Randomized Clinical Trials and COVID-19: Managing Expectations

Global landscape of COVID-19 research: a visualization analysis of randomized clinical trials

The emergence of COVID-19 in 2019 has resulted in a significant global health crisis. Consequently, extensive research was published to understand and mitigate the disease. In particular, randomized controlled trials (RCTs) have been considered the benchmark for assessing the efficacy and safety of interventions. Hence, the present study strives to present a comprehensive overview of the global research landscape pertaining to RCTs and COVID-19. A bibliometric analysis was performed using the Scopus database. The search parameters included articles published from 2020 to 2022 using keywords specifically related to COVID-19 and RCTs. The data were analyzed using various bibliometric indicators. The volume of publications, contributions of countries and institutions, funding agencies, active journals, citation analysis, co-occurrence analysis, and future research direction analysis were specifically analyzed. A total of 223,480 research articles concerning COVID-19 were published, with 3,727 of them related to RCTs and COVID-19. The ten most productive countries collectively produced 75.8% of the documents, with the United States leading the way by contributing 31.77%, followed by the UK with 14.03% (n = 523), China with 12.96% (n = 483) and Canada with 7.16% (n = 267). Trials (n = 173, 4.64%), BMJ Open (n = 81, 2.17%), PLOS One (n = 73, 1.96%) and JAMA Network Open (n = 53, 1.42%) were the most active journals in publishing articles related to COVID-19 RCTs. The co-occurrence analysis identified four clusters of research areas: the safety and effectiveness of COVID-19 vaccines, mental health strategies to cope with the impact of the pandemic, the use of monoclonal antibodies to treat patients with COVID-19, and systematic reviews and meta-analyses of COVID-19 research. This paper offers a detailed examination of the global research environment pertaining to RCTs and their use in the context of the COVID-19 pandemic. The comprehensive body of research findings was found to have been generated by the collaborative efforts of multiple countries, institutions, and funding organizations. The predominant research areas encompassed COVID-19 vaccines, strategies for mental health, monoclonal antibodies, and systematic reviews. This information has the potential to aid researchers, policymakers, and funders in discerning areas of weakness and establishing areas of priority.

Clinical Characteristics, Diagnosis, and Therapeutics of COVID-19: A Review

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that suddenly emerged at the end of December 2019 and caused coronavirus disease 2019 (COVID-19) continues to afflict humanity, not only seriously affecting healthcare systems but also leading to global social and economic imbalances. As of August 2022, there were approximately 580 million confirmed cases of COVID-19 and approximately 6.4 million confirmed deaths due to this disease. The data are sufficient to highlight the seriousness of SARS-CoV-2 infection. Although most patients with COVID-19 present primarily with respiratory symptoms, an increasing number of extrapulmonary systemic symptoms and manifestations have been associated with COVID-19. Since the outbreak of COVID-19, much has been learned about the disease and its causative agent. Therefore, great effort has been aimed at developing treatments and drug interventions to treat and reduce the incidence of COVID-19. In this narrative review, we provide a brief overview of the epidemiology, mechanisms, clinical manifestations, diagnosis, and therapeutics of COVID-19.

Best Practices from the American Society of Pain and Neuroscience (ASPN) for Clinical Research During a Pandemic or Emergency

The COVID-19 pandemic caught many areas of medicine in a state of unpreparedness for conducting research and completing ongoing projects during a global crisis, including the field of pain medicine. Waves of infection led to a disjointed ability to provide care and conduct clinical research. The American Society of Pain and Neuroscience (ASPN) Research Group has created guidance for pragmatic and ethical considerations for research during future emergency or disaster situations. This analysis uses governmental guidance, scientific best practices, and expert opinion to address procedure-based or device-based clinical trials during such times. Current literature offers limited recommendations on this important issue, and the findings of this group fill a void for protocols to improve patient safety and efficacy, especially as we anticipate the impact of future disasters and spreading global infectious diseases. We recommend local adaptations to best practices and innovations to enable continued research while respecting the stressors to the research subjects, investigator teams, health-care systems, and to local infrastructure.

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring

BACKGROUND

Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques.

METHODS

We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power.

RESULTS

The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions.

CONCLUSION

This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

Impact of dexamethasone and tocilizumab on hematological parameters in COVID-19 patients with chronic disease

BACKGROUND AND AIM

The most effective way to control severity and mortality rate of the novel coronavirus disease (COVID-19) is through sensitive diagnostic approaches and an appropriate treatment protocol. We aimed to identify the effect of adding corticosteroid and Tocilizumab to a standard treatment protocol in treating COVID-19 patients with chronic disease through hematological and lab biomarkers.

