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In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.
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BACKGROUND: We evaluated the radiologic, pulmonary functional, and antibody statuses of coronavirus disease 2019 (COVID-19) patients 6 and 18 months after discharge, comparing changes in status and focusing on risk factors for residual computed tomography (CT) abnormalities. METHODS: This prospective cohort study was conducted on COVID-19 patients discharged between April 2020 and January 2021. Chest CT, pulmonary function testing (PFT), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) measurements were performed 6 and 18 months after discharge. We evaluated factors associated with residual CT abnormalities and the correlation between lesion volume in CT (lesionvolume), PFT, and IgG levels. RESULTS: This study included 68 and 42 participants evaluated 6 and 18 months, respectively, after hospitalizations for COVID-19. CT abnormalities were noted in 22 participants (32.4%) at 6 months and 13 participants (31.0%) at 18 months. Lesionvolume was significantly lower at 18 months than 6 months (P < 0.001). Patients with CT abnormalities at 6 months showed lower forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC), and patients with CT abnormalities at 18 months exhibited lower FVC. FVC significantly improved between 6 and 18 months of follow-up (all P < 0.0001). SARS-CoV-2 IgG levels were significantly higher in patients with CT abnormalities at 6 and 18 months (P < 0.001). At 18-month follow-up assessments, age was associated with CT abnormalities (odds ratio, 1.17; 95% confidence interval, 1.03-1.32; P = 0.01), and lesionvolume showed a positive correlation with IgG level (r = 0.643, P < 0.001). CONCLUSION: At 18-month follow-up assessments, 31.0% of participants exhibited residual CT abnormalities. Age and higher SARS-CoV-2 IgG levels were significant predictors, and FVC was related to abnormal CT findings at 18 months. Lesionvolume and FVC improved between 6 and 18 months. TRIAL REGISTRATION: Clinical Research Information Service Identifier: KCT0008573.
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COVID-19 , Imunoglobulina G , Pulmão , Testes de Função Respiratória , SARS-CoV-2 , Tomografia Computadorizada por Raios X , Humanos , COVID-19/diagnóstico por imagem , Masculino , Feminino , Estudos Prospectivos , Pessoa de Meia-Idade , Imunoglobulina G/sangue , SARS-CoV-2/imunologia , SARS-CoV-2/isolamento & purificação , Idoso , Seguimentos , Pulmão/diagnóstico por imagem , Pulmão/patologia , Anticorpos Antivirais/sangue , Adulto , Volume Expiratório Forçado , Capacidade Vital , Fatores de RiscoRESUMO
PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. MATERIALS AND METHODS: In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). RESULTS: Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). CONCLUSIONS: The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.
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Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.
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Angiografia Coronária , Doença da Artéria Coronariana , Vasos Coronários , Aprendizado Profundo , Valor Preditivo dos Testes , Interpretação de Imagem Radiográfica Assistida por Computador , Calcificação Vascular , Humanos , Calcificação Vascular/diagnóstico por imagem , Calcificação Vascular/terapia , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/terapia , Vasos Coronários/diagnóstico por imagem , Prognóstico , Angiografia por Tomografia Computadorizada , Reprodutibilidade dos Testes , Índice de Gravidade de Doença , Inteligência Artificial , Técnicas de Imagem de Sincronização CardíacaRESUMO
PURPOSE: Coronary artery plaque burden, low attenuation non-calcified plaque (LAP), and pericoronary adipose tissue (PCAT) on coronary CT angiography (CCTA), have been linked to future cardiac events. The purpose of this study was to evaluate intra- and inter reader reproducibility in the quantification of coronary plaque burden and its characteristics using an artificial intelligence-enhanced semi-automated software. MATERIALS AND METHODS: A total of 10 women and 6 men, aged 52 (IQR 49-58) underwent CCTA using a Siemens Somatom Force, Somatom Definition AS and Somatom Definition Flash scanners. Two expert readers utilized dedicated semi-automatic software (vascuCAP, Elucid Bioimaging, Wenham, MA) to assess calcified plaque, low attenuation plaque and PCAT. Readers were blinded to all clinical information and repeated their analysis at 6 weeks in random order to minimize recall bias. Data analysis was performed on the right and left coronary arteries. Intra- and inter-reader reproducibility was compared using Pearson correlation coefficient, while absolute values between analyses and readers were compared with paired non-parametric tests. This is a sub-study of the Specialized Center of Research Excellence (SCORE) clinical trial (5U54AG062334). RESULTS: A total of 64 vessels from 16 patients were analyzed. Intra-reader Pearson correlation coefficients for calcified plaque volume, LAP volume and PCAT volumes were 0.96, 0.99 and 0.92 for reader 1 and 0.94, 0.94 and 0.95 for reader 2, respectively, (all p < 0.0001). Inter-reader Pearson correlation coefficients for calcified plaque volume, LAP and PCAT volumes were 0.92, 0.96 and 0.78, and 0.99, 0.99 and 0.93 on the second analyses, all had a p value <0.0001. There was no significant bias on the corresponding Bland-Altman analyses. CONCLUSION: Volume measurement of coronary plaque burden and PCAT volume can be performed with high intra- and inter-reader agreement.
