Vitamin C for COVID-19: real-time meta analysis of 74 studies (72 treatment studies and 2 sufficiency studies)
Abstract
Statistically significant lower risk is seen for mortality, ICU admission, hospitalization, and recovery. 24 studies from 24 independent teams in 12 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
21% [14‑27%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
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 30 of 72
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
6 RCTs
with 1,420 patients have not reported results (up to 3 years late).
The European Food Safety Authority has found evidence for a causal relationship between the intake of vitamin C and optimal immune system function1,2.
Early cessation of high dose IV treatment may result in a detrimental rebound
effect3.
Ongoing treatment is more effective than
early cessation: 33% [22‑42%] vs.
16% [-31‑46%].
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.
The quality of non-prescription supplements can vary widely and the quantity
of the active ingredient may be significantly lower than stated4-6.
Vitamin C for COVID-19 — Highlights
Vitamin C reduces
risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for ICU admission, and low confidence for progression.
6th treatment shown effective with ≥3 clinical studies in
September 2020, now with p = 0.000000028 from 72 studies, and recognized in 12 countries.
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 vitamin C 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, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 5.6 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 injury13-23 and
cognitive deficits15,20, cardiovascular
complications24-26, 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,27-32, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk33, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Vitamin C has been identified by the European Food Safety Authority (EFSA) as having sufficient evidence for a causal relationship between intake and optimal immune system function1,2,34.
Vitamin C plays a key role in the immune system, supporting the production and function of leukocytes, or white blood cells, which defend against infection and disease, including the production of lymphocytes, which make antibodies, and enhancing phagocytosis, the process by which immune system cells ingest and destroy viruses and infected cells.
Vitamin C is an antioxidant, protecting cells from damage caused by free radicals.
Vitamin C inhibits SARS-CoV-2 3CLpro35,36, inhibits SARS-CoV-2 infection by reducing ACE2 levels in a dose-dependent manner37, and may limit COVID-19 induced cardiac damage by acting as an antioxidant and potentially reducing the reactive oxygen species (ROS) production induced by the spike protein that contributes to the activation of profibrotic pathways25.
Vitamin C reduces inflammation, oxidative stress, and NETosis, supporting immune function and vascular protection38.
Intracellular levels of vitamin C decline during COVID-19 hospitalization suggesting ongoing utilization and depletion of vitamin C39.
Threonic acid, a metabolite of vitamin C, is lower in mild and severe cases, consistent with increased need for and metabolization of vitamin C with moderate infection, but more limited ability to produce threonic acid in severe infection due to depletion or existing lower levels of vitamin C40.
Symptomatic COVID-19 is associated with a lower frequency of natural killer (NK) cells, and vitamin C has been shown to improve NK cell numbers and functioning41,42.
Studies have shown efficacy with vitamin C for the common cold43 and acute respiratory tract infections44.
We analyze all significant
controlled studies of
vitamin C
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.
Vitamin C inhibits SARS-CoV-2 3CLpro35,36, inhibits SARS-CoV-2 infection by reducing ACE2 levels in a dose-dependent manner37, and may limit COVID-19 induced cardiac damage by acting as an antioxidant and potentially reducing the reactive oxygen species (ROS) production induced by the spike protein that contributes to the activation of profibrotic pathways25.
An In Vivo animal study supports the efficacy of vitamin C37.
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 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17
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, high dose IV studies, high-dose IV RCTs with early cessation vs. ongoing treatment, sufficiency studies, peer reviewed studies, and non-symptomatic vs. symptomatic results.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 21% [14‑27%] **** | 72 | 88,913 | 777 |
| After exclusions | 26% [16‑34%] **** | 41 | 56,087 | 497 |
| Peer-reviewed studiesPeer-reviewed | 22% [15‑29%] **** | 65 | 85,362 | 696 |
| Randomized Controlled TrialsRCTs | 20% [9‑30%] ** | 21 | 4,605 | 288 |
| Mortality | 19% [10‑28%] *** | 43 | 48,673 | 491 |
| VentilationVent. | 9% [-12‑27%] | 10 | 12,076 | 166 |
| ICU admissionICU | 14% [2‑24%] * | 8 | 10,468 | 72 |
| HospitalizationHosp. | 19% [7‑30%] ** | 16 | 19,095 | 146 |
| Recovery | 29% [22‑35%] **** | 10 | 2,182 | 98 |
| Cases | 3% [-16‑19%] | 7 | 32,285 | 96 |
| Viral | -5% [-73‑36%] | 2 | 236 | 18 |
| RCT mortality | 14% [-2‑28%] | 14 | 4,038 | 227 |
| RCT hospitalizationRCT hosp. | 9% [-9‑24%] | 8 | 856 | 101 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 39% [20‑53%] *** | 20% [11‑28%] **** | 19% [6‑30%] ** |
| After exclusions | 30% [-9‑55%] | 24% [10‑36%] ** | 26% [11‑38%] ** |
| Peer-reviewed studiesPeer-reviewed | 39% [20‑53%] *** | 23% [12‑32%] *** | 19% [6‑30%] ** |
| Randomized Controlled TrialsRCTs | 30% [10‑46%] ** | 19% [6‑30%] ** | |
| Mortality | 40% [-105‑82%] | 17% [7‑25%] ** | 29% [13‑42%] ** |
| VentilationVent. | 8% [-15‑27%] | 25% [-62‑65%] | |
| ICU admissionICU | 13% [-1‑26%] | 15% [-69‑57%] | |
| HospitalizationHosp. | 40% [24‑53%] **** | 10% [-6‑24%] | 29% [18‑38%] **** |
| Recovery | 25% [10‑37%] ** | 29% [21‑36%] **** | |
| Cases | 3% [-16‑19%] | ||
| Viral | -5% [-73‑36%] | ||
| RCT mortality | -204% [-7189‑87%] | 15% [-2‑29%] | |
| RCT hospitalizationRCT hosp. | 31% [-298‑88%] | 9% [-10‑24%] |
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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 high dose IV 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.
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Figure 14. Random effects meta-analysis for high-dose IV RCTs with early cessation vs. ongoing treatment.