MATERIALS AND METHODS

This study was performed retrospectively on 68 COVID-19 patients with chronic disease who were treated by different therapeutic protocols. The patients were categorized into four groups: control group represented the patients' lab results at admission before treatment protocols were applied; group 1 included patients treated with anticoagulants, Hydroxychloroquine, and antibiotics; group 2 comprised patients treated with Dexamethasone; and group 3 included patients treated with Dexamethasone and Tocilizumab.

RESULTS

The WBC and neutrophil counts were increased significantly in group 3 upon the treatment when they were compared with patients in group 1 (p = 0.004 and p = 0.001, respectively). The comparison of C-reactive Protein (CRP) level at admission was higher in group 3 than in group 1 with p = 0.030. After 10 days of treatment, CRP level was decreased in all groups, but in group 3 it was statistically significant (p = 0.002).

CONCLUSION

The study paves the way into the effectiveness of combining Dexamethasone with Tocilizumab in treatment COVID-19 patients with chronic diseases.

Impact of dexamethasone and tocilizumab on hematological parameters in COVID-19 patients with chronic disease

BACKGROUND AND AIM

The most effective way to control severity and mortality rate of the novel coronavirus disease (COVID-19) is through sensitive diagnostic approaches and an appropriate treatment protocol. We aimed to identify the effect of adding corticosteroid and Tocilizumab to a standard treatment protocol in treating COVID-19 patients with chronic disease through hematological and lab biomarkers.

MATERIALS AND METHODS

This study was performed retrospectively on 68 COVID-19 patients with chronic disease who were treated by different therapeutic protocols. The patients were categorized into four groups: control group represented the patients' lab results at admission before treatment protocols were applied; group 1 included patients treated with anticoagulants, Hydroxychloroquine, and antibiotics; group 2 comprised patients treated with Dexamethasone; and group 3 included patients treated with Dexamethasone and Tocilizumab.

RESULTS

The WBC and neutrophil counts were increased significantly in group 3 upon the treatment when they were compared with patients in group 1 (p=0.004 and p=0.001, respectively). The comparison of C-reactive Protein (CRP) level at admission was higher in group 3 than in group 1 with p=0.030. After 10 days of treatment, CRP level was decreased in all groups, but in group 3 it was statistically significant (p=0.002).

CONCLUSION

The study paves the way into the effectiveness of combining Dexamethasone with Tocilizumab in treatment COVID-19 patients with chronic diseases.

Machine learning-based scoring system to predict in-hospital outcomes in patients hospitalized with COVID-19

BACKGROUND

The evolution of patients hospitalized with coronavirus disease 2019 (COVID-19) is still hard to predict, even after several months of dealing with the pandemic.

AIMS

To develop and validate a score to predict outcomes in patients hospitalized with COVID-19.

METHODS

All consecutive adults hospitalized for COVID-19 from February to April 2020 were included in a nationwide observational study. Primary composite outcome was transfer to an intensive care unit from an emergency department or conventional ward, or in-hospital death. A score that estimates the risk of experiencing the primary outcome was constructed from a derivation cohort using stacked LASSO (Least Absolute Shrinkage and Selection Operator), and was tested in a validation cohort.

RESULTS

Among 2873 patients analysed (57.9% men; 66.6±17.0 years), the primary outcome occurred in 838 (29.2%) patients: 551 (19.2%) were transferred to an intensive care unit; and 287 (10.0%) died in-hospital without transfer to an intensive care unit. Using stacked LASSO, we identified 11 variables independently associated with the primary outcome in multivariable analysis in the derivation cohort (n=2313), including demographics (sex), triage vitals (body temperature, dyspnoea, respiratory rate, fraction of inspired oxygen, blood oxygen saturation) and biological variables (pH, platelets, C-reactive protein, aspartate aminotransferase, estimated glomerular filtration rate). The Critical COVID-19 France (CCF) risk score was then developed, and displayed accurate calibration and discrimination in the derivation cohort, with C-statistics of 0.78 (95% confidence interval 0.75-0.80). The CCF risk score performed significantly better (i.e. higher C-statistics) than the usual critical care risk scores.

CONCLUSIONS

The CCF risk score was built using data collected routinely at hospital admission to predict outcomes in patients with COVID-19. This score holds promise to improve early triage of patients and allocation of healthcare resources.

