Colchicine for COVID-19: real-time meta analysis of 56 studies
Abstract
Statistically significant lower risk is seen for mortality, ICU admission, hospitalization, and recovery. 26 studies from 26 independent teams in 15 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
27% [18‑36%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids the immune system or recovery but may have limited antiviral effects. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 22 of 56
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT which
is not generalizable to earlier usage.
No treatment or intervention
is 100% effective. All practical, effective, and safe means should be used
based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
are more effective.
Colchicine for COVID-19 — Highlights
Colchicine reduces
risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for ICU admission, low confidence for progression, and very low confidence for ventilation.
5th treatment shown effective with ≥3 clinical studies in
September 2020, now with p = 0.00000031 from 56 studies.
Outcome specific analyses and combined evidence from all
studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments.
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 colchicine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, 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 4.4 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 injury11-21 and
cognitive deficits13,18, cardiovascular
complications22-24, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,25-30, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk31, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
colchicine
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, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 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.
Figure 2. Treatment stages.
Table 1 shows potential mechanisms of action for
the treatment of COVID-19 using colchicine.
| Antiviral effects | Direct antiviral activity via inhibiting microtubule polymerization and viral entry. |
| Immunomodulatory effects | Potential prevention of an overactive immune response via modulation of immune cell functions, such as neutrophil chemotaxis, adhesion, and activation. |
| Anti-inflammatory effects | Reduction in inflammation and severity of cytokine storm via inibition of inflammasome activation and the release of pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α. |
| Prevention of microvascular thrombosis | Reduction in the risk of clot formation via antithrombotic properties, such as inhibiting platelet aggregation. |
| Cardioprotective effects | Mitigation of myocardial injury via reduced myocardial inflammation and oxidative stress, and inhibition of NLRP3 inflammasomes. |
An In Vitro study supports the efficacy of colchicine23.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Table 2 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, with different exclusions, and for specific outcomes.
Table 3 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 27% [18‑36%] **** | 56 | 33,066 | 959 |
| After exclusions | 39% [26‑49%] **** | 46 | 15,266 | 701 |
| Peer-reviewed studiesPeer-reviewed | 27% [18‑36%] **** | 53 | 32,545 | 943 |
| Randomized Controlled TrialsRCTs | 18% [6‑28%] ** | 30 | 27,021 | 632 |
| RCTs after exclusionsRCTs w/exc. | 26% [16‑35%] **** | 24 | 11,282 | 430 |
| Mortality | 28% [17‑37%] **** | 43 | 29,650 | 827 |
| VentilationVent. | 29% [-15‑56%] | 10 | 13,614 | 260 |
| ICU admissionICU | 34% [8‑52%] * | 9 | 1,389 | 174 |
| HospitalizationHosp. | 19% [11‑26%] **** | 21 | 12,818 | 333 |
| Recovery | 20% [7‑31%] ** | 15 | 12,936 | 199 |
| Cases | -9% [-29‑8%] | 4 | 2,559 | 42 |
| RCT mortality | 8% [-4‑19%] | 23 | 26,305 | 567 |
| RCT hospitalizationRCT hosp. | 21% [11‑29%] **** | 14 | 9,704 | 240 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 45% [-67‑82%] | 29% [19‑38%] **** | 12% [-21‑36%] |
| After exclusions | 45% [-67‑82%] | 44% [30‑55%] **** | 14% [-16‑37%] |
| Peer-reviewed studiesPeer-reviewed | 68% [40‑83%] *** | 29% [18‑38%] **** | 12% [-21‑36%] |
| Randomized Controlled TrialsRCTs | 2% [-235‑71%] | 18% [6‑29%] ** | |
| RCTs after exclusionsRCTs w/exc. | 2% [-235‑71%] | 27% [17‑36%] **** | |
| Mortality | 68% [33‑85%] ** | 26% [15‑36%] **** | 18% [-46‑54%] |
| VentilationVent. | 29% [-15‑56%] | ||
| ICU admissionICU | 34% [8‑52%] * | ||
| HospitalizationHosp. | 2% [-235‑71%] | 22% [14‑29%] **** | -10% [-45‑16%] |
| Recovery | 7% [-28‑33%] | 23% [9‑35%] ** | 7% [-70‑49%] |
| Cases | -9% [-29‑8%] | ||
| RCT mortality | 8% [-4‑19%] | ||
| RCT hospitalizationRCT hosp. | 2% [-235‑71%] | 21% [11‑29%] **** |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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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.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. 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 14 shows a comparison of results for RCTs and non-RCT studies.
Figure 15, 16, 17, 18, and 19
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCTs after exclusions, RCT mortality results, RCT mortality results after exclusions, and RCT hospitalization results.
RCT results are included in Table 2 and Table 3.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. 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.
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Figure 16. Random effects meta-analysis for RCTs after exclusions.
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.
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Figure 17. Random effects meta-analysis for RCT mortality results.
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Figure 18. Random effects meta-analysis for RCT mortality results after exclusions.
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Figure 19. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases34, and
analysis of double-blind RCTs has identified extreme levels of bias35.
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 109 treatments we have analyzed,
65% 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.
RCTs have a bias against finding an
effect for interventions that are widely available — patients that
believe they need the intervention are more likely to decline participation
and take the intervention. RCTs for colchicine are more likely to
enroll low-risk participants that do not need treatment to recover, making the
results less applicable to clinical practice. This bias is likely to be
greater for widely known treatments, and may be greater when the risk of a
serious outcome is overstated. This bias does not apply to the typical
pharmaceutical trial of a new drug that is otherwise unavailable.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 109 treatments37.
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 109 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.92‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.82‑1.03].
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, 48 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, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 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 20 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Diaz, very late stage, oxygen saturation <90% at baseline; very late stage, >80% on oxygen/ventilation at baseline.
Eikelboom, very late stage, oxygen saturation <90% at baseline.
Jalal, minimal details provided.
Karakaş, excessive unadjusted differences between groups.
Mahale, unadjusted results with no group details.
Oztas, excessive unadjusted differences between groups.
Recovery Collaborative Group, very late stage, 9 days since symptoms started, 32% baseline ventilation.
Rodriguez-Nava, substantial unadjusted confounding by indication likely; excessive unadjusted differences between groups; unadjusted results with no group details.
Shah, very late stage, >50% on oxygen/ventilation at baseline.
Vaziri, randomization resulted in significant baseline differences that were not adjusted for.
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Figure 20. 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 hours53,54.
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 cases55 |
| <24 hours | -33 hours symptoms56 |
| 24-48 hours | -13 hours symptoms56 |
| Inpatients | -2.5 hours to improvement57 |
Figure 21 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 109 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 21. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 109 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 variants59, for
example the Gamma variant shows significantly different characteristics60-63. 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 variants64,65.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use
of other treatments may significantly affect outcomes, including supplements,
other medications, or other interventions such as prone positioning.
Treatments may be synergistic66-77, therefore
efficacy may depend strongly on combined treatments.
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 note that pooled effects are no longer
required for colchicine as of September 2020.
Efficacy is now known for colchicine 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.
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.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
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 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 109
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 22 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 23 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 24 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 = 0.00000042 to p = 0.00000002.
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Figure 22. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 23. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 22. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.1 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 25 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 25. 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, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results81-84.
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 26 shows a scatter plot of
results for prospective and retrospective studies.
54% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
41% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 29% improvement,
compared to 31% for prospective
studies, showing similar results.
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Figure 26. 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 27 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 < 0.0585-92.