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 15. Random effects meta-analysis for sufficiency 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.
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Figure 16. 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.
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Figure 17. Random effects meta-analysis for non-symptomatic vs. symptomatic results.
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.
Figure 18 shows a comparison of results for RCTs and non-RCT studies.
Figure 19, 20, and 21
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 1 and Table 2.
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Figure 18. Results for RCTs and non-RCT studies.
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Figure 19. 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 20. Random effects meta-analysis for RCT mortality results.
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Figure 21. 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 biases56, and
analysis of double-blind RCTs has identified extreme levels of bias57.
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 vitamin C 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 treatments59.
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 see63,64.
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 22 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Abdulateef, unadjusted results with no group details.
Coskun, very late stage, ICU patients.
Darban, very late stage, ICU patients.
Elhadi, unadjusted results with no group details; very late stage, ICU patients.
Fowler, very late stage, ICU patients.
Gadhiya, substantial unadjusted confounding by indication likely.
Gavrielatou, very late stage, ICU patients.
Guldemir, unadjusted results with no group details.
Holt, significant unadjusted confounding possible.
Jang, very late stage, ECMO patients.
Krishnan, unadjusted results with no group details.
Kumar (B), very late stage, ICU patients.
Li, very late stage, ICU patients.
Majidi, very late stage, ICU patients.
Mohseni, unadjusted results with no group details.
Mulhem, substantial unadjusted confounding by indication likely; substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
Rahman, unadjusted results with no group details; significant unadjusted confounding possible.
Rana, very late stage, ICU patients.
Salehi, unadjusted results with no group details; very late stage, ICU patients.
Shehab, unadjusted results with no group details.
Suna, substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
Tu, unadjusted results with no group details.
Vishnuram, unadjusted results with no group details; minimal details of groups provided.
Yang (B), combined treatments may contribute significantly to the effect seen.
Yüksel, very late stage, ICU patients.
Zangeneh, very late stage, ICU patients.
Zhang, very late stage, ICU patients.
Zhao, substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
Zheng, substantial unadjusted confounding by indication likely; immortal time bias may significantly affect results; treatment start times unknown, treatment may not have started at baseline.
Özgülteki̇n, very late stage, ICU patients.
Özgünay, substantial unadjusted confounding by indication likely; very late stage, ICU patients.
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Figure 22. 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 hours102,103.
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 (C) et al. report only 2.5
hours improvement for inpatient treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases104 |
| <24 hours | -33 hours symptoms105 |
| 24-48 hours | -13 hours symptoms105 |
| Inpatients | -2.5 hours to improvement106 |
Figure 23 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 23. 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 variants108, for
example the Gamma variant shows significantly different characteristics109-112. 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 variants113,114.
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 synergistic115-126, 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 (B) et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Non-prescription supplements may show very wide variations in quality4,5.
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 vitamin C as of September 2020.
Efficacy is now known based on specific outcomes.
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 24 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 25 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 26 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 24. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 25. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 24. 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 27 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 27. 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.
Studies have also shown efficacy with vitamin C for the common cold43 and acute respiratory tract infections44.
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 results130-133.
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 28 shows a scatter plot of
results for prospective and retrospective treatment studies.
Prospective studies show 20% [8‑30%] improvement in meta
analysis, compared to 22% [13‑29%] for retrospective
studies, showing no significant difference.
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Figure 28. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Studies for vitamin C were primarily late treatment studies, in
contrast with typical patented treatments that were tested with early
treatment as recommended.
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Figure 29. Patented treatments received mostly early treatment studies, while low cost treatments were typically tested for late treatment.
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 30 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 < 0.05134-141.
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 30. 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. Vitamin C for COVID-19
lacks this because it is an inexpensive and widely available supplement.
In contrast, most COVID-19 vitamin C 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 vitamin C 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 alone115-126.
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 72
studies compare against other treatments, which may reduce the effect
seen.
7 of 72 studies
combine treatments. The results of
vitamin C
alone may differ.
5 of 21 RCTs use combined treatment.
6 other meta analyses show significant improvements with vitamin C for mortality7-10, progression11, severity7, and cases12.
NIH provides an analysis of vitamin C for
COVID-19154, concluding that there is insufficient evidence to
recommend for or against use. However, they appear not to have looked at the
majority of the evidence. For example, considering RCTs providing clinical
results for COVID-19 and vitamin C, they reference only97,155-157, and appear not to know about 17 other RCTs73,75,82,84,88,94,158-168
as shown in Figure 31.
Notably, the NIH selection does not correspond to the most relevant
and highest quality studies, for example including Zhang et al., with
very late treatment of ICU patients.
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Figure 31. Analysis by NIH is missing 17 RCTs.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors27-32,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk33, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 32 shows an overview of the results for vitamin C
in the context of multiple COVID-19 treatments, and Figure 33 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 32.
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 efficacy169.
Loading..
Loading..
Figure 33. Efficacy vs. cost for COVID-19 treatments.
Vitamin C is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, ICU admission, hospitalization, and recovery. 24 studies from 24 independent teams in 12 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
21% [14‑27%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
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.
2 sufficiency studies analyze outcomes based on serum levels,
showing 55% [-10‑81%] lower risk for patients with higher vitamin C levels.
Results are robust — in exclusion sensitivity analysis 30 of 72
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
The European Food Safety Authority has found evidence for a causal relationship between the intake of vitamin C and optimal immune system function1,2.
Early cessation of high dose IV treatment may result in a detrimental rebound
effect3.
Ongoing treatment is more effective than
early cessation: 33% [22‑42%] vs.
16% [-31‑46%].
Abdulateef:
Survey of 428 recovered COVID-19 patients in Iraq, showing fewer hospital visits for patients on prophylactic vitamin C or D. Hospitalization was lower for those on vitamin C, D, or zinc, without statistical significance.
Adhikari (B):
Very late stage (APACHE II 8 and 12 for non-critical and critical) RCT with publication delayed over a year showing higher ventilation and no significant difference in mortality with vitamin C.
