Tocilizumab for COVID-19: real-time meta analysis of 46 studies
ControlAbstract
Significantly lower risk is seen for ventilation and recovery. 20 studies from 20 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
8% [-8‑22%] lower risk, without reaching statistical significance.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
Tocilizumab currently has no early treatment studies.
All data and sources to reproduce this analysis are in the appendix.
Ghaempanah et al. present another meta analysis for tocilizumab, showing significant improvement for mortality.
Tocilizumab for COVID-19 — Highlights
Tocilizumab reduces risk with very high confidence for ventilation and recovery, and very low confidence for progression, however increased risk is seen with high confidence for hospitalization.
Real-time updates and corrections with a consistent protocol for 172 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in tocilizumab studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 5.0 months, compared to using all studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury3-15 and
cognitive deficits6,11, cardiovascular
complications16-20, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits21—the spike protein binds to fibrin leading to
fibrinolysis-resistant blood clots, thromboinflammation, and
neuropathology.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,22-29, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk30, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
tocilizumab
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 3 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Currently all tocilizumab studies use late treatment.
Figure 3. Treatment stages.
Table 1 summarizes the results for all studies, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes.
Figure 4, 5, 6, 7, 8, 9, 10, 11, and 12
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 8% [-8‑22%] | 46 | 20,172 | 1,728 |
| After exclusions | 7% [-9‑22%] | 44 | 19,500 | 1,693 |
| Peer-reviewed studiesPeer-reviewed | 9% [-6‑23%] | 44 | 20,043 | 1,721 |
| Randomized Controlled TrialsRCTs | 12% [5‑18%] ** | 13 | 6,688 | 918 |
| Mortality | 6% [-11‑20%] | 44 | 19,360 | 1,690 |
| VentilationVent. | 23% [14‑32%] **** | 8 | 4,591 | 769 |
| ICU admissionICU | 15% [-53‑52%] | 6 | 813 | 107 |
| HospitalizationHosp. | -28% [-63‑-0%] * | 3 | 254 | 42 |
| Recovery | 14% [7‑20%] **** | 10 | 5,170 | 219 |
| Viral | -7% [-29‑11%] | 2 | 123 | 36 |
| RCT mortality | 12% [4‑18%] ** | 12 | 6,644 | 892 |
Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for ICU admission.
Figure 8. Random effects meta-analysis for hospitalization.
Figure 9. Random effects meta-analysis for progression.
Figure 10. Random effects meta-analysis for recovery.
Figure 11. Random effects meta-analysis for viral clearance.
Figure 12. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Figure 13 shows a comparison of results for RCTs and non-RCT studies.
Figure 14 and 15
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 1.
Figure 13. Results for RCTs and non-RCT studies.
Figure 14. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 15. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases33, and
analysis of double-blind RCTs has identified extreme levels of bias34.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 172 treatments we have analyzed,
67% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
Figure 16.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 0.98 [0.92‑1.05]
across 172 treatments36.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 172 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 0.98 [0.92‑1.05]39. Similar results are found for all low-cost treatments, RR
1.00 [0.91‑1.10]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.84‑1.02].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see41,42.
Currently, 55 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 58% have been confirmed in RCTs, with a mean delay of 7.7 months (64% with 8.9 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to evaluate each trial on its own merits. RCTs for a
given medication and disease may be more reliable, however they may also be
less reliable. For off-patent medications, very high conflict of interest
trials may be more likely to be RCTs, and more likely to be large trials that
dominate meta analyses.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 17 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Biran, significant unadjusted confounding possible.
Kewan, unadjusted results with significant differences between groups.
Figure 17. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours45,46. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases47 |
| <24 hours | -33 hours symptoms48 |
| 24-48 hours | -13 hours symptoms48 |
| Inpatients | -2.5 hours to improvement49 |
Figure 18 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 172 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 172 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants51, for example the Gamma variant shows significantly
different characteristics52-55. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants56,57.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for tocilizumab as of December 2020. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 172
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 19 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 20 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 21 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000009 to p = 0.0000000039.
Figure 19. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 20. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 19. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 55 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.9 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 22 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 22. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results. Trials with patented drugs may have a financial conflict of interest that
results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to
date (CTRI/2021/05/033864 and CTRI/2021/08/0354242).
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 23 shows a scatter plot of
results for prospective and retrospective studies.
41% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
50% of prospective studies, consistent with a bias toward publishing negative results.
Figure 23. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 24 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.0578-85.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 24. Example funnel plot analysis for simulated perfect trials.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone60-76.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
1 of the 46
studies compare against other treatments, which may reduce the effect
seen.
Ghaempanah et al. present another meta analysis for tocilizumab, showing significant improvement for mortality.
Xie et al. present a review covering tocilizumab for COVID-19.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors22-29, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk30, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 25 shows an overview of the results for tocilizumab
in the context of multiple COVID-19 treatments, and Figure 26 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 25.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy168.
Figure 26. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for ventilation and recovery. 20 studies from 20 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
8% [-8‑22%] lower risk, without reaching statistical significance.
Ghaempanah et al. present another meta analysis for tocilizumab, showing significant improvement for mortality.
Retrospective 764 COVID-19 patients requiring ICU support showing lower mortality with tocilizumab. There was significantly higher use of HCQ+AZ in the treatment group, which was not adjusted for in the propensity score matching or Cox regression.
5% of tocilizumab patients did not receive HCQ compared to 17% of untreated patients (15% after PSM), suggesting substantial differences between the groups in treatment propensity (which may include other unreported treatments such as vitamin D).
Authors state that "An increase in use of hydroxychloroquine was noted in patients who received tocilizumab compared with those who did not receive tocilizumab, which we do not believe had a relevant effect on our findings because most observational studies have not reported a benefit for hydroxychloroquine among hospitalised patients".
However authors would know the actual effect of HCQ and AZ in their dataset. Lack of reporting and lack of adjustment suggests that HCQ+AZ may have been beneficial in this case (due to politics authors would not be allowed to report this).
HCQ/AZ may lack benefit or be harmful with excessive dosage and very late treatment. Tocilizumab treatment was delayed 3 days post admission - HCQ treatment likely started earlier based on local protocol, without RCT delays. Typical HCQ dose in the region was reasonable. The RECOVERY trial delivered roughly four times the five-day exposure of the Hackensack area hospital protocol and more than double even a ten-day course. Based on expected dose and treatment time, it is likely that HCQ/AZ use here was beneficial.
Confounding by time is also possible - HCQ use likely dropped during the end of the study period, and the correlation with tocilizumab treatment suggests potential changes in tocilizumab treatment propensity over time, which adds confounding due to other significant SOC changes during this early pandemic period.
Other potential significant confounders were not included in adjustments without explanation - for example after matching the tocilizumab treatment group had 2x the number of nursing home residents with higher baseline risk.
5% of tocilizumab patients did not receive HCQ compared to 17% of untreated patients (15% after PSM), suggesting substantial differences between the groups in treatment propensity (which may include other unreported treatments such as vitamin D).
Authors state that "An increase in use of hydroxychloroquine was noted in patients who received tocilizumab compared with those who did not receive tocilizumab, which we do not believe had a relevant effect on our findings because most observational studies have not reported a benefit for hydroxychloroquine among hospitalised patients".
However authors would know the actual effect of HCQ and AZ in their dataset. Lack of reporting and lack of adjustment suggests that HCQ+AZ may have been beneficial in this case (due to politics authors would not be allowed to report this).