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 27. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Colchicine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 colchicine trials have been run by
physicians on the front lines with the primary goal of finding the best
methods to save human lives and minimize the collateral damage caused by
COVID-19. While pharmaceutical companies are careful to run trials under
optimal conditions (for example, restricting patients to those most likely to
benefit, only including patients that can be treated soon after onset when
necessary, and ensuring accurate dosing), not all colchicine trials
represent the optimal conditions for efficacy.
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 alone66-77.
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 56
studies compare against other treatments, which may reduce the effect
seen.
8 of 56 studies
combine treatments. The results of
colchicine
alone may differ.
5 of 30 RCTs use combined treatment.
10 other meta analyses show significant improvements with colchicine for mortality1-8, oxygen therapy8, hospitalization9, and severity10.
Mitev et al. present a review covering colchicine for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors25-30,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk31, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 28 shows an overview of the results for colchicine
in the context of multiple COVID-19 treatments, and Figure 29 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 28.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 8,000+ proposed treatments show efficacy94.
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Figure 29. Efficacy vs. cost for COVID-19 treatments.
Colchicine is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, ICU admission, hospitalization, and recovery. 26 studies from 26 independent teams in 15 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
27% [18‑36%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids the immune system or recovery but may have limited antiviral effects. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 22 of 56
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT which
is not generalizable to earlier usage.
Absalón-Aguilar:
Very late stage RCT with 56 colchicine and 60 control patients in Mexico, showing no significant differences.
Alsultan:
Small RCT 49 severe condition hospitalized patients in Syria, showing lower mortality with colchicine and shorter hospitalization time with both colchicine and budesonide (all of these were not statistically significant).
Avanoglu Guler:
Retrospective 73 familial Mediterranean fever patients with COVID-19 in Turkey, showing significantly higher risk of hospitalization for respiratory support with non-adherence to colchicine treatment before the infection.
Brunetti:
PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.
Cecconi:
RCT 240 hospitalized patients with COVID-19 pneumonia, mean 9 days from the onset of symptoms, showing no significant differences with colchicine treatment. EudraCT 2020-001841-38.
Chevalier:
Retrospective 1,213 rheumatic disease patients in France, showing no significant difference with colchicine use in univariate analysis.
Correa-Rodríguez:
Retrospective 244 Behçet disease patients in Spain, showing no significant difference in outcomes with colchicine treatment. Confounding by indication may significantly affect results - colchicine may be prescribed more often for more serious cases, which may have a higher baseline risk for COVID-19.
Deftereos:
RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment.
Diaz:
Very late stage RCT (O2 88%, 84% on oxygen) with 1,279 hospitalized patients in Argentina, showing lower mortality and lower combined mortality/ventilation, statistically significant only for the combined outcome and per-protocol analysis. NCT04328480. COLCOVID.
Dorward:
Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.
Eikelboom (B):
Late (5.4 days) outpatient RCT showing no significant difference in outcomes with colchicine treatment. Authors include a meta analysis of 6 colchicine RCTs, however there were 19 RCTs as of the publication date105.
Eikelboom:
RCT very late stage (baseline SpO2 80%) patients, showing no significant differences with colchicine treatment.
Gaitán-Duarte:
RCT 633 hospitalized patients in Colombia, 153 treated with colchicine + rosuvastatin, not showing statistically significant differences in outcomes. Improved results were seen with the combination of emtricitabine/tenofovir disoproxil + rosuvastatin + colchicine. NCT04359095.
García-Posada:
Retrospective 209 hospitalized patients in Colombia, showing lower mortality with antibiotics + LMWH + corticosteroids + colchicine in multivariable analysis.
Gertner:
Open-label RCT 137 hospitalized COVID-19 patients, showing lower progression to ICU/step-down ICU and improved recovery with colchicine, both without statistical significance. The primary outcome was changed mid-trial due to the low number of patients progressing to severe disease.
Gorial:
RCT with 80 colchicine and 80 control patients, showing improved recovery with treatment. SOC included vitamin C, vitamin D, and zinc.
Hassan:
RCT 150 patients in Egypt showing no significant difference in outcomes with colchicine. SOC included vitamin C, D, and zinc. Colchicine 0.5mg tid days 1-3, bid days 4-7.
Hueda-Zavaleta:
Retrospective 200 patients with ARDS due to COVID-19 on invasive mechanical ventilation, showing no significant difference in mortality with colchicine treatment. The Cox proportional hazards result is from112.
Hueda-Zavaleta (B):
Retrospective 450 late stage (median oxygen saturation 86%) COVID+ hospitalized patients in Peru, showing lower mortality with colchicine treatment.
Hunt:
Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including colchicine. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment in some cases, and may be early or late treatment in other cases. Further reduction in mortality was seen with combinations of treatments.
Inokuchi:
RCT 38 low risk outpatients in Japan, showing no significant differences for colchicine and low-dose aspirin compared to loxoprofen. Hospitalization was lower, without statistical significance (4.3% vs. 13.3%, p=0.34). There were no critical cases, deaths, or severe adverse events in either group.
Colchicine: 1.0mg loading dose, followed approximately half a day later by 0.5mg twice daily for 10 doses, and then 0.5 mg once daily for four doses. Aspirin: 100mg daily for 10 days. Both groups received probiotics and acetaminophen.
Colchicine: 1.0mg loading dose, followed approximately half a day later by 0.5mg twice daily for 10 doses, and then 0.5 mg once daily for four doses. Aspirin: 100mg daily for 10 days. Both groups received probiotics and acetaminophen.
Jalal:
Open label RCT of colchicine showing improved recovery with treatment. Only the abstract is currently available. Colchicine 0.5mg bid for 14 days.
Karakaş:
Retrospective 356 hospitalized COVID-19 patients, shorter hospitalization time with colchicine treatment. There were no statistically significant differences for mortality or ICU admission. Significantly lower mortality was seen with higher dosage (1mg/day vs 0.5mg/day). More control patients were on oxygen at baseline (65% vs. 54%).
Kasiri:
Very late treatment (10 days from onset) RCT 110 patients in Iran, showing no significant difference in outcomes with colchicine. Colchicine 2mg loading dose followed by 0.5mg bid for 7 days.
Kevorkian:
Observational study in France with 28 hospitalized patients treated with prednisone/furosemide/colchicine/salicylate/direct anti-Xa inhibitor, and 40 control patients, showing lower combined mortality, ventilation, or high-flow oxygen therapy with treatment.
Landi:
Early terminated RCT with 14 colchicine, 13 edoxaban, 16 colchicine+edoxaban, and 16 control patients, showing no significant difference in outcomes with treatment up to 7 days after PCR diagnosis.
Lopes:
RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.
Madrid-García:
Retrospective 9,379 patients attending a rheumatology outpatient clinic in Spain, showing higher mortality and hospitalization with colchicine use, without statistical significance.
Mahale:
Retrospective 134 hospitalized COVID-19 patients in India, showing no significant difference with colchicine treatment in unadjusted results.
Manenti:
IPTW retrospective 141 COVID-19 patients (83% hospitalized), 71 treated with colchicine and 70 matched control patients, showing lower mortality and faster recovery with treatment.
Mareev:
Small trial with 21 colchicine patients and 22 control patients in Russia, showing improved recovery with treatment. The trial was originally an RCT, however randomization to the control arm was stopped after 5 patients, and 17 retrospective patients were added for comparison.
Mehrizi:
Retrospective study of 917,198 hospitalized COVID-19 cases covered by the Iran Health Insurance Organization over 26 months showing that antithrombotics, corticosteroids, and antivirals reduced mortality while diuretics, antibiotics, and antidiabetics increased it. Confounding makes some results very unreliable. For example, diuretics like furosemide are often used to treat fluid overload, which is more likely in ICU or advanced disease requiring aggressive fluid resuscitation. Hospitalization length has increased risk of significant confounding, for example longer hospitalization increases the chance of receiving a medication, and death may result in shorter hospitalization. Mortality results may be more reliable.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
Monserrat Villatoro:
PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate, and higher mortality with acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol.