A review of clinical efficacy data supporting emergency use authorization for COVID-19 therapeutics and lessons for future pandemics

Emergency Use Authorization (EUA) allows the US Food and Drug Administration (FDA) to expedite the availability of therapeutics in the context of a public health emergency. To date, an evidentiary standard for clinical efficacy to support an EUA has not yet been established. This review examines the clinical data submitted in support of EUA for antiviral and anti-inflammatory therapeutics for coronavirus disease 2019 (COVID-19) through December of 2021 and the resilience of the authorization as new clinical data arose subsequent to the authorization. In the vast majority of cases, EUA was supported by at least one well-powered randomized controlled trial (RCT) where statistically significant efficacy was demonstrated. This included branded medications already approved for use outside of the context of COVID-19. When used, the standard of a single RCT seemed to provide adequate evidence of clinical efficacy, such that subsequent clinical studies generally supported or expanded the EUA of the therapeutic in question. The lone generic agent that was granted EUA (chloroquine/hydroxychloroquine) was not supported by a well-controlled RCT, and the EUA was withdrawn within 3 months time. This highlighted not only the ambiguity of the EUA standard, but also the need to provide avenues through which high quality clinical evidence for the efficacy of a generic medication could be obtained. Therefore, maintaining the clinical trial networks assembled during the COVID-19 pandemic could be a critical component of our preparation for future pandemics. Consideration could also be given to establishing a single successful RCT as regulatory guidance for obtaining an EUA.

An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data

Objectives

Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.

Materials and Methods

An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis.

Results

The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period.

Discussion

An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time.

Conclusions

The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

Beyond COVID-19 and lessons learned in the United States

The COVID-19 pandemic severely tested the resilience of the US blood supply with wild fluctuations in blood donation and utilisation rates as community donation opportunities ebbed and hospitals post-poned elective surgery. Key stakeholders in transfusion services, blood centres, supply chains and manufacturers reviewed their experiences during the SARS-CoV-2 pandemic as well as available literature to describe successes, opportunities for improvement and lessons learned. The blood community found itself in uncharted territory responding to restriction of its access to donors (approximately 20% decrease) and some supplies; environmental adjustments to address staff and donor concerns about coronavirus transmission; and the development of a new product (COVID-19 convalescent plasma [CCP]). In assuring that the needs of the patients were paramount, the donation process was safe, that clinicians had access to CCP, and vendor relationships aligned, the blood banking community relearned its primary focus: improving patient outcomes.

Low versus high dose anticoagulation in patients with Coronavirus 2019 pneumonia at the time of admission to critical care units: A multicenter retrospective cohort study in the Beaumont healthcare system

Purpose

Coagulopathy is common in patients with COVID-19. The ideal approach to anticoagulation remains under debate. There is a significant variability in existing protocols for anticoagulation, and these are mostly based on sporadic reports, small studies, and expert opinion.

Materials and methods

This multicenter retrospective cohort study evaluated the association between anticoagulation dose and inpatient mortality among critically ill COVID-19 patients admitted to the intensive care units (ICUs) or step-down units (SDUs) of eight Beaumont Healthcare hospitals in Michigan, USA from March 10th to April 15th, 2020.

Results

Included were 578 patients with a median age of 64 years; among whom, 57.8% were males. Most patients (n = 447, 77.3%) received high dose and one in four (n = 131, 22.7%) received low dose anticoagulation. Overall mortality rate was 41.9% (n = 242). After adjusting for potential confounders (age, sex, race, BMI, ferritin level at hospital admission, intubation, comorbidities, mSOFA, and Padua score), administration of high anticoagulation doses at the time of ICU/SDU admission was associated with decreased inpatient mortality (OR 0.564, 95% CI 0.333–0.953, p = 0.032) compared to low dose.

Conclusion

Treatment with high dose anticoagulation at the time of ICU/SDU admission was associated with decreased adjusted mortality among critically ill adult patients with COVID-19.

A multivariate model for successful publication of intensive care medicine randomized controlled trials in the highest impact factor journals: the SCOTI score

Background

Critical care randomized controlled trials (RCTs) are often published in high-impact journals, whether general journals [the New England Journal of Medicine (NEJM), The Lancet, the Journal of the American Medical Association (JAMA)] or critical care journals [Intensive Care Medicine (ICM), the American Journal of Respiratory and Critical Care Medicine (AJRCCM), Critical Care Medicine (CCM)]. As rejection occurs in up to 97% of cases, it might be appropriate to assess pre-submission probability of being published. The objective of this study was to develop and internally validate a simplified score predicting whether an ongoing trial stands a chance of being published in high-impact general journals.