HCQ/AZ may lack benefit or be harmful with excessive dosage and very late treatment. Tocilizumab treatment was delayed 3 days post admission - HCQ treatment likely started earlier based on local protocol, without RCT delays. Typical HCQ dose in the region was reasonable. The RECOVERY trial delivered roughly four times the five-day exposure of the Hackensack area hospital protocol and more than double even a ten-day course. Based on expected dose and treatment time, it is likely that HCQ/AZ use here was beneficial.
Confounding by time is also possible - HCQ use likely dropped during the end of the study period, and the correlation with tocilizumab treatment suggests potential changes in tocilizumab treatment propensity over time, which adds confounding due to other significant SOC changes during this early pandemic period.
Other potential significant confounders were not included in adjustments without explanation - for example after matching the tocilizumab treatment group had 2x the number of nursing home residents with higher baseline risk.
\Retrospective 65 severe COVID-19 patients showing no statistically significant differences with tocilizumab.
Retrospective case-control study of 128 hospitalized COVID-19 patients with severe respiratory impairment showing no significant difference in 30-day mortality with intravenous tocilizumab treatment.
Retrospective 53 critically ill COVID-19 patients showing no difference in mortality, need for renal replacement therapy, use of antibiotics, or positive cultures with tocilizumab treatment.
Retrospective 1,225 hospitalized patients showing lower mortality/intubation with tocilizumab.
PSM retrospective 112 hospitalized COVID-19 patients showing no significant difference in ICU admission or mortality with tocilizumab.
Retrospective 1,538 hospitalized patients in Italy, showing only HCQ associated with reduced mortality. Authors analyze mortality amongst those that were alive at day 7 to avoid survival time bias due to drug recording requiring a minimum of 5 days treatment. Results were similar when analyzing the entire population.
Retrospective 115 mechanically ventilated COVID-19 patients showing no mortality benefit with tocilizumab treatment.
Retrospective cohort study of 161 hospitalized patients with severe COVID-19 pneumonia and persistent hypoxia showing survival benefit with tocilizumab.
RCT 803 critically ill COVID-19 patients showing improved outcomes with tocilizumab and sarilumab. There was only 48 sarilumab patients and the model used shrinks the posterior distribution for each intervention effect toward the overall estimate for the combined drugs. The concurrent event counts for sarilumab may be more accurate.
Retrospective 544 patients with severe COVID-19 pneumonia showing lower mechanical ventilation or death with tocilizumab.
Retrospective 607 patients showing higher mortality with tocilizumab use.
Retrospective 3,924 critically ill COVID-19 patients showing lower mortality with tocilizumab.
Two open-label RCTs of 97 and 91 critically ill COVID-19 patients in France showing no significant differences with tocilizumab or sarilumab.
RCT 130 hospitalized COVID-19 patients with moderate or severe pneumonia showing tocilizumab did not reduce WHO-CPS scores below 5 at day 4 (primary outcome), but reduced the proportion of patients needing noninvasive ventilation, high-flow oxygen, mechanical ventilation, or death by day 14. There was no significant difference in 28-day mortality.
Retrospective 88 hospitalized COVID-19 patients showing no significant difference in clinical improvement or mortality with tocilizumab treatment.
Retrospective 26,445 hospitalized COVID-19 patients in the USA, showing higher mortality with tocilizumab.
RCT 4,116 hospitalized COVID-19 patients showing significantly lower mortality with tocilizumab.
Retrospective 2,512 hospitalized patients showing lower mortality with tocilizumab treatment within a subset of ICU patients, 143 treated with tocilizumab with adequate data, and 413 control patients.
Retrospective 51 hospitalized COVID-19 patients showing significant reduction in vasopressor support duration with tocilizumab treatment, but no significant difference in other outcomes.
Retrospective 111 critically ill COVID-19 patients showing that tocilizumab treatment was associated with increased secondary bacterial infections, higher mortality (35.2% vs 19.3%, p=0.020), and a trend toward more fungal infections (5.6% vs 0%, p=0.112).
Retrospective 93 hospitalized COVID-19 patients with cytokine storm showing no significant difference between anakinra and tocilizumab treatment after adjusting for baseline characteristics.
Retrospective 224 hospitalized COVID-19 patients in the USA, showing no significant difference in mortality with tocilizumab.
Retrospective 1,229 hospitalized COVID-19 patients in Spain showing that tocilizumab was associated with decreased mortality in patients with high C-reactive protein (CRP) levels (>150 mg/L) but not in those with lower CRP levels.
Prospective cohort study of 138 hospitalized COVID-19 patients showing lower mortality with tocilizumab.
Retrospective 79 COVID-19 patients with ARDS undergoing noninvasive ventilation showing lower risk of intubation or death with tocilizumab treatment.
PSM retrospective 77 hospitalized patients receiving tocilizumab treatment and matched controls, showing no significant difference in outcomes.
Retrospective 42 hospitalized COVID-19 patients treated with tocilizumab showing no significant differences in mortality compared to matched controls.
Retrospective 74 hospitalized COVID-19 patients treated with tocilizumab and 74 matched controls, showing higher mortality with tocilizumab.
RCT 85 patients in the USA showing no significant differences with tocilizumab treatment.
Retrospective 778 hospitalized COVID-19 patients with hyperinflammatory state showing tocilizumab associated with lower mortality.
The 1:2 PSM analysis may be the most accurate because it provides documented, tight covariate balance across >30 baseline characteristics while automatically discarding patients whose propensity scores fall outside the zone of common support. The trimming eliminates the destabilising extreme weights that can silently bias inverse-probability–weighted estimates - yet the matched cohort still contains nearly two-thirds of the original sample, preserving adequate power. Importantly, hazard ratios for both tocilizumab and high-dose corticosteroids converge across the matched and time-dependent models, whereas IPTW produces the most extreme point estimates without providing any weight-distribution or post-weight balance diagnostics to justify that extra optimism. Given the absence of those diagnostics and the strong, transparent balance achievable through matching, the matched-cases analysis offers the most credible and least assumption-dependent assessment of treatment effect in this study.
The 1:2 PSM analysis may be the most accurate because it provides documented, tight covariate balance across >30 baseline characteristics while automatically discarding patients whose propensity scores fall outside the zone of common support. The trimming eliminates the destabilising extreme weights that can silently bias inverse-probability–weighted estimates - yet the matched cohort still contains nearly two-thirds of the original sample, preserving adequate power. Importantly, hazard ratios for both tocilizumab and high-dose corticosteroids converge across the matched and time-dependent models, whereas IPTW produces the most extreme point estimates without providing any weight-distribution or post-weight balance diagnostics to justify that extra optimism. Given the absence of those diagnostics and the strong, transparent balance achievable through matching, the matched-cases analysis offers the most credible and least assumption-dependent assessment of treatment effect in this study.
Retrospective case-control study of 193 hospitalized COVID-19 patients showing a non-statistically significant trend toward lower mortality with tocilizumab treatment. When excluding intubated patients, tocilizumab was associated with significantly lower mortality.
RCT 452 hospitalized patients with severe COVID-19 pneumonia showing no significant difference in clinical status or mortality at day 28 with tocilizumab. There was significantly lower ICU admission for patients not in the ICU at baseline.
Retrospective 246 hospitalized patients with severe COVID-19 pneumonia showing significantly lower mortality with tocilizumab treatment.
Prospective analysis of 96 hospitalized patients with severe worsening COVID-19 pneumonia showing that tocilizumab treatment was associated with reduced need for ventilatory support and shorter time to oxygen withdrawal, however there was no significant difference in mortality.
Retrospective 506 hospitalized COVID-19 patients in Spain showing lower mortality with tocilizumab.