Mostafaie:
RCT with 60 patients treated with colchicine and phenolic monoterpenes and 60 control patients in Iran, showing lower mortality with treatment. NCT04392141.
Ozcifci:
Prospective analysis of 1,047 Behçet’s syndrome patients in Turkey, showing no significant difference in cases with colchicine use.
Oztas:
Retrospective 635 HCQ users and 643 household contacts, showing higher risk with colchicine in unadjusted results.
Patients with conditions leading to the use of colchicine may have significantly different baseline risk, e.g.127.
Patients with conditions leading to the use of colchicine may have significantly different baseline risk, e.g.127.
Pascual-Figal:
RCT with 52 colchicine patients and 51 control patients, showing lower risk of clinical deterioration with treatment. COL-COVID. NCT04350320.
Perricone:
RCT 152 hospitalized patients in Italy, showing no significant difference in outcomes with colchicine treatment. Table 2 shows 13% of patients treated with antivirals in the colchicine arm, however 16.9% were treated with one specific antiviral (HCQ).
Pimenta Bonifácio:
Open label RCT late stage hospitalized patients in Brazil with 14 colchicine and 16 SOC patients, showing lower mortality and improved recovery with treatment, without statistical significance. Authors note that the colchicine group had one patient with SOFA ≥7 vs. zero for SOC, however both groups had one patient intubated and SOC had more patients not requiring high-flow oxygen (12 vs. 8).
The journal version of this paper falsely states: "Ixekizumab, colchicine, and IL-2 were demonstrated to be safe but ineffective".
The pre-print more accurately represents the improved but not statistically significant results:
"The colchicine arm presented the lowest mortality rate (0%), while the low dose IL-2 had the highest (21.4%) by day 28 post-enrollment. The frequency of adverse events was lowest in the colchicine group (7.3%). None of the differences observed was statistically significant. Interpretation: Colchicine added to SOC performed better than Ixekizumab, low-dose IL-2, or SOC alone for hospitalized patients with moderate to critical Covid-19 in this exploratory study. Larger studies are needed to confirm these findings."
The journal version of this paper falsely states: "Ixekizumab, colchicine, and IL-2 were demonstrated to be safe but ineffective".
The pre-print more accurately represents the improved but not statistically significant results:
"The colchicine arm presented the lowest mortality rate (0%), while the low dose IL-2 had the highest (21.4%) by day 28 post-enrollment. The frequency of adverse events was lowest in the colchicine group (7.3%). None of the differences observed was statistically significant. Interpretation: Colchicine added to SOC performed better than Ixekizumab, low-dose IL-2, or SOC alone for hospitalized patients with moderate to critical Covid-19 in this exploratory study. Larger studies are needed to confirm these findings."
Pinzón:
Retrospective 301 pneumonia patients in Colombia showing lower mortality with colchicine treatment.
Pourdowlat:
RCT 202 patients in Iran, 102 treated with colchicine, showing lower hospitalization and improved clinical outcomes with treatment.
Rahman:
RCT 300 patients in Bangladesh, published 2 years after completion, showing significantly lower mortality with treatment at 28 days (not significant at 14 days). 1.2mg colchicine on day 1 followed by 0.6mg for 13 days.
Recovery Collaborative Group:
RCT with 5,610 colchicine and 5,730 control patients showing mortality RR 1.01 [0.93-1.10]. Very late stage treatment, median 9 days after symptom onset, baseline 32% ventilation (5% invasive). ISRCTN 50189673.
Dose frequency was halved for patients receiving a moderate CYP3A4 inhibitor, patients with an estimated glomerular filtration rate of less than 30 mL/min per 1·73m², and those with an estimated bodyweight of less than 70kg.
Dose frequency was halved for patients receiving a moderate CYP3A4 inhibitor, patients with an estimated glomerular filtration rate of less than 30 mL/min per 1·73m², and those with an estimated bodyweight of less than 70kg.
Rodriguez-Nava:
Retrospective 313 patients, mostly critical stage and mostly requiring respiratory support. Confounding by indication likely.
Salehzadeh:
Open label RCT with 100 hospitalized patients in Iran, 50 treated with colchicine, showing shorter hospitalization time with treatment. There were no deaths.
Sandhu:
Prospective cohort study of hospitalized patients in the USA, 34 treated with colchicine, showing lower mortality and intubation with treatment.
Scarsi:
Retrospective 122 colchicine patients and 140 control patients in Italy, showing lower mortality with treatment.
Shah:
RCT 250 late stage (80% on oxygen) hospitalized patients in the USA, showing no significant differences with combined colchicine/rosuvastatin treatment.
There was a trend towards increased risk, which authors note may be due to chance because the patients enrolled in the treatment arm were in more serious condition, for example, patients in the treatment arm were more frequently on oxygen, more frequently on HFNC/NIV, and had higher mean SOFA scores.
Colchicine 0.6mg two times daily for 3 days followed by 0.6mg daily, and high-intensity rosuvastatin 40mg daily.
There was a trend towards increased risk, which authors note may be due to chance because the patients enrolled in the treatment arm were in more serious condition, for example, patients in the treatment arm were more frequently on oxygen, more frequently on HFNC/NIV, and had higher mean SOFA scores.
Colchicine 0.6mg two times daily for 3 days followed by 0.6mg daily, and high-intensity rosuvastatin 40mg daily.
Sunil Naik:
RCT 122 hospitalized patients in India, showing improved recovery with colchicine treatment. All patients received aspirin. There was one death and higher progression in the colchicine arm, however 3 patients in the colchicine arm had baseline ordinal scores ≥5, while no patients in the control arm did.
Sáenz-Aldea:
Retrospective 86,652 patients in Spain, showing no significant difference in cases and hospitalization with colchicine use. The different risk for patients prescribed colchicine may not be fully adjusted for. See139.
Tardif:
RCT for relatively low risk outpatients, 2235 treated with colchicine a mean of 5.3 days after the onset of symptoms, and 2253 controls, showing lower mortality, ventilation, and hospitalization with treatment.
This study was submitted to NEJM which delayed for ~6 months and then said they were not interested, then to JAMA which delayed for ~6 months and then said they were not interested, and then to the Lancet which delayed for ~6 months and then said they were not interested, and finally was published in Lancet Respiratory Medicine141.
This study was submitted to NEJM which delayed for ~6 months and then said they were not interested, then to JAMA which delayed for ~6 months and then said they were not interested, and then to the Lancet which delayed for ~6 months and then said they were not interested, and finally was published in Lancet Respiratory Medicine141.
Topless:
UK Biobank retrospective showing a higher risk of COVID-19 cases and mortality for patients with gout. Among patients with gout, mortality risk was lower for those on colchicine, OR 1.06 [0.60-1.89], compared to those without colchicine, OR 1.38 [1.08-1.76].
Valerio Pascua:
Retrospective 65 ICU patients in the USA and Honduras, showing shorter ICU stay with combined treatment including colchicine, LMWH, tocilizumab, dexamethasone, and methylprednisolone.
Vaziri:
RCT 179 hospitalized COVID-19 patients showing lower mortality, ICU admission, and hospitalization duration with colchicine plus phenolic monoterpenes compared to standard care alone. The intervention group received 0.8 mg/day colchicine and 45 mg/day phenolic monoterpenes extracted from nigella sativa and Trachyspermum ammi in addition to standard care (lopinavir/ritonavir). No serious side effects were reported. Baseline SpO2 was significantly lower in the control group, although there was no significant difference in severity according to NIH guidelines.