Methods

A cohort of critical care RCTs published between 1999 and 2018 in the three highest impact medical journals (NEJM, The Lancet, JAMA) or the three highest impact critical care journals (ICM, AJRCCM, CCM) was split into two samples (derivation cohort, validation cohort) to develop and internally validate the simplified score. Primary outcome was journal of publication assessed as high-impact general journal (NEJM, The Lancet, JAMA) or critical care journal (ICM, AJRCCM, CCM).

Results

A total of 968 critical care RCTs were included in the predictive cohort and split into a derivation cohort (n = 510) and a validation cohort (n = 458). In the derivation cohort, the sample size (P value < 0.001), the number of centers involved (P value = 0.01), mortality as primary outcome (P value = 0.002) or a composite item including mortality as primary outcome (P value = 0.004), and topic [ventilation (P value < 0.001) or miscellaneous (P value < 0.001)] were independent factors predictive of publication in high-impact general journals, compared to high-impact critical care journals. The SCOTI score (Sample size, Centers, Outcome, Topic, and International score) was developed with an area under the ROC curve of 0.84 (95% Confidence Interval, 0.80–0.88) in validation by split sample.

Conclusions

The SCOTI score, developed and validated by split sample, accurately predicts the chances of a critical care RCT being published in high-impact general journals, compared to high-impact critical care journals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00954-x.

Definitions and elements of endpoints in phase III randomized trials for the treatment of COVID-19: a cross-sectional analysis of trials registered in ClinicalTrials.gov

BACKGROUND

There are several challenges in designing clinical trials for the treatment of novel infectious diseases, such as COVID-19. In particular, the definition of endpoints related to the severity, time frame, and clinical course remains unclear. Therefore, we conducted a cross-sectional analysis of phase III randomized trials for COVID-19 registered at ClinicalTrials.gov .

METHODS

We collected the data from ClinicalTrials.gov on March 31, 2021, by specifying the following search conditions under Advanced Search: Condition or disease: (COVID-19) OR (SARS-CoV-2); Study type: Interventional Studies; Study Results: All Studies; Recruitment: Not yet recruiting, Recruiting, Enrolling by invitation, Active, Not recruiting, Suspended, Completed; Sex: All; and Phase: Phase 3. From the downloaded search results, we selected trials that met the following criteria: Primary Purpose: Treatment; Allocation: Randomized. We manually transcribed information not included in the downloaded file, such as Primary Outcome Measures, Secondary Outcome Measures, Time Frame, and Inclusion Criteria. In the analysis, we examined primary and secondary endpoints in trials with severe and non-severe patients, including the types of endpoints, time frame, clinical course, and sample size.

RESULTS

A total of 406 trials were included in the analysis. The median numbers of endpoints in trials with severe and non-severe patients were 9 and 7, respectively. Approximately 25% of the trials used multiple primary endpoints. Regardless of the type of endpoint, the time frames were longer in the trials with severe patients than in the trials with non-severe patients. In the evaluation of the clinical course, worsening was often considered in binary endpoints, and improvement was considered in time-to-event endpoints. The sample size was the largest in clinical trials using binary endpoints.

CONCLUSIONS

Endpoints can differ with respect to severity, and the clinical course and time frame are important for defining endpoints. This study provides information that can facilitate the achievement of a consensus for the endpoints in evaluating COVID-19 treatments.

Clinical trials with antiviral drugs against COVID-19: some progress and many shattered hopes

Vaccines and drugs are the cornerstones in the fight against the SARS-CoV-2 pandemic. While vaccines were a success story, the development of antiviral drugs against SARS-CoV-2 turned out to be difficult. For an accelerated use of antivirals in the clinic, most SARS-CoV-2 antivirals represented repurposed drugs. The present article summarizes the outcomes of clinical trials with antiviral drugs in COVID-19 patients. Many antiviral drugs failed to demonstrate beneficial effects or showed mixed results. One reason for the low success rate of clinical trials was shortcomings of antiviral tests in cell culture systems and another reason was the abundance of ill-coordinated and underpowered clinical trials. However, large pragmatic clinical trials particularly of the British RECOVERY trial series demonstrated that even under emergency situation drug trials can be conducted in a timely way such that the therapy of COVID-19 patients can be based on evidence basis instead on expert opinion or even worse on political pressure.