RCT 377 hospitalized COVID-19 patients with pneumonia showing that tocilizumab significantly reduced mechanical ventilation or death by day 28, however there was no difference in mortality alone.
RCT 126 hospitalized COVID-19 patients with pneumonia showing no significant benefit of tocilizumab treatment.
RCT 180 hospitalized patients with moderate to severe COVID-19 in India showing no significant difference in disease progression with tocilizumab.
Retrospective 154 mechanically ventilated COVID-19 patients showing improved survival with tocilizumab. However, tocilizumab was associated with significantly higher rates of superinfection, particularly ventilator-associated pneumonia.
RCT 243 hospitalized patients with COVID-19 showing no significant difference in intubation or death with tocilizumab compared to placebo. Authors hypothesize that elevated interleukin-6 levels may represent host responses to infection rather than components of a self-amplifying inflammatory loop that would benefit from suppression.
RCT 40 hospitalized COVID-19 patients in Iran showing no significant improvement in mortality, clinical outcomes, or oxygen therapy requirements with tocilizumab treatment. The study found no significant differences between the tocilizumab and standard of care groups in the number of patients who recovered (70.6% vs. 78.9%), hospitalization duration, or time to clinical improvement.
PSM retrospective 274 hospitalized COVID-19 patients showing no difference in mortality with tocilizumab.
RCT 129 hospitalized patients with severe or critical COVID-19 in Brazil showing no benefit and potential harm with tocilizumab treatment.
RCT 65 COVID-19 patients in China showing improvement in hypoxia recovery with tocilizumab treatment, especially in moderate patients with bilateral pulmonary lesions.
Retrospective 2,196 COVID-19 patients in Taiwan (49% mild cases, 44% moderate, 7% severe) showing higher mortality with tocilizumab, without statistical significance.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are tocilizumab and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of tocilizumab for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
Studies with major unexplained data issues, for example major outcome data that
is impossible to be correct with no response from the authors, are excluded.
This is a living analysis and is updated regularly.
Figure 27.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
reported then they are both used in specific outcome analyses, while mortality
is used for pooled analysis.
If symptomatic
results are reported at multiple times, we use the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral outcomes.
When basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
An IPD meta-analysis confirms that intermediate viral load reduction
is more closely associated with hospitalization/death than later
viral load reduction169.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to Zhang et al.
Reported confidence intervals and p-values are used when available,
and adjusted values are used when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted results have preference over unadjusted
results for a more serious outcome when the adjustments significantly alter
results. When needed, conversion between reported p-values and
confidence intervals followed Altman, Altman (B), and Fisher's exact
test was used to calculate p-values for event data. If continuity
correction for zero values is required, we use the reciprocal of the opposite
arm with the sum of the correction factors equal to 1173.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.13.4) with
scipy (1.15.3), pythonmeta (1.26), numpy (2.3.0), statsmodels (0.14.4), and plotly (6.1.2).
Forest plots are computed using PythonMeta174
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.2 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective45,46.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/tzmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Biran, 10/31/2020, retrospective, USA, peer-reviewed, 29 authors, study period 1 March, 2020 - 22 April, 2020, trial NCT04347993 (history), excluded in exclusion analyses: significant unadjusted confounding possible. | risk of death, 29.0% lower, HR 0.71, p = 0.004, treatment 205, control 416, propensity score matching, Kaplan–Meier. |
| Campochiaro, 6/30/2020, retrospective, Italy, peer-reviewed, 16 authors, study period 13 March, 2020 - 19 March, 2020, trial NCT04318366 (history). | risk of death, 53.1% lower, RR 0.47, p = 0.15, treatment 5 of 32 (15.6%), control 11 of 33 (33.3%), NNT 5.6. |
| risk of mechanical ventilation, 106.2% higher, RR 2.06, p = 0.43, treatment 4 of 32 (12.5%), control 2 of 33 (6.1%). | |
| risk of no hospital discharge, 27.2% lower, RR 0.73, p = 0.32, treatment 12 of 32 (37.5%), control 17 of 33 (51.5%), NNT 7.1. | |
| risk of no improvement, 20.7% lower, RR 0.79, p = 0.61, treatment 10 of 32 (31.2%), control 13 of 33 (39.4%), NNT 12. | |
| Canziani, 11/30/2020, retrospective, Italy, peer-reviewed, 23 authors, study period 15 March, 2020 - 22 April, 2020, trial NCT04317092 (history). | risk of death, 18.0% lower, HR 0.82, p = 0.57, treatment 64, control 64, adjusted per study, multivariable, Cox proportional hazards. |
| Carvalho, 7/15/2020, retrospective, Brazil, preprint, 6 authors, study period 21 March, 2020 - 31 May, 2020. | risk of death, 165.6% higher, RR 2.66, p = 0.30, treatment 5 of 29 (17.2%), control 4 of 24 (16.7%), odds ratio converted to relative risk. |
| Chilimuri, 10/24/2020, retrospective, USA, peer-reviewed, 12 authors. | risk of death/intubation, 60.0% lower, HR 0.40, p = 0.008, treatment 83, control 685, adjusted per study, propensity score weighting, multivariable, Cox proportional hazards. |
| Colaneri, 5/9/2020, retrospective, Italy, peer-reviewed, median age 63.5, 11 authors, study period 14 March, 2020 - 27 March, 2020, SMACORE trial. | risk of death, 18.2% lower, RR 0.82, p = 0.85, treatment 5 of 21 (23.8%), control 19 of 91 (20.9%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable. |
| risk of ICU admission, 87.5% lower, RR 0.12, p = 0.43, treatment 3 of 21 (14.3%), control 12 of 91 (13.2%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable. | |
| De Rosa, 5/1/2021, retrospective, Italy, peer-reviewed, 20 authors, average treatment delay 6.0 days. | risk of death, 41.0% higher, OR 1.41, p = 0.38, treatment 97, control 1,239, adjusted per study, patients alive at day 7, multivariable, RR approximated with OR. |
| Fisher, 2/28/2021, retrospective, USA, peer-reviewed, 5 authors, study period 10 March, 2020 - 2 April, 2020. | risk of death, 4.0% higher, OR 1.04, p = 0.96, treatment 45, control 70, adjusted per study, multivariable, RR approximated with OR. |
