Diagnosis Of Coronary Artery Disease Research Articles

Single Coronary Artery as a Rare Developmental Variant in Cardiac Computed Tomography Angiography

Cardiac computed tomography angiography (CCTA) is a non-invasive method for the diagnosis of coronary artery disease. In addition to the assessment of possible stenoses in the coronary arteries, this method also allows the assessment of other abnormalities of coronary and extracoronary heart structures. CCTA is the optimal method for assessing the relationship of coronary arteries to other anatomical structures; thus, it is used as a method of diagnosing developmental variants of coronary circulation. We present images of a single left coronary artery in a 384-slice CCTA in a 69-year-old Caucasian female patient with non-specific chest pain and low intermediate cardiovascular risk as an example of a rare developmental coronary variant. In conclusion, the importance of CCTA as a method of diagnosing developmental variations of the heart and vessels should be emphasized.

ECG-based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease.

The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning. The CDG can reflect anomalous functional information in coronary artery disease (CAD). We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECG-diagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA. Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52-66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794-0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790-0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = -.395; p < .001). The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has the potential to be used as a screening tool for suspected CAD patients before CCTA.

Utility of neutrophil-to-high-density lipoprotein-cholesterol ratio in patients with coronary artery disease: a narrative review.

The neutrophil-to-high-density lipoprotein-cholesterol ratio (NHR) is thought to reflect inflammatory status and dyslipidaemia, both of which play significant roles in coronary artery disease (CAD). The objective of this narrative review is to summarise the results of studies that have explored the utility of NHR for the diagnosis and management of CAD. The PubMed, Google Scholar, Scopus, Embase and Web of Science databases were searched for articles related to NHR from their inception to October 2022. Seven relevant articles were obtained for review. There were unclear relationships of NHR with age, sex, smoking status, hypertension and diabetes. However, NHR had a sensitivity and specificity as high as 94.8% and 59%, respectively, for the identification of significant coronary stenosis. NHR was also a superior predictor of prognosis to conventional parameters. NHR had a sensitivity and specificity as high as 77.6% and 74.2%, respectively, for the prediction of adverse events, including mortality, associated with acute coronary syndrome. Thus, NHR could be used in clinical cardiovascular medicine for risk stratification and the prediction of the short-term and long-term outcomes of CAD. However, more studies are required before a quantitative assessment of the efficacy of NHR for use in patient management can be completed.

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation.

Automatic segmentation of coronary arteries provides vital assistance to enable accurate and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task of coronary artery segmentation (CAS) remains highly challenging due to the large-scale variations exhibited by coronary arteries, their complicated anatomical structures and morphologies, as well as the low contrast between vessels and their background. To comprehensively tackle these challenges, we propose a novel multi-attention, multi-scale 3D deep network for CAS, which we call CAS-Net. Specifically, we first propose an attention-guided feature fusion (AGFF) module to efficiently fuse adjacent hierarchical features in the encoding and decoding stages to capture more effectively latent semantic information. Then, we propose a scale-aware feature enhancement (SAFE) module, aiming to dynamically adjust the receptive fields to extract more expressive features effectively, thereby enhancing the feature representation capability of the network. Furthermore, we employ the multi-scale feature aggregation (MSFA) module to learn a more distinctive semantic representation for refining the vessel maps. In addition, considering that the limited training data annotated with a quality golden standard are also a significant factor restricting the development of CAS, we construct a new dataset containing 119 cases consisting of coronary computed tomographic angiography (CCTA) volumes and annotated coronary arteries. Extensive experiments on our self-collected dataset and three publicly available datasets demonstrate that the proposed method has good segmentation performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. Compared with U-Net3D, the proposed method significantly improves the Dice similarity coefficient (DSC) by at least 4% on each dataset, due to the synergistic effect among the three core modules, AGFF, SAFE, and MSFA. Our implementation is released at https://github.com/Cassie-CV/CAS-Net.

Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks.

