Título de la rs

ABSTRACT

Objective

To provide a timely, rigorous and continuously updated summary of the available evidence on the role of ivermectin in the treatment of patients with COVID-19.

Methods

We adapted an already published common protocol for multiple parallel systematic reviews to the specificities of this question. Eligible studies were non-randomised studies evaluating the effect of ivermectin versus placebo or no treatment. We conducted searches in the L·OVE (Living OVerview of Evidence) platform for COVID-19, a system that maps PICO questions to a repository maintained through regular searches in electronic databases, preprint servers, trial registries and other resources relevant to COVID-19. All the searches covered the period until August 28, 2020. No date or language restrictions were applied. Two reviewers independently evaluated potentially eligible studies according to predefined selection criteria, and extracted data on study characteristics, methods, outcomes, and risk of bias, using a predesigned, standardised form. We intended to perform meta-analyses, using random-effect models when possible, 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. We will resubmit it every time the conclusions change or whenever there are substantial updates.

Results

Our search strategy yielded 88 references. Finally, we included 2 non randomised studies evaluating ivermectin in addition to standard care versus standard care alone. We were not able to perform a meta-analyses, thus the results were presented as a narrative synthesis. In patients with COVID-19, the evidence is very uncertain about the effect of ivermectin on all-cause mortality, invasive mechanical ventilation, adverse effects leading to discontinuation, time to viral clearance and length of hospital stay.

Conclusions

The evidence is insufficient to balance the benefits of ivermectin, if any, with the adverse effects and costs.

PROSPERO Registration number: CRD42020189554

Keywords

COVID-19, Coronavirus disease, Severe Acute Respiratory Syndrome Coronavirus 2, Coronavirus Infections, Systematic Review, Ivermectin, Antiparasitic Agents

KEY MESSAGES:

  • Lopinavir/ritonavir may reduce mortality, but the magnitude of the reduction varies across different risk groups (low certainty evidence).
  • Lopinavir/ritonavir may reduce the risk of developing respiratory failure, acute respiratory distress syndrome or requiring invasive mechanical ventilation (low certainty evidence).
  • Lopinavir/ritonavir may not lead to a substantial increase in the risk of serious adverse effects, but probably increases the total number of adverse events in patients with COVID-19.
  • There are multiple ongoing trials that should shed light on the actual role of lopinavir/ritonavir in patients with COVID-19.

Summary of Findings table

Table 1: Summary of Findings table.

Introduction

COVID-19 is an infection caused by the SARS-CoV-2 coronavirus [1]. It was first identified in Wuhan, China, on December 31, 2019 [2]; six months later, more than fifteen million cases of contagion had been identified across 188 countries [3]. On March 11, 2020, WHO characterised the COVID-19 outbreak as a pandemic [1]. While the majority of cases result in mild symptoms, some might progress to pneumonia, acute respiratory distress syndrome and death [4], [5], [6]. The case fatality rate reported across countries, settings and age groups is highly variable, but it ranges from about 0.5% to 10% [7]. In hospitalised patients it has been reported to be higher than 10% in some centres [8]. One of the strategies underway to identify effective interventions for COVID-19 is repurposing drugs that have been used for the treatment of other diseases. Ivermectin is a broad spectrum antiparasitic agent which has been used for several years in the treatment of various helminth infections including filariasis, intestinal nematodes, strongyloidiasis and larva migrans. It’s also widely used in the treatment of ectoparasitic infections like scabies. Ivermectin has shown in-vitro antiviral activity against a broad range of RNA and DNA viruses [9], [10] including Zika, Dengue, Yellow Fever, Influenza, but with no evidence of clinical benefit in humans for these infections [11]. A recent in vitro study reported that ivermectin can inhibit viral replication in Vero/hSLAM cells infected with SARS-CoV-2 virus. Also, no toxicity was observed with ivermectin at any of the concentrations studied [12]. Considering these results and its effectiveness in the treatment of other infectious diseases with a known-safety profile, ivermectin has been suggested as a possible treatment in the context of the COVID-19 pandemic in 2019/2020. Using innovative and agile processes, taking advantage of technological tools, and resorting to the collective effort of several research groups, this living systematic review aims to provide a timely, rigorous and continuously updated summary of the evidence available on the effects of ivermectin in patients with COVID-19.