| Gokhale, 7/31/2020, retrospective, India, peer-reviewed, 5 authors, study period 20 April, 2020 - 5 June, 2020. | risk of death, 38.4% lower, HR 0.62, p = 0.046, treatment 70, control 91, adjusted per study, multivariable, Cox proportional hazards. |
| Gordon, 4/22/2021, Randomized Controlled Trial, multiple countries, peer-reviewed, 62 authors, trial NCT02735707 (history) (REMAP-CAP). | risk of death, 21.7% lower, RR 0.78, p = 0.03, treatment 98 of 350 (28.0%), control 127 of 355 (35.8%), NNT 13, concurrent control patients. |
| Guaraldi, 8/31/2020, retrospective, Italy, peer-reviewed, median age 67.0, 34 authors, study period 21 February, 2020 - 24 March, 2020. | risk of death, 62.0% lower, HR 0.38, p = 0.01, treatment 13 of 125 (10.4%), control 73 of 179 (40.8%), NNT 3.3, adjusted per study, multivariable, Cox proportional hazards. |
| risk of death/intubation, 39.0% lower, HR 0.61, p = 0.02, treatment 125, control 179, adjusted per study, multivariable, Cox proportional hazards. | |
| Guisado-Vasco, 10/15/2020, retrospective, Spain, peer-reviewed, median age 69.0, 25 authors. | risk of death, 140.1% higher, OR 2.40, p = 0.02, treatment 132, control 475, adjusted per study, multivariable, RR approximated with OR. |
| Gupta, 1/1/2021, retrospective, USA, peer-reviewed, median age 62.0, 342 authors, study period 4 March, 2020 - 10 May, 2020. | risk of death, 29.0% lower, HR 0.71, p = 0.007, treatment 433, control 3,491. |
| Hermine, 2/3/2022, Randomized Controlled Trial, France, peer-reviewed, 6 authors, study period 31 March, 2020 - 20 April, 2020, trial NCT04324073 (history) (CORIMUNO-TOCI-2). | risk of death, 33.0% lower, HR 0.67, p = 0.33, treatment 49, control 43. |
| Hermine (B), 1/1/2021, Randomized Controlled Trial, France, peer-reviewed, median age 64.0, 594 authors, study period 31 March, 2020 - 18 April, 2020, trial NCT04331808 (history) (CORIMUNO-TOCI-1). | risk of death, 6.9% lower, RR 0.93, p = 1.00, treatment 7 of 63 (11.1%), control 8 of 67 (11.9%), NNT 121, day 28. |
| risk of death, 24.1% higher, RR 1.24, p = 0.77, treatment 7 of 63 (11.1%), control 6 of 67 (9.0%), day 14. | |
| risk of mechanical ventilation, 62.0% lower, RR 0.38, p = 0.047, treatment 5 of 63 (7.9%), control 14 of 67 (20.9%), NNT 7.7, day 14. | |
| risk of progression, 33.5% lower, RR 0.66, p = 0.18, treatment 15 of 63 (23.8%), control 24 of 67 (35.8%), NNT 8.3, noninvasive ventilation, mechanical ventilation, or death, day 14. | |
| Hill, 11/22/2020, retrospective, USA, peer-reviewed, 15 authors, study period 19 March, 2020 - 24 April, 2020. | risk of death, 43.0% lower, HR 0.57, p = 0.27, treatment 43, control 45, propensity score weighting, Cox proportional hazards. |
| risk of no improvement, 8.0% lower, HR 0.92, p = 0.86, treatment 43, control 45, propensity score weighting, Cox proportional hazards. | |
| Ho, 10/31/2023, retrospective, USA, peer-reviewed, 9 authors, study period 1 January, 2020 - 31 August, 2021. | risk of death, 290.0% higher, OR 3.90, p < 0.001, treatment 424, control 26,021, adjusted per study, multivariable, RR approximated with OR. |
| Horby, 5/31/2021, Randomized Controlled Trial, United Kingdom, peer-reviewed, mean age 63.6, 33 authors, study period 23 April, 2020 - 24 January, 2021, trial NCT04381936 (history) (RECOVERY). | risk of death, 11.8% lower, RR 0.88, p = 0.005, treatment 621 of 2,022 (30.7%), control 729 of 2,094 (34.8%), NNT 24. |
| risk of mechanical ventilation, 20.7% lower, RR 0.79, p = 0.002, treatment 265 of 1,754 (15.1%), control 343 of 1,800 (19.1%), NNT 25. | |
| risk of no hospital discharge, 14.0% lower, RR 0.86, p < 0.001, treatment 872 of 2,022 (43.1%), control 1,050 of 2,094 (50.1%), NNT 14. | |
| Ip, 5/25/2020, retrospective, USA, peer-reviewed, 32 authors. | risk of death, 24.0% lower, HR 0.76, p = 0.06, treatment 134, control 413. |
| Kewan, 7/31/2020, retrospective, USA, peer-reviewed, 6 authors, study period 13 March, 2020 - 19 April, 2020, excluded in exclusion analyses: unadjusted results with significant differences between groups. | risk of death, 23.2% higher, RR 1.23, p = 1.00, treatment 3 of 28 (10.7%), control 2 of 23 (8.7%). |
| risk of no hospital discharge, 39.6% higher, RR 1.40, p = 0.27, treatment 17 of 28 (60.7%), control 10 of 23 (43.5%). | |
| ventilation time, 30.0% lower, relative time 0.70, p = 0.16, treatment median 7.0 IQR 10.0 n=28, control median 10.0 IQR 10.0 n=23. | |
| hospitalization time, 57.1% higher, relative time 1.57, p = 0.16, treatment median 11.0 IQR 16.25 n=28, control median 7.0 IQR 8.5 n=23. | |
| Kimmig, 10/28/2020, retrospective, USA, peer-reviewed, 9 authors, study period 1 March, 2020 - 27 April, 2020. | risk of death, 82.3% higher, RR 1.82, p = 0.09, treatment 19 of 54 (35.2%), control 11 of 57 (19.3%). |
| Langer-Gould, 10/31/2020, retrospective, USA, peer-reviewed, 8 authors, study period 1 March, 2020 - 30 April, 2020, this trial compares with another treatment - results may be better when compared to placebo. | risk of death, 117.4% higher, HR 2.17, p = 0.53, treatment 24 of 52 (46.2%), control 9 of 41 (22.0%), inverted to make HR<1 favor treatment, Cox proportional hazards. |
| Maeda, 7/28/2020, retrospective, USA, peer-reviewed, 4 authors. | risk of death, no change, OR 1.00, p = 1.00, treatment 23, control 201, propensity score weighting, RR approximated with OR. |
| Martínez-Sanz, 2/28/2021, retrospective, Spain, peer-reviewed, 7 authors, study period 31 January, 2020 - 23 April, 2020. | risk of death, 35.0% lower, HR 0.65, p = 0.51, all patients. |
| risk of death, 66.0% lower, HR 0.34, p = 0.005, CRP >150 mg/L. | |
| risk of death, 21.0% higher, HR 1.21, p = 0.55, CRP ≤150 mg/L. | |
| Masiá, 10/31/2020, prospective, Spain, peer-reviewed, median age 64.0, 10 authors, study period 10 March, 2020 - 17 April, 2020. | risk of death, 79.6% lower, RR 0.20, p = 0.04, treatment 2 of 76 (2.6%), control 8 of 62 (12.9%), NNT 9.7. |
| risk of ICU admission, 8.2% lower, RR 0.92, p = 1.00, treatment 9 of 76 (11.8%), control 8 of 62 (12.9%), NNT 94. | |
| hospitalization time, 44.4% higher, relative time 1.44, p < 0.001, treatment median 13.0 IQR 9.8 n=76, control median 9.0 IQR 7.0 n=62. | |
| risk of no viral clearance, 68.0% higher, HR 1.68, p = 0.51, treatment 29, control 29, adjusted per study, propensity score matching, multivariable, Cox proportional hazards. | |
| Menzella, 9/29/2020, retrospective, Italy, peer-reviewed, median age 65.0, 19 authors, study period 10 March, 2020 - 14 April, 2020. | risk of death, 45.0% lower, HR 0.55, p = 0.19, treatment 41, control 38, adjusted per study, multivariable, Cox proportional hazards. |