Authors have combined what was to be two separate trials into one trial, however there are very large differences between the trials. The results for each source trial are shown separately here158,159.
eTable 15 shows that results in LOVIT-COVID were substantially better than those in REMAP-CAP. eTable 13 shows improved survival for LOVIT-COVID and worse survival for REMAP-CAP (authors provide mortality breakdown only for hospital survival):
LOVIT-COVID shows 85% and 82% probability of superiority of vitamin C (critical and non-critical).
REMAP-CAP shows 12% and 7% probability of superiority of vitamin C.
Notably, LOVIT-COVID patients were blinded, while REMAP-CAP was open-label, introducing additional opportunity for bias on this highly politicized treatment. REMAP-CAP had more patients and dominates the combined results.
eFigure 8b also shows that the REMAP-CAP results were initially positive, switching to negative around September 2021. Authors note that they were unable to explain this reversal. The overall negative result is only due to the larger number of patients in the REMAP-CAP later time period.
Results for intubation are much worse than mortality, with statistically significant higher intubation for the treatment group. Hypothetically, if the actual risk matched other trials (~20% lower risk in meta analysis of 18 RCTs at the time), and there was something causing biased intubation of treatment patients in this mostly open-label trial, we may get the observed results whereby intubation is significantly worse due to the bias, but this has a muted effect on mortality which may reflect the change in risk due to intubation combined with that due to treatment.
Results varied dramatically over time. For example, during 22 Jan ‐ 21 Apr 2021, the probability of superiority for vitamin C was 1.0 for critical and 0.97 for non-critical (eTable 17).
There were dramatic changes in randomization proportions and in overall clinical outcomes over time, leading to potential issues and inaccuracies in the attempted adjustment for confounding by time.
The very long delay between the end of the trial and publication also raises questions.
See also Hemilä et al. which shows that the poor results may be explained by a rebound effect due to the abrupt termination of treatment after 4 days.
NCT04401150 (LOVIT-COVID) and NCT02735707 (REMAP-CAP). 50mg/kg vitamin C administered intravenously over 30-60 minutes every 6 hours for 4 days.
Authors have combined what was to be two separate trials into one trial, however there are very large differences between the trials. The results for each source trial are shown separately here158,159.
eTable 15 shows that results in LOVIT-COVID were substantially better than those in REMAP-CAP. eTable 13 shows improved survival for LOVIT-COVID and worse survival for REMAP-CAP (authors provide mortality breakdown only for hospital survival):
LOVIT-COVID shows 85% and 82% probability of superiority of vitamin C (critical and non-critical).
REMAP-CAP shows 12% and 7% probability of superiority of vitamin C.
Notably, LOVIT-COVID patients were blinded, while REMAP-CAP was open-label, introducing additional opportunity for bias on this highly politicized treatment. REMAP-CAP had more patients and dominates the combined results.
eFigure 8b also shows that the REMAP-CAP results were initially positive, switching to negative around September 2021. Authors note that they were unable to explain this reversal. The overall negative result is only due to the larger number of patients in the REMAP-CAP later time period.
Results for intubation are much worse than mortality, with statistically significant higher intubation for the treatment group. Hypothetically, if the actual risk matched other trials (~20% lower risk in meta analysis of 18 RCTs at the time), and there was something causing biased intubation of treatment patients in this mostly open-label trial, we may get the observed results whereby intubation is significantly worse due to the bias, but this has a muted effect on mortality which may reflect the change in risk due to intubation combined with that due to treatment.
Results varied dramatically over time. For example, during 22 Jan ‐ 21 Apr 2021, the probability of superiority for vitamin C was 1.0 for critical and 0.97 for non-critical (eTable 17).
There were dramatic changes in randomization proportions and in overall clinical outcomes over time, leading to potential issues and inaccuracies in the attempted adjustment for confounding by time.
The very long delay between the end of the trial and publication also raises questions.
See also Hemilä et al. which shows that the poor results may be explained by a rebound effect due to the abrupt termination of treatment after 4 days.
NCT04401150 (LOVIT-COVID) and NCT02735707 (REMAP-CAP). 50mg/kg vitamin C administered intravenously over 30-60 minutes every 6 hours for 4 days.
Adhikari:
Very late stage (APACHE II 8 and 12 for non-critical and critical) RCT with publication delayed over a year showing higher ventilation and no significant difference in mortality with vitamin C.
Authors have combined what was to be two separate trials into one trial, however there are very large differences between the trials. The results for each source trial are shown separately here158,159.
eTable 15 shows that results in LOVIT-COVID were substantially better than those in REMAP-CAP. eTable 13 shows improved survival for LOVIT-COVID and worse survival for REMAP-CAP (authors provide mortality breakdown only for hospital survival):
LOVIT-COVID shows 85% and 82% probability of superiority of vitamin C (critical and non-critical).
REMAP-CAP shows 12% and 7% probability of superiority of vitamin C.
Notably, LOVIT-COVID patients were blinded, while REMAP-CAP was open-label, introducing additional opportunity for bias on this highly politicized treatment. REMAP-CAP had more patients and dominates the combined results.
eFigure 8b also shows that the REMAP-CAP results were initially positive, switching to negative around September 2021. Authors note that they were unable to explain this reversal. The overall negative result is only due to the larger number of patients in the REMAP-CAP later time period.
Results for intubation are much worse than mortality, with statistically significant higher intubation for the treatment group. Hypothetically, if the actual risk matched other trials (~20% lower risk in meta analysis of 18 RCTs at the time), and there was something causing biased intubation of treatment patients in this mostly open-label trial, we may get the observed results whereby intubation is significantly worse due to the bias, but this has a muted effect on mortality which may reflect the change in risk due to intubation combined with that due to treatment.
Results varied dramatically over time. For example, during 22 Jan ‐ 21 Apr 2021, the probability of superiority for vitamin C was 1.0 for critical and 0.97 for non-critical (eTable 17).
There were dramatic changes in randomization proportions and in overall clinical outcomes over time, leading to potential issues and inaccuracies in the attempted adjustment for confounding by time.
The very long delay between the end of the trial and publication also raises questions.
See also Hemilä et al. which shows that the poor results may be explained by a rebound effect due to the abrupt termination of treatment after 4 days.