The coronary artery disease reporting and data system (CAD-RADS™) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS™ in suspected coronary artery disease (CAD) patients. Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADS™ with commercially-available automated segmentation and manual postprocessing in a retrospective validation cohort. Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no significant difference between the DL-based and the reader-based CAD-RADS™ grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 ± 1.80 min to 5.02 ± 0.8 min (p < 0.001). The standardised reporting of DL-based CAD-RADS™ in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active learning. This is achieved through selective query of challenging samples for labeling. To the best of our knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers determine whether a patient's three main coronary arteries are stenotic or not. The fourth classifier predicts whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The training is performed once more using the samples labeled so far. The interleaved phases of labeling and training are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the three main coronary arteries as a sample label and considering the two remaining arteries as sample features.

The Relationship Between Smoking Cessation and Time Perspective in Patients With Coronary Artery Disease

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease (CVD) and has a high mortality and morbidity rate. Smoking is one of its important risk factors. The time perspective is the basic cognitive process by which individuals perceive, interpret, and interact with their environment and describes the impact of attention on the past, present, and future. It was hypothesized that this cognitive process can have an effect on decisions to use or quit smoking. Thus, this study evaluated the relationship between smoking cessation and time perspective. Materials and Methods: Overall, 170 patients who were hospitalized with the diagnosis of CAD in Bandar Abbas Shahid Mohammadi hospital were selected for inclusion in this cross-sectional descriptive study. They all had a history of smoking. The subjects were divided into two groups of smokers and previous smokers (smoking cessation at least 3 months ago). Finally, Zimbardo Time Perspective Inventory was used to evaluate the time perspective. Results: In general, 170 patients were surveyed, including 151 males and 19 females. Based on the results, 140 people were smokers and 30 had quit smoking. Those quitting smoking received higher scores on past-positive and future time perspectives (P&lt;0.05). Patients who were smokers had higher scores in the present-hedonistic, present-fatalistic, and future transcendent perspectives, but they were not statistically significant. Conclusion: Although previous evidence confirmed a positive relationship between present time perspective and risky behaviors, our findings failed to prove it. However, people with future time perspectives had less tendency to drug addiction and smoking.

Impact of omicron wave and associated control measures in Shanghai on health management and psychosocial well-being of patients with chronic conditions.

The objective of this cross-sectional study was to investigate health management, well-being, and pandemic-related perspectives of chronic disease patients in the context of stringent measures, and associated correlates. A self-report survey was administered during the Omicron wave lockdown in Shanghai, China. Items from the Somatic Symptom Scale (SSS) and Symptom Checklist-90 were administered, as well as pandemic-related items. Overall, 1,775 patients (mostly married females with hypertension) were recruited through a community family physician group. Mean SSS scores were 36.1 ± 10.5/80, with 41.5% scoring in the elevated range (i.e., >36). In an adjusted model, being female, diagnosis of coronary artery disease and arrhythmia, perceived impact of pandemic on life, health condition, change to exercise routine, tolerance of control measures, as well as perception of future and control measures were significantly associated with greater distress. One-quarter perceived the pandemic had a permanent impact on their life, and 44.1% perceived at least a minor impact. One-third discontinued exercise due to the pandemic. While 47.6% stocked up on their medications before the lockdown, their supply was only enough for two weeks; 17.5% of participants discontinued use. Chief among their fears were inability to access healthcare (83.2%), and what they stated they most needed to manage their condition was medication access (65.6%). Since 2020 when we assessed a similar cohort, distress and perceived impact of the pandemic have worsened. Greater access to cardiac rehabilitation in China could address these issues.

Improving Coronary Artery Disease Prediction

Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis

Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model's performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.

Classification, diagnosis and clinical strategy of congenital coronary artery disease in children.

Some of the congenital coronary artery diseases in children have potential life-threatening complications. In addition to anatomical classification, the peadiatricians should pay more attention to the risk of adverse cardiac events classification; and then, they can eventrually make the personalized guidance suggestions and treatment decisions according to different diseases.

A systemic review of rubidium-82 PET contrasted with 99mTc-MIBI SPECT for imaging coronary artery disease.