| risk of death/intubation, 56.0% lower, HR 0.44, p = 0.02, treatment 41, control 38, adjusted per study, multivariable, Cox proportional hazards. | |
| Okoh, 10/5/2020, retrospective, USA, peer-reviewed, 4 authors, study period 10 March, 2020 - 10 April, 2020. | risk of death, 166.7% higher, RR 2.67, p = 0.21, treatment 4 of 20 (20.0%), control 3 of 40 (7.5%). |
| risk of ICU admission, 344.4% higher, RR 4.44, p = 0.01, treatment 10 of 30 (33.3%), control 3 of 40 (7.5%). | |
| Patel, 10/20/2020, retrospective, USA, peer-reviewed, 27 authors, study period 16 March, 2020 - 17 April, 2020. | risk of death, 9.3% lower, RR 0.91, p = 0.80, treatment 21, control 21, combined. |
| risk of death, 33.3% lower, RR 0.67, p = 0.72, treatment 4 of 21 (19.0%), control 6 of 21 (28.6%), NNT 10, severe. | |
| risk of death, 11.1% higher, RR 1.11, p = 1.00, treatment 7 of 21 (33.3%), control 6 of 20 (30.0%), critical. | |
| Pettit, 8/21/2020, retrospective, USA, peer-reviewed, 7 authors, study period 1 March, 2020 - 25 April, 2020. | risk of death, 70.6% higher, RR 1.71, p = 0.05, treatment 29 of 74 (39.2%), control 17 of 74 (23.0%). |
| Reid, 2/10/2025, Randomized Controlled Trial, USA, preprint, 1 author, trial NCT04479358 (history) (COVIDOSE-2). | risk of death, 56.0% higher, RR 1.56, p = 1.00, treatment 3 of 50 (6.0%), control 1 of 26 (3.8%), all patients. |
| risk of death, 66.2% lower, RR 0.34, p = 1.00, treatment 0 of 25 (0.0%), control 1 of 26 (3.8%), NNT 26, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), 120mg. | |
| risk of death, 212.0% higher, RR 3.12, p = 0.35, treatment 3 of 25 (12.0%), control 1 of 26 (3.8%), 40mg. | |
| recovery time, 2.5% higher, relative time 1.02, p = 0.94, treatment 25, control 26, all patients. | |
| recovery time, 20.0% lower, relative time 0.80, p = 0.09, treatment mean 4.0 (±1.28) n=25, control mean 5.0 (±2.6) n=26, 120mg. | |
| recovery time, 40.0% higher, relative time 1.40, p = 0.15, treatment mean 7.0 (±6.38) n=25, control mean 5.0 (±2.6) n=26, 40mg. | |
| Rodríguez-Baño, 2/28/2021, retrospective, Spain, peer-reviewed, 8 authors, study period 2 February, 2020 - 31 March, 2020, trial NCT04355871 (history) (SAMI-COVID). | risk of death, 78.0% lower, HR 0.22, p = 0.04, treatment 88, control 176, propensity score matching. |
| risk of death/intubation, 58.0% lower, HR 0.42, p = 0.03, treatment 88, control 176, propensity score matching. | |
| Rojas-Marte, 6/19/2020, retrospective, USA, peer-reviewed, mean age 60.0, 18 authors, study period 8 March, 2020 - 25 April, 2020. | risk of death, 21.0% lower, RR 0.79, p = 0.11, treatment 43 of 96 (44.8%), control 55 of 97 (56.7%), NNT 8.4. |
| Rosas, 4/22/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, median age 60.9, 23 authors, study period 3 April, 2020 - 28 May, 2020, trial NCT04320615 (history) (COVACTA). | risk of death, 1.5% higher, RR 1.01, p = 1.00, treatment 58 of 294 (19.7%), control 28 of 144 (19.4%). |
| risk of mechanical ventilation, 24.0% lower, RR 0.76, p = 0.16, treatment 51 of 183 (27.9%), control 33 of 90 (36.7%), NNT 11. | |
| risk of ICU admission, 40.8% lower, RR 0.59, p = 0.04, treatment 27 of 127 (21.3%), control 23 of 64 (35.9%), NNT 6.8. | |
| recovery time, 22.2% lower, relative time 0.78, p = 0.18, treatment mean 14.0 (±21.9) n=294, control mean 18.0 (±39.8) n=144, median time to improvement ≥2 categories. | |
| Rossi, 10/17/2020, retrospective, France, peer-reviewed, mean age 67.7, 13 authors, study period 14 March, 2020 - April 2020, trial NCT04366206 (history). | risk of death, 64.0% lower, HR 0.36, p = 0.003, treatment 84, control 84, propensity score matching, propensity score weighting, Cox proportional hazards. |
| risk of death/intubation, 60.0% lower, HR 0.40, p = 0.001, treatment 84, control 84, propensity score matching, propensity score weighting, Cox proportional hazards. | |
| Roumier, 11/14/2020, retrospective, France, peer-reviewed, mean age 60.0, 29 authors, study period 9 March, 2020 - 11 April, 2020. | risk of death, 32.0% lower, HR 0.68, p = 0.34, treatment 49, control 47. |
| risk of mechanical ventilation, 42.0% lower, HR 0.58, p = 0.03, treatment 49, control 47. | |
| risk of no hospital discharge, 45.1% lower, HR 0.55, p = 0.03, treatment 49, control 47, inverted to make HR<1 favor treatment. | |
| Ruiz-Antorán, 12/6/2020, retrospective, Spain, peer-reviewed, 25 authors, study period 3 March, 2020 - 20 April, 2020, trial EUPAS34415 (TOCICOV). | risk of death, 25.9% lower, HR 0.74, p = 0.001, treatment 268, control 238, propensity score weighting. |
| Salama, 1/7/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, mean age 55.9, 20 authors, trial NCT04372186 (history) (EMPACTA). | risk of death, 21.5% higher, RR 1.22, p = 0.72, treatment 26 of 249 (10.4%), control 11 of 128 (8.6%), day 28. |
| risk of death/intubation, 44.0% lower, HR 0.56, p = 0.03, treatment 249, control 128, day 28. | |
| Salvarani, 1/1/2021, Randomized Controlled Trial, Italy, peer-reviewed, median age 60.0, 39 authors, study period 31 March, 2020 - 11 June, 2020, trial NCT04346355 (history) (RCT-TCZ-COVID-19). | risk of death, 110.0% higher, RR 2.10, p = 0.61, treatment 2 of 60 (3.3%), control 1 of 63 (1.6%), day 30. |
| risk of ICU admission, 26.0% higher, RR 1.26, p = 0.76, treatment 6 of 60 (10.0%), control 5 of 63 (7.9%), day 30. | |
| risk of no hospital discharge, 26.0% higher, RR 1.26, p = 0.76, treatment 6 of 60 (10.0%), control 5 of 63 (7.9%), day 30. | |
| risk of no hospital discharge, 5.0% higher, RR 1.05, p = 1.00, treatment 17 of 60 (28.3%), control 17 of 63 (27.0%), day 14. | |
| Soin, 5/31/2021, Randomized Controlled Trial, India, peer-reviewed, median age 56.0, 20 authors, study period 30 May, 2020 - 31 August, 2020, trial CTRI/2020/05/025369 (COVINTOC). | risk of death, 29.1% lower, RR 0.71, p = 0.40, treatment 11 of 91 (12.1%), control 15 of 88 (17.0%), NNT 20. |
| risk of mechanical ventilation, 4.1% higher, RR 1.04, p = 1.00, treatment 14 of 91 (15.4%), control 13 of 88 (14.8%). | |
| risk of progression, 29.7% lower, RR 0.70, p = 0.47, treatment 8 of 91 (8.8%), control 11 of 88 (12.5%), NNT 27. | |
| risk of ICU admission, 7.3% higher, RR 1.07, p = 0.49, treatment 71 of 91 (78.0%), control 64 of 88 (72.7%). | |
| Somers, 7/11/2020, retrospective, USA, peer-reviewed, 22 authors. | risk of death, 45.0% lower, HR 0.55, p = 0.02, treatment 78, control 76, propensity score weighting. |
| Stone, 12/10/2020, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, median age 59.8, 48 authors, study period 20 April, 2020 - 15 June, 2020, trial NCT04356937 (history) (BACC Bay). | risk of death, 52.0% higher, HR 1.52, p = 0.54, treatment 9 of 161 (5.6%), control 3 of 82 (3.7%), adjusted per study, day 28. |