Genetic predisposition to COVID-19 may increase the risk of hypertension disorders in pregnancy: A two-sample Mendelian randomization study

AIMS

The aim of this study was to apply the Mendelian randomization (MR) design to explore the potential causal association between COVID-19 and the risk of hypertension disorders in pregnancy.

METHODS

Our primary genetic instrument comprised 8 single-nucleotide polymorphisms (SNPs) associated with COVID-19 at genome-wide significance. Data on the associations between the SNPs and the risk of hypertension disorders in pregnancy were obtained from study based on a very large cohort of European population. The random-effects inverse-variance weighted method was conducted for the main analyses, with a complementary analysis of the weighted median and MR-Egger approaches.

RESULTS

Using IVW, we found that genetically predicted COVID-19 was significantly positively associated with hypertension disorders in pregnancy, with an odds ratio (OR) of 1.111 [95% confidence interval (CI) 1.042-1.184; P = 0.001]. Weighted median regression also showed directionally similar estimates [OR 1.098 (95% CI, 1.013-1.190), P = 0.023]. Both funnel plots and MR-Egger intercepts suggest no directional pleiotropic effects observed.

CONCLUSIONS

Our findings provide direct evidence that there is a shared genetic predisposition so that patients infected with COVID-19 may be causally associated with increased risk of hypertension disorders in pregnancy.

Understanding and predicting COVID-19 clinical trial completion vs. cessation

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Clinicaltrial.gov. Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs. cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from ClinicalTrial.gov to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs. cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs. ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs.

Vitamin C and D supplementation and the severity of COVID-19: A protocol for systematic review and meta-analysis

BACKGROUND

The COVID-19 pandemic has rapidly spread to other countries, causing numerous deaths and challenges for organizations and health professionals. Diet and nutrition invariably influence the competence of the immune system and determine the risk and severity of infections. Studies have already been published on the relationships through which vitamins C and D can mitigate the severity of infections such as COVID-19. In this context, this protocol describes a systematic review intended to analyze if vitamin C and D supplementation can reduce the severity of Covid-19.

METHODS

This protocol was developed based on the recommendations of PRISMA-P. In order to accomplish the systematic review, we will carry out searches in the PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect databases in the quest for control case studies that analyze the supplementation and evolution of patients with COVID-19. There will be no limitations related to language or publication time. The searches will be carried out by 2 independent researchers who will select the articles, and then the duplicate studies will be removed, while the suitable ones will be selected using the Rayyan QCRI application. In order to assess the risk of bias, we will use the instrument proposed by the National Heart, Lung and Blood Institute. Moreover, we will carry out metaanalyses and subgroup analyses according to the conditions of the included data.

RESULTS

This review will assess the association between vitamin C and D supplementation and the reduction in the severity of COVID-19.

CONCLUSION

The findings of this systematic review will summarize the latest evidence for the association between vitamin C and D supplementation and COVID-19 through a systematic review and meta-analysis.

RECORD OF SYSTEMATIC REVIEW

CRD42021255763.

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

BACKGROUND

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials.

METHODS AND FINDINGS

A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d-1 (95% CI: 1.06 to 1.27 d-1), 0.777 d-1 (0.716 to 0.838 d-1), and 0.450 d-1 (0.378 to 0.522 d-1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation.

CONCLUSIONS

In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

Sleep quality and associated factors during the COVID-19 epidemic among community non-medical anti-epidemic Workers of Wuhan, China

BACKGROUND

Since the outbreak of Coronavirus Disease 2019 (COVID-19) in December 2019, community non-medical anti-epidemic workers have played an important role in the prevention of COVID-19 in China. The present study aimed to assess sleep quality and its associated factors among community non-medical anti-epidemic workers.

METHOD

A survey was conducted using anonymous online questionnaire to collect information from 16 March 2020 to 24 March 2020. A total of 474 participants were included, with a 94.23% completion rate. The questionnaire contained demographic data, physical symptoms, and contact history with COVID-19. The researchers assessed perceived social support by the Multidimensional Scale of Perceived Social Support (MSPSS), assessed perceived stress by the Perceived Stress Scale (PSS), and measured sleep quality by the Pittsburgh Sleep Quality Index (PSQI) questionnaire.

RESULTS

Among the participants, 46.20% reported poor sleep quality. A binary logistic regression revealed that having educational background of junior college or above, being a member of the police force, having contacted individuals with confirmed or suspected COVID-19 infection, having chronic disease(s), having illness within 2 weeks, and having high or moderate perceived stress were significant factors associated with an increased risk of poor sleep quality.

