The vanishing atrial mass.

HEFCEThis is the final version of the article. It first appeared from Oxford University Press via https://doi.org10.1093/ehjci/jew12

An unusual finding in a 57-year-old woman with new onset hypertension and a diastolic murmur.

CLINICAL INTRODUCTION: A 57-year-old woman presented to our clinic with breathlessness brought on while walking uphill. She had been recently diagnosed with systemic hypertension. There was no known family history of cardiac disease, or prior smoking habit. On examination, pulse was 73 bpm and blood pressure 155/73 mm Hg, which was asymmetrical in her arms. Auscultation revealed a readily audible early diastolic murmur in the aortic area and bilateral subclavian bruits. ECG showed sinus rhythm with no abnormality. Transthoracic echocardiography demonstrated mild-to-moderate aortic regurgitation, and normal left ventricular size and function. The ascending aorta was mildly dilated (41 mm), with para-aortic thickening noted. Owing to the abnormal appearance of the aortic wall, cardiac MRI, and subsequently 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scan was performed (figure 1). QUESTION: Which complication of the underlying disease is evident in figure 1, panel C? Aortic aneurysmAortic dissectionAortic thrombusCoronary artery aneurysmCoronary sinus fistula

Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions. METHODS AND FINDINGS: Using data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain). CONCLUSIONS: Our AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the "information gain" achieved by considering more risk factors in the predictive model was significantly higher than the "modeling gain" achieved by adopting complex predictive models

Editor's Choice - Calcification of Thoracic and Abdominal Aneurysms is Associated with Mortality and Morbidity.

INTRODUCTION: Cardiovascular events are common in people with aortic aneurysms. Arterial calcification is a recognised predictor of cardiovascular outcomes in coronary artery disease. Whether calcification within abdominal and thoracic aneurysm walls is correlated with poor cardiovascular outcomes is not known. PATIENTS AND METHODS: Calcium scores were derived from computed tomography (CT) scans of consecutive patients with either infrarenal (AAA) or descending thoracic aneurysms (TAA) using the modified Agatston score. The primary outcome was subsequent all cause mortality during follow-up. Secondary outcomes were cardiovascular mortality and morbidity. RESULTS: A total of 319 patients (123 TAA and 196 AAA; median age 77 [71-84] years, 72% male) were included with a median follow-up of 30 months. The primary outcome occurred in 120 (37.6%) patients. In the abdominal aortic aneurysm group, the calcium score was significantly related to both all cause mortality and cardiac mortality (odds ratios (OR) of 2.246 (95% CI 1.591-9.476; p < 0.001) and 1.321 (1.076-2.762; p = 0.003)) respectively. In the thoracic aneurysm group, calcium score was significantly related to all cause mortality (OR 6.444; 95% CI 2.574-6.137; p < 0.001), cardiac mortality (OR 3.456; 95% CI 1.765-4.654; p = 0.042) and cardiac morbidity (OR 2.128; 95% CI 1.973-4.342; p = 0.002). CONCLUSIONS: Aortic aneurysm calcification, in either the thoracic or the abdominal territory, is significantly associated with both higher overall and cardiovascular mortality. Calcium scoring, rapidly derived from routine CT scans, may help identify high risk patients for treatment to reduce risk

Noninvasive Molecular Imaging of Disease Activity in Atherosclerosis.

Major focus has been placed on the identification of vulnerable plaques as a means of improving the prediction of myocardial infarction. However, this strategy has recently been questioned on the basis that the majority of these individual coronary lesions do not in fact go on to cause clinical events. Attention is, therefore, shifting to alternative imaging modalities that might provide a more complete pan-coronary assessment of the atherosclerotic disease process. These include markers of disease activity with the potential to discriminate between patients with stable burnt-out disease that is no longer metabolically active and those with active atheroma, faster disease progression, and increased risk of infarction. This review will examine how novel molecular imaging approaches can provide such assessments, focusing on inflammation and microcalcification activity, the importance of these processes to coronary atherosclerosis, and the advantages and challenges posed by these techniques.M.R.D and D.E.N are supported by the British Heart Foundation (CH/09/002 to D.E.N., FS/14/78/31020 to M.R.D). M.R.D is the recipient of the Sir Jules Thorn Biomedical Research Award 2015 (M.R.D.) E.A. research is supported by R01HL 114805 and R01HL 109506.This is the final version of the article. It first appeared from Lippincott, Williams & Wilkins via http://dx.doi.org/10.1161/CIRCRESAHA.116.30797

Using the local density approximation and the LYP, BLYP, and B3LYP functionals within Reference--State One--Particle Density--Matrix Theory

