Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Predicting the Loan Default using Logistic Regression Model

Under the direction of Dr. Giancarlo Schrementi Predicting loan default is an important problem for managing risk at banks. Banks began to emerge as key players in the lending market since industrial revolution and they would use the collateral to minimize their risk. Loan default is an important issue for banks because it can lead to bank’s insolvency and can have broader impact on economy. Hence, it is important to manage the risk of loan defaults to promote financial stability and economic growth. Previous studies have been done in this field to predict the probability of loan default using logistic regression, machine learning models and python programming models. This study examines using a logistic regression model in predicting the probability of loan default of customers. A logistic regression is a statistical analysis method to predict a binary outcome, such as yes or no, based on prior observations of a data set. Thus, a logistic regression model is used here because loan default is a binary prediction problem i.e. (a loan is defaulted or not) and logistic regression is commonly used in binary prediction. The dataset is taken from the Kaggle dataset repository, an open dataset platform, and contains a wide assortment of features, half of them being categorical and half being quantitative. The data has highly unbalanced class proportions, as most customers do not default. The methods include exploratory data analysis, data wrangling and cleansing, feature selection, and evaluating the resulting model

Back to the Future: Mathematical Predictions //VAC 112

Sujata Bhandari, Predicting Loan Default using Logistic Regression Anh Doan, Statistics and Machine Learning: Regression and Forecasting Linh T. Pham, Implementation of Binomial and Black-Scholes Option Pricing Models in Python to Predict Amazon European Option Premiums Marella Fernandez, Social Factors Contributing to Academic Success: A Statistical Analysis Moderator: Dr. Molly Lync

Anesthesia for penetrating injury of the oropharynx in a child: a case report

Abstract Background Penetrating injury of the oropharynx occurs frequently in children, however, anesthetic management is seldom described in such cases. Case presentation A 2-year old child came to the emergency room with a toothbrush impacted in the gingivobuccal sulcus making airway management difficult. We used a simple yet unique approach to secure the airway safely given the lack of pediatric size fibreoptic and videolaryngoscopes in our emergency operation theatre. The patient was kept in Pediatric ICU and watched for any complications and discharged on the 4th postoperative day. Conclusions Thus, ingenious non-invasive techniques to secure the airway can prevent the patient from undergoing surgical tracheostomy

Assessments of ecosystem service indicators and stakeholder's willingness to pay for selected ecosystem services in the Chure region of Nepal

The Chure region of Nepal—the area sandwiched between the hills in the north and the plains in the south—is considered an ecologically fragile, structurally weak, and highly erosion-prone region. The forest in the Chure region provides several ecosystem services to people living in the downstream areas. However, assessment and quantification of ecosystem services in this region are very limited. This study, conducted in a watershed of the Chure region of western Nepal, combined local users' perspectives with experts' opinions to identify and rank ecosystem services based on land use types, to investigate the downstream users' willingness to pay for ecosystem services, and to explore the socio-economic factors affecting their willingness to pay. The study found that forests offered the highest number of ecosystem goods and services in this area. Local people were familiar with 10 different ecosystem services provided by the watershed and ranked drinking water service at the top. The downstream beneficiaries would be willing to pay a higher amount for drinking water service than they were currently paying if the quality of the service and its sustainability were assured. The amount they were willing to pay for ecosystem services increased significantly with monthly income. The results of this study are useful for other areas in which an upstream–downstream linkage exists and the upstream communities play a crucial role in maintaining ecosystem functions and the resulting supply of ecosystem services to the downstream communities