Villamañán:
Retrospective 111 hospitalized COVID-19 pneumonia patients treated with colchicine and 111 matched controls, showing lower mortality with colchicine treatment.
Yadollahzadeh:
Open-label RCT with 52 severe COVID-19 pneumonia patients showing no significant differences in mortality with colchicine. All patients received infliximab and remdesivir.
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 colchicine 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 colchicine 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.
This is a living analysis and is updated regularly.
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
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used 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 test status. 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.
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 to145.
Reported confidence intervals and p-values were used when available,
using adjusted values 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 1148.
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
(3.13.0) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta149
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 0.8.0 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 effective53,54.
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/ometa.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.
| Hassan, 6/13/2023, Randomized Controlled Trial, Egypt, preprint, 6 authors, study period July 2021 - August 2022. | risk of hospitalization, 40.0% higher, RR 1.40, p = 0.76, treatment 7 of 50 (14.0%), control 5 of 50 (10.0%). |
| risk of no recovery, 3.6% lower, RR 0.96, p = 1.00, treatment 27 of 50 (54.0%), control 28 of 50 (56.0%), NNT 50. | |
| Hunt, 6/29/2022, retrospective, USA, peer-reviewed, 8 authors, study period 1 March, 2020 - 10 September, 2020, dosage not specified. | risk of death, 68.0% lower, RR 0.32, p = 0.003, treatment 9 of 402 (2.2%), control 1,603 of 26,106 (6.1%), NNT 26, adjusted per study, day 30. |
| Inokuchi, 3/19/2024, Randomized Controlled Trial, Japan, peer-reviewed, 16 authors, study period 27 July, 2021 - 6 September, 2021, average treatment delay 1.8 days, this trial compares with another treatment - results may be better when compared to placebo, this trial uses multiple treatments in the treatment arm (combined with aspirin) - results of individual treatments may vary. | risk of hospitalization, 67.4% lower, RR 0.33, p = 0.55, treatment 1 of 23 (4.3%), control 2 of 15 (13.3%), NNT 11, day 28. |
| prolonged symptoms, 23.8% lower, RR 0.76, p = 0.72, treatment 8 of 21 (38.1%), control 6 of 12 (50.0%), NNT 8.4. | |
| days until ≤37°C, 17.0% higher, relative time 1.17, p = 0.60, treatment 21, control 12. |
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.
| Absalón-Aguilar, 11/9/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 18 authors, study period May 2020 - April 2021, dosage 1.5mg day 1, 1mg days 2-10. | risk of death, 28.6% lower, RR 0.71, p = 0.74, treatment 4 of 56 (7.1%), control 6 of 60 (10.0%), NNT 35. |
| progression to critical or death, 17.0% lower, OR 0.83, p = 0.67, treatment 56, control 60, primary outcome, RR approximated with OR. | |
| risk of no recovery, 13.0% higher, RR 1.13, p = 0.59, treatment 56, control 60, Kaplan–Meier. | |
| Alsultan, 12/31/2021, Randomized Controlled Trial, Syria, peer-reviewed, 11 authors, dosage 2mg day 1, 1mg days 2-5. | risk of death, 35.7% lower, RR 0.64, p = 0.70, treatment 3 of 14 (21.4%), control 7 of 21 (33.3%), NNT 8.4. |
| Brunetti, 9/14/2020, retrospective, propensity score matching, USA, peer-reviewed, baseline oxygen required 86.4%, 7 authors, dosage 1.2mg daily. | risk of death, 72.7% lower, RR 0.27, p = 0.03, treatment 3 of 33 (9.1%), control 11 of 33 (33.3%), NNT 4.1, PSM. |
| risk of no hospital discharge, 72.7% lower, RR 0.27, p = 0.03, treatment 3 of 33 (9.1%), control 11 of 33 (33.3%), NNT 4.1, PSM. | |
| Cecconi, 6/2/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, mean age 65.0, 31 authors, study period August 2020 - March 2021, average treatment delay 9.0 days, dosage 1mg day 1, 0.5mg days 2-5. | risk of death, 29.4% lower, RR 0.71, p = 0.62, treatment 7 of 119 (5.9%), control 10 of 120 (8.3%), NNT 41. |
| risk of mechanical ventilation, 49.6% lower, RR 0.50, p = 0.29, treatment 5 of 119 (4.2%), control 10 of 120 (8.3%), NNT 24. | |
| risk of ICU admission, 20.8% lower, RR 0.79, p = 0.67, treatment 11 of 119 (9.2%), control 14 of 120 (11.7%), NNT 41. | |
| combined NIV/ICU/ventilation/death, 15.3% lower, RR 0.85, p = 0.62, treatment 21 of 119 (17.6%), control 25 of 120 (20.8%), NNT 31, primary outcome. | |
| Deftereos, 6/24/2020, Randomized Controlled Trial, Greece, peer-reviewed, baseline oxygen required 62.9%, 49 authors, study period 3 April, 2020 - 27 April, 2020, dosage 2mg day 1, 1mg days 2-21, trial NCT04326790 (history) (GRECCO-19). | risk of death, 77.3% lower, RR 0.23, p = 0.19, treatment 1 of 55 (1.8%), control 4 of 50 (8.0%), NNT 16. |
| risk of mechanical ventilation, 81.8% lower, RR 0.18, p = 0.10, treatment 1 of 55 (1.8%), control 5 of 50 (10.0%), NNT 12. | |
| risk of clinical deterioration, 87.4% lower, RR 0.13, p = 0.046, treatment 1 of 55 (1.8%), control 7 of 50 (14.0%), NNT 8.2, odds ratio converted to relative risk. | |
| Diaz, 12/29/2021, Randomized Controlled Trial, Argentina, peer-reviewed, 101 authors, study period 17 April, 2020 - 28 March, 2021, dosage 2mg day 1, 1mg days 2-14, trial NCT04328480 (history), excluded in exclusion analyses: very late stage, oxygen saturation <90% at baseline; very late stage, >80% on oxygen/ventilation at baseline. | risk of death, 12.0% lower, HR 0.88, p = 0.30, treatment 131 of 640 (20.5%), control 142 of 639 (22.2%), NNT 57, adjusted per study, Cox proportional hazards, primary outcome. |
| risk of death/intubation, 17.0% lower, HR 0.83, p = 0.08, treatment 160 of 640 (25.0%), control 184 of 639 (28.8%), NNT 26, adjusted per study, Cox proportional hazards, primary outcome. | |
| risk of death/intubation, 52.0% lower, HR 0.48, p = 0.60, treatment 6 of 93 (6.5%), control 13 of 102 (12.7%), NNT 16, adjusted per study, subset not on supplemental oxygen, Cox proportional hazards. | |
| risk of death, 17.0% lower, HR 0.83, p = 0.30, treatment 98 of 515 (19.0%), control 140 of 634 (22.1%), NNT 33, adjusted per study, PP, Cox proportional hazards. | |