| risk of mechanical ventilation, 35.0% lower, HR 0.65, p = 0.36, treatment 11 of 161 (6.8%), control 8 of 82 (9.8%), NNT 34, adjusted per study, day 28. | |
| risk of death/intubation, 17.0% lower, HR 0.83, p = 0.65, treatment 17 of 161 (10.6%), control 10 of 82 (12.2%), NNT 61, adjusted per study, day 28. | |
| risk of progression, 11.0% higher, HR 1.11, p = 0.76, treatment 31 of 161 (19.3%), control 14 of 82 (17.1%), adjusted per study, clinical worsening on ordinal scale, day 28. | |
| Talaschian, 2/20/2024, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, mean age 59.6, 10 authors, study period 10 July, 2020 - 10 December, 2020. | risk of death, 39.7% higher, RR 1.40, p = 0.71, treatment 5 of 17 (29.4%), control 4 of 19 (21.1%). |
| risk of no hospital discharge, 47.4% lower, HR 0.53, p = 0.26, treatment 17, control 19, inverted to make HR<1 favor treatment. | |
| risk of no recovery, 39.7% higher, RR 1.40, p = 0.71, treatment 5 of 17 (29.4%), control 4 of 19 (21.1%). | |
| recovery time, 11.1% higher, relative time 1.11, p = 0.71, treatment median 10.0 IQR 7.0 n=17, control median 9.0 IQR 13.0 n=19. | |
| Tsai, 11/5/2020, retrospective, USA, peer-reviewed, mean age 63.0, 4 authors, study period 1 March, 2020 - 5 May, 2020. | risk of death, no change, RR 1.00, p = 1.00, treatment 18 of 66 (27.3%), control 18 of 66 (27.3%), propensity score matching. |
| Veiga, 1/20/2021, Randomized Controlled Trial, Brazil, peer-reviewed, mean age 57.0, 36 authors, study period 8 May, 2020 - 17 July, 2020, trial NCT04403685 (history) (TOCIBRAS). | risk of death, 129.7% higher, RR 2.30, p = 0.09, treatment 14 of 65 (21.5%), control 6 of 64 (9.4%), day 28. |
| risk of no recovery, 1.0% lower, RR 0.99, p = 0.96, treatment 65, control 64, relative SOFA score, day 15. | |
| Wang (B), 12/31/2020, Randomized Controlled Trial, China, peer-reviewed, 26 authors, study period 13 February, 2020 - 13 March, 2020, trial ChiCTR2000029765. | risk of progression, 27.1% lower, RR 0.73, p = 0.53, treatment 7 of 24 (29.2%), control 8 of 20 (40.0%), NNT 9.2. |
| risk of no recovery, 54.4% lower, RR 0.46, p = 0.41, treatment 2 of 34 (5.9%), control 4 of 31 (12.9%), NNT 14. | |
| risk of no recovery, 79.2% lower, RR 0.21, p = 0.03, treatment 2 of 24 (8.3%), control 8 of 20 (40.0%), NNT 3.2, hypoxia recovery. | |
| hospitalization time, 8.3% higher, relative time 1.08, p = 0.35, treatment median 26.0 IQR 10.0 n=34, control median 24.0 IQR 13.0 n=31. | |
| time to viral-, 6.2% higher, relative time 1.06, p = 0.54, treatment median 17.0 IQR 8.0 n=34, control median 16.0 IQR 9.5 n=31. | |
| Yen, 8/20/2024, retrospective, Taiwan, peer-reviewed, 6 authors, study period 1 January, 2022 - 31 July, 2022. | risk of death, 58.0% higher, HR 1.58, p = 0.06, treatment 67, control 2,129, adjusted per study, multivariable, Cox proportional hazards. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
Ghaempanah et al., Does tocilizumab have an effect on the clinical outcomes in COVID-19 patients? A meta-analysis of randomized control trials, Journal of Pharmaceutical Policy and Practice, doi:10.1186/s40545-023-00662-w.
Ryu et al., Fibrin drives thromboinflammation and neuropathology in COVID-19, Nature, doi:10.1038/s41586-024-07873-4.
Rong et al., Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19, Cell Host & Microbe, doi:10.1016/j.chom.2024.11.007.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Wang et al., SARS-CoV-2 membrane protein induces neurodegeneration via affecting Golgi-mitochondria interaction, Translational Neurodegeneration, doi:10.1186/s40035-024-00458-1.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
AlTaweel et al., An In-Depth Insight into Clinical, Cellular and Molecular Factors in COVID19-Associated Cardiovascular Ailments for Identifying Novel Disease Biomarkers, Drug Targets and Clinical Management Strategies, Archives of Microbiology & Immunology, doi:10.26502/ami.936500177.
Saha et al., COVID-19 beyond the lungs: Unraveling its vascular impact and cardiovascular complications—mechanisms and therapeutic implications, Science Progress, doi:10.1177/00368504251322069.
Trender et al., Changes in memory and cognition during the SARS-CoV-2 human challenge study, eClinicalMedicine, doi:10.1016/j.eclinm.2024.102842.
Dugied et al., Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization, Communications Biology, doi:10.1038/s42003-025-07933-z.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Wu et al., Decoding the genome of SARS-CoV-2: a pathway to drug development through translation inhibition, RNA Biology, doi:10.1080/15476286.2024.2433830.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Biran et al., Tocilizumab among patients with COVID-19 in the intensive care unit: a multicentre observational study, The Lancet Rheumatology, doi:10.1016/S2665-9913(20)30277-0.
Kewan et al., Tocilizumab for treatment of patients with severe COVID–19: A retrospective cohort study, eClinicalMedicine, doi:10.1016/j.eclinm.2020.100418.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Chen et al., Synergistic Inhibition of SARS-CoV-2 Replication Using Disulfiram/Ebselen and Remdesivir, ACS Pharmacology & Translational Science, doi:10.1021/acsptsci.1c00022.
Hempel et al., Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties, Chemical Science, doi:10.1039/D1SC01494C.
Schultz et al., Pyrimidine inhibitors synergize with nucleoside analogues to block SARS-CoV-2, Nature, doi:10.1038/s41586-022-04482-x.
Ohashi et al., Potential anti-COVID-19 agents, cepharanthine and nelfinavir, and their usage for combination treatment, iScience, doi:10.1016/j.isci.2021.102367.
Al Krad et al., The protease inhibitor Nirmatrelvir synergizes with inhibitors of GRP78 to suppress SARS-CoV-2 replication, bioRxiv, doi:10.1101/2025.03.09.642200.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Xie et al., The role of reactive oxygen species in severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection-induced cell death, Cellular & Molecular Biology Letters, doi:10.1186/s11658-024-00659-6.
Reid et al., COVIDOSE-2: A Multi-center, Randomized, Controlled Phase 2 Trial Comparing Early Administration of Low-dose Tocilizumab to Standard of Care in Hospitalized Patients With COVID-19 Pneumonitis Not Requiring Invasive Ventilation, NCT04479358, clinicaltrials.gov/study/NCT04479358.
Yen et al., Predictors for cause-specific and timing of deaths in patients with COVID-19: a cohort study in Taiwan, BMC Infectious Diseases, doi:10.1186/s12879-024-09654-w.
Talaschian et al., Tocilizumab Failed to Reduce Mortality in Severe COVID-19 Patients: Results from a Randomized Controlled Clinical Trial, Iranian Journal of Allergy, Asthma and Immunology, doi:10.18502/ijaai.v23i1.14956.
