Medication therapy strategies for the coronavirus disease 2019 (COVID-19): recent progress and challenges

Health System- and Patient-Related Factors Associated with COVID-19 Mortality among Hospitalized Patients in Limpopo Province of South Africa's Public Hospitals

South Africa has recorded the highest COVID-19 morbidity and mortality compared to other African regions. Several authors have linked the least amount of death in African countries with under-reporting due to poor health systems and patients' health-seeking behaviors, making the use of clinical audits more relevant for establishing the root causes of health problems, and improving quality patient care outcomes. Clinical audits, such as mortality audits, have a significant role in improving quality health care services, but very little is documented about the outcomes of the audits. Therefore, the study sought to determine the health care system and patient-related factors associated with COVID-19 mortality by reviewing the COVID-19 inpatient mortality audit narration reports. This was a retrospective qualitative research approach of all hospitalized COVID-19 patients, resulting in death between the first and second COVID-19 pandemic waves. Thematic analysis employed inductive coding to identify themes from mortality audits from all 41 public hospitals in Limpopo Province, South Africa. Four themes with seventeen sub-themes emerged: sub-standard emergency medical care provided, referral system inefficiencies contributed to delays in access to health care services, the advanced age of patients with known and unknown comorbidities, and poor management of medical supplies and equipment, as a health system and patient-related factors that contributed to the high mortality of COVID-19 patients. There is a need to routinely conduct clinical audits to identify clinical challenges and make recommendations for health promotion, risk communication, and community engagement. We recommend reviewing and expanding the scope of practice for health-care providers during epidemics and pandemics that include aspects such as task-shifting.

COVID-19 Pharmacotherapy: Drug Development, Repurposing of Drugs, and the Role of Pharmacogenomics

The SARS-CoV-2 virus has been the subject of intense pharmacological research. Various pharmacotherapeutic approaches including antiviral and immunotherapy are being explored. A pandemic, however, cannot depend on the development of new drugs; the time required for conventional drug discovery and development is far too lengthy. As such, repurposing drugs is being used as a viable approach for identifying pharmacological agents for COVID-19 infections. Evaluation of repurposed drug candidates with pharmacogenomic analysis is being used to identify near-term pharmacological remedies for COVID-19.

Adherence of Clinical Practice Guidelines for Pharmacologic Treatments of Hospitalized Patients With COVID-19 to Trustworthy Standards: A Systematic Review

IMPORTANCE

The COVID-19 pandemic created the need for rapid and urgent guidance for clinicians to manage COVID-19 among patients and prevent transmission.

OBJECTIVE

To appraise the quality of clinical practice guidelines (CPGs) using the National Academy of Medicine (NAM) criteria.

EVIDENCE REVIEW

A search of MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials to December 14, 2020, and a search of related articles to February 28, 2021, that included CPGs developed by societies or by government or nongovernment organizations that reported pharmacologic treatments of hospitalized patients with COVID-19. Teams of 2 reviewers independently abstracted data and assessed CPG quality using the 15-item National Guideline Clearinghouse Extent of Adherence to Trustworthy Standards (NEATS) instrument.

FINDINGS

Thirty-two CPGs were included in the review. Of these, 25 (78.1%) were developed by professional societies and emanated from a single World Health Organization (WHO) region. Overall, the CPGs were of low quality. Only 7 CPGs (21.9%) reported funding sources, and 12 (37.5%) reported conflicts of interest. Only 5 CPGs (15.6%) included a methodologist, described a search strategy or study selection process, or synthesized the evidence. Although 14 CPGs (43.8%) made recommendations or suggestions for or against treatments, they infrequently rated confidence in the quality of the evidence (6 of 32 [18.8%]), described potential benefits and harms (6 of 32 [18.8%]), or graded the strength of the recommendations (5 of 32 [15.6%]). External review, patient or public perspectives, or a process for updating were rare. High-quality CPGs included a methodologist and multidisciplinary collaborations involving investigators from 2 or more WHO regions.

CONCLUSIONS AND RELEVANCE

In this review, few COVID-19 CPGs met NAM standards for trustworthy guidelines. Approaches that prioritize engagement of a methodologist and multidisciplinary collaborators from at least 2 WHO regions may lead to the production of fewer, high-quality CPGs that are poised for updates as new evidence emerges.

TRIAL REGISTRATION

PROSPERO Identifier: CRD42021245239.

A systematic review on AI/ML approaches against COVID-19 outbreak

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.