. registry, the study identified patients aged≥65 years who were referred for stress CMR for evaluation of myocardial inducible ischemia. The primary outcome was defined as CV death or nonfatal myocardial infarction, whereas the secondary outcome was defined as any primary outcome, hospitalization for unstable angina, hospitalization for congestive heart failure, and unplanned late coronary artery bypass grafting. The associations of CMR findings with CV outcomes adjusted to clinical risk markers and health care cost spending were determined. Among 1,780 patients (aged 73 ± 5.7 years; 46% female), study investigators observed 144 primary eventsand 323 secondary events, over a median follow-up of 4.8 years. The presence of inducible ischemia and late gadolinium enhancement (LGE) was associated with incrementally higher event rates. Patients with neither inducible ischemia nor LGE experienced a<1% annualized rate of primary outcome. In a multivariable model adjusted for CV risk factors, inducible ischemia and LGE maintained an independent association with primary (HR: 2.80 [95%CI: 1.93-4.05]; P< 0.001; and HR: 1.85 [95%CI: 1.21-2.82]; P = 0.004, respectively) and secondary (HR: 2.46 [95%CI: 1.90-3.19]; P< 0.001; and HR: 1.72 [95%CI: 1.30-2.27]; P< 0.001, respectively) outcomes. Rates of revascularization, as well as downstream costs for patients without CMR-detected inducible ischemia, remained low throughout the follow-up period. In a multicenter cohort of Medicare-eligible older patients, stress CMR was effective in providing risk stratification. (Stress CMR Perfusion Imaging in the United States [SPINS] study; NCT03192891).

Exercise ECG for Detection of Asymptomatic Coronary Artery Disease in Patients with Type 2 Diabetes Mellitus

Exercise ECG for Detection of Asymptomatic Coronary Artery Disease in Patients with Type 2 Diabetes Mellitus

Biomarker Profiling of Nitrosative Stress and Inflammation in Early Detection and Prognosis of Coronary Artery Disease

Biomarker Profiling of Nitrosative Stress and Inflammation in Early Detection and Prognosis of Coronary Artery Disease

Comparative Analysis of T2 and T1 MRI for Coronary Artery Disease Detection

Coronary artery disease (CAD) remains a significant contributor to global mortality. Non-invasive imaging modalities are indispensable for the diagnosis and management of CAD. Magnetic Resonance Imaging (MRI) has emerged as a promising tool due to its inherent lack of ionizing radiation and its capacity to provide detailed anatomical and functional insights. This article presents a comprehensive review of the current literature, comparing the utilization of T2 and T1 MRI techniques in CAD detection. We highlight the advantages, limitations, and specific clinical applications of each modality. T1 MRI with contrast enhancement excels in visualizing coronary artery walls and plaques, assessing myocardial perfusion, and detecting scarring. In contrast, T2 MRI, without the need for contrast agents, is invaluable for detecting myocardial edema, a key indicator in the diagnosis of acute myocardial infarction. The selection between T2 and T1 MRI should be judiciously based on the specific clinical question and patient considerations.

The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.

Cardiovascular diseases remain the leading cause of global mortality, underscoring the critical need for accurate and timely diagnosis. This narrative review examines the current applications and future potential of artificial intelligence (AI) and machine learning (ML) in cardiovascular imaging. We discuss the integration of these technologies across various imaging modalities, including echocardiography, computed tomography, magnetic resonance imaging, and nuclear imaging techniques. The review explores AI-assisted diagnosis in key areas such as coronary artery disease detection, valve disorders assessment, cardiomyopathy classification, arrhythmia detection, and prediction of cardiovascular events. AI demonstrates promise in improving diagnostic accuracy, efficiency, and personalized care. However, significant challenges persist, including data quality standardization, model interpretability, regulatory considerations, and clinical workflow integration. We also address the limitations of current AI applications and the ethical implications of their implementation in clinical practice. Future directions point towards advanced AI architectures, multimodal imaging integration, and applications in precision medicine and population health management. The review emphasizes the need for ongoing collaboration between clinicians, data scientists, and policymakers to realize the full potential of AI in cardiovascular imaging while ensuring ethical and equitable implementation. As the field continues to evolve, addressing these challenges will be crucial for the successful integration of AI technologies into cardiovascular care, potentially revolutionizing diagnostic capabilities and improving patient outcomes.

CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography

<scp>CADICA</scp>: A new dataset for coronary artery disease detection by using invasive coronary angiography

Utility of Magnetocardiography and Stress Speckle Tracking in Detection of Coronary Artery Disease.