NCT04401150 (LOVIT-COVID) and NCT02735707 (REMAP-CAP). 50mg/kg vitamin C administered intravenously over 30-60 minutes every 6 hours for 4 days.
Authors have combined what was to be two separate trials into one trial, however there are very large differences between the trials. The results for each source trial are shown separately here158,159.
eTable 15 shows that results in LOVIT-COVID were substantially better than those in REMAP-CAP. eTable 13 shows improved survival for LOVIT-COVID and worse survival for REMAP-CAP (authors provide mortality breakdown only for hospital survival):
LOVIT-COVID shows 85% and 82% probability of superiority of vitamin C (critical and non-critical).
REMAP-CAP shows 12% and 7% probability of superiority of vitamin C.
Notably, LOVIT-COVID patients were blinded, while REMAP-CAP was open-label, introducing additional opportunity for bias on this highly politicized treatment. REMAP-CAP had more patients and dominates the combined results.
eFigure 8b also shows that the REMAP-CAP results were initially positive, switching to negative around September 2021. Authors note that they were unable to explain this reversal. The overall negative result is only due to the larger number of patients in the REMAP-CAP later time period.
Results for intubation are much worse than mortality, with statistically significant higher intubation for the treatment group. Hypothetically, if the actual risk matched other trials (~20% lower risk in meta analysis of 18 RCTs at the time), and there was something causing biased intubation of treatment patients in this mostly open-label trial, we may get the observed results whereby intubation is significantly worse due to the bias, but this has a muted effect on mortality which may reflect the change in risk due to intubation combined with that due to treatment.
Results varied dramatically over time. For example, during 22 Jan ‐ 21 Apr 2021, the probability of superiority for vitamin C was 1.0 for critical and 0.97 for non-critical (eTable 17).
There were dramatic changes in randomization proportions and in overall clinical outcomes over time, leading to potential issues and inaccuracies in the attempted adjustment for confounding by time.
The very long delay between the end of the trial and publication also raises questions.
See also Hemilä et al. which shows that the poor results may be explained by a rebound effect due to the abrupt termination of treatment after 4 days.
NCT04401150 (LOVIT-COVID) and NCT02735707 (REMAP-CAP). 50mg/kg vitamin C administered intravenously over 30-60 minutes every 6 hours for 4 days.
Akbar:
Retrospective 10,000 adults in Qatar, showing lower risk of COVID-19 cases with vitamin C supplementation, without statistical significance. Authors do not analyze COVID-19 severity.
Al Sulaiman:
Retrospective 158 critically ill patients receiving vitamin C and propensity matched controls, showing mortality OR 0.77 [0.48-1.23], and statistically significantly lower thrombosis, OR 0.42 [0.18-0.94]. 1000mg of vitamin C was given daily.
Aldwihi:
Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
Asoudeh:
Retrospective 250 recovered COVID-19 patients, showing lower risk of severe cases with higher vitamin C intake.
Baguma:
Retrospective COVID+ hospitalized patients in Uganda, 385 patients receiving vitamin C treatment, showing higher mortality with treatment, without statistical significance.
Behera:
Retrospective matched case-control prophylaxis study for HCQ, ivermectin, and vitamin C with 372 healthcare workers, showing lower COVID-19 incidence for all treatments, with statistical significance reached for ivermectin.
HCQ OR 0.56, p = 0.29
Ivermectin OR 0.27, p < 0.001
Vitamin C OR 0.82, p = 0.58
HCQ OR 0.56, p = 0.29
Ivermectin OR 0.27, p < 0.001
Vitamin C OR 0.82, p = 0.58
Bejan:
Retrospective 9,748 COVID-19 patients in the USA showing lower risk of mortality, ventilation, and ICU admission with vitamin C prophylaxis, without statistical significance.
Boerenkamp:
Analysis of serum and intracellular vitamin C levels in hospitalized COVID-19 patients. Low vitamin C levels were common with 36% having serum levels <26 μmol/L and 15% <11 μmol/L.
Intracellular vitamin C levels in peripheral blood mononuclear cells (PBMCs) were low at admission and declined during hospitalization, suggesting ongoing utilization and depletion of vitamin C stores.
Critical patients had higher odds of low serum vitamin C levels. There was a weak negative correlation between serum vitamin C levels and severity, without statistical significance.
Intracellular vitamin C levels in peripheral blood mononuclear cells (PBMCs) were low at admission and declined during hospitalization, suggesting ongoing utilization and depletion of vitamin C stores.
Critical patients had higher odds of low serum vitamin C levels. There was a weak negative correlation between serum vitamin C levels and severity, without statistical significance.
Boukef:
150 patient vitamin C early treatment RCT with results not reported over 1.5 years after completion.
Coppock:
RCT with 66 very late stage (8 days from symptom onset) hospitalized patients, 44 treated with vitamin C and 22 control patients, showing no significant differences with treatment.
Corrao:
Prospective study of 146 hospitalized COVID-19 patients showing shorter hospitalization with high-dose intravenous vitamin C. 104 patients received 10g of vitamin C intravenously daily for 3 days and 42 patients received only standard care. Mortality was lower with treatment (8.7% vs 14.3%) without statistical significance. Treatment was associated with significantly shorter hospitalization in multivariable analysis (−4.95, p=0.041). No adverse events were reported in the vitamin C group.
Coskun:
Retrospective 78 ICU patients in Turkey, showing lower mortality with high-dose vitamin C treatment, without statistical significance. The SOFA score was significantly better with treatment at day 4.
Authors incorrectly state that "HDVC treatment did not reduce the short-term mortality...". Mortality was lower with treatment, although not statistically significant given the sample size.
6g of vitamin C daily in 4 equal doses every 6h, for a total of 96h.
Authors incorrectly state that "HDVC treatment did not reduce the short-term mortality...". Mortality was lower with treatment, although not statistically significant given the sample size.
6g of vitamin C daily in 4 equal doses every 6h, for a total of 96h.
Darban:
Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences.
Doocy:
Prospective study of 144 hospitalized COVID-19 patients in the DRC and South Sudan, showing lower mortality with vitamin C treatment.
