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Abstract Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen’s Kappa = 0.89, P

Abstract The presentation and identification of cardiovascular disease in women pose unique diagnostic challenges compared to men, and underrecognized conditions in this patient population may lead to clinical mismanagement. This article reviews the sex differences in cardiovascular disease, explores the diagnostic and prognostic role of cardiovascular magnetic resonance (CMR) in the spectrum of cardiovascular disorders in women, and proposes the added value of CMR compared to other imaging modalities. In addition, this article specifically reviews the role of CMR in cardiovascular diseases occurring more frequently or exclusively in female patients, including Takotsubo cardiomyopathy, connective tissue disorders, primary pulmonary arterial hypertension and peripartum cardiomyopathy. Gaps in knowledge and opportunities for further investigation of sex-specific cardiovascular differences by CMR are also highlighted.

Objective: To evaluate the predictive value of Computed Tomography Angiography (CTA) measurements of the RVOT for transcatheter valve sizing. Background: Transcatheter pulmonary valve replacement (TPVR) provides an alternative to surgery in patients with right ventricular outflow tract (RVOT) dysfunction. We studied 18 patients who underwent catheterization for potential TPVR to determine whether CT imaging can be used to accurately predict implant size. Methods: Cases were grouped by RVOT characteristics: native or transannular patch (n = 8), conduit (n = 5) or bioprosthetic valve (n = 5). TPVR was undertaken in 14/18 cases, after balloon-sizing was used to confirm suitability and select implant size. Retrospective CT measurements of the RVOT (circumference-derived (Dcirc) and area-derived (Darea) diameters) were obtained at the level of the annulus, bioprosthesis or conduit. Using manufacturer sizing guidance, a valve size was generated and a predicted valve category assigned: (1) 29 mm. Predicted and implanted valves were compared for inter-rater agreement using Cohen’s kappa coefficient. Results: The median age of patients was 37 years old (IQR: 30–49); 55% were male. Diagnoses included: Tetralogy of Fallot (12/18), d-Transposition repair (3/18), congenital pulmonary stenosis (2/18) and carcinoid heart disease (1/18). Measurements of Darea (κ = 0.697, p

Background. In end-stage liver disease, alterations in iron metabolism can lead to iron overload and development of iron overload cardiomyopathy. In liver transplant candidates, evaluation for cardiac iron overload and dysfunction can help to identify candidates at increased risk for peritransplant morbidity and mortality, though recommendations for pretransplant evaluation of cardiac iron overload are not standardized. Cardiac Magnetic Resonance Imaging T2* (CMRI-T2*) is a validated method to quantify cardiac iron deposition, with normal T2* value of 20 ms or greater. In this study, we sought to identify the incidence and predictors of iron overload by CMRI-T2* and to evaluate the impact of cardiac and iron overload on morbidity and mortality after liver transplantation. Methods. In this retrospective single-center cohort study, all liver transplant candidates who underwent a pretransplant CMRI-T2* between January 1, 2008, and June 30, 2016, were included to analyze the association between clinical characteristics and low T2* using logistic regression. Results. One hundred seventy-nine liver transplant candidates who received CMRI-T2* were included. Median age was 57 years, 73.2% were male, and 47.6% were white. 49.7% had hepatitis C and 2.8% had hemochromatosis. Median Model for End-Stage Liver Disease score was 25. 65.2% were Child-Pugh C. In multivariable logistic regression, T2* less than 20 ms (n = 35) was associated with Model for End-Stage Liver Disease score of 25 or greater (odds ratio [OR], 3.65; P = 0.007), Child-Pugh C (OR, 3.42; P = 0.03), and echocardiographic systolic ejection fraction less than 65% (OR, 2.24; P = 0.01). Posttransplant heart failure occurred exclusively in recipients with T2* less than 15 ms. Survival was worse in T2* 10 to 14.9 versus T2* of 20 ms or greater (hazard ratio, 3.85; P = 0.003), but not for 15 to 19.9 versus T2* of 20 ms or greater. Conclusions. Severity of liver disease and systolic dysfunction is associated with T2* less than 20 ms, though there was no difference in posttransplant outcomes between T2* 15 to 19.9 and T2* 20 ms or greater, suggesting that individuals with T2* of 15 ms or greater may be suitable transplant candidates. CMRI-T2* is an additional diagnostic tool in evaluating transplant candidates at high risk for posttransplant cardiac complications.