Introduction: Coronary artery disease (CAD) is a leading cause of death and disability in developed countries. While exercise testing is recommended for diagnosing stable angina pectoris, its limited sensitivity and specificity have been questioned. Myocardial strain is a promising predictor of significant CAD. Aim: To evaluate the utility of myocardial strain obtained by 2D speckle tracking at rest and under stress combined with stress magnetocardiography for detecting CAD in patients with stable or low-risk unstable angina pectoris. Methods: A total of 108 patients meeting the inclusion criteria underwent coronary angiography within 48 h of admission. Myocardial strain was assessed using 2D speckle tracking at rest and during dobutamine stress alongside stress magnetocardiography. Results: Global longitudinal strain at stress showed a moderate correlation with significant CAD (r = 0.41, p <0.0001) and with coronary occlusion severity (r = 0.62, p <0.0001). Strain at stress had a sensitivity of 74.1% and specificity of 76.7% for detecting CAD at a cut-off value of -19.1. The ST fluctuation rate from magnetocardiography demonstrated the highest sensitivity for CAD detection. Conclusions: Longitudinal strain parameters and stress magnetocardiography are effective non-invasive methods for predicting CAD in patients with stable angina, potentially reducing the need for invasive assessments.

Detection of Coronary Artery Disease With Coronary Computed Tomography Angiography and Stress Testing in Candidates for Liver Transplant

Cardiac complications are the leading cause of morbidity and mortality in liver transplant (LT) recipients. Prior guidelines recommended stress testing to exclude coronary artery disease (CAD), though recent guidelines recommend coronary computed tomography angiography (CCTA). We aimed to assess the prevalence and predictors of CAD on CCTA and compare CCTA with stress testing in consecutive adult LT candidates undergoing CAD non-invasive assessment between 2020 to 2023. Patients who underwent a stress test between 01/2020 and 12/2020 formed the stress cohort, and patients who underwent CCTA between 01/2021 and 09/2023 formed the CCTA cohort. There were 141 in the stress test cohort and 269 patients in the CCTA cohort. Stress tests were non-diagnostic or inconclusive in 18 (12.8%) whereas CCTA was non-diagnostic in 6 (2.2%) patients. In patients evaluated with CCTA, mean coronary artery calcium (CAC) score was 332 ± 716 AU, with moderate or greater (>50%) stenosis in 33 (12.3%) patients. New CAD was diagnosed in 158 (58.7%) using CCTA and in 5 (3.5%) patients using stress tests. Clinically actionable CAD (CAC >100) on CCTA was present in 96 (35.7%) patients. The number of CAD risk factors were associated with the presence of CAD on CCTA. In conclusion, there was a high burden of CAD, mainly non-obstructive, in a large cohort of LT candidates undergoing CAD testing over a 4-year period. The current recommended risk-based evaluation of LT candidates using CCTA as a first line test was feasible and effective. Diagnosis of clinically actionable CAD on CCTA provides a vast opportunity for optimizing cardiac care in LT candidates and recipients.

Deep learning fusion framework for automated coronary artery disease detection using raw heart sound signals

One of the most common cardiovascular diseases is coronary artery disease (CAD). Thus, it is crucial for early CAD diagnosis to control disease progression. Computer-aided CAD detection often converts heart sounds into graphics for analysis. However, this method relies heavily on the subjective experience of experts. Therefore, in this study, we proposed a method for CAD detection using raw heart sound signals by constructing a fusion framework with two CAD detection models: a multidomain feature model and a medical multidomain feature fusion model. We collected heart sound signal datasets from 400 participants, extracting 206 multidomain features and 126 medical multidomain features. The designed framework fused the same one-dimensional deep learning features with different multidomain features for CAD detection. The experimental results showed that the multidomain feature model and the medical multidomain feature fusion model achieved areas under the curve (AUC) of 94.7 % and 92.7 %, respectively, demonstrating the effectiveness of the fusion framework in integrating one-dimensional and cross-domain heart sound features through deep learning algorithms, providing an effective solution for noninvasive CAD detection.

Integrating Toe Brachial Index and longitudinal strain echocardiography for detecting coronary artery disease in patients with diabetic foot syndrome.

Coronary artery disease (CAD) is a common problem amongst diabetic foot syndrome (DFS) patients, associated with peripheral arterial disease. This analytic cross-sectional study investigates the diagnostic efficacy of the Toe Brachial Index (TBI) in the detection of CAD in 62 DFS patients. The presence of CAD was assessed by longitudinal strain echocardiography, a sensitive method that provides a more accurate measure of intrinsic left ventricular contractility than left ventricular ejection fraction, especially in diabetic patients. Univariate and multivariate logistic regression identified CAD-associated factors. Receiver operating characteristic curve evaluated TBI and toe pressure's diagnostic performance for CAD. p-Values < 0.05 were considered significant. There was a significant association between TBI and CAD, with each 0.01 increase in TBI associated with a 15% decrease in the odds of CAD development (odds ratio = 0.85, 95% CI: 0.72-0.99, p = 0.039). TBI demonstrated an area under the curve of 0.854, a sensitivity of 80.0% and a specificity of 66.7% at a cut-off of 0.69. Additionally, toe pressure exhibited an area under the curve of 0.845, sensitivity of 74.0% and specificity of 75.0% at a cut-off of 68.0 mmHg. Overall accuracy for TBI and toe pressure was 77.4% and 74.2%, respectively, indicating their potential for CAD risk stratification in the DFS population. This study highlights a significant association between low TBI and the presence of CAD in DFS patients. Consequently, TBI emerges as a valuable screening tool for identifying CAD within this population.