Elhadi:
Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
Fogleman:
Early terminated low-risk patient RCT with 32 low-dose vitamin C, 32 melatonin, and 34 placebo patients, showing faster resolution of symptoms with melatonin in spline regression analysis, and no significant difference for vitamin C. All patients recovered with no serious outcomes reported. Baseline symptoms scores were higher in the melatonin and vitamin C arms (median 27 and 24 vs. 18 for placebo).
Fowler:
RCT 47 ICU patients showing no significant differences with vitamin C treatment.
Gadhiya:
Retrospective 283 patients in the USA showing higher mortality with all treatments (not statistically significant). Confounding by indication is likely. In the supplementary appendix, authors note that the treatments were usually given for patients that required oxygen therapy. Oxygen therapy and ICU admission (possibly, the paper includes ICU admission for model 2 in some places but not others) were the only variables indicating severity used in adjustments.
Galindo:
Estimated 160 patient vitamin C late treatment RCT with results not reported over 2 years after estimated completion.
Gao:
Retrospective 76 COVID-19 patients, 46 treated with intravenous high-dose vitamin C, showing lower mortality and improved oxygen requirements with treatment. Dosage was 6g intravenous infusion per 12hr on the first day, and 6g once for the following 4 days.
Gavrielatou:
Retrospective 113 consecutive mechanically ventilated COVID+ ICU patients in Greece, 10 receiving high dose IV vitamin C, showing lower mortality with treatment, without statistical significance (p=0.11).
The associated meta analysis includes only 11 studies, while there are currently 72 studies, 43 with mortality results. Authors only include critical patients, however not all studies with critical patients are included, for example84,95,101,162. The meta analysis also uses unadjusted results, while PSM, Cox proportional hazards, or KM results are reported by97,99,171,179. For97 authors use 28 day mortality, while the study reports longer term in-hospital mortality.
The associated meta analysis includes only 11 studies, while there are currently 72 studies, 43 with mortality results. Authors only include critical patients, however not all studies with critical patients are included, for example84,95,101,162. The meta analysis also uses unadjusted results, while PSM, Cox proportional hazards, or KM results are reported by97,99,171,179. For97 authors use 28 day mortality, while the study reports longer term in-hospital mortality.
Guan:
Retrospective 2,746 individuals in China showing significantly lower incidence of COVID-19 symptoms and fever with higher vitamin C intake, with a dose response relationship.
Guldemir:
Retrospective 477 COVID+ public transportation workers in Turkey, showing lower risk of hospitalization with vitamin C use in unadjusted results.
Hakamifard:
RCT with 38 patients treated with vitamin C and vitamin E, and 34 control patients, showing lower ICU admission with treatment, but not statistically significant.
Hamidi-Alamdari:
RCT 80 hospitalized patients with severe COVID-19, 40 treated with methylene blue + vitamin C + N-acetylcysteine, showing lower mortality, shorter hospitalization, and significantly improved SpO2 and respiratory distress with treatment.
He:
60 patient vitamin C late treatment RCT with results not reported over 3 years after completion.
Hess:
Retrospective 100 severe condition hospitalized patients in the USA, 25 treated with high dose IV vitamin C, showing lower mechanical ventilation and cardiac arrest, and increased length of survival with treatment. 3g IV vitamin C every 6h for 7 days.
Holt:
Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.
Izzo:
Long COVID trial comparing L-arginine + vitamin C with multivitamin treatment (vitamin B1, B2, B6, B12, nicotinamide, folic acid, pantothenic acid), showing significant improvement in symptoms with L-arginine + vitamin C treatment.
JamaliMoghadamSiahkali:
Small late stage RCT for the addition of vitamin C to HCQ and lopinavir/ritonavir, with 30 treatment and 30 control patients, finding a significant reduction in temperature and a significant improvement in oxygenation after 3 days in the vitamin C group. However, hospitalization time was longer and there was no significant difference in mortality.
Jang:
Retrospective 19 COVID-19 ECMO patients in South Korea, showing a higher rate of weaning from ECMO with vitamin C treatment, without statistical significance. Authors perform multivariate analysis but do not provide full results, only reporting p > 0.05.
Krishnan:
Retrospective 152 mechanically ventilated patients in the USA showing unadjusted lower mortality with vitamin C, vitamin D, HCQ, and zinc treatment, statistically significant only for vitamin C.
Kumar (B):
RCT 60 ICU patients in India, showing no significant difference in outcomes with vitamin C. Mortality was lower in the vitamin C arm despite having more severe cases at baseline (87% vs. 67%). 1 gram intravenous vitamin C 8 hourly for four days.
Kumari:
RCT 150 hospitalized patients in Pakistan showing 26% faster recovery, p < 0.0001. 36% lower mortality, not statistically significant due to the small number of events. Dosage was 50 mg/kg/day of intravenous vitamin C.
Kyagambiddwa:
Retrospective 246 severe COVID-19 patients in Uganda, showing lower mortality with vitamin C treatment, without statistical significance (p = 0.06).
Labbani-Motlagh:
RCT 74 patients in Iran, showing no significant differences in outcomes with high dose vitamin C treatment. Tables 1b and 2a show conflicting baseline SOFA scores. The percentages of patients receiving antiviral treatments and corticosteroids are switched between the text and Table 1b. Authors indicate ICU admission was an outcome, but the result is not provided. AKI was lower with treatment, though not reaching statistical significance.
Lamontagne:
392 patient vitamin C late treatment RCT with results not reported over 1.5 years after completion. The companion non-COVID trial NCT03680274 has reported results.
Li:
PSM retrospective 8 ICU patients treated with vitamin C and 24 matched controls, showing no significant difference. Authors note that "it is possible for the delayed timing of IV vitamin C to have blunted the beneficial effects as these patients may have already progressed to the late fibroproliferative phase or ARDS". IV vitamin C 1.5 grams every 6 hours.
Liu:
Estimated 608 patient vitamin C late treatment RCT with results not reported over 1 year after estimated completion.
Louca:
Survey analysis of dietary supplements showing no significant difference in PCR+ cases with vitamin C usage in the UK, however significant reductions were found in the US and Sweden. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
Loucera:
Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, azithromycin, aspirin, vitamin D, vitamin C, and budesonide. Since only hospitalized patients are included, results do not reflect different probabilities of hospitalization across treatments.
