Evidence synthesis relevant to COVID-19: a protocol for multiple systematic reviews and overviews of systematic reviews

Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study

OBJECTIVES

A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses.

STUDY DESIGN

After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI.

RESULTS

Of the 3,999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n = 64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs. 3.5, P < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119).

CONCLUSION

AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.

A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

OBJECTIVE

To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).

METHODS

We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.

RESULTS

The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).

CONCLUSION

A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Bias as a source of inconsistency in ivermectin trials for COVID-19: A systematic review. Ivermectin's suggested benefits are mainly based on potentially biased results

OBJECTIVE

The objective of this systematic review is to summarize the effects of ivermectin for the prevention and treatment of patients with COVID-19 and to assess inconsistencies in results from individual studies with focus on risk of bias due to methodological limitations.

STUDY DESIGN AND SETTING

We searched the L.OVE platform through July 6, 2021 and included randomized trials (RCTs) comparing ivermectin to standard or other active treatments. We conducted random-effects pairwise meta-analysis, assessed the certainty of evidence using the GRADE approach and performed sensitivity analysis excluding trials with risk of bias.

RESULTS

We included 29 RCTs which enrolled 5592 cases. Overall, the certainty of the evidence was very low to low suggesting that ivermectin may result in important benefits. However, after excluding trials classified as "high risk" or "some concerns" in the risk of bias assessment, most estimates of effect changed substantially: Compared to standard of care, low certainty evidence suggests that ivermectin may not reduce mortality (RD 7 fewer per 1000) nor mechanical ventilation (RD 6 more per 1000), and moderate certainty evidence shows that it probably does not increase symptom resolution or improvement (RD 14 more per 1000) nor viral clearance (RD 12 fewer per 1000).

CONCLUSION

Ivermectin may not improve clinically important outcomes in patients with COVID-19 and its effects as a prophylactic intervention in exposed individuals are uncertain. Previous reports concluding important benefits associated with ivermectin are based on potentially biased results reported by studies with substantial methodological limitations. Further research is needed.

Shortcomings of Rapid Clinical Information Dissemination: Lessons From a Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has led to an acceleration of clinical information dissemination to unprecedented speeds, a phenomenon only partially explained by formal efforts of the scientific community. These have ranged from the establishment of open-source platforms for review of article preprints to the elimination of journal paywalls for COVID-19-related publications. In addition, informal efforts that rely on various modern media platforms that promote, repackage, and synthesize information have played substantial adjunctive roles, many of which did not exist during the severe acute respiratory syndrome pandemic of 2003. Although these latter efforts have greatly bolstered the speed of knowledge dissemination, their unregulated nature subjects them to risk for facilitating the spread of misinformation. In our opinion, the role of modern media in influencing clinical knowledge dissemination was not adequately examined even before the pandemic and therefore remains largely unchecked. In this article, we examine the spread of information in the field of COVID-19 and neurologic disorders, develop a simple model that maps various modern media tools on to the dissemination pipeline, and critically examine its components. Through this exercise, we identify opportunities for the scientific community to regulate and safeguard the clinical knowledge dissemination process, with implications both for the pandemic and beyond.

The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System

OBJECTIVES

We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases.

METHODS AND RESULTS

We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system.

CONCLUSIONS

The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.

Student support systems for undergraduate medical students during the COVID-19 pandemic: a systematic narrative review of the literature

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has boosted medical students' vulnerability to various problems. Given the stressful nature of medical disciplines, considerable attention must be paid to student support systems during pandemics. This study aimed to review the current literature regarding medical student support systems systematically.

METHODS

We performed a systematic review of six databases and grey literature sources in addition to a hand search in the references of the articles on April 5, 2021. We included all studies about support for undergraduate medical students delivered in response to the COVID-19 pandemic. In conducting this review, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

RESULTS

A total of 3646 articles were retrieved from the databases, and 16 additional papers were extracted from other sources. After removing duplicates, we screened 2434 titles and abstracts according to our criteria. Among them, 32 full-text articles were assessed for eligibility. Ultimately, 10 studies were included for review. We identified two major themes: (a) academic support and (b) mental health support. All of the included studies utilized online methods whether for transitioning from previous support systems or developing novel approaches. Students and faculty members seemed to be receptive to these new systems. Despite indicating outstanding program outcomes, most studies merely described the positive effects of the program rather than providing a precise evaluation.

