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Background: There is conflicting evidence on the role of acetylsalicylic acid (ASA) use in the development of cardiac allograft vasculopathy (CAV). Methods: A nationwide prospective two‐center study investigated changes in the coronary artery vasculature by highly automated 3‐D optical coherence tomography (OCT) analysis at 1 month and 12 months after heart transplant (HTx). The influence of ASA use on coronary artery microvascular changes was analyzed in the overall study cohort and after propensity score matching for selected clinical CAV risk factors. Results: In total, 175 patients (mean age 52 ± 12 years, 79% male) were recruited. During the 1‐year follow‐up, both intimal and media thickness progressed, with ASA having no effect on its progression. However, detailed OCT analysis revealed that ASA use was associated with a lower increase in lipid plaque (LP) burden (p =.013), while it did not affect the other observed pathologies. Propensity score matching of 120 patients (60 patient pairs) showed similar results, with ASA use associated with lower progression of LPs (p =.002), while having no impact on layered fibrotic plaque (p =.224), calcification (p =.231), macrophage infiltration (p =.197), or the absolute coronary artery risk score (p =.277). According to Kaplan–Meier analysis, ASA use was not associated with a significant difference in survival (p =.699) Conclusion: This study showed a benefit of early ASA use after HTx on LP progression. However, ASA use did not have any impact on the progression of other OCT‐observed pathologies or long‐term survival. [ABSTRACT FROM AUTHOR]

Background: Automated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability. Purpose: In this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n‐D with limited annotation cost. Methods: Deep LOGISMOS combines deep‐learning‐based pre‐segmentation of objects of interest provided by our convolutional neural network, FilterNet+, and our 3D multi‐objects LOGISMOS framework (layered optimal graph image segmentation of multiple objects and surfaces) that uses newly designed trainable machine‐learned cost functions. In the paradigm of assisted annotation, multi‐object JEI for efficient editing of automated Deep LOGISMOS segmentation was employed to form a new larger training set with significant decrease of manual tracing effort. Results: We have evaluated our method on 350 lower leg (left/right) T1‐weighted MR images from 93 subjects (47 healthy, 46 patients with muscular morbidity) by fourfold cross‐validation. Compared with the fully manual annotation approach, the annotation cost with assisted annotation is reduced by 95%, from 8 h to 25 min in this study. The experimental results showed average Dice similarity coefficient (DSC) of 96.56±0.26%$96.56\pm 0.26 \%$ and average absolute surface positioning error of 0.63 pixels (0.44 mm) for the five 3D muscle compartments for each leg. These results significantly improve our previously reported method and outperform the state‐of‐the‐art nnUNet method. Conclusions: Our proposed approach can not only dramatically reduce the expert's annotation efforts but also significantly improve the segmentation performance compared to the state‐of‐the‐art nnUNet method. The notable performance improvements suggest the clinical‐use potential of our new fully automated simultaneous segmentation of calf muscle compartments. [ABSTRACT FROM AUTHOR]

BACKGROUND: The association between genetic polymorphisms and early cardiac allograft vasculopathy (CAV) development is relatively unexplored. Identification of genes involved in the CAV process may offer new insights into pathophysiology and lead to a wider range of therapeutic options. METHODS: This prospective study of 109 patients investigated 44 single nucleotide polymorphisms (SNPs) within the susceptibility loci potentially related to coronary artery disease, carotid artery intima-media thickness (cIMT), and in nitric oxide synthase gene. Genotyping was done by the Fluidigm SNP Type assays and Fluidigm 48.48 Dynamic Array IFC. The intima thickness progression (IT) was evaluated by coronary optical coherence tomography (OCT) performed 1 month and 12 months after heart transplantation (HTx). RESULTS: During the first post-HTx year, the mean intima thickness (IT) increased by 24.0 ± 34.2 μm (p < 0.001) and lumen area decreased by -0.9 ± 1.8 mm² (p < 0.001). The rs1570360 (A/G) SNP of the vascular endothelial growth factor A (VEGFA) gene showed the strongest association with intima thickness progression, even in the presence of the traditional CAV risk factors. SNPs previously related to carotid artery intima-media thickness rs11785239 (PRAG1), rs6584389 (PAX2), rs13225723 (LINC02577) and rs17477177 (CCDC71L), were among the five most significantly associated with IT progression but lost their significance once traditional CAV risk factors had been added. CONCLUSION: Results of this study suggest that genetic variability may play an important role in CAV development. The vascular endothelial growth factor A gene SNP rs1570360 showed the strongest association with intima thickness (IT) progression measured by OCT, even in the presence of the traditional CAV risk factors. [ABSTRACT FROM AUTHOR]

Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016–2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope. [ABSTRACT FROM AUTHOR]