Madamombe:
Retrospective 672 COVID-19 patients in Zimbabwe, showing lower mortality with vitamin C treatment.
Mahto:
Retrospective 689 healthcare workers in India, showing no significant difference in IgG positivity with vitamin C prophylaxis.
Majidi:
RCT 100 ICU patients in Iran, 31 treated with vitamin C, showing lower mortality with treatment.
Mohseni:
Retrospective 603 patients in Iran, 34 taking vitamin C supplements, showing increased risk of COVID-19 cases in unadjusted results. IR.SHOUSHTAR.REC.1399.015.
Mousaviasl:
RCT 401 hospitalized COVID-19 patients showing no significant differences with low-dose oral vitamin C (1000mg daily for 5 days).
Mulhem:
Retrospective database analysis of 3,219 hospitalized patients in the USA. Very different results in the time period analysis (Table S2), and results significantly different to other studies for the same medications (e.g., heparin OR 3.06 [2.44-3.83]) suggest significant confounding by indication and confounding by time.
Nimer:
Retrospective 2,148 COVID-19 recovered patients in Jordan, showing lower risk of severity and hospitalization with vitamin C prophylaxis, without statistical significance.
Patel:
Retrospective 176 hospitalized patients, 96 treated with oral vitamin C (from 500mg to 1500mg daily), showing lower mortality with treatment.
Pourhoseingholi:
Prospective study of 2,468 hospitalized COVID-19 patients in Iran, showing no significant difference with vitamin C treatment. IR.MUQ.REC.1399.013.
Rahman:
Retrospective 416 non-hospitalized and 184 hospitalized COVID-19 patients in Bangladesh, showing higher acetaminophen and lower vitamin C usage for hospitalized patients. Confounding may be significant and baseline details per treatment group are not provided, however fever and symptomatic patients were more common in the non-hospitalized group. Note there is an alignment mismatch in Table 1.
Rana:
RCT 278 COVID-19 ICU patients in Pakistan, showing lower mortality and ventilation, and shorter length of stay with high dose vitamin C treatment, without statistical significance. 30 grams IV vitamin C for four days.
Ried:
RCT 237 patients in Turkey, 162 treated with IV vitamin C in addition to HCQ/AZ/zinc/vitamin D used for all patients, showing significantly faster recovery with the addition of IV vitamin C.
97% of patients were vitamin D deficient, and lower vitamin D levels were associated with ICU admission and longer hospital stay.
Only 1 of 237 hospitalized patients died (average age 63, range 22-99) - a 70-year-old patient with heart and lung disease and severely deficient vitamin D levels (6 nmol/L). IV vitamin C (sodium ascorbate) was given as 50 mg/kg every six hours on day 1, followed by 100 mg/kg every six hours (four times daily, 400 mg/kg/day) for seven days. NCT04395768.
97% of patients were vitamin D deficient, and lower vitamin D levels were associated with ICU admission and longer hospital stay.
Only 1 of 237 hospitalized patients died (average age 63, range 22-99) - a 70-year-old patient with heart and lung disease and severely deficient vitamin D levels (6 nmol/L). IV vitamin C (sodium ascorbate) was given as 50 mg/kg every six hours on day 1, followed by 100 mg/kg every six hours (four times daily, 400 mg/kg/day) for seven days. NCT04395768.
Salehi:
Retrospective 125 mechanically ventilated ICU patients in Iran, showing no significant difference with vitamin C treatment in unadjusted results.
Seely:
Early terminated low-risk population (no hospitalization) very late treatment (mean 8 days) RCT with 44 patients treated with vitamin C, D, K, and zinc, and 46 control patients, showing no significant differences.
Authors acknowledge that the very late treatment is a major limitation, noting that in an ideal setting, "patients would begin taking therapeutic interventions immediately after noticing symptoms". Authors note that patients already had a low symptom burden at baseline and that "it is likely that the majority of the participants had almost fully recovered before starting treatment."
Authors note that most participants were young, had few comorbidities and had excellent self-rated health at baseline, leaving less room for improvement.
There was low compliance with completing surveys. Data from only 64% of patients was in the main analysis.
Authors claim "high internal validity", but the loss of data was statistically significantly different between arms, without analysis or mention. Since the study involves widely available treatments, one possibility is that patients in the control arm who feel sick may be more likely to independently take the treatments (via supplementation or food/sun exposure), believing that they are in the control arm or that additional dosing is safe, and they may then feel it's inappropriate to continue submitting the surveys.
Discussion is biased, stating that "evidence for the use of these products in people with COVID-19 is limited", however there were 219 controlled studies at the time, including 8, 27, and 16 RCTs for vitamin C, D, and zinc. Authors claim high similarity between arms however there was 60% vs. 41% male patients, and 88% vs. 68% of patients that received a third dose.
Authors claim that treatment "showed no beneficial effects for overall health or symptom burden". However 48% lower ER visits is beneficial, and most outcomes show a benefit. The only statistically significant effect was the loss of data, however significant clinical effects are not expected based on the small sample, very late treatment, event rates, and outcomes.
Authors acknowledge that the very late treatment is a major limitation, noting that in an ideal setting, "patients would begin taking therapeutic interventions immediately after noticing symptoms". Authors note that patients already had a low symptom burden at baseline and that "it is likely that the majority of the participants had almost fully recovered before starting treatment."
Authors note that most participants were young, had few comorbidities and had excellent self-rated health at baseline, leaving less room for improvement.
There was low compliance with completing surveys. Data from only 64% of patients was in the main analysis.
Authors claim "high internal validity", but the loss of data was statistically significantly different between arms, without analysis or mention. Since the study involves widely available treatments, one possibility is that patients in the control arm who feel sick may be more likely to independently take the treatments (via supplementation or food/sun exposure), believing that they are in the control arm or that additional dosing is safe, and they may then feel it's inappropriate to continue submitting the surveys.