Methods

This manuscript complies with the ‘Preferred Reporting Items for Systematic reviews and Meta-Analyses’ (PRISMA) guidelines for reporting systematic reviews and meta-analyses [13] (see Appendix 1 - PRISMA Checklist). A protocol stating the shared objectives and methodology of multiple evidence syntheses (systematic reviews and overviews of systematic reviews) to be conducted in parallel for different questions relevant to COVID-19 was published elsewhere [14]. The review was registered in PROSPERO with the number CRD42020189554 and a full protocol was made available [15].

Search strategies

Electronic searches We used the search strategies already developed in the L·OVE (Living OVerview of Evidence) platform (https://www.iloveevidence.com), a system that maps the evidence to different research questions. The full methods to maintain L·OVE are described in the website, but the process to devise the search strategies can be briefly described as: Identification of terms relevant to the population and intervention components of the search strategy, applying Word2vec technology [16] to the corpus of documents available in Epistemonikos Database.

  • Discussion of terms with content and methods experts to identify relevant, irrelevant and missing terms.

  • Creation of a sensitive boolean strategy encompassing all the relevant terms

  • Iterative analysis of articles missed by the boolean strategy, and refinement of the strategy accordingly.

All the information in the L·OVE platform comes from a repository developed and maintained by Epistemonikos Foundation through the screening of different sources relevant to COVID-19. At the time of releasing this article, this repository included more than 66989 articles relevant to Coronavirus disease, coming from the following databases, trial registries, preprint servers and websites relevant to COVID-19: Epistemonikos database, Pubmed, EMBASE, ICTRP Search Portal, Clinicaltrials.gov, ISRCTN registry, Chinese Clinical Trial Registry, IRCT - Iranian Registry of Clinical Trials, EU Clinical Trials Register: Clinical trials for covid-19, NIPH Clinical Trials Search (Japan) - Japan Primary Registries Network (JPRN) (JapicCTI, JMACCT CTR, jRCT, UMIN CTR), UMIN-CTR - UMIN Clinical Trials Registry, JRCT - Japan Registry of Clinical Trials, JAPIC Clinical Trials Information, Clinical Research Information Service (CRiS), Republic of Korea, ANZCTR - Australian New Zealand Clinical Trials Registry, ReBec - Brazilian Clinical Trials Registry, CTRI - Clinical Trials Registry - India, DRKS - German Clinical Trials Register, LBCTR - Lebanese Clinical Trials Registry, TCTR - Thai Clinical Trials Registry, NTR - The Netherlands National Trial Register,PACTR - Pan African Clinical Trial Registry, REPEC - Peruvian Clinical Trial Registry, SLCTR - Sri Lanka Clinical Trials Registry, medRxiv Preprints, bioRxiv Preprints, SSRN Preprints, WHO COVID-19 database.

The last version of the methods, the total number of sources screened, and a living flow diagram and report of the project is updated regularly on the website [17]. The repository is continuously updated [17] and the information is transmitted in real time to the L·OVE platform, however, it was last checked for this review the day before release on August 28, 2020. The searches covered the period from the inception date of each database, and no study design, publication status or language restriction was applied. The following strategy was used to retrieve from the repository the articles potentially eligible for this review. (coronavir* OR coronovirus* OR betacoronavir* OR “beta-coronavirus” OR “beta-coronaviruses” OR “corona virus” OR “virus corona” OR “corono virus” OR “virus corono” OR hcov* OR “covid-19” OR covid19* OR “covid 19” OR “2019-ncov” OR cv19* OR “cv-19” OR “cv 19” OR “n-cov” OR ncov* OR (wuhan* and (virus OR viruses OR viral)) OR sars* OR sari OR (covid* and (virus OR viruses OR viral)) OR “severe acute respiratory syndrome” OR mers* OR “middle east respiratory syndrome” OR “middle-east respiratory syndrome” OR “covid-19-related” OR “2019-ncov-related” OR “cv-19-related” OR “n-cov-related”) AND (ivermectin* OR stromectol* OR soolantra*)

