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Supranormal ejection fraction by echocardiography in clinically referred patient populations has been associated with an increased risk of cardiovascular disease (CVD). The prognostic implication of supranormal left ventricular ejection fraction (LVEF)-assessed by cardiac magnetic resonance (CMR)-in healthy, community-dwelling individuals is unknown.The purpose of this study is to investigate the prognostic implication of supranormal LVEF as assessed by CMR and its inter-relationship with stroke volume among community-dwelling adults without CVD.Participants from the MESA (Multi-Ethnic Study of Atherosclerosis) and DHS (Dallas Heart Study) cohorts free of CVD who underwent CMR with LVEF above the normal CMR cutoff (≥57%) were included. The association between cohort-specific LVEF categories and risk of clinically adjudicated major adverse cardiovascular events (MACE) was assessed using adjusted Cox models. Subgroup analysis was also performed to evaluate the association of LVEF and risk of MACE among individuals stratified by left ventricular stroke volume index.The study included 4,703 participants from MESA and 2,287 from DHS with 727 and 151 MACE events, respectively. In adjusted Cox models, the risk of MACE was highest among individuals in LVEF Q4 (vs Q1) in both cohorts after accounting for potential confounders (MESA: HR = 1.27 [95% CI: 1.01-1.60], P = 0.04; DHS: HR = 1.72 [95% CI: 1.05-2.79], P = 0.03). A significant interaction was found between the continuous measures of LVEF and left ventricular stroke volume index (P interaction = 0.02) such that higher LVEF was significantly associated with an increased risk of MACE among individuals with low but not high stroke volume.Among community-dwelling adults without CVD, LVEF in the supranormal range is associated with a higher risk of adverse cardiovascular outcomes, particularly in those with lower stroke volume.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19–positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19–positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19–positive and COVID-19–negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.

Objective: Abdominal aortic aneurysms are defined as a 50% or greater increase in infrarenal aortic diameter (IAD). However, normal IAD has not been defined for all ethnic groups as minorities have been underrepresented in most studies. The aim of the study was to assess ethnic differences in IAD and infrarenal aortic area (AoA) adjusting for the effects of age, gender and body size in the general population. Methods: Participants (2,515) in a population based study underwent high-resolution magnetic resonance imaging (MRI) of the abdominal aorta. Analyses of variance and multiple regression analyses were used to assess the relationship between race/ethnicity, age, gender and body size and IAD and aortic area. Subjects with AAA detected by MRI (defined as IAD ≥ 3.0 cm) were excluded from the analysis. Results: Decreasing age, female sex, Hispanic ethnicity, and lower height were independent predictors of reduced IAD by multivariate linear regression (all P < 0.001). Of these, female sex and Hispanic ethnicity were the factors more strongly associated with aortic size. Female sex was associated with 0.27 cm reduction in IAD and Hispanic ethnicity with 0.39 cm reduction in IAD. Similarly, decreasing age, female sex, Hispanic ethnicity, and lower height were independent predictors of reduced AoA. Female sex was associated with a 51 cm 2 reduction in AoA and Hispanic ethnicity with 11.1 cm 2 reduction in AoA. Although Hispanics had higher BMI than blacks and whites ( P =.01), and lower height values than blacks and whites (P Conclusions: Ethnic differences exist in infrarenal aortic diameter. Despite larger body size, Hispanics have significantly lower IAD than blacks and whites in the general population. The reduced aortic size in Hispanics suggests that the thresholds for abdominal aortic aneurysm diagnosis, rupture and repair may be lower and need to be established.

Introduction: White Matter Hyperintensities (WMH) have been implicated as a risk factor for motor and cognitive decline, dementia and stroke. A standard for use in clinical settings is needed to identify which cases are advanced or meet thresholds with implications for degree of impairment. The system of Fazekas is a promising candidate due to its simplicity and strong correlation with outcomes. The original Fazekas categorized deep and periventricular WMH separately. Advanced periventricular and deep WMH were later shown to have equivalent pathology and etiology. Subsequent work by De Carli showed the classification of deep and periventricular WMH on axial images to be arbitrary and suggested a common etiology for lesser degrees of WMH as well. We therefore adapted the Fazekas criteria to consider deep and periventricular WMH jointly. Questions persist about low reproducibility of grading systems. In a preliminary analysis we found frequent disagreement classifying lesions less than 3mm in diameter and decided to incorporate this threshold into our criteria to determine the effect on reproducibility and correlation of our final model with automated volumes. Hypothesis: Modification of the Fazekas grading system to consider deep and periventricular WMH jointly is less arbitrary. Simple modifications of such a system will result in a high degree of reproducibility and high correlation with automated WMH volumes. Methods: Axial 3T FLAIR MR images of the brain were obtained from community dwelling subjects. Grading measurements were applied to diameter of deep WMH and thickness of periventricular WMH. The initial system defined Grade 0 as no WMH, Grade 1 as < 10mm, Grade 2 as >=10mm but =20mm. 52 studies were read separately by 2 reviewers. We then revised grade 0 to include intensities < 3mm and grade 1 as >=3mm but Results: After modifications, inter-rater agreement for Fazekas white matter score increased from 72% to 89% with kappa increased from 0.45 to 0.78. There was good correlation of grade and automated volume (ANOVA R-Square: 0.52 P Conclusion: Advances in understanding of White Matter Hyperintensities suggest periventricular and deep lesions should be rated jointly. This modification was applied to the Fazekas system with excellent reproducibility after implementation of a 3mm size threshold as well as high agreement with automated volumes ammong community dwelling subjects.

To the Editor: Perhonen and co-workers ([4][1]) report that existence in simulated and actual microgravity leads to cardiac atrophy. I thank the authors for their interesting, convincing, and important work. In theirdiscussion, they speculate that chronic lack of cardiac distension in microgravity