99mTc-sestamibi single photon emission tomography (SPECT) method is widely used for imaging coronary artery disease (CAD). 82-Rubidium-PET is an alternative method that can be used to perform the same purpose. This study aims to determine whether 82-Rubidium-PET can offer extra value over 99mTc-sestamibi SPECT in imaging CAD. To achieve the study aim, a systematic review of the literature for the 2 tracers were conducted. The aim of the systemic review was to find every related previous study that corresponded to well-defined scientific criteria. The analysis of the results was restricted to peer-reviewed papers in order to avoid any selective outcome reports. Besides, extra analysis was carried out to curb or avoid any ascertainment bias. The qualified studies selected for this research were then assessed for the risk of bias. Furthermore, the details of the methods were rechecked to ensure that they were comparable, before synthesizing the results. Eighteen original studies were selected and included in the final analysis out of 803 articles identified at the initial research. Overall, the mean value of sensitivity and specificity for diagnosis of CAD was 84.3% and 75.4%, respectively for technetium 99m sestamibi (99mTc-MIBI). On the other hand, for 82-Rubidium-PET, the mean value of sensitivity and specificity for diagnosis of CAD was 81% and 81%, respectively. The accuracy of diagnostics of these imaging modalities was dependent on the radiotracers and stress agent used in these studies, with 99mTc-MIBI achieving the highest diagnostic value. This study concludes that 99mTc-MIBI-SPECT has higher diagnostic value for diagnosing CAD compared to 82-Rubidium-PET. This indicates that 99mTc-MIBI-SPECT is a more valuable technique for predicting CAD. Besides, for the stress agents used to stimulate the heart and increase workload, this research/study recommends the use of adenosine for the SPECT and the use of dipyridamole for positron emission tomography. However, it suggests the need for more systemic and theoretical studies to assess the real value of 82-Rubidium-PET and the value of stress agents.

MicroRNAs as Potential Biomarkers in Coronary Artery Disease.

Coronary artery disease (CAD) is the leading cause of mortality globally. Although substantial advances have been made in the diagnosis, management, and risk stratification of CAD, there is still a need for novel diagnostic biomarkers and new therapeutic targets to prevent the epidemic of the disease. Recently, growing evidence has linked dysregulated microRNAs (miRNAs) to cardiovascular diseases, including CAD. miRNAs are endogenous, stable, single-stranded, short, non-coding RNAs, and may have utility as diagnostic and prognostic biomarkers for CAD. Dysregulated miRNAs are involved in regulating lipid and glucose homeostasis pathways, reninangiotensin- aldosterone pathways, inflammation, endothelial and vascular smooth cell phenotypes promoting atherosclerotic plaque development, progression, and instability. Additionally, miRNAs are stable and easily accessible in the extracellular space, may reside in microvesicles, and are detectable in serum or plasma, making them attractive biomarkers for the diagnosis and prognosis of cardiovascular disease. Accumulating studies suggest that miRNAs could be useful biomarkers for early discrimination of patients presenting with myocarditis or Takotsubo syndrome from those with a diagnosis of acute myocardial infarction, early prognostication of patients presenting with acute coronary syndromes, and accurate detection of left ventricular remodeling after a chronic or acute ischemic event. Moreover, miRNAs represent potential novel therapeutic targets for CAD or other cardiovascular diseases. This review provides an overview of the effects of the entire spectrum of CAD, its major risk factors, and complications on levels of circulating miRNAs, as well as the limitations and challenges of their potential clinical applications.

Incidental Coronary Artery Disease on Routine CT Coronary Angiography –An evidence-based approach

Atherosclerotic coronary artery disease is the leading cause of high morbidity and mortality across the globe [1]. Coronary CT angiography has emerged as a highly accurate non-invasive diagnostic modality for the diagnosis of atherosclerotic coronary artery disease [2]. Coronary CT angiography should be considered as an alternative to stress imaging technique for ruling out atherosclerotic coronary artery disease in patients with the lower range of intermediate pre-test probability for atherosclerotic stable coronary artery disease. However, coronary CT angiography is not recommended as a screening test in asymptomatic individuals without clinical suspicion of coronary artery disease. CT Coronary angiography has been performed routinely before many of the cardiac and non-cardiac procedures like TAVI, atrial fibrillation ablation prior to the organ transplantation. Many a times, advised by Family Physician or patients themselves for a routine screening for atherosclerotic coronary artery disease undergo CT coronary angiography. When incidental atherosclerotic coronary artery disease is found in such patients who do not have any typical or atypical symptom suggestive of coronary artery disease then question arises how to approach this patient as far as the risk estimation for future adverse cardiovascular event and prognostication is concerned. It also becomes imperative to decide the line of management for this patient. To answer these two pertinent questions, we need to understand the behavior and natural history of atherosclerosis &amp; performance of CT coronary angiography in diagnosis and in predicting the prognosis of atherosclerotic disease.

