Patient-reported distress at a cancer center during the COVID-19 pandemic.

Assessments of health-related quality of life (HRQOL) are conducted by health systems to improve patient-centered care. Studies have shown that the COVID-19 pandemic poses unique stressors for patients with cancer. This study investigates change in self-reported global health scores in patients with cancer before and during the COVID-19 pandemic. In this single-institution retrospective cohort study, patients who completed the Patient-Reported Outcomes Measurement Information System (PROMIS) at a comprehensive cancer center before and during the COVID-19 pandemic were identified. Surveys were analyzed to assess change in the global mental health (GMH) and global physical health (GPH) scores at different time periods (pre-COVID: 3/1/5/2019-3/15/2020, surge1: 6/17/2020-9/7/2020, valley1: 9/8/2020-11/16/2020, surge2: 11/17/2020-3/2/2021, and valley2: 3/3/2021-6/15/2021). A total of 25,192 surveys among 7209 patients were included in the study. Mean GMH score for patients before the COVID-19 pandemic (50.57) was similar to those during various periods during the pandemic: surge1 (48.82), valley1 (48.93), surge2 (48.68), valley2 (49.19). Mean GPH score was significantly higher pre-COVID (42.46) than during surge1 (36.88), valley1 (36.90), surge2 (37.33) and valley2 (37.14). During the pandemic, mean GMH (49.00) and GPH (37.37) scores obtained through in-person were similar to mean GMH (48.53) and GPH (36.94) scores obtained through telehealth. At this comprehensive cancer center, patients with cancer reported stable mental health and deteriorating physical health during the COVID-19 pandemic as indicated by the PROMIS survey. Modality of the survey (in-person versus telehealth) did not affect scores.

Concurrent Cardio-Cerebral Infarctions in COVID-19: A Systematic Review of Published Case Reports/Series.

Twelve CCI patients were studied with confirmed or suspected COVID-19 infection. The majority of these patients were males (83.3%) with a median age of 55 years from three geographical locations, constituting the Middle East (7), Spain (3), and the USA (1). In 6 patients, IgG/IgM was positive for COVID-19, 4 with high pretest probability and 2 with positive RT-PCR. Type 2 DM, hyperlipidemia, and smoking were the primary risk factors. Right-sided neurological impairments and verbal impairment were the most common symptoms. Our analysis found 8 (66%) synchronous occurrences. In 58.3% of cases, neuroimaging showed left Middle Cerebral Artery (MCA) infarct and 33.3% right. Carotid artery thrombosis (16.6%), tandem occlusion (8.3%), and carotid stenosis (1%) were also reported in imaging. Dual antiplatelet therapy (DAPT) and anticoagulants were conservative therapies (10). Two AMI patients had aspiration thrombectomy, while three AIS patients had intravenous thrombolysis/tissue plasminogen activator (IVT-tPA), 2 had mechanical thrombectomy (MT), and 1 had decompressive craniotomy. Five had COVID-19-positive chest X-rays, whereas 4 were normal. four of 8 STEMI and 3 NSTEMI/UA patients complained chest pain. LV, ICA, and pulmonary embolism were further complications (2). Upon discharge, 7 patients (70%) had residual deficits while 1 patient unfortunately died.

Advancement of deep learning in pneumonia/Covid-19 classification and localization: A systematic review with qualitative and quantitative analysis.

Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid-19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X-ray) and Covid-19 (real-time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid-19 can be detected instantly from chest X-rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid-19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community-acquired pneumonia, viral pneumonia, and Covid-19 from images of chest X-rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one-stop solution to beginners and current researchers interested in this field.

Life-Threatening Diffuse Alveolar Hemorrhage as an Initial Presentation of Microscopic Polyangiitis: COVID-19 as a Likely Culprit.

Microscopic polyangiitis (MPA) is a systemic small vessel vasculitis but it is rare to see life-threatening diffuse alveolar hemorrhage (DAH) in MPA as an initial presentation. MPA more commonly presents with renal involvement and develops pulmonary manifestations later in the disease course. Our patient is a 77-year-old female with a recent history of recovered COVID-19 infection who presented with sudden onset fever, dyspnea, and hemoptysis for three days. She was diagnosed with MPA because of the new-onset DAH, a strongly positive myeloperoxidase (MPO) antibody, and the low likelihood of another etiology. The patient was treated with pulse-dose steroids and plasmapheresis while being on mechanical ventilation. This case highlights the importance of the prompt recognition of DAH as an initial presentation of MPA and illustrates the possible role of COVID-19 in inciting autoimmune conditions.