Ho et al., A Retrospective Cohort Study Assessing the Impact of Statin Therapy on Hospital Length of Stay and Inpatient Mortality in COVID-19 Patients, HCA Healthcare Journal of Medicine, doi:10.36518/2689-0216.1546.
Hermine et al., Effect of interleukin-6 receptor antagonists in critically ill adult patients with COVID-19 pneumonia: two randomised controlled trials of the CORIMUNO-19 Collaborative Group, European Respiratory Journal, doi:10.1183/13993003.02523-2021.
Horby et al., Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial, The Lancet, doi:10.1016/S0140-6736(21)00676-0.
Soin et al., Tocilizumab plus standard care versus standard care in patients in India with moderate to severe COVID-19-associated cytokine release syndrome (COVINTOC): an open-label, multicentre, randomised, controlled, phase 3 trial, The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(21)00081-3.
De Rosa et al., Risk Factors for Mortality in COVID-19 Hospitalized Patients in Piedmont, Italy: Results from the Multicenter, Regional, CORACLE Registry, Journal of Clinical Medicine, doi:10.3390/jcm10091951.
Mohandas et al., Clinical review of COVID-19 patients presenting to a quaternary care private hospital in South India: A retrospective study, Clinical Epidemiology and Global Health, doi:10.1016/j.cegh.2021.100751.
Gordon et al., Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2100433.
Rosas et al., Tocilizumab in Hospitalized Patients with Severe Covid-19 Pneumonia, New England Journal of Medicine, doi:10.1056/NEJMoa2028700.
Rodríguez-Baño et al., Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19), Clinical Microbiology and Infection, doi:10.1016/j.cmi.2020.08.010.
Martínez-Sanz et al., Effects of tocilizumab on mortality in hospitalized patients with COVID-19: a multicentre cohort study, Clinical Microbiology and Infection, doi:10.1016/j.cmi.2020.09.021.
Fisher et al., Tocilizumab in the treatment of critical COVID-19 pneumonia: A retrospective cohort study of mechanically ventilated patients, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2020.12.021.
Veiga et al., Effect of tocilizumab on clinical outcomes at 15 days in patients with severe or critical coronavirus disease 2019: randomised controlled trial, BMJ, doi:10.1136/bmj.n84.
Salama et al., Tocilizumab in Patients Hospitalized with Covid-19 Pneumonia, New England Journal of Medicine, doi:10.1056/NEJMoa2030340.
Hermine (B) et al., Effect of Tocilizumab vs Usual Care in Adults Hospitalized With COVID-19 and Moderate or Severe Pneumonia, JAMA Internal Medicine, doi:10.1001/jamainternmed.2020.6820.
Salvarani et al., Effect of Tocilizumab vs Standard Care on Clinical Worsening in Patients Hospitalized With COVID-19 Pneumonia, JAMA Internal Medicine, doi:10.1001/jamainternmed.2020.6615.
Gupta et al., Association Between Early Treatment With Tocilizumab and Mortality Among Critically Ill Patients With COVID-19, JAMA Internal Medicine, doi:10.1001/jamainternmed.2020.6252.
Wang (B) et al., Tocilizumab Ameliorates the Hypoxia in COVID-19 Moderate Patients with Bilateral Pulmonary Lesions: A Randomized, Controlled, Open-Label, Multicenter Trial, SSRN Electronic Journal, doi:10.2139/ssrn.3667681.
Stone et al., Efficacy of Tocilizumab in Patients Hospitalized with Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2028836.
Ruiz-Antorán et al., Combination of Tocilizumab and Steroids to Improve Mortality in Patients with Severe COVID-19 Infection: A Spanish, Multicenter, Cohort Study, Infectious Diseases and Therapy, doi:10.1007/s40121-020-00373-8.
Canziani et al., Interleukin-6 receptor blocking with intravenous tocilizumab in COVID-19 severe acute respiratory distress syndrome: A retrospective case-control survival analysis of 128 patients, Journal of Autoimmunity, doi:10.1016/j.jaut.2020.102511.
Hill et al., Tocilizumab in hospitalized patients with COVID‐19: Clinical outcomes, inflammatory marker kinetics, and safety, Journal of Medical Virology, doi:10.1002/jmv.26674.
Roumier et al., Tocilizumab for Severe Worsening COVID-19 Pneumonia: a Propensity Score Analysis, Journal of Clinical Immunology, doi:10.1007/s10875-020-00911-6.
Tsai et al., Impact of tocilizumab administration on mortality in severe COVID-19, Scientific Reports, doi:10.1038/s41598-020-76187-y.
Langer-Gould et al., Early identification of COVID-19 cytokine storm and treatment with anakinra or tocilizumab, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2020.07.081.
Masiá et al., Impact of interleukin-6 blockade with tocilizumab on SARS-CoV-2 viral kinetics and antibody responses in patients with COVID-19: A prospective cohort study, EBioMedicine, doi:10.1016/j.ebiom.2020.102999.
Kimmig et al., IL-6 Inhibition in Critically Ill COVID-19 Patients Is Associated With Increased Secondary Infections, Frontiers in Medicine, doi:10.3389/fmed.2020.583897.
Chilimuri et al., Tocilizumab use in patients with moderate to severe COVID‐19: A retrospective cohort study, Journal of Clinical Pharmacy and Therapeutics, doi:10.1111/jcpt.13303.
Patel et al., Use of the IL‐6R antagonist tocilizumab in hospitalized COVID‐19 patients, Journal of Internal Medicine, doi:10.1111/joim.13163.
Rossi et al., Effect of Tocilizumab in Hospitalized Patients with Severe COVID-19 Pneumonia: A Case-Control Cohort Study, Pharmaceuticals, doi:10.3390/ph13100317.
Guisado-Vasco et al., Clinical characteristics and outcomes among hospitalized adults with severe COVID-19 admitted to a tertiary medical center and receiving antiviral, antimalarials, glucocorticoids, or immunomodulation with tocilizumab or cyclosporine: A retrospective observational study (COQUIMA cohort), eClinicalMedicine, doi:10.1016/j.eclinm.2020.100591.
Okoh et al., Tocilizumab use in COVID‐19‐associated pneumonia, Journal of Medical Virology, doi:10.1002/jmv.26471.
Menzella et al., Efficacy of tocilizumab in patients with COVID-19 ARDS undergoing noninvasive ventilation, Critical Care, doi:10.1186/s13054-020-03306-6.
Guaraldi et al., Tocilizumab in patients with severe COVID-19: a retrospective cohort study, The Lancet Rheumatology, doi:10.1016/S2665-9913(20)30173-9.
Pettit et al., Late onset infectious complications and safety of tocilizumab in the management of COVID‐19, Journal of Medical Virology, doi:10.1002/jmv.26429.
Gokhale et al., Tocilizumab improves survival in patients with persistent hypoxia in severe COVID-19 pneumonia, eClinicalMedicine, doi:10.1016/j.eclinm.2020.100467.
Maeda et al., The association of interleukin‐6 value, interleukin inhibitors, and outcomes of patients with COVID‐19 in New York City, Journal of Medical Virology, doi:10.1002/jmv.26365.
Carvalho et al., Effects of Tocilizumab in Critically Ill Patients With COVID-19: A Quasi-Experimental Study, medRxiv, doi:10.1101/2020.07.13.20149328.
Somers et al., Tocilizumab for Treatment of Mechanically Ventilated Patients With COVID-19, Clinical Infectious Diseases, doi:10.1093/cid/ciaa954.