CONCLUSION

Demographic factors, physical symptoms, history of contact with COVID-19, and perceived stress are significantly associated with poor sleep quality of community non-medical anti-epidemic workers. Thus, targeting these factors might be helpful in enhancing sleep quality of community workers.

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

An ethics framework for consolidating and prioritizing COVID-19 clinical trials

Given the dearth of established safe and effective interventions to respond to COVID-19, there is an urgent ethical imperative to conduct meaningful clinical research. The good news is that interventions to be tested are not in short supply. Unfortunately, the human and material resources needed to conduct these trials are finite. It is essential that trials be robust and meet enrollment targets and that lower-quality studies not be permitted to displace higher-quality studies, delaying answers to critical questions. Yet, with few exceptions, existing research review bodies and processes are not designed to ensure these conditions are satisfied. To meet this challenge, we offer guidance for research institutions about how to ethically consolidate and prioritize COVID-19 clinical trials, while recognizing that consolidation and prioritization should also take place upstream (among manufacturers and funders) and at a higher level (e.g. nationally). In our proposed three-stage process, trials must first meet threshold criteria. Those that do are evaluated in a second stage to determine whether the institution has sufficient capacity to support all proposed trials. If it does not, the third stage entails evaluating studies against two additional sets of comparative prioritization criteria: those specific to the study and those that aim to advance diversification of an institution's research portfolio. To implement these criteria fairly, we propose that research institutions form COVID-19 research prioritization committees. We briefly discuss some important attributes of these committees, drawing on the authors' experiences at our respective institutions. Although we focus on clinical trials of COVID-19 therapeutics, our guidance should prove useful for other kinds of COVID-19 research, as well as non-pandemic research, which can raise similar challenges due to the scarcity of research resources.

Discovering symptom patterns of COVID-19 patients using association rule mining

BACKGROUND

The COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public. Under such circumstances, understanding COVID-19 symptoms is vital in initial triage; it is crucial to distinguish the severity of cases for effective management and treatment. This study aimed to discover symptom patterns and overall symptom rules, including rules disaggregated by age, sex, chronic condition, and mortality status, among COVID-19 patients.

METHODS

This study was a retrospective analysis of COVID-19 patient data made available online by the Wolfram Data Repository through May 27, 2020. We applied a widely used rule-based machine learning technique called association rule mining to identify frequent symptoms and define patterns in the rules discovered.

RESULT

In total, 1,560 patients with COVID-19 were included in the study, with a median age of 52 years. The most frequently occurring symptom was fever (67%), followed by cough (37%), malaise/body soreness (11%), pneumonia (11%), and sore throat (8%). Myocardial infarction, heart failure, and renal disease were present in less than 1% of patients. The top ten significant symptom rules (out of 71 generated) showed cough, septic shock, and respiratory distress syndrome as frequent consequents. If a patient had a breathing problem and sputum production, then, there was higher confidence of that patient having a cough; if cardiac disease, renal disease, or pneumonia was present, then there was a higher confidence of septic shock or respiratory distress syndrome. Symptom rules differed between younger and older patients and between male and female patients. Patients who had chronic conditions or died of COVID-19 had more severe symptom rules than those patients who did not have chronic conditions or survived of COVID-19. Concerning chronic condition rules among 147 patients, if a patient had diabetes, prerenal azotemia, and coronary bypass surgery, there was a certainty of hypertension.

CONCLUSION

The most frequently reported symptoms in patients with COVID-19 were fever, cough, pneumonia, and sore throat; while 1% had severe symptoms, such as septic shock, respiratory distress syndrome, and respiratory failure. Symptom rules differed by age and sex. Patients with chronic disease and patients who died of COVID-19 had severe symptom rules more specifically, cardiovascular-related symptoms accompanied by pneumonia, fever, and cough as consequents.

Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats

BACKGROUND

Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials.