CONCLUSION

There are several methods of supporting medical students who are experiencing unprecedented changes in their educational trajectory. Due to substantial differences in undergraduate medical education in different regions of the world, cultural and contextual-oriented support is indispensable for developing a safe learning environment. Future research should investigate the question of the extent to which online support can supersede in-person strategies.

Use of gloves for the prevention of COVID-19 in healthy population: A living systematic review protocol

BACKGROUND AND AIMS

The efficacy of using gloves by the general population to prevent COVID-19 is unknown. We aim to determine the efficacy of routine glove use by the general healthy population in preventing COVID-19. This is the protocol of a living systematic review.

METHODS

We adapted an already published common protocol for multiple parallel systematic reviews to the specificities of this question. We will conduct searches in PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), grey literature, and in a centralized repository in L·OVE (Living OVerview of Evidence). L·OVE is a platform that maps PICO questions to evidence from Epistemonikos database. In response to the COVID-19 emergency, L·OVE was adapted to expand the range of evidence it covers and customized to group all COVID-19 evidence in one place. The search will cover the period until the day before submission to a journal. We will include randomized trials evaluating the effect of use of gloves in healthy population to prevent COVID-19 disease. Randomized trials evaluating the effect of use of gloves during outbreaks caused by MERS-CoV and SARS-CoV, and nonrandomized studies in COVID-19 will be searched in case no direct evidence from randomized trials is found.Two reviewers will independently screen each study for eligibility, extract data, and assess the risk of bias. We will perform random-effects meta-analyses and use GRADE to assess the certainty of the evidence for each outcome.A living, web-based version of this review will be openly available during the COVID-19 pandemic. We will resubmit it if the conclusions change or there are substantial updates.

Maternal and perinatal outcomes related to COVID-19 and pregnancy: An overview of systematic reviews

Introduction

Evidence about coronavirus disease 2019 (COVID‐19) and pregnancy has rapidly increased since December 2019, making it difficult to make rigorous evidence‐based decisions. The objective of this overview of systematic reviews is to conduct a comprehensive analysis of the current evidence on prognosis of COVID‐19 in pregnant women.

Material and methods

We used the Living OVerview of Evidence (L·OVE) platform for COVID‐19, which continually retrieves studies from 46 data sources (including PubMed/MEDLINE, Embase, other electronic databases, clinical trials registries, and preprint repositories, among other sources relevant to COVID‐19), mapping them into PICO (population, intervention, control, and outcomes) questions. The search covered the period from the inception date of each database to 13 September 2020. We included systematic reviews assessing outcomes of pregnant women with COVID‐19 and/or their newborns. Two authors independently screened the titles and abstracts, assessed full texts to select the studies that met the inclusion criteria, extracted data, and appraised the risk of bias of each included systematic review. We measured the overlap of primary studies included among the selected systematic reviews by building a matrix of evidence, calculating the corrected covered area, and assessing the level of overlap for every pair of systematic reviews.

Results

Our search yielded 1132 references. 52 systematic reviews met inclusion criteria and were included in this overview. Only one review had a low risk of bias, three had an unclear risk of bias, and 48 had a high risk of bias. Most of the included reviews were highly overlapped among each other. In the included reviews, rates of maternal death varied from 0% to 11.1%, admission to intensive care from 2.1% to 28.5%, preterm deliveries before 37 weeks from 14.3% to 61.2%, and cesarean delivery from 48.3% to 100%. Regarding neonatal outcomes, neonatal death varied from 0% to 11.7% and the estimated infection status of the newborn varied between 0% and 11.5%.

Conclusions

Only one of 52 systematic reviews had a low risk of bias. Results were heterogeneous and the overlap of primary studies was frequently very high between pairs of systematic reviews. High‐quality evidence syntheses of comparative studies are needed to guide future clinical decisions.