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Angiografia por Tomografia Computadorizada , Angiografia Coronária , Doença da Artéria Coronariana , Vasos Coronários , Placa Aterosclerótica , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Angiografia por Tomografia Computadorizada/métodos , Angiografia Coronária/métodos , Doença da Artéria Coronariana/diagnóstico , Doença da Artéria Coronariana/diagnóstico por imagem , Vasos Coronários/diagnóstico por imagem , Variações Dependentes do Observador , Placa Aterosclerótica/diagnóstico por imagem , Placa Aterosclerótica/diagnóstico , Reprodutibilidade dos TestesRESUMO
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.
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Inteligência Artificial , Cardiopatias , Humanos , Cardiopatias/diagnóstico por imagem , Imageamento por Ressonância Magnética , Processamento de Imagem Assistida por ComputadorRESUMO
BACKGROUND: Although a coronary artery calcium (CAC) of ≥1,000 is a subclinical atherosclerosis threshold to consider combination lipid-lowering therapy, differentiating very high from high atherosclerotic cardiovascular disease (ASCVD) risk in this patient population is not well-defined. OBJECTIVES: Among persons with a CAC of ≥1,000, the authors sought to identify risk factors equating with very high-risk ASCVD mortality rates. METHODS: The authors studied 2,246 asymptomatic patients with a CAC of ≥1,000 from the CAC Consortium without a prior ASCVD event. Cox proportional hazards regression modelling was performed for ASCVD mortality during a median follow-up of 11.3 years. Crude ASCVD mortality rates were compared with those reported for secondary prevention trial patients classified as very high risk, defined by ≥2 major ASCVD events or 1 major event and ≥2 high-risk conditions (1.4 per 100 person-years). RESULTS: The mean age was 66.6 years, 14% were female, and 10% were non-White. The median CAC score was 1,592 and 6% had severe left main (LM) CAC (vessel-specific CAC ≥300). Diabetes (HR: 2.04 [95% CI: 1.47-2.83]) and severe LM CAC (HR: 2.32 [95% CI: 1.51-3.55]) were associated with ASCVD mortality. The ASCVD mortality per 100 person-years for all patients was 0.8 (95% CI: 0.7-0.9), although higher rates were observed for diabetes (1.4 [95% CI: 0.8-1.9]), severe LM CAC (1.3 [95% CI: 0.6-2.0]), and both diabetes and severe LM CAC (7.1 [95% CI: 3.4-10.8]). CONCLUSIONS: Among asymptomatic patients with a CAC of ≥1,000 without a prior index event, diabetes, and severe LM CAC define very high risk ASCVD, identifying individuals who may benefit from more intensive prevention therapies across several domains, including low-density lipoprotein-cholesterol lowering.
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Angiografia Coronária , Doença da Artéria Coronariana , Diabetes Mellitus , Valor Preditivo dos Testes , Calcificação Vascular , Humanos , Feminino , Masculino , Calcificação Vascular/diagnóstico por imagem , Calcificação Vascular/mortalidade , Idoso , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/mortalidade , Doença da Artéria Coronariana/terapia , Pessoa de Meia-Idade , Medição de Risco , Fatores de Risco , Fatores de Tempo , Diabetes Mellitus/epidemiologia , Diabetes Mellitus/mortalidade , Prognóstico , Angiografia por Tomografia Computadorizada , Doenças Assintomáticas , Índice de Gravidade de Doença , Vasos Coronários/diagnóstico por imagem , Fatores de Risco de Doenças CardíacasRESUMO
PURPOSE OF REVIEW: Bias in artificial intelligence (AI) models can result in unintended consequences. In cardiovascular imaging, biased AI models used in clinical practice can negatively affect patient outcomes. Biased AI models result from decisions made when training and evaluating a model. This paper is a comprehensive guide for AI development teams to understand assumptions in datasets and chosen metrics for outcome/ground truth, and how this translates to real-world performance for cardiovascular disease (CVD). RECENT FINDINGS: CVDs are the number one cause of mortality worldwide; however, the prevalence, burden, and outcomes of CVD vary across gender and race. Several biomarkers are also shown to vary among different populations and ethnic/racial groups. Inequalities in clinical trial inclusion, clinical presentation, diagnosis, and treatment are preserved in health data that is ultimately used to train AI algorithms, leading to potential biases in model performance. Despite the notion that AI models themselves are biased, AI can also help to mitigate bias (e.g., bias auditing tools). In this review paper, we describe in detail implicit and explicit biases in the care of cardiovascular disease that may be present in existing datasets but are not obvious to model developers. We review disparities in CVD outcomes across different genders and race groups, differences in treatment of historically marginalized groups, and disparities in clinical trials for various cardiovascular diseases and outcomes. Thereafter, we summarize some CVD AI literature that shows bias in CVD AI as well as approaches that AI is being used to mitigate CVD bias.