CONCLUSION

Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

Treatment with an Anti-CK2 Synthetic Peptide Improves Clinical Response in COVID-19 Patients with Pneumonia. A Randomized and Controlled Clinical Trial

The instrumental role of CK2 in the SARS-CoV-2 infection has pointed out this protein kinase as promising therapeutic target in COVID-19. Anti-SARS-CoV-2 activity has been reported by CK2 inhibitors in vitro; however, no anti-CK2 clinical approach has been investigated in COVID-19. This trial aimed to explore the safety and putative clinical benefit of CIGB-325, an anti-CK2 peptide previously assessed in cancer patients. A monocentric, controlled, and therapeutic exploratory trial of intravenous CIGB-325 in adults hospitalized with COVID-19 was performed. Twenty patients were randomly assigned to receive CIGB-325 (2.5 mg/kg/day during 5-consecutive days) plus standard-of-care (10 patients) or standard-of-care alone (10 patients). Adverse events were classified by the WHO Adverse Reaction Terminology. Parametric and nonparametric statistical analyses were performed according to the type of variable. Considering the small sample size, differences between groups were estimated by Bayesian analysis. CIGB-325 induced transient mild and/or moderate adverse events such as pruritus, flushing, and rash in some patients. Both therapeutic regimens were similar with respect to SARS-CoV-2 clearance in nasopharynx swabs over time. However, CIGB-325 significantly reduced the median number of pulmonary lesions (9.5 to 5.5, p = 0.042) at day 7 and the proportion of patients with such an effect was also higher according to Bayesian analysis (pDif > 0; 0.951). Also, CIGB-325 significantly reduced the CPK (p = 0.007) and LDH (p = 0.028) plasma levels at day 7. Our preliminary findings suggest that this anti-CK2 clinical approach could be combined with standard-of-care in COVID-19 in larger studies.

General medical publications during COVID-19 show increased dissemination despite lower validation

Background

The COVID-19 pandemic has yielded an unprecedented quantity of new publications, contributing to an overwhelming quantity of information and leading to the rapid dissemination of less stringently validated information. Yet, a formal analysis of how the medical literature has changed during the pandemic is lacking. In this analysis, we aimed to quantify how scientific publications changed at the outset of the COVID-19 pandemic.

Methods

We performed a cross-sectional bibliometric study of published studies in four high-impact medical journals to identify differences in the characteristics of COVID-19 related publications compared to non-pandemic studies. Original investigations related to SARS-CoV-2 and COVID-19 published in March and April 2020 were identified and compared to non-COVID-19 research publications over the same two-month period in 2019 and 2020. Extracted data included publication characteristics, study characteristics, author characteristics, and impact metrics. Our primary measure was principal component analysis (PCA) of publication characteristics and impact metrics across groups.

Results

We identified 402 publications that met inclusion criteria: 76 were related to COVID-19; 154 and 172 were non-COVID publications over the same period in 2020 and 2019, respectively. PCA utilizing the collected bibliometric data revealed segregation of the COVID-19 literature subset from both groups of non-COVID literature (2019 and 2020). COVID-19 publications were more likely to describe prospective observational (31.6%) or case series (41.8%) studies without industry funding as compared with non-COVID articles, which were represented primarily by randomized controlled trials (32.5% and 36.6% in the non-COVID literature from 2020 and 2019, respectively).

Conclusions

In this cross-sectional study of publications in four general medical journals, COVID-related articles were significantly different from non-COVID articles based on article characteristics and impact metrics. COVID-related studies were generally shorter articles reporting observational studies with less literature cited and fewer study sites, suggestive of more limited scientific support. They nevertheless had much higher dissemination.

Recent Developments on Therapeutic and Diagnostic Approaches for COVID-19

The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious public health threat worldwide with millions of people at risk in a growing number of countries. Though there are no clinically approved antiviral drugs and vaccines for COVID-19, attempts are ongoing for clinical trials of several known antiviral drugs, their combination, as well as development of vaccines in patients with confirmed COVID-19. This review focuses on the latest approaches to diagnostics and therapy of COVID-19. We have summarized recent progress on the conventional therapeutics such as antiviral drugs, vaccines, anti-SARS-CoV-2 antibody treatments, and convalescent plasma therapy which are currently under extensive research and clinical trials for the treatment of COVID-19. The developments of nanoparticle-based therapeutic and diagnostic approaches have been also discussed for COVID-19. We have assessed recent literature data on this topic and made a summary of current development and future perspectives.

Characteristics of Clinical Trials Launched Early in the COVID-19 Pandemic in the US and in France

Based on hierarchical classification and logistic regression of early US and French COVID-19 clinical trials we show that despite the registration of a large number of trials, only a minority had characteristics usually associated with providing robust and relevant evidence.

The Landscape of Emerging Randomized Clinical Trial Evidence for COVID-19 Disease Stages: A Systematic Review of Global Trial Registries

Purpose

A multitude of randomized controlled trials (RCTs) have emerged in response to the novel coronavirus disease (COVID-19) pandemic. Understanding the distribution of trials among various settings is important to guide future research priorities and efforts. The purpose of this review was to describe the emerging evidence base of COVID-19 RCTs by stages of disease progression, from pre-exposure to hospitalization.