Other sources

In order to identify articles that might have been missed in the electronic searches, we proceeded as follows:

  • Screened the reference lists of other systematic reviews.

  • Scanned the reference lists of selected guidelines, narrative reviews and other documents.

Eligibility criteria

We included non-randomised studies evaluating patients infected with SARS-CoV-2 of any severity. The intervention of interest was ivermectin at any dosage, duration, timing or route of administration. The comparison of interest was placebo (ivermectin plus standard of care versus placebo plus standard of care) or no treatment (ivermectin plus standard of care versus standard of care). Our primary outcome of interest was all-cause mortality at longest follow-up. Secondary outcomes were hospital admission and adverse effects leading to discontinuation. We also extracted information on the following outcomes: invasive mechanical ventilation, time to viral clearance, length of hospital stay and serious adverse effects. We did not consider the outcomes as an inclusion criteria during the selection process. Any article meeting all the criteria except for the outcome criterion was preliminarily included and assessed in full text.

Selection of studies

The results of the searches in the individual sources were de-duplicated by an algorithm that compares unique identifiers (database ID, DOI, trial registry ID), and citation details (i.e. author names, journal, year of publication, volume, number, pages, article title, and article abstract). Then, the information matching the search strategy was sent in real-time to the L·OVE platform where at least two authors independently screened the titles and abstracts yielded against the inclusion criteria. We obtained the full reports for all titles that appeared to meet the inclusion criteria or required further analysis and then decided about their inclusion. We recorded the reasons for excluding trials in any stage of the search and outlined the study selection process in a PRISMA flow diagram which we adapted for the purpose of this project.

Extraction and management of data

Using standardised forms, two reviewers independently extracted the following data from each included trial: study design, setting, participant characteristics (including disease severity and age) and study eligibility criteria; details about the administered intervention and comparison, including dose, duration and timing (i.e. time after diagnosis); the outcomes assessed and the time they were measured; the source of funding of the study and the conflicts of interest disclosed by the investigators; the risk of bias assessment for each individual study. We resolved disagreements by discussion, with one arbiter adjudicating unresolved disagreements.

Risk of bias assessment

The risk of bias for each study was assessed by using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS‐I) [18], a tool for assessing risk of bias in non‐randomised studies of interventions considering the following domains: (1) bias due to confounding, (2) bias in selection of participants into the study, (3) bias in classification of interventions, (4) bias due to deviations from intended interventions (effect of assignment to intervention), (5) bias due to missing data, (6) bias in measurement of outcomes and (7) bias in the selection of the reported result. We judged each domain as low risk, moderate risk, serious risk, critical risk, or no information, and evaluated individual bias items as described in ROBINS-I guidance. We considered the following factors as baseline potential confounders:

  • Age
  • Comorbidity (e.g. cardiovascular disease, renal disease, eye disease, liver disease)
  • Co-interventions
  • Severity, as defined by the authors (i.e respiratory failure vs respiratory distress syndrome vs ICU requirement).

Measures of treatment effect

For dichotomous outcomes, we expressed the estimate of treatment effect of an intervention as risk ratios (RR) along with 95% confidence intervals (CI). For continuous outcomes, we used the mean difference and standard deviation (SD) to summarise the data using a 95% CI. If continuous outcomes would have been reported using different scales, the treatment effect would have been expressed as a standardised mean difference with 95% CI.

