Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

ELAM : a model for acceptance and use of e-learning by teachers and students

Use of technology to facilitate learning is accepted to be of value across educational institutions. Government of India has taken cognizance of the institutional support required for resources in e-learning and formulated the national mission on education through ICT. However, the focus is still largely on getting the infrastructure and creating the e-learning content. It is necessary to consider the individual factors that play an important role in the adoption of e-learning. For example, attitude of students and teachers towards e-learning may affect their acceptance of the technology in the teaching-learning process. While there have been studies to understand the factors of the instructors (e.g release time for staff to engage in e-learning) and students (e.g. learning style) in acceptance of e-learning separately, a comprehensive view that considers both students and teachers in the same model is lacking (Jung, et. al., 2008; Nanayakkara 2007). To addresses this research gap, this paper considers the attitudes of students and the teachers that determine intention and actual use of the e-learning technology simultaneously in the model of e-learning. We present a conceptual framework for understanding acceptance of e-learning technology. Our model, ELAM, is based on the Unified Theory of Acceptance and Use of Technology (Venkatesh, et. al. 2003). ELAM (e-learning acceptance model) identifies the key factors in acceptance of e-learning as measured by behavioural intention to use the technology and actual usage. The four determinants of e-learning acceptance are --- (i) performance expectancy, (ii) effort expectancy, (iii) social influence and (iv) facilitating conditions. Performance expectancy is based on beliefs about perceived usefulness, interactivity and flexibility. Effort expectancy is based on beliefs about ease of learning, perceived ease of use and self-efficacy. Social influence is based on subjective norm and image. In developing countries, wherein educational institutions depend on governmental support to get the infrastructure and determine policies, institutional support plays a crucial role in the acceptance of e-learning. Hence, the model includes facilitating conditions as one of the determinants of e-learning acceptance. The following factors are included in this variable --- reliable infrastructure, institutional policies, training and support. As e-learning is associated with individualization of the teaching-learning process, the learning style of the student and teaching style of the teacher is an important factor affecting the adoption process. These factors are considered as mediators affecting the relation between performance expectancy beliefs and behavioural intention to use e-learning. The main contribution of the paper is that it presents a framework to understand e-learning acceptance as governed by the teacher, student and institutional factors