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Inteligência Artificial , Doenças Cardiovasculares , Feminino , Masculino , Humanos , Doenças Cardiovasculares/diagnóstico por imagem , Algoritmos , ViésRESUMO
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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American Heart Association , Inteligência Artificial , Humanos , Aprendizado de Máquina , Coração , Imageamento por Ressonância MagnéticaRESUMO
In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.
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Inteligência Artificial , Redes Neurais de Computação , Humanos , Registros Eletrônicos de Saúde , Processamento de Linguagem Natural , Diagnóstico por ImagemRESUMO
BACKGROUND: The development of thoracic aortic calcium (TAC) temporally precedes coronary artery calcium more often in women versus men. Whether TAC density and area confer sex-specific differences in atherosclerotic cardiovascular disease (ASCVD) risk is unknown. METHODS: We studied 5317 primary prevention patients who underwent coronary artery calcium scoring on noncontrast cardiac gated computed tomography with TAC >0. The Agatston TAC score (Agatston units), density (Hounsfield units), and area (mm2) were compared between men and women. Cox proportional hazards regression calculated adjusted hazard ratios for TAC density-area groups with ASCVD mortality, adjusting for traditional risk factors, coronary artery calcium, and TAC. Multinomial logistic regression calculated adjusted odds ratios for the association between traditional risk factors and TAC density-area groups. RESULTS: The mean age was 60.7 years, 38% were women, and 163 ASCVD deaths occurred over a median of 11.7-year follow-up. Women had higher median TAC scores (97 versus 84 Agatston units; P=0.004), density (223 versus 210 Hounsfield units; P<0.001), and area (37 versus 32 mm2; P=0.006) compared with men. There was a stepwise higher incidence of ASCVD deaths across increasing TAC density-area groups in men though women with low TAC density relative to TAC area (3.6 per 1000 person-years) had survival probability commensurate with the high-density-high-area group (4.8 per 1000 person-years). Compared with low TAC density-area, low TAC density/high TAC area conferred a 3.75-fold higher risk of ASCVD mortality in women (adjusted hazard ratio, 3.75 [95% CI, 1.13-12.44]) but not in men (adjusted hazard ratio, 1.16 [95% CI, 0.48-2.84]). Risk factors most strongly associated with low TAC density/high TAC area differed in women (diabetes: adjusted odds ratio, 2.61 [95% CI, 1.34-5.07]) versus men (hypertension: adjusted odds ratio, 1.45 [95% CI, 1.11-1.90]). CONCLUSIONS: TAC density-area phenotypes do not consistently associate with ASCVD mortality though low TAC density relative to area may be a marker of increased ASCVD risk in women.