Strategy for data synthesis

The results of the search and the selection of the studies is presented, by means of the corresponding flow chart, according to recommendations of the PRISMA statement [13]. For any outcomes where it was not possible to calculate an effect estimate, a narrative synthesis is presented, describing the studies in terms of the direction and the size of effects, and any available measure of precision For any outcomes where data was available from more than one trial, we conducted a formal quantitative synthesis (meta-analysis) for studies clinically homogeneous using RevMan 5 [19], using the inverse variance method with the random-effects model. We assessed inconsistency by visual inspection of the forest plots and using the I² index. For any outcomes where it was not possible to calculate an effect estimate, we formulated a narrative synthesis, describing the studies in terms of the direction and the size of effects, and any available measure of precision.

Subgroup and sensitivity analysis

As few trials were found, we did not perform sensitivity or subgroup analysis.

Assessment of certainty of evidence

The certainty of the evidence for all outcomes was judged using the Grading of Recommendations Assessment, Development and Evaluation working group methodology (GRADE Working Group) [20], across the domains of risk of bias, consistency, directness, precision and reporting bias. For the main comparisons and outcomes, we prepared a Summary of Findings (SoF) table [21], [22] and also an interactive Summary of Findings table (http://isof.epistemonikos.org/). The SoF table is presented at the beginning of the manuscript.

Living evidence synthesis

An artificial intelligence algorithm deployed in the Coronavirus/COVID-19 topic of the L·OVE platform provides instant notification of articles with a high likelihood of being eligible. The authors review them, decide upon inclusion, and update the living web version of the review accordingly. This review is part of a larger project set up to produce multiple parallel systematic reviews relevant to COVID-19 [14].

Results

Risk of bias in the included studies

Both included trials had issues with bias due to confounding, classification of interventions and bias due to deviations from intended interventions, so they were rated as ‘serious risk of bias’. Neither study carried out an analysis to control for confounding variables in all outcomes assessed. One study reported unbalanced co-interventions between intervention and control groups [24] and in the second study this information was not reported [25].

Table 4 summarises the risk of bias assessments and details of each assessment are presented in Appendix 3.

Table 4- Risk of bias in the included studies assessed by ROBINS-I tool.

pegar acá tabla 4

Efficacy of ivermectin in the treatment of patients with COVID-19.

Both included studies [24], [25] are comparative, non-randomised studies. However, one [24] corresponds to a retrospective cohort and the other is an interventional study with a synthetic controlled arm (SCA) [25]. These differences, along with their study characteristics, were considered to be heterogeneity enough to not perform meta-analyses. Thus, the results on the impact of ivermectin for the treatment of COVID-19 are presented as a narrative synthesis.

The main results are summarised in the Summary of Findings table.

Primary outcome: All-cause mortality.

Both studies reported this outcome [24], [25], but the evidence is very uncertain about the effect of ivermectin on mortality.

#Funnel plot

#Gráfico de forest plot

Secondary outcomes

Invasive mechanical ventilation One study [24] reported the number of patients intubated in each group, but the evidence is very uncertain about the effect of ivermectin on invasive mechanical ventilation.

#Funnel plot

#Gráfico de forest plot

Adverse effects leading to discontinuation

One study [25] reported adverse effects, but the evidence is very uncertain about the effect of ivermectin on adverse effects leading to discontinuation.

#Funnel plot

#Gráfico de forest plot

Other outcomes

Time to viral clearance

One study [25] reported this outcome, but the evidence is very uncertain about the effect of ivermectin on time to viral clearance.

#Funnel plot

#Gráfico de forest plot

Length of hospital stay

Both studies reported this outcome [24], [25], but the evidence is very uncertain about the effect of ivermectin on length of hospital stay.

#Funnel plot

#Gráfico de forest plot

Extracorporeal membrane oxygenation

None of the included studies reported this outcome.