The Relationship Between Coronary Slow Flow and Myocardial Ischaemia Evaluated with Timi Frame Count and Myocardial Perfusion Scintigraphy

Objective: Coronary slow flow (CSF) is known as a form or early stage of common atherosclerotic disease. Myocardial perfusion scintigraphy (MPS) is a valuable technique in the diagnosis of coronary artery disease and prediction of prognosis. The aim of this study was to investigate the relationship between the myocardial defect score and ischaemia in patients with CSF. Methods: A total of 168 patients who applied with the complaint of angina pectoris and underwent SPECT as a non-invasive test followed by coronary angiography were included in this retrospective study. 9 patient was excluded from the study for various reasons. The study population comprised determined with CSF and no obstructive stricture in the coronary arteries and with normal flow. The mean age of the patients was 56±12 years. The scores obtained from Quantitative Perfusion SPECT (QPS) and Quantitative Gated SPECT (QGS) software were used in the myocardial perfusion evaluation. The TIMI frame counts were compared with the myocardial defect and ischaemia scores. The TIMI frame count method was used in the determination of CSF. Results: In patients with slow flow in the circumflex (Cx) coronary artery, the stress total perfusion defect Cx (sTPD-Cx) was found to be 0.1 (range, 0.0-1.3), and in those with normal flow, it was 0.0 (range, 0.0-0.28) (p=0.002). The stress score Cx (sscore-Cx) was found to be 1.0 (range, 0.0-3.0) in patients with slow flow and 0.0 (range, 0.0-2.0) in those with normal flow (p=0.031). A linear correlation was determined between the Cx TIMI frame count and the sTPD-Cx and sscore-Cx values (r=0.207, p=0.009; r=0.159, p=0.045). No relationship was found between slow flow and the defect and ischemia scores in other myocardial regions. Conclusion: In patients with slow flow in the Cx coronary artery, the sTPD-Cx and the sscore-Cx values were found to be significantly high. Although at a weak level, a linear correlation was found between the Cx TIMI frame count and the sTPD-Cx and the sscore-Cx values

Abstract 43: Genetic, Sociodemographic, Lifestyle, and Clinical Risk Factors of Coronary Artery Disease Recurrent Events: Population-Based Cohort Study

Introduction: Contemporary guidelines remain to uniformly classify patients with established atherosclerotic cardiovascular disease as high risk despite substantial heterogeneities in risk factors distributions. We examined the independent associations and relative predictabilities of genetic, sociodemographic, lifestyle, and clinical risk factors on coronary artery disease (CAD) recurrence. Methods: From population-based UK Biobank cohort, we identified 7,024 participants aged 40-69 years with established CAD at enrollment. Based on multivariable Cox regression model, we evaluated the hazard ratio (HR) and discrimination index of age at enrollment, age at first CAD diagnosis, sex, smoking, physical activity, diet, sleep, socioeconomic deprivation, body mass index, blood pressure, lipids, glucose, lipoprotein(a), C-reactive protein, estimated glomerular filtration rate, statin prescription, and CAD polygenic risk score (PRS) on first CAD recurrence since enrollment. Results: Over a median [IQR] follow-up of 11.6 [4.0] years, 2,003 (28.5%) recurrent CAD events occurred. Of studied risk factors, the HR [95% CI] for CAD recurrence was the most pronounced with current smoking (1.35 [1.13-1.61]) and per SD increase in age at first CAD (0.74 [0.67-0.82]). Moreover, age at enrollment, Townsend Deprivation Index, sleep quality, cholesterols, glucose, renal function, C-reactive protein, and CAD PRS also significantly associated with recurrence. CAD PRS (C index: 0.58 [0.57-0.59]) largely predicted CAD recurrence, followed by high-density lipoprotein (HDL) cholesterol (0.57 [0.57-0.58]) and age at first CAD (0.57 [0.56-0.57]). In addition to traditional risk factors, a comprehensive model improved the C index from 0.64 [0.63-0.65] to 0.68 [0.67 to 0.69]. Conclusion: Genetic risk, age at first CAD event, and HDL cholesterol contributed the most to CAD recurrence. These features may identify high risk subgroups for treatment intensification or trials for new therapeutic strategies among those with CAD.