Campochiaro et al., Efficacy and safety of tocilizumab in severe COVID-19 patients: a single-centre retrospective cohort study, European Journal of Internal Medicine, doi:10.1016/j.ejim.2020.05.021.
Rojas-Marte et al., Outcomes in patients with severe COVID-19 disease treated with tocilizumab: a case–controlled study, QJM: An International Journal of Medicine, doi:10.1093/qjmed/hcaa206.
Ip et al., Hydroxychloroquine and Tocilizumab Therapy in COVID-19 Patients - An Observational Study, PLoS ONE, doi:10.1371/journal.pone.0237693.
Colaneri et al., Tocilizumab for Treatment of Severe COVID-19 Patients: Preliminary Results from SMAtteo COvid19 REgistry (SMACORE), Microorganisms, doi:10.3390/microorganisms8050695.
Li et al., Immune modulation: the key to combat SARS-CoV-2 induced myocardial injury, Frontiers in Immunology, doi:10.3389/fimmu.2025.1561946.
Qudus et al., SARS-CoV-2-ORF-3a Mediates Apoptosis Through Mitochondrial Dysfunction Modulated by the K+ Ion Channel, International Journal of Molecular Sciences, doi:10.3390/ijms26041575.
Kumar (B) et al., Advancements in the development of antivirals against SARS-Coronavirus, Frontiers in Cellular and Infection Microbiology, doi:10.3389/fcimb.2025.1520811.
Wu (B) et al., A20 as a Potential Therapeutic Target for COVID‐19, Immunity, Inflammation and Disease, doi:10.1002/iid3.70127.
Andreevich Shoichet et al., SARS-CoV-2 Treatment: Current Perceptions and Perspectives, Journal of Microbiota, doi:10.5812/jmb-157269.
Rath et al., A Glimpse for the subsistence from pandemic SARS-CoV-2 infection, Bioorganic Chemistry, doi:10.1016/j.bioorg.2024.107977.
Thom et al., Future applications of host direct therapies for infectious disease treatment, Frontiers in Immunology, doi:10.3389/fimmu.2024.1436557.
Paranga et al., Cytokine Storm in COVID-19: Exploring IL-6 Signaling and Cytokine-Microbiome Interactions as Emerging Therapeutic Approaches, International Journal of Molecular Sciences, doi:10.3390/ijms252111411.
Liang et al., Repurposing existing drugs for the treatment ofCOVID-19/SARS-CoV-2: A review of pharmacological effects and mechanism of action, Heliyon, doi:10.1016/j.heliyon.2024.e35988.
Ebrahimi et al., Systems biology approaches to identify driver genes and drug combinations for treating COVID-19, Scientific Reports, doi:10.1038/s41598-024-52484-8.
Saranya et al., Systems medicine framework for repurposable drug combinations for COVID-19 comorbidities, Medicine in Omics, doi:10.1016/j.meomic.2024.100038.
Jaimes-Castelán et al., Drugs and natural products for the treatment of COVID-19 during 2020, the first year of the pandemic, Boletín Médico del Hospital Infantil de México, doi:10.24875/bmhim.23000016.
Agamah et al., Network-based multi-omics-disease-drug associations reveal drug repurposing candidates for COVID-19 disease phases, ScienceOpen, doi:10.58647/DRUGARXIV.PR000010.v1.
Sharma et al., Reviewing the insights of SARS-CoV-2: Its Epidemiology, Pathophysiology, and Potential Preventive Measures in Traditional Medicinal System, Clinical Traditional Medicine and Pharmacology, doi:10.1016/j.ctmp.2024.200147.
Choi et al., Review of COVID-19 Therapeutics by Mechanism: From Discovery to Approval, Journal of Korean Medical Science, doi:10.3346/jkms.2024.39.e134.
Pal et al., Pharmacotherapeutics for cytokine storm in COVID-19, Stem Cells, doi:10.1016/B978-0-323-95545-4.00003-7.
Fragkou et al., Review of trials currently testing treatment and prevention of COVID-19, Clinical Microbiology and Infection, doi:10.1016/j.cmi.2020.05.019.
Beg et al., Are herbal drugs effective in COVID management? A review to demystify the current facts and claims, ScienceOpen, doi:10.14293/s2199-1006.1.sor-.ppxfif7.v2.
Mushebenge et al., Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors, International Journal of Molecular Sciences, doi:10.3390/ijms242115518.
Vlasova-St. Louis et al., COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine, Reports, doi:10.3390/reports6040045.
Yuan et al., The role of cell death in SARS-CoV-2 infection, Signal Transduction and Targeted Therapy, doi:10.1038/s41392-023-01580-8.
Khan et al., Possible therapeutic targets for SARS-CoV-2 infection and COVID-19, Journal of Allergy & Infectious Diseases, doi:10.46439/allergy.2.028.
Mushebenge (B) et al., Assessing the Potential Contribution of in Silico Studies in Discovering Drug Candidates that Interact with Various SARS-CoV-2 Receptors, MDPI AG, doi:10.20944/preprints202308.0434.v1.
Gudima et al., Antiviral Therapy of COVID-19, International Journal of Molecular Sciences, doi:10.3390/ijms24108867.
Wang (C) et al., Inflammasomes: a rising star on the horizon of COVID-19 pathophysiology, Frontiers in Immunology, doi:10.3389/fimmu.2023.1185233.
Oliver et al., Different drug approaches to COVID-19 treatment worldwide: an update of new drugs and drugs repositioning to fight against the novel coronavirus, Therapeutic Advances in Vaccines and Immunotherapy, doi:10.1177/25151355221144845.
Xue et al., Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS‐CoV‐2, MedComm, doi:10.1002/mco2.254.
Pati et al., Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network, Multimedia Tools and Applications, doi:10.1007/s11042-023-15270-8.
Ceja-Gálvez et al., Severe COVID-19: Drugs and Clinical Trials, Journal of Clinical Medicine, doi:10.3390/jcm12082893.
Nayak et al., Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease, Journal of Personalized Medicine, doi:10.3390/jpm13040664.
Kumar (C) et al., COVID-19 therapeutics: Clinical application of repurposed drugs and futuristic strategies for target-based drug discovery, Genes & Diseases, doi:10.1016/j.gendis.2022.12.019.
Astasio-Picado et al., Therapeutic Targets in the Virological Mechanism and in the Hyperinflammatory Response of Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2), Applied Sciences, doi:10.3390/app13074471.
Raghav et al., Potential treatments of COVID-19: Drug repurposing and therapeutic interventions, Journal of Pharmacological Sciences, doi:10.1016/j.jphs.2023.02.004.
Basu et al., Therapeutics for COVID-19 and post COVID-19 complications: An update, Current Research in Pharmacology and Drug Discovery, doi:10.1016/j.crphar.2022.100086.
Heimfarth et al., Drug repurposing and cytokine management in response to COVID-19: A review, International Immunopharmacology, doi:10.1016/j.intimp.2020.106947.
Mateja et al., The choice of viral load endpoint in early phase trials of COVID-19 treatments aiming to reduce 28-day hospitalization and/or death, The Journal of Infectious Diseases, doi:10.1093/infdis/jiaf282.
Zhang et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Please send us corrections, updates, or comments.
c19early involves the extraction of 200,000+ datapoints from
thousands of papers. Community updates
help ensure high accuracy.
Treatments and other interventions are complementary.
All practical, effective, and safe
means should be used based on risk/benefit analysis.
No treatment or intervention is 100% available and effective for all current
and future variants.
We do not provide medical advice. Before taking any medication,
consult a qualified physician who can provide personalized advice and details
of risks and benefits based on your medical history and situation. IMA and WCH
provide treatment protocols.