Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes coronavirus disease 2019 (COVID-19) has resulted in a global health crisis. Prior to the arrival of this viral pandemic, the world was already plagued with a significant burden of cardiovascular disease. With the introduction of the novel virus, the world now faces a double jeapordy. Early reports have suggested an increased risk of death in individuals with underlying cardio-metabolic disorders. The exact effects of COVID-19 on the cardiovascular system are not well determined, however lessons from prior viral epidemics suggest that such infections can trigger acute coronary syndromes, arrhythmias and heart failure via direct and indirect mechanisms. In this article, we aimed to discuss the effects and potential underlying mechanisms of COVID -19 as well as potential implications of treatments targeted against this virus on the cardiovascular system.

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Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm’s performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Purpose:The goal of this study was to assess global cerebral glucose uptake in subjects with known cardiovascular risk factors by employing a quantitative 18 F-fluorodeoxyglucosepositron emission tomography/computed tomography (FDG-PET/CT) technique. We hypothesized that at-risk subjects would demonstrate decreased global brain glucose uptake compared to healthy controls. Methods:We compared 35 healthy male controls and 14 male subjects at increased risk for cardiovascular disease (CVD) as assessed by the Systematic Coronary Risk Evaluation tool. All subjects were grouped into two age-matched cohorts: younger (<50 years) and older (≥50 years). The global standardized uptake value mean (Avg SUVmean) was measured by mapping regions of interest (ROIs) of the entire brain across the supratentorial structures and cerebellum. Wilcoxon's rank-sum test was used to assess the differences in Avg SUVmean between controls and at-risk subjects.Results: Younger subjects demonstrated higher brain Avg SUVmean than older subjects.In addition, in both age strata, the 10-year risk for fatal cardiovascular disease according to the Systematic Coronary Risk Evaluation tool, was significantly greater in the at-risk groups than in healthy controls (younger: p=0.0304; older: p=0.0436). In the younger cohort, at-risk subjects demonstrated significantly lower brain Avg SUVmean than healthy controls (p=0.0355). In the older cohort, at-risk subjects similarly had lower Avg SUVmean than controls (p=0.0343). Conclusions:Global brain glucose uptake appears to be influenced by chronic cardiovascular risk factors. Therefore, FDG-PET/CT may play a role in determining the importance of CVD on brain function and its potential for monitoring the efficacy of various therapeutic interventions.

Aims The ketogenic diet (KD) is standard-of-care to achieve myocardial glucose suppression (MGS) for assessing inflammation using fluorine-18 fluorodeoxyglucose–positron emission tomography (FDG-PET). As KD protocols remain highly variable between centres (including estimation of nutrient intake by dietary logs for adequacy of dietary preparation), we aimed to assess the predictive utility of nutrient intake in achieving MGS. Methods and results Nineteen healthy participants underwent short-term KD, with FDG-PET performed after 1 and 3 days of KD (goal carbohydrate intake &lt;20 g/day). Nutrient consumption was estimated from dietary logs using nutrition research software. The area under receiver operating characteristics (AUROC) of macronutrients (carbohydrate, fat, and protein intake) for predicting MGS was analysed. The association between 133 nutrients and 4 biomarkers [beta-hydroxybutyrate (BHB), non-esterified fatty acids, insulin, and glucagon] with myocardial glucose uptake was assessed using mixed effects regression with false discovery rate (FDR) correction. Median (25th–75th percentile) age was 29 (25–34) years, 47% were women, and 42% were non-white. Median (25th–75th percentile) carbohydrate intake (g) was 18.7 (13.1-30.7), 16.9 (10.4-28.7), and 21.1 (16.6-29.0) on Days 1–3. No macronutrient intake (carbohydrate, fat, or protein) predicted MGS (c-statistic 0.45, 0.53, 0.47, respectively). Of 133 nutrients and 4 biomarkers, only BHB was associated with myocardial glucose uptake after FDR correction (corrected P-value 0.003). Conclusions During highly supervised, short-term KD, approximately half of patients meet strict carbohydrate goals. Yet, in healthy volunteers, dietary review does not provide reassurance for adequacy of myocardial preparation since no clear thresholds for carbohydrate or fat intake reliably predict MGS.