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Aterosclerose , Doenças Cardiovasculares , Doença da Artéria Coronariana , Calcificação Vascular , Masculino , Humanos , Feminino , Pessoa de Meia-Idade , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/epidemiologia , Doença da Artéria Coronariana/complicações , Cálcio , Doenças Cardiovasculares/epidemiologia , Medição de Risco/métodos , Aterosclerose/diagnóstico por imagem , Aterosclerose/epidemiologia , Fatores de Risco , Calcificação Vascular/complicaçõesRESUMO
INTRODUCTION: Although pericoronary adipose tissue (PCAT) is a component of the epicardial adipose tissue (EAT) depot, they may have different associations to coronary artery disease (CAD). We explored relationships between pericoronary adipose tissue mean attenuation (PCATMA) and EAT measurements in coronary CT angiography (CCTA) in patients with and without CAD. MATERIAL AND METHODS: CCTA scans of 185 non-CAD and 81 CAD patients (86.4% >50% stenosis) were included and retrospectively analyzed. PCATMA and EAT density/volume were measured and analyzed by sex, including associations with age, risk factors and tube voltage using linear regression models. RESULTS: In non-CAD and CAD, mean PCATMA and EAT volume were higher in men than in women (non-CAD: -92.5 ± 10.6HU vs -96.2 ± 8.4HU, and 174.4 ± 69.1 cm3 vs 124.1 ± 57.3 cm3; CAD: -92.2 ± 9.0HU vs -97.4 ± 9.7HU, and 193.6 ± 62.5 cm3 vs 148.5 ± 50.5 cm3 (p < 0.05)). EAT density was slightly lower in men than women in non-CAD (-96.4 ± 6.3HU vs -94.4 ± 5.5HU (p < 0.05)), and similar in CAD (-98.2 ± 5.2HU vs 98.2 ± 6.4HU). There was strong correlation between PCATMA and EAT density (non-CAD: r = 0.725, p < 0.001, CAD: r = 0.686, p < 0.001) but no correlation between PCATMA and EAT volume (non-CAD: r = 0.018, p = 0.81, CAD: r = -0.055, p = 0.63). A weak inverse association was found between EAT density and EAT volume (non-CAD: r = -0.244, p < 0.001, CAD: r = -0.263, p = 0.02). In linear regression models, EAT density was significantly associated with PCATMA in both non-CAD and CAD patients independent of risk factors and tube voltage. CONCLUSION: In CAD and non-CAD patients, EAT density, but not EAT volume, showed significant associations with PCATMA. Compared to women, men had higher PCATMA and EAT volume independently of disease status, but similar or slightly lower EAT density. Differences in trends and relations of PCATMA and EAT by sex could indicate that personalized interpretation and thresholding is needed.
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Doença da Artéria Coronariana , Humanos , Masculino , Feminino , Doença da Artéria Coronariana/diagnóstico por imagem , Estudos Retrospectivos , Angiografia Coronária , Tomografia Computadorizada por Raios X/efeitos adversos , Pericárdio/diagnóstico por imagem , Tecido Adiposo/diagnóstico por imagemRESUMO
Dual-energy computed tomography (DECT) acquires images using two energy spectra and offers a variation of reconstruction techniques for improved cardiac imaging. Virtual monoenergetic images decrease artifacts improving coronary plaque and stent visualization. Further, contrast attenuation is increased allowing significant reduction of contrast dose. Virtual non-contrast reconstructions enable coronary artery calcium scoring from contrast-enhanced scans. DECT provides advanced plaque imaging with detailed analysis of plaque components, indicating plaque stability. Extracellular volume assessment using DECT offers noninvasive detection of myocardial fibrosis. This review aims to outline the current cardiac applications of DECT, summarize recent literature, and discuss their findings.
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Coração , Imagem Radiográfica a Partir de Emissão de Duplo Fóton , Humanos , Coração/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Imagem Radiográfica a Partir de Emissão de Duplo Fóton/métodosRESUMO
Coronary artery calcium (CAC) is the measure of subclinical coronary artery atherosclerosis most strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk. However, CAC is rarely reported in the inpatient setting to guide chest pain management. We present a case of very high CAC in a 64-year-old woman with hypertension, type 2 diabetes, and hyperlipidemia presenting with dyspnea. Initial electrocardiogram (ECG) demonstrated normal conduction with a heart rate of 76 beats/min, but new T-wave inversions in V1-V4 and a high-sensitivity troponin-I (hsTnI) value of 6 ng/L (normal < 6 ng/L). Repeat ECG in the emergency department showed normal sinus rhythm (heart rate of 80 beats/min); however, it subsequently demonstrated a left bundle branch block (LBBB) with a repeat hsTnI of 7 ng/L. Stress testing with pharmacologic single-photon emission computerized tomography did not show scintigraphic evidence of ischemia but noted extensive CAC and a concern for balanced ischemia. Subsequent coronary computed tomography angiography (CCTA) showed nonobstructive disease and a total Agatston CAC score of 1262. Invasive evaluation with left heart catheterization was deferred given the patient's unchanged symptoms and CCTA findings. Statin therapy was intensified and aspirin, metoprolol succinate, and antihypertension therapies were continued. Initiation of glucose-lowering therapy and lipoprotein(a) testing was strongly recommended on follow-up. Our case suggests that CAC ⩾ 1000 may be incidentally associated with transient LBBB during the workup of coronary artery disease. Here, we specifically show that functional testing that incorporates measurement of CAC burden can help to improve ASCVD-preventive pharmacotherapy initiation and intensification beyond the identification of obstructive disease alone.