#Funnel plot

#Gráfico de forest plot

Discussion

We performed a comprehensive search of the literature and we found 3 non-randomised studies evaluating the effect of ivermectin in patients with COVID-19 [23], [24], [25]. The first identify study [23] published in April of 2020 gained recognition for its favourable results towards the use of ivermectin, but its validity has largely been questioned [27] and since its publication has been retracted.

The 2 remaining comparative, non-randomised studies have shown little to no difference regarding its potential therapeutic effect, but the limitations related to their study design and additional quality issues are critical when assessing the certainty of the evidence. Given the uncertainty regarding the effect of ivermectin, we were not able to balance the benefits of ivermectin, if any, with the adverse effects and costs [24], [25].

Despite these issues, many researchers have supported the use of ivermectin. We have identified 46 ongoing randomised trials evaluating the effectiveness of ivermectin, despite World Health Organization (WHO) excluding ivermectin from its cosponsored Solidarity Trial for COVID-19 treatments [28]. Systematic reviews are the gold standard to collect and summarize the available evidence regarding a scientific question. However, the traditional model for conducting reviews has several limitations, including a high demand for time and resources [29] and a rapid obsolescence [30]. Amidst the COVID-19 crisis, researchers should make their best effort to answer the urgent needs of health decision makers yet without giving up scientific accuracy. Information is being produced at a vertiginous speed [31], so alternative models are needed.

One potential solution to these shortfalls are rapid reviews, a form of knowledge synthesis that streamlines or omits specific methods of a traditional systematic review in order to move faster. Unfortunately, in many cases, this rapidity comes at the cost of quality [32]. Furthermore, they do not solve the issue of obsolescence. Living systematic reviews do address that issue [33]. They are continually updated by incorporating relevant new evidence as it becomes available, at a substantial effort. So, an approach combining these two models might prove more successful in providing the scientific community and other interested parties with evidence that is actionable, rapidly and efficiently produced, up to date, and of the highest quality [34].

This review is part of a larger project set up to put such an approach into practice. This project aims to produce multiple parallel living systematic reviews relevant to COVID-19 following the higher standards of quality in evidence synthesis production [14]. We believe that our methods are well suited to handle the abundance of evidence that is to come, including evidence on the role of ivermectin in patients with COVID-19. We have identified multiple ongoing studies addressing this question which will provide valuable evidence to inform researchers and decision makers in the near future. The main limitation of our review results from the insufficiency of the existing evidence to inform decisions. We hope that the substantial number of trials that are expected to be completed in the next months will shed some light on the role of ivermectin in the treatment of patients with COVID-19. During the COVID-19 pandemic we will maintain a living, web-based, openly available version of this review, and we will resubmit the review when randomised trials are available. In parallel, we intend to conduct another review with an expanded search to incorporate evidence from non-randomised studies.

Notes

Acknowledgements

The members of the COVID-19 L·OVE Working Group and Epistemonikos Foundation have made possible to build the systems and compile the information needed by this project. Epistemonikos is a collaborative effort, based on the ongoing volunteer work of over a thousand contributors since 2012.

Roles and contributions

All the review authors drafted and revised the protocol, conducted article screening and data collection, and drafted and revised the review. The COVID-19 L·OVE Working Group was created by Epistemonikos and a number of expert teams in order to provide decision makers with the best evidence related to COVID-19. Up-to-date information about the group and its member organisations is available here: epistemonikos.cl/working-group.

Competing interests

All authors declare no financial relationships with any organisation that might have a real or perceived interest in this work. There are no other relationships or activities that might have influenced the submitted work. Funding This project was not commissioned by any organisation and did not receive external funding. Epistemonikos Foundation is providing training, support and tools at no cost for all the members of the COVID-19 L·OVE Working Group.

PROSPERO registration number CRD42020189554

Ethics

As researchers will not access information that could lead to the identification of an individual participant, obtaining ethical approval was waived.

Data sharing

All data related to the project will be available. Epistemonikos Foundation will grant access to data.

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