Tailored PRISMA 2020 flow diagrams for living systematic reviews: a methodological survey and a proposal

Background: While the PRISMA flow diagram is widely used for reporting standard systematic reviews (SRs), it was not designed for capturing the results of continual searches for studies in living systematic reviews (LSRs). The objectives of this study are (1) to assess how published LSRs report on the flow of studies through the different phases of the review for the different updates; (2) to propose an approach to reporting on that flow. Methods: For objective 1, we identified all LSRs published up to April 2021. We abstracted information regarding their general characteristics and how they reported on search results. For objective 2, we based our proposal for tailored PRISMA approaches on the findings from objective 1, as well as on our experience with conducting Cochrane LSRs. Results: We identified 279 living publications relating to 76 LSRs. Of the 279 publications, 11% were protocols, 23% were base versions (i.e., the first version), 50% were partial updates (i.e., does not include all typical sections of an SR), and 16% were full updates (i.e., includes all typical sections of an SR). We identified six ways to reporting the study flow: base separately, each update separately (38%); numbers not reported (32%); latest update separately, all previous versions combined (20%); base separately, all updates combined (7%); latest update version only (3%); all versions combined (0%). We propose recording in detail the results of the searches to keep track of all identified records. For structuring the flow diagram, we propose using one of four approaches. Conclusion: We identified six ways for reporting the study flow through the different phases of the review for the different update versions. We propose to document in detail the study flow for the different search updates and select one of our four tailored PRISMA diagram approaches to present that study flow.

Tailored PRISMA 2020 flow diagrams for living systematic reviews: a methodological survey and a proposal

Background: While the PRISMA flow diagram is widely used for reporting standard systematic reviews (SRs), it was not designed for capturing the results of continual searches for studies in living systematic reviews (LSRs). The objectives of this study are (1) to assess how published LSRs report on the flow of studies through the different phases of the review for the different updates; (2) to propose an approach to reporting on that flow. Methods: For objective 1, we identified all LSRs published up to July 2020. We abstracted information regarding their general characteristics and how they reported on search results. For objective 2, we based our proposal for tailored PRISMA approaches on the findings from objective 1, as well as on our experience with conducting Cochrane LSRs. Results: We identified 108 living publications relating to 32 LSRs. Of the 108 publications, 7% were protocols, 24% were base versions (i.e., the first version), 62% were partial updates (i.e., does not include all typical sections of an SR), and 7% were full updates (i.e., includes all typical sections of an SR). We identified six ways to reporting the study flow: base separately, each update separately (38%); numbers not reported (32%); latest update separately, all previous versions combined (20%); base separately, all updates combined (7%); latest update version only (3%);  all versions combined (0%). We propose recording in detail the results of the searches to keep track of all identified records. For structuring the flow diagram, we propose using one of four approaches. Conclusion: We identified six ways for reporting the study flow through the different phases of the review for the different update versions. We propose to document in detail the study flow for the different search updates and select one of our four tailored PRISMA diagram approaches to present that study flow.

PRISMA flow diagrams for living systematic reviews: a methodological survey and a proposal

Background: While the PRISMA flow diagram is widely used for reporting standard systematic reviews (SRs), it was not designed for capturing the results of continual searches for studies in living systematic reviews (LSRs). The objectives of this study are (1) to assess how published LSRs report on the flow of studies through the different phases of the review for the different updates; (2) to propose an approach to reporting on that flow. Methods: For objective 1, we identified all LSRs published up to July 2020. We abstracted information regarding their general characteristics and how they reported on search results. For objective 2, we based our proposal for tailored PRISMA approaches on the findings from objective 1, as well as on our experience with conducting Cochrane LSRs. Results: We identified 108 living publications relating to 32 LSRs. Of the 108 publications, 7% were protocols, 24% were base versions (i.e., the first version), 62% were partial updates (i.e., does not include all typical sections of an SR), and 7% were full updates (i.e., includes all typical sections of an SR). We identified six ways to reporting the study flow: base separately, each update separately (38%); numbers not reported (32%); latest update separately, all previous versions combined (20%); base separately, all updates combined (7%); latest update version only (3%);  all versions combined (0%). We propose recording in detail the results of the searches to keep track of all identified records. For structuring the flow diagram, we propose using one of four approaches. Conclusion: We identified six ways for reporting the study flowthrough the different phases of the review for the different update versions. We propose to document in detail the study flow for the different search updates and select one of our four tailored PRISMA diagram approaches to present that study flow.

How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.

Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review

BACKGROUND AND PURPOSE

The objective of our systematic review is to identify prognostic factors that may be used in decision-making related to the care of patients infected with COVID-19.

DATA SOURCES

We conducted highly sensitive searches in PubMed/MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL) and Embase. The searches covered the period from the inception date of each database until April 28, 2020. No study design, publication status or language restriction were applied.