| risk of death/intubation, 25.0% lower, HR 0.75, p = 0.02, treatment 117 of 515 (22.7%), control 181 of 634 (28.5%), NNT 17, adjusted per study, PP, Cox proportional hazards. | |
| Dorward, 9/23/2021, Randomized Controlled Trial, United Kingdom, peer-reviewed, 21 authors, study period 4 March, 2021 - 26 May, 2021, average treatment delay 6.0 days, dosage 0.5mg days 1-14. | risk of death, 69.7% lower, RR 0.30, p = 0.43, treatment 0 of 156 (0.0%), control 1 of 120 (0.8%), NNT 120, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of death/hospitalization, 29.8% higher, RR 1.30, p = 0.66, treatment 6 of 156 (3.8%), control 4 of 133 (3.0%), odds ratio converted to relative risk, concurrent randomisation. | |
| risk of death/hospitalization, 22.1% lower, RR 0.78, p = 0.59, treatment 6 of 156 (3.8%), control 119 of 1,145 (10.4%), odds ratio converted to relative risk, including control patients before the colchicine arm started. | |
| risk of no recovery, 6.4% higher, HR 1.06, p = 0.67, treatment 156, control 133, inverted to make HR<1 favor treatment, time to alleviation of symptoms, concurrent randomisation. | |
| Eikelboom (B), 10/10/2022, Randomized Controlled Trial, Canada, peer-reviewed, mean age 45.0, 31 authors, study period 27 August, 2020 - 10 February, 2022, average treatment delay 5.4 days, dosage 1.2mg days 1-3, 0.6mg days 4-28, trial NCT04324463 (history) (ACT outpatient). | risk of death, 9.0% higher, HR 1.09, p = 0.84, treatment 12 of 1,939 (0.6%), control 11 of 1,942 (0.6%). |
| risk of death/hospitalization, 2.0% higher, HR 1.02, p = 0.93, treatment 66 of 1,939 (3.4%), control 65 of 1,942 (3.3%), primary outcome. | |
| risk of hospitalization, 2.0% higher, HR 1.02, p = 0.92, treatment 62 of 1,939 (3.2%), control 61 of 1,942 (3.1%). | |
| Eikelboom, 10/10/2022, Randomized Controlled Trial, multiple countries, peer-reviewed, mean age 56.0, 29 authors, study period 2 October, 2020 - 10 February, 2022, average treatment delay 7.0 days, dosage 1.8mg day 1, 1.2mg days 2-28, trial NCT04324463 (history) (ACT inpatient), excluded in exclusion analyses: very late stage, oxygen saturation <90% at baseline. | risk of death, 8.0% higher, HR 1.08, p = 0.38, treatment 264 of 1,304 (20.2%), control 249 of 1,307 (19.1%). |
| risk of progression, 4.0% higher, HR 1.04, p = 0.58, treatment 368 of 1,304 (28.2%), control 356 of 1,307 (27.2%), high-flow oxygen, ventilation, or death. | |
| risk of progression, 2.0% lower, HR 0.98, p = 0.84, treatment 246 of 1,304 (18.9%), control 252 of 1,307 (19.3%), NNT 241, high-flow oxygen or ventilation. | |
| Gaitán-Duarte, 7/10/2021, Randomized Controlled Trial, Colombia, peer-reviewed, 17 authors, study period 24 August, 2020 - 20 March, 2021, average treatment delay 10.0 days, dosage 0.5mg days 1-14, this trial uses multiple treatments in the treatment arm (combined with rosuvastatin) - results of individual treatments may vary, trial NCT04359095 (history). | risk of death, 22.0% lower, HR 0.78, p = 0.38, treatment 22 of 153 (14.4%), control 28 of 161 (17.4%), NNT 33, adjusted per study, Cox proportional hazards. |
| García-Posada, 3/6/2021, retrospective, Colombia, peer-reviewed, 8 authors, dosage not specified, this trial uses multiple treatments in the treatment arm (combined with antibiotics, LMWH, and corticosteroidsPERIOD:5/20-8/20) - results of individual treatments may vary. | risk of death, 56.9% lower, RR 0.43, p = 0.01, treatment 48 of 99 (48.5%), control 59 of 110 (53.6%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Gertner, 5/15/2024, Randomized Controlled Trial, USA, peer-reviewed, mean age 58.0, 8 authors, study period 25 January, 2021 - 29 November, 2021, trial NCT04756128 (history) (COLTREXONE). | ICU/stepdown, 65.0% lower, OR 0.35, p = 0.11, treatment 67, control 70, adjusted per study, multivariable, RR approximated with OR. |
| recovery, 43.2% lower, OR 0.57, p = 0.14, treatment 67, control 70, adjusted per study, inverted to make OR<1 favor treatment, multivariable, day 5, RR approximated with OR. | |
| day of recovery, 24.2% lower, HR 0.76, p = 0.12, treatment 67, control 70, adjusted per study, inverted to make HR<1 favor treatment, multivariable, Cox proportional hazards, primary outcome. | |
| HFNC/NIPPV, 34.0% lower, OR 0.66, p = 0.34, treatment 67, control 70, adjusted per study, multivariable, RR approximated with OR. | |
| hospitalization time, 20.0% lower, relative time 0.80, p = 0.13, treatment 67, control 70, adjusted per study, multivariable. | |
| Gorial, 4/12/2022, Randomized Controlled Trial, Iraq, peer-reviewed, 6 authors, dosage 1mg days 1-7, 0.5mg days 8-15. | risk of death, 66.7% lower, RR 0.33, p = 0.62, treatment 1 of 80 (1.2%), control 3 of 80 (3.8%), NNT 40. |
| risk of no recovery, 62.8% lower, HR 0.37, p < 0.001, treatment 80, control 80, inverted to make HR<1 favor treatment, Cox proportional hazards. | |
| Hueda-Zavaleta, 12/13/2022, retrospective, Peru, peer-reviewed, 9 authors, study period April 2020 - April 2021. | risk of death, 33.0% higher, HR 1.33, p = 0.33, treatment 18 of 52 (34.6%), control 33 of 148 (22.3%), Cox proportional hazards, Cox result from Chen et al. |
| Hueda-Zavaleta (B), 6/10/2021, retrospective, Peru, peer-reviewed, 6 authors, dosage not specified. | risk of death, 54.0% lower, HR 0.46, p = 0.03, treatment 10 of 50 (20.0%), control 109 of 301 (36.2%), NNT 6.2, adjusted per study, multivariable. |
| Jalal, 5/5/2022, Randomized Controlled Trial, Iraq, peer-reviewed, 3 authors, study period 8 May, 2021 - 18 June, 2021, trial NCT04867226 (history), excluded in exclusion analyses: minimal details provided. | hospitalization time, 24.1% lower, relative time 0.76, p = 0.009, treatment 36, control 44. |
| Karakaş, 1/31/2022, retrospective, Turkey, peer-reviewed, 11 authors, dosage 1mg daily, 0.5mg for 37 patients, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of death, 12.7% lower, RR 0.87, p = 0.72, treatment 16 of 165 (9.7%), control 19 of 171 (11.1%), NNT 71. |
| risk of ICU admission, 16.0% lower, RR 0.84, p = 0.50, treatment 30 of 165 (18.2%), control 37 of 171 (21.6%), NNT 29. | |
| hospitalization time, 25.0% lower, relative time 0.75, p < 0.001, treatment 165, control 171. | |
| Kasiri, 1/16/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 54.6, 6 authors, study period February 2021 - May 2021, average treatment delay 10.0 days, trial IRCT20190804044429N5. | risk of death, 7.3% lower, RR 0.93, p = 1.00, treatment 6 of 55 (10.9%), control 6 of 51 (11.8%), NNT 117. |