The utility of delta troponin in diagnosing significant coronary artery disease in patients with symptomatic atrial fibrillation.

Troponin I (cTnI) elevation is common in patients with atrial fibrillation (AF) but does not reliably indicate underlying coronary ischemia. We investigated whether dynamic changes in cTnI value (delta troponin) are useful in revealing significant coronary artery disease (sCAD) in patients presenting with symptomatic AF. We conducted a retrospective case-control study analyzing serial cTnI values in 231 patients presenting with symptomatic AF who had an objective assessment for underlying CAD within 6 months of the index admission. Diagnostic performance of an elevated cTnI (>0.04 μg/L) only, and elevated cTnI coupled with Youden Index derived cutoffs for absolute and relative changes in troponin, for distinguishing patients with sCAD, was evaluated. A total of 107 patients had an elevated cTnI on serial measurements. In this group, the area under the receiver operating characteristic curve was 0.69 [95% confidence interval (CI), 0.56-0.81] for relative delta troponin and 0.71 (95% CI, 0.59-0.83) for absolute delta troponin. The optimal diagnostic cutoff for relative delta troponin was > -0.42, and > -0.055 μg/L for absolute delta troponin. The specificity of elevated troponin to diagnose sCAD increased from 56 to 77% when relative delta troponin was added, and to 88% with absolute delta troponin. Although the sensitivity of cTnI elevation (57.1%) decreased to 50% for relative and 35.7% for absolute delta troponin, the negative predictive values were high and similar at 86%. When added to the troponin peak, delta troponin is a promising test for the diagnosis of significant coronary artery disease in patients presenting with symptomatic AF with elevated cTnI. This result requires prospective validation in a larger cohort of patients.

Automatic Quantitative Coronary Analysis Based on Deep Learning

As a core technique to quantitatively assess the stenosis severity of coronary arteries, quantitative coronary analysis (QCA) is urgently supposed to become more automated and intelligent, especially for regions lacking expertise and technology. The existing QCA methods highly depend on manual operation, which is time-consuming and subject to personal experience. This study innovatively proposes a fully automatic QCA workflow based on artificial intelligence (AI-QCA), which can quickly and accurately make a quantitative assessment of stenosis severity. The whole AI-QCA workflow mainly consists of three parts: the boundary-aware segmentation on the coronary angiogram (CAG) images, the AI-enabled coronary artery tree construction, and the diameter fitting and stenosis detection. Experiments show that the precision, recall, and F1 score of the segmentation, evaluated on 1322 CAGs, are 0.866, 0.897, and 0.879, respectively. Furthermore, the RMSE between diameter stenosis assessed by AI-QCA and manual QCA served by senior experts, evaluated on 249 CAGs, is 0.064, and the Pearson coefficient is 0.765. Meanwhile, the operation time can be reduced from tens of minutes to several seconds by AI-QCA. As a conclusion, the proposed AI-QCA is able to quickly quantify stenosis parameters as accurately as senior experts, which is significant for the intelligent diagnosis and treatment of coronary artery disease.

Chinese expert consensus on the application of optical coherence tomography in the interventional diagnosis and treatment of coronary artery disease

我国经皮冠状动脉介入治疗（PCI）数量快速增长，光学相干断层成像（OCT）在指导和优化PCI中具有重要的价值，逐渐发展成为心血管介入医生进行精准PCI不可或缺的工具，但OCT在我国的使用率仍较低且不规范。鉴于OCT在冠心病介入诊疗中的价值及我国的应用现状，专家组总结了既往OCT在冠心病介入诊疗中的临床证据，结合国内外相关指南、共识及专家建议等，形成了规范及推广OCT在中国冠心病介入诊疗中应用的专家共识，以减少心血管不良事件的发生，改善广大冠心病患者的预后。.