STUDY SELECTION AND DATA EXTRACTION

We included studies that assessed patients with confirmed or suspected SARS-CoV-2 infectious disease and examined one or more prognostic factors for mortality or disease severity. Reviewers working in pairs independently screened studies for eligibility, extracted data and assessed the risk of bias. We performed meta-analyses and used GRADE to assess the certainty of the evidence for each prognostic factor and outcome.

RESULTS

We included 207 studies and found high or moderate certainty that the following 49 variables provide valuable prognostic information on mortality and/or severe disease in patients with COVID-19 infectious disease: Demographic factors (age, male sex, smoking), patient history factors (comorbidities, cerebrovascular disease, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, cardiac arrhythmia, arterial hypertension, diabetes, dementia, cancer and dyslipidemia), physical examination factors (respiratory failure, low blood pressure, hypoxemia, tachycardia, dyspnea, anorexia, tachypnea, haemoptysis, abdominal pain, fatigue, fever and myalgia or arthralgia), laboratory factors (high blood procalcitonin, myocardial injury markers, high blood White Blood Cell count (WBC), high blood lactate, low blood platelet count, plasma creatinine increase, high blood D-dimer, high blood lactate dehydrogenase (LDH), high blood C-reactive protein (CRP), decrease in lymphocyte count, high blood aspartate aminotransferase (AST), decrease in blood albumin, high blood interleukin-6 (IL-6), high blood neutrophil count, high blood B-type natriuretic peptide (BNP), high blood urea nitrogen (BUN), high blood creatine kinase (CK), high blood bilirubin and high erythrocyte sedimentation rate (ESR)), radiological factors (consolidative infiltrate and pleural effusion) and high SOFA score (sequential organ failure assessment score).

CONCLUSION

Identified prognostic factors can help clinicians and policy makers in tailoring management strategies for patients with COVID-19 infectious disease while researchers can utilise our findings to develop multivariable prognostic models that could eventually facilitate decision-making and improve patient important outcomes.

SYSTEMATIC REVIEW REGISTRATION

Prospero registration number: CRD42020178802. Protocol available at: https://www.medrxiv.org/content/10.1101/2020.04.08.20056598v1.

Breakthrough healthcare technologies in the COVID-19 era: a unique opportunity for cardiovascular practitioners and patients

INTRODUCTION

The Coronavirus disease 2019 (COVID-19) pandemic, caused by symptomatic severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) infection, has wreaked havoc globally, challenging the healthcare, economical, technological and social status quo of developing but also developed countries. For instance, the COVID-19 scare has reduced timely hospital admissions for ST-elevation myocardial infarction in Europe and the USA, causing unnecessary deaths and disabilities. While the emergency is still ongoing, enough efforts have been put to study and tackle this condition such that a comprehensive perspective and synthesis on the potential role of breakthrough healthcare technologies is possible. Indeed, current state-of-the-art information technologies can provide a unique opportunity to adapt and adjust to the current healthcare needs associated with COVID-19, either directly or indirectly, and in particular those of cardiovascular patients and practitioners.

EVIDENCE ACQUISITION

We searched several biomedical databases, websites and social media, including PubMed, Medscape, and Twitter, for smartcare approaches suitable for application in the COVID-19 pandemic.

EVIDENCE SYNTHESIS

We retrieved details on several promising avenues for present and future healthcare technologies, capable of substantially reduce the mortality, morbidity, and resource use burden of COVID-19 as well as that of cardiovascular disease. In particular, we have found data supporting the importance of data sharing, model sharing, preprint archiving, social media, medical case sharing, distance learning and continuous medical education, smartphone apps, telemedicine, robotics, big data analysis, machine learning, and deep learning, with the ultimate goal of optimization of individual prevention, diagnosis, tracing, risk-stratification, treatment and rehabilitation.

CONCLUSIONS

We are confident that refinement and command of smartcare technologies will prove extremely beneficial in the short-term, but also dramatically reshape cardiovascular practice and healthcare delivery in the long-term future, for COVID-19 as well as other diseases.