| risk of mechanical ventilation, 7.3% lower, RR 0.93, p = 1.00, treatment 6 of 55 (10.9%), control 6 of 51 (11.8%), NNT 117. | |
| risk of ICU admission, 23.6% higher, RR 1.24, p = 0.63, treatment 12 of 55 (21.8%), control 9 of 51 (17.6%). | |
| risk of no recovery, 27.9% lower, RR 0.72, p = 0.59, treatment 7 of 55 (12.7%), control 9 of 51 (17.6%), NNT 20, day 14. | |
| risk of no recovery, 11.7% lower, RR 0.88, p = 0.69, treatment 20 of 55 (36.4%), control 21 of 51 (41.2%), NNT 21, day 7. | |
| recovery time, 14.3% lower, relative time 0.86, p = 0.06, treatment 55, control 51. | |
| Kevorkian, 6/30/2021, retrospective, France, peer-reviewed, 11 authors, study period 9 January, 2020 - 30 November, 2020, this trial uses multiple treatments in the treatment arm (combined with prednisone, furosemide, salicylate, direct anti-Xa inhibitor) - results of individual treatments may vary. | risk of mortality, ventilation, or high-flow oxygen therapy, 95.7% lower, OR 0.04, p < 0.001, treatment 28, control 40, adjusted per study, multivariable, RR approximated with OR. |
| Landi, 5/28/2024, Randomized Controlled Trial, multiple countries, peer-reviewed, 11 authors, trial NCT04516941 (history) (CONVINCE). | risk of hospitalization, 4.0% higher, RR 1.04, p = 0.98, treatment 30, control 29. |
| risk of oxygen therapy, 5.0% higher, RR 1.05, p = 0.97, treatment 30, control 29. | |
| risk of analgesic need, 40.0% lower, RR 0.60, p = 0.97, treatment 30, control 29. | |
| risk of no viral clearance, 11.0% lower, RR 0.89, p = 0.77, treatment 30, control 29. | |
| Lopes, 8/12/2020, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, baseline oxygen required 93.0%, median age 54.5 (treatment) 55.0 (control), 34 authors, study period 11 April, 2020 - 30 August, 2020, average treatment delay 9.5 (treatment) 8.0 (control) days, dosage 1.5mg days 1-5, 1mg days 6-10. | risk of death, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 36 (0.0%), control 2 of 36 (5.6%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 50.0% lower, RR 0.50, p = 0.67, treatment 2 of 36 (5.6%), control 4 of 36 (11.1%), NNT 18. | |
| hospitalization time, 22.2% lower, relative time 0.78, p < 0.01, treatment 36, control 36. | |
| Mahale, 12/31/2020, retrospective, India, peer-reviewed, 22 authors, study period 22 March, 2020 - 21 May, 2020, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 7.2% higher, RR 1.07, p = 0.83, treatment 11 of 39 (28.2%), control 25 of 95 (26.3%). |
| Manenti, 3/24/2021, retrospective, Italy, peer-reviewed, 24 authors, study period 1 March, 2020 - 10 April, 2020, dosage 1mg days 1-21. | risk of death, 76.0% lower, HR 0.24, p = 0.005, treatment 71, control 70, adjusted per study, propensity score weighting. |
| risk of no recovery, 44.4% lower, RR 0.56, p = 0.048, treatment 71, control 70, adjusted per study, inverted to make RR<1 favor treatment, propensity score weighting. | |
| Mareev, 2/28/2021, retrospective, Russia, peer-reviewed, 21 authors, dosage 1mg days 1-3. | risk of death, 79.6% lower, RR 0.20, p = 0.49, treatment 0 of 21 (0.0%), control 2 of 22 (9.1%), NNT 11, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| ΔSHOCS-COVID, 50.0% lower, RR 0.50, p = 0.06, treatment 21, control 22, ΔSHOCS-COVID score, primary outcome. | |
| SHOCS-COVID, 71.4% lower, RR 0.29, p = 0.002, treatment 21, control 22, SHOCS-COVID score. | |
| NEWS-2, 66.7% lower, RR 0.33, p = 0.06, treatment 21, control 22, inverted to make RR<1 favor treatment, NEWS-2 score. | |
| hospitalization time, 25.7% lower, relative time 0.74, p = 0.08, treatment 21, control 22. | |
| Mehrizi, 12/18/2023, retrospective, Iran, peer-reviewed, 10 authors, study period 1 February, 2020 - 20 March, 2022. | risk of death, 13.0% higher, OR 1.13, p < 0.001, RR approximated with OR. |
| Mostafaie, 4/20/2021, Randomized Controlled Trial, Iran, preprint, 1 author, study period 1 April, 2020 - 1 November, 2020, dosage not specified, this trial uses multiple treatments in the treatment arm (combined with phenolic monoterpenes) - results of individual treatments may vary, trial NCT04392141 (history). | risk of death, 83.3% lower, RR 0.17, p = 0.11, treatment 1 of 60 (1.7%), control 6 of 60 (10.0%), NNT 12, primary outcome. |
| hospitalization time, 34.7% lower, relative time 0.65, p < 0.001, treatment 59, control 54. | |
| Pascual-Figal, 9/11/2021, Randomized Controlled Trial, Spain, peer-reviewed, 14 authors, study period 30 April, 2020 - 4 December, 2020, dosage 1.5mg day 1, 1mg days 2-8, 0.5mg days 9-36, trial NCT04350320 (history). | risk of death, 80.2% lower, RR 0.20, p = 0.24, treatment 0 of 52 (0.0%), control 2 of 51 (3.9%), NNT 26, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of mechanical ventilation, 80.2% lower, RR 0.20, p = 0.24, treatment 0 of 52 (0.0%), control 2 of 51 (3.9%), NNT 26, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of ICU admission, 51.0% lower, RR 0.49, p = 0.44, treatment 2 of 52 (3.8%), control 4 of 51 (7.8%), NNT 25. | |
| risk of 7-point scale, 87.5% lower, RR 0.13, p = 0.03, treatment 3 of 52 (5.8%), control 7 of 51 (13.7%), adjusted per study, odds ratio converted to relative risk, deterioration ≥1 point, multivariable, primary outcome. | |
| risk of 7-point scale, 80.2% lower, RR 0.20, p = 0.24, treatment 0 of 52 (0.0%), control 2 of 51 (3.9%), NNT 26, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), deterioration ≥2 points. | |
| hospitalization time, 14.6% higher, relative time 1.15, p = 0.34, treatment 52, control 51. | |
| Perricone, 10/31/2022, Randomized Controlled Trial, Italy, peer-reviewed, mean age 69.1, 40 authors, study period 18 April, 2020 - 12 May, 2021, dosage 1.5mg daily, 2mg daily for >100kg, trial NCT04375202 (history) (COLVID-19). | risk of death, 36.4% higher, RR 1.36, p = 0.77, treatment 7 of 77 (9.1%), control 5 of 75 (6.7%). |
| risk of progression, 7.1% higher, RR 1.07, p = 1.00, treatment 11 of 77 (14.3%), control 10 of 75 (13.3%), mechanical ventilation, ICU, or death, primary outcome. | |
| risk of mechanical ventilation, 29.9% higher, RR 1.30, p = 1.00, treatment 4 of 77 (5.2%), control 3 of 75 (4.0%). | |
| risk of ICU admission, 75.6% lower, RR 0.24, p = 0.21, treatment 1 of 77 (1.3%), control 4 of 75 (5.3%), NNT 25. | |
| hospitalization time, 4.1% lower, relative time 0.96, p = 0.69, treatment mean 14.1 (±10.4) n=77, control mean 14.7 (±8.1) n=75. | |