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Aterosclerose , Doença da Artéria Coronariana , Diabetes Mellitus Tipo 2 , Hipercalcemia , Feminino , Humanos , Pessoa de Meia-Idade , Bloqueio de Ramo/diagnóstico , Bloqueio de Ramo/complicações , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Doença da Artéria Coronariana/diagnóstico , Doença da Artéria Coronariana/diagnóstico por imagem , Arritmias Cardíacas , Hipercalcemia/complicações , Isquemia , Angiografia Coronária/métodos , Medição de Risco , Fatores de RiscoRESUMO
Objective: Coronary heart disease is a leading cause of death and disability. Although psychological stress has been identified as an important potential contributor, mechanisms by which stress increases risk of heart disease and mortality are not fully understood. The purpose of this study was to assess mechanisms by which stress acts through the brain and heart to confer increased CHD risk. Methods: Coronary Heart Disease patients (N=10) underwent cardiac imaging with [Tc-99m] sestamibi single photon emission tomography at rest and during a public speaking mental stress task. Patients returned for a second day and underwent positron emission tomography imaging of the brain, heart, bone marrow, aorta (indicating inflammation) and subcutaneous adipose tissue, after injection of [18F]2-fluoro-2-deoxyglucose for assessment of glucose uptake followed mental stress. Patients with (N=4) and without (N=6) mental stress-induced myocardial ischemia were compared for glucose uptake in brain, heart, adipose tissue and aorta with mental stress. Results: Patients with mental stress-induced ischemia showed a pattern of increased uptake in the heart, medial prefrontal cortex, and adipose tissue with stress. In the heart disease group as a whole, activity increase with stress in the medial prefrontal brain and amygdala correlated with stress-induced increases in spleen (r=0.69, p=0.038; and r=0.69, p=0.04 respectfully). Stress-induced frontal lobe increased uptake correlated with stress-induced aorta uptake (r=0.71, p=0.016). Activity in insula and medial prefrontal cortex was correlated with post-stress activity in bone marrow and adipose tissue. Activity in other brain areas not implicated in stress did not show similar correlations. Increases in medial prefrontal activity with stress correlated with increased cardiac glucose uptake with stress, suggestive of myocardial ischemia (r=0.85, p=0.004). Conclusions: These findings suggest a link between brain response to stress in key areas mediating emotion and peripheral organs involved in inflammation and hematopoietic activity, as well as myocardial ischemia, in Coronary Heart Disease patients.
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The field of coronary plaque analysis is advancing including more quantitative analysis of coronary artery diseases such as plaque burden, high-risk plaque features, computed tomography-derived fractional flow reserve, and radiomics. Although these biomarkers have shown great promise for the diagnosis and prognosis of cardiac patients in a research setting, many of these advanced analyses are labour and time intensive and therefore hard to implement in daily clinical practice. Artificial intelligence (AI) is playing an increasing role in supporting the quantification of these new biomarkers. AI offers the opportunity to increase efficiency, reduce human error and reader variability and to increase the accuracy of diagnosis and prognosis by automating many processing and supporting clinicians in their decision-making. With the use of AI these novel analysis approaches for coronary artery disease can be made feasible for clinical practice without increasing cost and workload and potentially improve patient care.
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Microcirculatory dysfunction during psychological stress may lead to diffuse myocardial ischemia. We developed a novel quantification method for diffuse ischemia during mental stress (dMSI) and examined its relationship with outcomes after a myocardial infarction (MI). We studied 300 patients ≤ 61 years of age (50% women) with a recent MI. Patients underwent myocardial perfusion imaging with mental stress and were followed for 5 years. dMSI was quantified from cumulative count distributions of rest and stress perfusion. Focal ischemia was defined in a conventional fashion. The main outcome was a composite outcome of recurrent MI, heart failure hospitalizations, and cardiovascular death. A dMSI increment of 1 standard deviation was associated with a 40% higher risk for adverse events (HR 1.4, 95% CI 1.2-1.5). Results were similar after adjustment for viability, demographic and clinical factors and focal ischemia. In sex-specific analysis, higher levels of dMSI (per standard deviation increment) were associated with 53% higher risk of adverse events in women (HR 1.5, 95% CI 1.2-2.0) but not in men (HR 0.9, 95% CI 0.5-1.4), P 0.001. A novel index of diffuse ischemia with mental stress was associated with recurrent events in women but not in men after MI.