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Among patients in whom a decision had already been made to obtain CCA, 64-slice CTCA was reliable for ruling out significant CAD in patients with stable and unstable anginal syndromes. A positive 64-slice CTCA scan often overestimates the severity of atherosclerotic obstructions and requires further testing to guide patient management.

Prosthetic heart valve (PHV) dysfunction is rare but potentially life-threatening. Although often challenging, establishing the exact cause of PHV dysfunction is essential to determine the appropriate treatment strategy. In clinical practice, a comprehensive approach that integrates several parameters of valve morphology and function assessed with 2D/3D transthoracic and transoesophageal echocardiography is a key to appropriately detect and quantitate PHV dysfunction. Cinefluoroscopy, multidetector computed tomography, cardiac magnetic resonance imaging, and to a lesser extent, nuclear imaging are complementary tools for the diagnosis and management of PHV complications. The present document provides recommendations for the use of multimodality imaging in the assessment of PHVs.

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Our results suggest that the Diamond-Forrester model overestimates the probability of CAD especially in women. We updated the predictive effects of age, sex, type of chest pain, and hospital setting which improved model performance and we extended it to include patients of 70 years and older.

Cardiac toxicity is one of the most concerning side effects of anti-cancer therapy. The gain in life expectancy obtained with anti-cancer therapy can be compromised by increased morbidity and mortality associated with its cardiac complications. While radiosensitivity of the heart was initially recognized only in the early 1970s, the heart is regarded in the current era as one of the most critical dose-limiting organs in radiotherapy. Several clinical studies have identified adverse clinical consequences of radiation-induced heart disease (RIHD) on the outcome of long-term cancer survivors. A comprehensive review of potential cardiac complications related to radiotherapy is warranted. An evidence-based review of several imaging approaches used to detect, evaluate, and monitor RIHD is discussed. Recommendations for the early identification and monitoring of cardiovascular complications of radiotherapy by cardiac imaging are also proposed.

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.Design Retrospective pooled analysis of individual patient data.Setting 18 hospitals in Europe and the United States.Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measuresObstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. ResultsWe included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory.Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates. IntroductionIn the United States, about 10.2 million people have chest pain complaints each year, 1 and more than 1.1 million diagnostic procedures of catheter based coronary angiography are performed on inpatients each year. 2 In a recent report based on the national cardiovascular data registry of the American College of Cardiology, 3 only 41% of patients undergoing elective procedures of catheter based coronary angiographies are diagnosed with obstructive coronary artery disease. The report's authors concluded that better risk stratification was needed, underlined by decision analyses showing that the choice of further diagnostic investigation in patients with chest pain depends primarily on the pretest probability of coronary artery disease. [4][5][6] The American College of Cardiology/American Heart Associatio...

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.