| Pimenta Bonifácio, 4/28/2022, Randomized Controlled Trial, Brazil, peer-reviewed, mean age 48.9, 18 authors, study period 5 January, 2021 - 30 July, 2021, dosage 1.5mg days 1-3, 1mg days 4-28, trial NCT04724629 (history) (STRUCK). | risk of death, 78.9% lower, RR 0.21, p = 0.49, treatment 0 of 14 (0.0%), control 2 of 16 (12.5%), NNT 8.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of no improvement, 84.9% lower, RR 0.15, p = 0.23, treatment 0 of 14 (0.0%), control 3 of 16 (18.8%), NNT 5.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| Pinzón, 10/23/2020, retrospective, Colombia, preprint, 9 authors, dosage 1mg days 1-14. | risk of death, 34.5% lower, RR 0.65, p = 0.18, treatment 14 of 145 (9.7%), control 23 of 156 (14.7%), NNT 20, odds ratio converted to relative risk. |
| Pourdowlat, 2/2/2022, Randomized Controlled Trial, Iran, peer-reviewed, 18 authors, study period 26 March, 2020 - 30 September, 2020. | risk of hospitalization, 72.8% lower, RR 0.27, p = 0.004, treatment 5 of 102 (4.9%), control 18 of 100 (18.0%), NNT 7.6. |
| relative improvement in dyspnea, 37.5% better, RR 0.62, p = 0.03, treatment 89, control 63, excluding 5 treatment and 37 control patients that needed hospitalization/other interventions. | |
| relative improvement in Ct score, 22.4% better, RR 0.78, p = 0.048, treatment 89, control 63, excluding 5 treatment and 37 control patients that needed hospitalization/other interventions. | |
| Rahman, 11/16/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Bangladesh, peer-reviewed, 14 authors, study period June 2020 - November 2020, dosage 1.2mg day 1, 0.6mg days 2-14, trial NCT04527562 (history). | risk of death, 71.0% lower, HR 0.29, p = 0.04, treatment 4 of 146 (2.7%), control 13 of 146 (8.9%), NNT 16, Cox proportional hazards, day 28. |
| risk of progression, 71.0% lower, HR 0.29, p = 0.04, treatment 4 of 146 (2.7%), control 13 of 146 (8.9%), NNT 16, 2 point deterioration, Cox proportional hazards, day 28. | |
| risk of death, 61.0% lower, HR 0.39, p = 0.26, treatment 2 of 146 (1.4%), control 5 of 146 (3.4%), NNT 49, Cox proportional hazards, day 14. | |
| risk of mechanical ventilation, 51.0% lower, HR 0.49, p = 0.41, treatment 2 of 146 (1.4%), control 4 of 146 (2.7%), NNT 73, Cox proportional hazards, day 14. | |
| risk of progression, 56.0% lower, HR 0.44, p = 0.17, treatment 4 of 146 (2.7%), control 9 of 146 (6.2%), NNT 29, 2 point deterioration, Cox proportional hazards, day 14, primary outcome. | |
| Recovery Collaborative Group, 5/18/2021, Randomized Controlled Trial, United Kingdom, peer-reviewed, 35 authors, study period 27 November, 2020 - 4 March, 2021, average treatment delay 9.0 days, dosage 1.5mg day 1, 1mg days 2-10, dose for days 2-10 halved for certain patients, trial NCT04381936 (history) (RECOVERY), excluded in exclusion analyses: very late stage, 9 days since symptoms started, 32% baseline ventilation. | risk of death, 1.0% higher, RR 1.01, p = 0.77, treatment 1,173 of 5,610 (20.9%), control 1,190 of 5,730 (20.8%). |
| risk of mechanical ventilation, 18.0% higher, RR 1.18, p = 0.06, treatment 259 of 3,815 (6.8%), control 228 of 3,962 (5.8%). | |
| risk of death/intubation, 2.0% higher, RR 1.02, p = 0.47, treatment 1,344 of 5,342 (25.2%), control 1,343 of 5,469 (24.6%). | |
| risk of no hospital discharge, 2.0% higher, RR 1.02, p = 0.44, treatment 1,709 of 5,610 (30.5%), control 1,698 of 5,730 (29.6%), inverted to make RR<1 favor treatment. | |
| Rodriguez-Nava, 11/5/2020, retrospective, USA, peer-reviewed, median age 68.0, 8 authors, dosage not specified, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; excessive unadjusted differences between groups; unadjusted results with no group details. | risk of death, 5.5% lower, RR 0.94, p = 0.87, treatment 16 of 52 (30.8%), control 85 of 261 (32.6%), NNT 56, unadjusted. |
| Salehzadeh, 9/21/2020, Randomized Controlled Trial, Iran, peer-reviewed, median age 56.0, 3 authors, study period 21 May, 2020 - 20 June, 2020, average treatment delay 6.28 (treatment) 8.12 (control) days, trial IRCT20200418047126N1. | hospitalization time, 22.7% lower, relative time 0.77, p = 0.001, treatment 50, control 50. |
| Sandhu, 10/27/2020, prospective, USA, peer-reviewed, 4 authors, dosage 1.2mg days 1-3, 0.6mg days 4-15. | risk of death, 41.7% lower, RR 0.58, p < 0.001, treatment 16 of 34 (47.1%), control 63 of 78 (80.8%), NNT 3.0. |
| risk of mechanical ventilation, 52.9% lower, RR 0.47, p < 0.001, treatment 16 of 34 (47.1%), control 68 of 68 (100.0%), NNT 1.9. | |
| risk of no hospital discharge, 41.7% lower, RR 0.58, p < 0.001, treatment 16 of 34 (47.1%), control 63 of 78 (80.8%), NNT 3.0. | |
| Scarsi, 9/14/2020, retrospective, Italy, peer-reviewed, 28 authors, dosage 1mg daily. | risk of death, 84.9% lower, HR 0.15, p < 0.001, treatment 122, control 140. |
| Shah, 2/24/2023, Randomized Controlled Trial, USA, peer-reviewed, median age 61.0, 23 authors, study period October 2020 - September 2021, this trial uses multiple treatments in the treatment arm (combined with rosuvastatin) - results of individual treatments may vary, trial NCT04472611 (history) (COLSTAT), excluded in exclusion analyses: very late stage, >50% on oxygen/ventilation at baseline. | risk of death, 75.0% higher, RR 1.75, p = 0.54, treatment 7 of 125 (5.6%), control 4 of 125 (3.2%), day 60. |
| risk of death, 100% higher, RR 2.00, p = 0.50, treatment 6 of 125 (4.8%), control 3 of 125 (2.4%), day 30. | |
| risk of mechanical ventilation, 200.0% higher, RR 3.00, p = 0.28, treatment 6 of 125 (4.8%), control 2 of 125 (1.6%). | |
| risk of severe case, 46.2% higher, RR 1.46, p = 0.34, treatment 19 of 125 (15.2%), control 13 of 125 (10.4%), day 60, primary outcome. | |
| risk of severe case, 72.7% higher, RR 1.73, p = 0.17, treatment 19 of 125 (15.2%), control 11 of 125 (8.8%), day 30, primary outcome. | |
| Sunil Naik, 1/21/2023, Randomized Controlled Trial, India, peer-reviewed, 3 authors, trial CTRI/2021/03/032060. | risk of death, 169.4% higher, RR 2.69, p = 1.00, treatment 1 of 62 (1.6%), control 0 of 43 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of progression, 108.1% higher, RR 2.08, p = 0.64, treatment 3 of 62 (4.8%), control 1 of 43 (2.3%). | |
| recovery, 7.3% lower, RR 0.93, p = 0.21, treatment 62, control 43, relative improvement in ordinal score. | |
| risk of no recovery, 15.0% lower, RR 0.85, p = 0.06, treatment 49 of 62 (79.0%), control 40 of 43 (93.0%), NNT 7.1, ordinal score ≤1. | |
