Comparing Telehealth-Based vs. In-Person Soft Skills Training for Persons with Disabilities During COVID-19: A Pilot Study

Work-related soft skills (e.g. requesting help and problem solving) are critical for employment success. This pilot study evaluated the feasibility of a Direct Skills Teaching (DST) group intervention to teach work-related help-seeking skills to persons with disabilities. The four-session group intervention ("Asking for Help”) was delivered in person or via telehealth to 74 participants with disabilities during COVID-19. Participants reported increased confidence with asking employers for help and reported high intervention satisfaction. In-person and telehealth soft skills groups achieved similar positive outcomes, and findings support their use to teach groups help-seeking skills with a preplanned curriculum. © 2023 ASGW.

THE DEADLY PACE

TOPIC: Cardiovascular Disease TYPE: Medical Student/Resident Case Reports INTRODUCTION: Myocardial perforation remains as a rare but important complication of pacemaker insertion. Perforation can occur acutely within 24 h after implantation, sub-acutely between 24 h to one month after implantation and chronically occurring more than one month after implantation. (Sidhu). Acute perforations are well described at medical literature, but chronic perforations are much less common and can be difficult to diagnose (Ahmed). This is the case of atypical presentation of a chronic right ventricular perforation caused by RV ICD lead presenting with tamponade and left sided compression atelectasis. CASE PRESENTATION: A 73-year-old Male with PMHx of A-Fib, CHF class II, A-HTN, NIDDM, RA, PUD, TAA, S/P ICD placement 3 months before presentation, that comes to the ED with pleuritic chest pain, dizziness, SOB and fatigue of 3 months of evolution. Patient states that the symptoms slowly worsened over the last 3 days. At physical examination was found with BP 90/40, HR 105 bpm, RR 21rpm Sat 95% in acute distress, using accessory muscles with muffled heart sounds and + JVD. COVID-19 Ag was negative. EKG shows sinus tachycardia and low voltage on precordial leads. A bedside echocardiogram shows large pericardial effusion with tamponade physiology, ventricular perforation and emergent pericardiocentesis was performed. A total of 515 ml of bloody pericardial fluid was removed and patient vital signs improves. Chest CT scan later confirmed the presence of pericardial fluid accumulation and left sided compression atelectasis. The ICD lead was removed and relocated at the ventricular septum to avoid ventricular perforation in the future. DISCUSSION: In rare circumstances when a large effusion develops over time, it may cause compression atelectasis of the surrounding bronchi and lung (Amin). Compression atelectasis caused by ventricular perforation are better described in cases of acute or subacute ventricular perforation at the medical literature. The most common presentation of chronic perforation is SOB and hypoxemia. This patient presentation after 3 months of ICD placement, with sudden onset cardiac tamponade and no hypoxemia is an atypical presentation of chronic ventricular perforation. The rapid fluid accumulation over time in the pericardium, is what causes that this space stretches to accommodate the excess fluid, that later cause a compression atelectasis of the surrounding bronchi and lung (Amin). CONCLUSIONS: This patient atypical presentation of ventricular perforation wasn't described before in the literature. Is very important not to assume that any perforation may the sequelae of a recent intervention and can present acutely like in this life-threatening clinical scenario. As clinicians we need to keep in mind that ventricular perforation may present with large effusion causing compression atelectasis, with or without tamponade physiology. REFERENCE #1: Sidhu B, Rajani R, Rinaldi C. Chronic Ventricular lead perforation: Expect the Unexpected. Clin Case Rep. 2019, 7: 455-468 REFERENCE #2: Ahmed A, Shokr M, Lieberman R. Subacute right ventricular perforation by pacemaker lead causing left sided hemothorax and epicardial hematoma. Case Reports in Cardiology. 2017: 1-5 REFERENCE #3: Amin H, Gyawali B, Chaudhuri D. A large pericardial effusion culminating in left lung collapse. Cureus. 2019, 11: e5287 DISCLOSURES: No relevant relationships by Jesse Aleman, source=Web Response No relevant relationships by Francisco Caraballo, source=Web Response No relevant relationships by Greisha Gonzalez Santiago, source=Web Response

Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death.

"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.