The Impact of the Covid19 Pandemic on Patients with Peripheral Artery Disease

Background The impact of COVID-19 on major adverse lower extremity (MALE) and cardiovascular events (MACE) in patients with peripheral artery disease (PAD) is unknown. Methods Using the VA Corporate Data Warehouse, Veterans with PAD were identified. Rates of MALE (amputation or lower extremity revascularization [LER]), and MACE (death, MI, or coronary revascularization) were assessed in pre-pandemic (3/11/2019-3/10/2020), early-pandemic (3/11/2020-3/10/2021), and late-pandemic (3/11/2021-3/10/2022) periods. Outcomes were compared using Kaplan-Meier method. Results Of 418,042 Veterans (mean age 72 yrs) with PAD, 76.7% were white and 96.8% male. Furthermore, 89.2% had HTN, 60.4% diabetes, 49.3% CAD, 21.6% heart failure, and 20.5% atrial fibrillation. From 3/11/2019 to 3/10/2022, 3,100 had amputation, 8,187 had LER, & 2,229 had MACE. Amputation rates declined and continued to decline in early- and late-pandemic period (306 to 268 to 235;p<0.001;rates per 100k). Rates of LER declined initially and stabilized in late-pandemic period (951 to 587 to 609;p < 0.001;rates per 100k). MACE did not change significantly. (215 to 168 to 202;p<0.001;rates per 100k). Conclusion Amputation rates in Veterans with PAD did not increase despite a clinically significant decline in LER. Given the known efficacy of noninvasive therapies in PAD, these data suggest that there is a need to re-evaluate appropriate indications for LER and amputation. [Formula presented]Copyright © 2023 American College of Cardiology Foundation

Differentiation of COVID-19 Conditions using Mediastinum Shape in Chest X-ray Images

In this work, an attempt has been made to analyze the shape variations in mediastinum for differentiation of Coronavirus Disease-2019 (COVID-19) and normal conditions in chest X-ray images. For this, the images are obtained from a publicly available dataset. Segmentation of mediastinum from the raw images is performed using Reaction Diffusion Level Set (RDLS) method. Shape-based features are extracted from the delineated mediastinum masks and are statistically analyzed. Further, the features are fed to two classifiers, namely, multi-layer perceptron and support vector machine for differentiation of normal and COVID-19 images. From the results, it is observed that the employed RDLS method is able to delineate mediastinum from the raw chest X-ray images. Eight shape features are observed to be statistically significant. The mean values of these features are found to be distinctly higher for COVID-19 images as compared to normal images. Area under the curve of greater than 76.9% is achieved for both the classifiers. It appears that mediastinum could be used as a region of interest for computerized detection and mass screening of the disease. © 2022 The Author(s), published by De Gruyter.

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks (Mar, 10.1007/s10489-020-01941-8, 2021)

Figure 4 in the original article unfortunately contained an error.