| recovery, 24.3% lower, RR 0.76, p = 0.02, treatment 62, control 43, relative improvement in CT score. | |
| Tardif, 1/27/2021, Double Blind Randomized Controlled Trial, multiple countries, peer-reviewed, 44 authors, study period 23 March, 2020 - 21 January, 2021, average treatment delay 5.3 days, dosage 1mg days 1-3, 0.5mg days 4-30, trial NCT04322682 (history) (COLCORONA). | risk of death, 43.9% lower, RR 0.56, p = 0.30, treatment 5 of 2,235 (0.2%), control 9 of 2,253 (0.4%), NNT 569, odds ratio converted to relative risk. |
| risk of death/hospitalization, 20.0% lower, RR 0.80, p = 0.08, treatment 104 of 2,235 (4.7%), control 131 of 2,253 (5.8%), NNT 86, odds ratio converted to relative risk, primary outcome. | |
| risk of mechanical ventilation, 46.8% lower, RR 0.53, p = 0.09, treatment 11 of 2,235 (0.5%), control 21 of 2,253 (0.9%), NNT 227, odds ratio converted to relative risk. | |
| risk of hospitalization, 20.0% lower, RR 0.80, p = 0.09, treatment 101 of 2,235 (4.5%), control 128 of 2,253 (5.7%), NNT 86, odds ratio converted to relative risk. | |
| Valerio Pascua, 1/7/2021, retrospective, multiple countries, peer-reviewed, 19 authors, study period 10 June, 2020 - 6 August, 2020, average treatment delay 6.1 days, dosage 1.5mg day 1, 1mg days 2-5, varied by location, this trial uses multiple treatments in the treatment arm (combined with LMWH, tocilizumab, dexamethasone, methylprednisolone) - results of individual treatments may vary. | risk of death, 22.8% lower, RR 0.77, p = 0.60, treatment 5 of 35 (14.3%), control 12 of 30 (40.0%), NNT 3.9, adjusted per study, odds ratio converted to relative risk, multivariable. |
| ICU time, 39.9% lower, relative time 0.60, p = 0.03, treatment 35, control 30, adjusted per study, multivariable. | |
| Vaziri, 3/6/2024, Randomized Controlled Trial, Iran, peer-reviewed, mean age 54.2, 11 authors, study period April 2020 - December 2020, this trial uses multiple treatments in the treatment arm (combined with phenolic monoterpenes) - results of individual treatments may vary, trial NCT04392141 (history), excluded in exclusion analyses: randomization resulted in significant baseline differences that were not adjusted for. | risk of death, 81.2% lower, RR 0.19, p = 0.03, treatment 2 of 108 (1.9%), control 7 of 71 (9.9%), NNT 12, after 14 day followup. |
| risk of death, 89.0% lower, RR 0.11, p = 0.02, treatment 1 of 108 (0.9%), control 6 of 71 (8.5%), NNT 13, in hospital. | |
| risk of ICU admission, 86.9% lower, RR 0.13, p = 0.002, treatment 2 of 108 (1.9%), control 10 of 71 (14.1%), NNT 8.2. | |
| hospitalization time, 34.7% lower, relative time 0.65, p < 0.001, treatment mean 4.17 (±1.34) n=108, control mean 6.39 (±2.59) n=71. | |
| Villamañán, 3/23/2023, retrospective, Spain, peer-reviewed, median age 79.0, 10 authors, study period March 2020 - June 2020. | risk of death, 41.9% lower, RR 0.58, p = 0.03, treatment 19 of 111 (17.1%), control 32 of 111 (28.8%), NNT 8.5, odds ratio converted to relative risk. |
| Yadollahzadeh, 2/29/2024, Randomized Controlled Trial, Iran, peer-reviewed, 10 authors, trial IRCT20200325046854N2. | risk of death, 33.3% lower, RR 0.67, p = 0.54, treatment 6 of 26 (23.1%), control 9 of 26 (34.6%), NNT 8.7. |
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.
| Avanoglu Guler, 7/21/2022, retrospective, Turkey, peer-reviewed, median age 39.5, 14 authors. | risk of oxygen therapy, 78.8% lower, RR 0.21, p = 0.04, treatment 6 of 66 (9.1%), control 3 of 7 (42.9%), NNT 3.0, inverted to make RR<1 favor treatment, odds ratio converted to relative risk. |
| Chevalier, 3/22/2023, retrospective, France, peer-reviewed, mean age 70.3, 24 authors. | risk of death, 27.8% higher, RR 1.28, p = 0.54, treatment 5 of 21 (23.8%), control 111 of 569 (19.5%), odds ratio converted to relative risk. |
| risk of hospitalization, 7.6% lower, RR 0.92, p = 0.83, treatment 15 of 116 (12.9%), control 180 of 1,097 (16.4%), odds ratio converted to relative risk. | |
| Correa-Rodríguez, 9/19/2022, retrospective, Spain, peer-reviewed, mean age 44.0, 6 authors. | risk of oxygen therapy, 149.7% higher, RR 2.50, p = 1.00, treatment 1 of 163 (0.6%), control 0 of 81 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of hospitalization, 149.7% higher, RR 2.50, p = 1.00, treatment 1 of 163 (0.6%), control 0 of 81 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). | |
| risk of no recovery, 7.1% lower, RR 0.93, p = 1.00, treatment 13 of 24 (54.2%), control 7 of 12 (58.3%), NNT 24, full recovery at 6 months. | |
| risk of case, 0.6% lower, RR 0.99, p = 1.00, treatment 24 of 163 (14.7%), control 12 of 81 (14.8%), NNT 1100. | |
| Madrid-García, 1/31/2021, retrospective, Spain, peer-reviewed, 8 authors, study period 1 March, 2020 - 20 May, 2020. | risk of death, 37.1% higher, HR 1.37, p = 0.57. |
| risk of hospitalization, 137.0% higher, HR 2.37, p = 0.20, GBM. | |
| Monserrat Villatoro, 1/8/2022, retrospective, propensity score matching, Spain, peer-reviewed, 18 authors. | risk of death, 80.0% lower, OR 0.20, p = 0.02, RR approximated with OR. |
| Ozcifci, 11/25/2021, prospective, Turkey, peer-reviewed, 13 authors, study period 1 April, 2020 - 30 April, 2021. | risk of case, 4.0% lower, RR 0.96, p = 0.72, treatment 130 of 616 (21.1%), control 85 of 421 (20.2%), odds ratio converted to relative risk. |
| Oztas, 3/21/2022, retrospective, Turkey, peer-reviewed, 15 authors, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of hospitalization, 406.3% higher, RR 5.06, p = 0.12, treatment 5 of 635 (0.8%), control 1 of 643 (0.2%). |
| risk of symptomatic case, 72.7% higher, RR 1.73, p = 0.07, treatment 29 of 635 (4.6%), control 17 of 643 (2.6%). | |
| risk of case, 24.4% higher, RR 1.24, p = 0.35, treatment 43 of 635 (6.8%), control 35 of 643 (5.4%). | |
| Sáenz-Aldea, 1/13/2023, retrospective, Spain, peer-reviewed, 8 authors. | risk of hospitalization, 8.0% higher, OR 1.08, p = 0.68, treatment 36 of 3,060 (1.2%) cases, 459 of 56,785 (0.8%) controls, case control OR. |
| risk of case, 12.0% higher, OR 1.12, p = 0.68, treatment 140 of 29,817 (0.5%) cases, 459 of 56,875 (0.8%) controls, NNT 9.0, case control OR. | |
| Topless, 1/28/2022, retrospective, database analysis, United Kingdom, peer-reviewed, 6 authors, dosage not specified. | risk of death, 23.2% lower, OR 0.77, p = 0.12, relative odds for patients with gout, model 2, RR approximated with OR. |
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