For closed-shell systems, the local density approximation (LDA) and the LYP, BLYP, and B3LYP functionals are shown to be compatible with reference-state one-particle density-matrix theory, where this recently introduced formalism is based on Brueckner-orbital theory and an energy functional that includes exact exchange and a non-universal correlation-energy functional. The method is demonstrated to reduce to a density functional theory when the exchange-correlation energy-functional has a simplified form, i.e., its integrand contains only the coordinates of two electron, say r1 and r2, and it has a Dirac delta function -- delta(r1 - r2) -- as a factor. Since Brueckner and Hartree--Fock orbitals are often very similar, any local exchange functional that works well with Hartree--Fock theory is a reasonable approximation with reference-state one-particle density-matrix theory. The LDA approximation is also a reasonable approximation. However, the Colle--Salvetti correlation-energy functional, and the LYP variant, are not ideal for the method, since these are universal functionals. Nevertheless, they appear to provide reasonable approximations. The B3LYP functional is derived using a linear combination of two functionals: One is the BLYP functional; the other uses exact exchange and a correlation-energy functional from the LDA.Comment: 26 Pages, 0 figures, RevTeX 4, Submitted to Mol. Phy

Bench-to-bedside review : targeting antioxidants to mitochondria in sepsis

Peer reviewedPublisher PD

Does Vascular Calcification Accelerate Inflammation?: A Substudy of the dal-PLAQUE Trial.

BACKGROUND: Atherosclerosis is an inflammatory condition with calcification apparent late in the disease process. The extent and progression of coronary calcification predict cardiovascular events. Relatively little is known about noncoronary vascular calcification. OBJECTIVES: This study investigated noncoronary vascular calcification and its influence on changes in vascular inflammation. METHODS: A total of 130 participants in the dal-PLAQUE (Safety and efficacy of dalcetrapib on atherosclerotic disease using novel non-invasive multimodality imaging) study underwent fluorodeoxyglucose positron emission tomography/computed tomography at entry and at 6 months. Calcification of the ascending aorta, arch, carotid, and coronary arteries was quantified. Cardiovascular risk factors were related to arterial calcification. The influences of baseline calcification and drug therapy (dalcetrapib vs. placebo) on progression of calcification were determined. Finally, baseline calcification was related to changes in vascular inflammation. RESULTS: Age >65 years old was consistently associated with higher baseline calcium scores. Arch calcification trended to progress more in those with calcification at baseline (p = 0.055). There were no significant differences between progression of vascular calcification with dalcetrapib compared to that with placebo. Average carotid target-to-background ratio indexes declined over 6 months if carotid calcium was absent (single hottest slice [p = 0.037], mean of maximum target-to-background ratio [p = 0.010], and mean most diseased segment [p < 0.001]), but did not significantly change if calcification was present at baseline. CONCLUSIONS: Across multiple arterial regions, higher age is consistently associated with higher calcium scores. The presence of vascular calcification at baseline is associated with progressive calcification; in the carotid arteries, calcification appears to influence vascular inflammation. Dalcetrapib therapy did not affect vascular calcification.The study was supported by F. Hoffmann-La Roche Ltd, Basel, Switzerland. Some editorial assistance was provided by Prime Healthcare and was funded by F. Hoffmann-La Roche Ltd, Basel, Switzerland. Partial support is acknowledged from NIH/NHLBI R01 HL071021 (ZAF). We thank Elisabetta Damonte for helping with statistical analyses.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.jacc.2015.10.05

Imaging Atherosclerosis.

Advances in atherosclerosis imaging technology and research have provided a range of diagnostic tools to characterize high-risk plaque in vivo; however, these important vascular imaging methods additionally promise great scientific and translational applications beyond this quest. When combined with conventional anatomic- and hemodynamic-based assessments of disease severity, cross-sectional multimodal imaging incorporating molecular probes and other novel noninvasive techniques can add detailed interrogation of plaque composition, activity, and overall disease burden. In the catheterization laboratory, intravascular imaging provides unparalleled access to the world beneath the plaque surface, allowing tissue characterization and measurement of cap thickness with micrometer spatial resolution. Atherosclerosis imaging captures key data that reveal snapshots into underlying biology, which can test our understanding of fundamental research questions and shape our approach toward patient management. Imaging can also be used to quantify response to therapeutic interventions and ultimately help predict cardiovascular risk. Although there are undeniable barriers to clinical translation, many of these hold-ups might soon be surpassed by rapidly evolving innovations to improve image acquisition, coregistration, motion correction, and reduce radiation exposure. This article provides a comprehensive review of current and experimental atherosclerosis imaging methods and their uses in research and potential for translation to the clinic.J.M.T. is supported by a Wellcome Trust research training fellowship (104492/Z/14/Z). M.D is supported by the British Heart Foundation (FS/14/78/31020). N.R.E. is supported by a research training fellowship from the Dunhill Medical Trust (RTF44/0114). A.J.B. is supported by the British Heart Foundation. J.H.F.R. is part-supported by the HEFCE, the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, and the Wellcome Trust.This is the final version of the article. It first appeared from the American Heart Association via http://dx.doi.org/10.1161/CIRCRESAHA.115.30624