Discussion is biased, stating that "evidence for the use of these products in people with COVID-19 is limited", however there were 219 controlled studies at the time, including 8, 27, and 16 RCTs for vitamin C, D, and zinc. Authors claim high similarity between arms however there was 60% vs. 41% male patients, and 88% vs. 68% of patients that received a third dose.
Authors claim that treatment "showed no beneficial effects for overall health or symptom burden". However 48% lower ER visits is beneficial, and most outcomes show a benefit. The only statistically significant effect was the loss of data, however significant clinical effects are not expected based on the small sample, very late treatment, event rates, and outcomes.
Sharif:
Retrospective 962 COVID-19 patients in Bangladesh, showing significantly lower severity with vitamin C, vitamin D, and zinc supplementation, and improved results from the combination of all three.
Sharmin:
Estimated 50 patient vitamin C late treatment RCT with results not reported over 3 years after estimated completion.
Shehab:
Retrospective survey-based analysis of 349 COVID-19 patients, showing no significant difference with vitamin C prophylaxis in unadjusted analysis. REC/UG/2020/03.
Simsek:
Retrospective 139 hospitalized patients in Turkey, 58 treated with high dose vitamin C, showing improved kidney functioning with treatment. Mortality was lower with treatment, but not reaching statistical significance with the small sample size.
Sinnberg:
Analysis of 74 COVID-19 patients and 8 controls in Germany, showing low vitamin C levels associated with mortality. There was no significant difference for vitamin A, D, or E levels. Very few group details are provided, for example the age of patients in the control group and each severity group is not provided.
Su:
Retrospective 616 patients in China showing increased risk of disease progression with vitamin C treatment.
Suna:
Retrospective 323 hospitalized patients, 153 treated with vitamin C, showing no significant differences. Patients in each group were in different time periods, with the vitamin C group first. Time based confounding is possible due to improvements in SOC.
Tan:
PSM retrospective 207 hospitalized patients in China, 46 treated with diammonium glycyrrhizinate and vitamin C, showing lower risk of ARDS with treatment.
Tehrani:
RCT 54 late stage patients, 18 treated with IV vitamin C (2g every 6h for 5 days), showing significant relative improvements in oxygen saturation and respiratory rate.
Thomas:
Small 214 low-risk outpatient RCT showing non-statistically significant faster recovery with zinc and with vitamin C. A secondary analysis concludes that vitamin C increases recovery rate by 71% (p = 0.036)196. See also197.
Tu:
Retrospective 180 hospitalized COVID-19 patients in Sierra Leone, showing lower mortality with vitamin C treatment in unadjusted results.
Usanma Koban:
Retrospective 126 patients in Turkey, showing no significant difference in PCR+ at day 14 with vitamin C treatment.
Vaisi:
Analysis of nutrient intake and COVID-19 outcomes for 3,996 people in Iran, showing lower risk of COVID-19 hospitalization with sufficient vitamin A, vitamin C, and selenium intake, with statistical significance for vitamin A and selenium.
Vishnuram:
Retrospective 8,634 hospitalized patients in India, showing lower mortality with high-dose vitamin C in unadjusted results. No group details are provided, the text and table appear to show different results, and some numbers do not match.
Yang (B):
Prospective study of 60 patients in China with three arms: SOC, SOC+TCM, and SOC+TCM+high dose vitamin C, showing successively faster recovery with the addition of TCM and the addition of high dose vitamin C. TCM included inhaled vitamin C 10g, 3-7 times per day. IV vitamin C 10g/60kg twice a day, and oral vitamin C 3g three times a day. Group C vs. group A includes combined treatment with TCM, while group C vs. group B both include vitamin C (high vs. low dose).
Yüksel:
PSM retrospective 86 ICU patients on mechanical ventilation in Turkey, showing lower mortality with high dose vitamin C treatment (≥200mg/kg for 4 days).
Zangeneh:
Retrospective 193 ICU patients in Iran, showing no significant difference with vitamin C treatment.
Zhang:
Small RCT for high dose vitamin C for ICU patients showing reduced (but not statistically significant) mortality. Dosage was 12g of vitamin C/50ml every 12 hours for 7 days at a rate of 12ml/hour.
Zhao:
PSM retrospective 110 patients, 55 treated with high-dose IV vitamin C, showing lower progression to severe disease with treatment. Patients in each group were in different time periods, time based confounding is likely due to SOC improving over time. ChiCTR2000033050.
Zheng:
Retrospective 397 severe COVID-19 patients in China, showing worse outcomes with vitamin C treatment, without statistical significance. IV vitamin C 2-4g/day. Subject to confounding by indication and immortal time bias. Exclusion criteria were (a) the duration of hospitalization was less than 3 days; (b) vitamin C treatment started before admission; and (c) the length of vitamin C use was less than 3 days. Includes vitamin C use started at any time during hospitalization, for many patients this was >15 days later (Figure A2). Duration of treatment varied widely (Figure A1). Treatment was determined by clinicians according to the condition of each patient.
Özgülteki̇n:
Retrospective 43 ICU patients in Turkey, 21 treated with vitamin C, showing no significant difference in mortality and increased renal failure. Treatment included stage 1 AKI patients. Vitamin C 45-50 g/day for 5 days.
Özgünay:
Retrospective 160 ICU patients, 32 with raised neutrophil/lymphocyte ratio treated with vitamin C, showing no significant differences.
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 "vitamin C", "ascorbic acid" 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 vitamin C 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 to200.
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 1203.
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 PythonMeta204
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 effective102,103.