Effects of the COVID-19 pandemic on supply and use of blood for transfusion

The COVID-19 pandemic has major implications for blood transfusion. There are uncertain patterns of demand, and transfusion institutions need to plan for reductions in donations and loss of crucial staff because of sickness and public health restrictions. We systematically searched for relevant studies addressing the transfusion chain-from donor, through collection and processing, to patients-to provide a synthesis of the published literature and guidance during times of potential or actual shortage. A reduction in donor numbers has largely been matched by reductions in demand for transfusion. Contingency planning includes prioritisation policies for patients in the event of predicted shortage. A range of strategies maintain ongoing equitable access to blood for transfusion during the pandemic, in addition to providing new therapies such as convalescent plasma. Sharing experience and developing expert consensus on the basis of evolving publications will help transfusion services and hospitals in countries at different stages in the pandemic.

A systematic review of trial registry entries for randomized clinical trials investigating COVID-19 medical prevention and treatment

AIM

To identify investigated interventions for COVID-19 prevention or treatment via trial registry entries on planned or ongoing randomised clinical trials. To assess these registry entries for recruitment status, planned trial size, blinding and reporting of mortality.

METHODS

We identified trial registry entries systematically via the WHO International Clinical Trials Registry Platform and 33 trial registries up to June 23, 2020. We included relevant trial registry entries for randomized clinical trials investigating medical preventive, adjunct or supportive therapies and therapeutics for treatment of COVID-19. Studies with non-random and single-arm design were excluded. Trial registry entries were screened by two authors independently and data were systematically extracted.

RESULTS

We included 1303 trial registry entries from 71 countries investigating 381 different single interventions. Blinding was planned in 47% of trials. Sample size was >200 participants in 40% of trials and a total of 611,364 participants were planned for inclusion. Mortality was listed as an outcome in 57% of trials. Recruitment was ongoing in 54% of trials and completed in 8%. Thirty-five percent were multicenter trials. The five most frequent investigational categories were immune modulating drugs (266 trials (20%)), unconventional medicine (167 trials (13%)), antimalarial drugs (118 trials (9%)), antiviral drugs (100 trials (8%)) and respiratory adjuncts (78 trials (6%)). The five most frequently tested uni-modal interventions were: chloroquine/hydroxychloroquine (113 trials with 199,841 participants); convalescent plasma (64 trials with 11,840 participants); stem cells (51 trials with 3,370 participants); tocilizumab (19 trials with 4,139 participants) and favipiravir (19 trials with 3,210 participants).

CONCLUSION

An extraordinary number of randomized clinical trials investigating COVID-19 management have been initiated with a multitude of medical preventive, adjunctive and treatment modalities. Blinding will be used in only 47% of trials, which may have influence on future reported treatment effects. Fifty-seven percent of all trials will assess mortality as an outcome facilitating future meta-analyses.

Lopinavir-ritonavir for COVID-19: A living systematic review

Objective

Provide a timely, rigorous, and continuously updated summary of the evidence on the role of lopinavir/ritonavir in the treatment of patients with COVID-19.

Methods

We conducted searches in the special L·OVE (Living OVerview of Evidence) platform for COVID-19, a system that performs regular searches in PubMed, Embase, CENTRAL, and other 33 sources. We searched for randomized trials and non-randomized studies evaluating the effect of lopinavir/ritonavir versus placebo or no treatment in patients with COVID-19. Two reviewers independently evaluated potentially eligible studies, according to predefined selection criteria, and extracted data using a predesigned standardized form. We performed meta-analyses using random-effect models and assessed overall certainty in evidence using the GRADE approach. A living, web-based version of this review will be openly available during the COVID-19 pandemic.

Results

Our search strategy yielded 862 references. Finally, we identified 12 studies, including two randomized trials, evaluating lopinavir/ritonavir, in addition to standard care versus standard care alone in 250 adult inpatients with COVID-19. The evidence from randomized trials shows lopinavir/ritonavir may reduce mortality (relative risk: 0.77; 95% confidence interval: 0.45 to 1.3; low certainty evidence), but the anticipated magnitude of the absolute reduction in mortality, varies across different risk groups. Lopinavir/ritonavir also had a slight reduction in the risk of requiring invasive mechanical ventilation, developing respiratory failure, or acute respiratory distress syndrome. However, it did not lead to any difference in the duration of hospitalization and may lead to an increase in the number of total adverse effects. The overall certainty of the evidence was low or very low.

Conclusions

For severe and critical patients with COVID-19, lopinavir/ritonavir might play a role in improving outcomes, but the available evidence is still limited. A substantial number of ongoing studies should provide valuable evidence to inform researchers and decision-makers soon.