Methods

We collated trial data across international registries: ClinicalTrials.gov; International Standard Randomised Controlled Trial Number Registry; Chinese Clinical Trial Registry; Clinical Research Information Service; EU Clinical Trials Register; Iranian Registry of Clinical Trials; Japan Primary Registries Network; German Clinical Trials Register (up to 7 October 2020). Active COVID-19 RCTs in international registries were eligible for inclusion. We extracted trial status, intervention(s), control, sample size, and clinical context to generate descriptive frequencies, network diagram illustrations, and statistical analyses including odds ratios and the Mann–Whitney U-test.

Results

Our search identified 11503 clinical trials registered for COVID-19 and identified 2388 RCTs. After excluding 45 suspended RCTs and 480 trials with unclear or unreported disease stages, 1863 active RCTs were included and categorized into four broad disease stages: pre-exposure (n=107); post-exposure (n=208); outpatient treatment (n=266); hospitalization, including the intensive care unit (n=1376). Across all disease stages, most trials had two arms (n=1500/1863, 80.52%), most often included (hydroxy)chloroquine (n=271/1863, 14.55%) and were US-based (n=408/1863, 21.90%). US-based trials had lower odds of including (hydroxy)chloroquine than trials in other countries (OR: 0.63, 95% CI: 0.45–0.90) and similar odds of having two arms compared to other geographic regions (OR: 1.05, 95% CI: 0.80–1.38).

Conclusion

There is a marked difference in the number of trials across settings, with limited studies on non-hospitalized persons. Focus on pre- and post-exposure, and outpatients, is worthwhile as a means of reducing infections and lessening the health, social, and economic burden of COVID-19.

Reporting and design of randomized controlled trials for COVID-19: A systematic review

Background

The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs).

Purpose

The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs.

Data sources

We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications.

Study selection

After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints.

Data extraction

Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted.

Data synthesis

Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data.

Conclusions

The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Causal inference methods for small non-randomized studies: Methods and an application in COVID-19

The usual development cycles are too slow for the development of vaccines, diagnostics and treatments in pandemics such as the ongoing SARS-CoV-2 pandemic. Given the pressure in such a situation, there is a risk that findings of early clinical trials are overinterpreted despite their limitations in terms of size and design. Motivated by a non-randomized open-label study investigating the efficacy of hydroxychloroquine in patients with COVID-19, we describe in a unified fashion various alternative approaches to the analysis of non-randomized studies. A widely used tool to reduce the impact of treatment-selection bias are so-called propensity score (PS) methods. Conditioning on the propensity score allows one to replicate the design of a randomized controlled trial, conditional on observed covariates. Extensions include the g-computation approach, which is less frequently applied, in particular in clinical studies. Moreover, doubly robust estimators provide additional advantages. Here, we investigate the properties of propensity score based methods including three variations of doubly robust estimators in small sample settings, typical for early trials, in a simulation study. R code for the simulations is provided.

Artificial intelligence in COVID-19 drug repurposing

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Data monitoring committees for clinical trials evaluating treatments of COVID-19

The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.

Targeted Nanotherapeutics for Respiratory Diseases: Cancer, Fibrosis, and Coronavirus

Systemic delivery of therapeutics for treatment of lung diseases has several limitations including poor organ distribution of delivered payload with relatively low accumulation of active substances in the lungs and severe adverse side effects. In contrast, nanocarrier based therapeutics provide a broad range of opportunities due to their ability to encapsulate substances with different aqueous solubility, transport distinct types of cargo, target therapeutics specifically to the deceased organ, cell, or cellular organelle limiting adverse side effects and increasing the efficacy of therapy. Moreover, many nanotherapeutics can be delivered by inhalation locally to the lungs avoiding systemic circulation. In addition, nanoscale based delivery systems can be multifunctional, simultaneously carrying out several tasks including diagnostics, treatment and suppression of cellular resistance to the treatment. Nanoscale delivery systems improve the clinical efficacy of conventional therapeutics allowing new approaches for the treatment of respiratory diseases which are difficult to treat or possess intrinsic or acquired resistance to treatment. The present review summarizes recent advances in the development of nanocarrier based therapeutics for local and targeted delivery of drugs, nucleic acids and imaging agents for diagnostics and treatment of various diseases such as cancer, cystic fibrosis, and coronavirus.