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/cmeta.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.
| Boukef, 2/28/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Tunisia, trial NCT05670444 (history). | 150 patient RCT with results unknown and over 1.5 years late. |
| Madamombe, 3/21/2023, retrospective, Zimbabwe, peer-reviewed, 9 authors, study period April 2020 - April 2022, dosage not specified. | risk of death, 53.0% lower, OR 0.47, p < 0.001, adjusted per study, multivariable, RR approximated with OR. |
| Rahman, 11/8/2023, retrospective, Bangladesh, peer-reviewed, 5 authors, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details; significant unadjusted confounding possible. | risk of hospitalization, 40.5% lower, RR 0.60, p < 0.001, treatment 128 of 476 (26.9%), control 56 of 124 (45.2%), NNT 5.5. |
| Ried, 11/25/2021, Randomized Controlled Trial, Turkey, peer-reviewed, 3 authors, study period January 2021 - June 2021, average treatment delay 4.0 days, dosage 50mg/kg qid day 1, 100mg/kg qid days 2-7, trial ACTRN12620000557932. | risk of no recovery, 30.6% lower, RR 0.69, p = 0.008, treatment 69 of 162 (42.6%), control 46 of 75 (61.3%), NNT 5.3, mid-recovery, day 15. |
| Su, 12/23/2020, retrospective, China, peer-reviewed, 9 authors, study period 20 January, 2020 - 30 April, 2020, dosage 10000mg days 1-3, 5-15g per day for at least 3 days. | risk of progression, 135.3% higher, HR 2.35, p = 0.18, adjusted per study, binary logistic regression. |
| improvement time, 34.2% worse, relative time 1.34, p = 0.04, adjusted per study, inverted to make RR<1 favor treatment, Cox proportional hazards. | |
| Thomas, 2/12/2021, Randomized Controlled Trial, USA, peer-reviewed, 11 authors, study period 8 April, 2020 - 11 February, 2021, dosage 8000mg days 1-10, trial NCT04342728 (history) (COVIDAtoZ). | risk of death, 204.2% higher, RR 3.04, p = 0.49, treatment 1 of 48 (2.1%), control 0 of 50 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of hospitalization, 30.6% lower, RR 0.69, p = 1.00, treatment 2 of 48 (4.2%), control 3 of 50 (6.0%), NNT 55. | |
| recovery time, 17.9% lower, relative time 0.82, p = 0.15, treatment mean 5.5 (±3.7) n=48, control mean 6.7 (±4.4) n=50, mean time to a 50% reduction in symptoms, primary outcome. | |
| Usanma Koban, 6/7/2022, retrospective, Turkey, peer-reviewed, 3 authors, study period 1 March, 2020 - 30 September, 2020, dosage not specified. | risk of no viral clearance, 33.0% lower, OR 0.67, p = 0.73, treatment 31, control 95, adjusted per study, multivariable, day 14, RR approximated with OR. |
| Zhao, 4/22/2021, retrospective, propensity score matching, China, peer-reviewed, 15 authors, average treatment delay 4.0 days, dosage 100mg/kg days 1-7, excluded in exclusion analyses: substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically. | risk of progression, 72.0% lower, RR 0.28, p = 0.03, treatment 4 of 55 (7.3%), control 12 of 55 (21.8%), NNT 6.9, adjusted per study, PSM. |
| time to viral-, 7.7% higher, relative time 1.08, p = 0.79, treatment 55, control 55, PSM. |
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.
| Adhikari (B), 10/25/2023, Double Blind Randomized Controlled Trial, multiple countries, peer-reviewed, 82 authors, dosage 50mg/kg qid days 1-4, trial NCT04401150 (history) (LOVIT-COVID). | risk of death, 27.8% lower, HR 0.72, p = 0.19, treatment 190, control 194, combined. |
| risk of death, 27.5% lower, HR 0.72, p = 0.34, treatment 84, control 97, inverted to make HR<1 favor treatment, LOVIT-COVID critical. | |
| risk of death, 28.1% lower, HR 0.72, p = 0.37, treatment 106, control 97, inverted to make HR<1 favor treatment, LOVIT-COVID non-critical. | |
| Adhikari, 10/25/2023, Randomized Controlled Trial, multiple countries, peer-reviewed, 82 authors, dosage 50mg/kg qid days 1-4, trial NCT04401150 (history) (REMAP-CAP). | risk of death, 19.5% higher, HR 1.19, p = 0.08, treatment 1,303, control 903, combined. |
| risk of death, 16.3% higher, HR 1.16, p = 0.22, treatment 953, control 434, inverted to make HR<1 favor treatment, REMAP-CAP critical. | |
| risk of death, 26.6% higher, HR 1.27, p = 0.19, treatment 350, control 469, inverted to make HR<1 favor treatment, REMAP-CAP non-critical. | |
| risk of mechanical ventilation, 35.1% higher, HR 1.35, p = 0.04, treatment 1,032, control 528, inverted to make HR<1 favor treatment, combined trials, critical. | |
| risk of mechanical ventilation, 69.5% higher, HR 1.69, p = 0.008, treatment 454, control 563, inverted to make HR<1 favor treatment, combined trials, non-critical. | |
| Al Sulaiman, 4/2/2021, retrospective, propensity score matching, Saudi Arabia, preprint, 12 authors, dosage 1000mg days 1-11. | risk of death, 14.9% lower, RR 0.85, p = 0.27, treatment 46 of 142 (32.4%), control 59 of 142 (41.5%), NNT 11, odds ratio converted to relative risk, PSM. |
| Baguma, 12/28/2021, retrospective, Uganda, preprint, 16 authors, study period March 2020 - October 2021, dosage not specified. | risk of death, 48.5% higher, RR 1.48, p = 0.54, treatment 385, control 96, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable, control prevalance approximated with overall prevalence. |
| Coppock, 3/19/2022, Randomized Controlled Trial, USA, peer-reviewed, 14 authors, dosage 300mg/kg day 1, 600mg/kg day 2, 900mg/kg days 3-6. | risk of progression, 5.0% lower, HR 0.95, p = 0.64, treatment 4 of 44 (9.1%), control 2 of 22 (9.1%), adjusted per study, within 36 hours. |
| risk of no improvement, 49.7% better, RR 0.50, p = 0.16, treatment 6 of 44 (13.6%), control 6 of 22 (27.3%), NNT 7.3, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, within 36 hours. | |
| risk of no hospital discharge, 22.5% lower, RR 0.78, p = 0.07, treatment 31 of 44 (70.5%), control 20 of 22 (90.9%), NNT 4.9, within 36 hours. | |
| Corrao, 7/8/2024, prospective, Italy, peer-reviewed, 7 authors, trial NCT04323514 (history). | risk of death, 39.4% lower, RR 0.61, p = 0.37, treatment 9 of 104 (8.7%), control 6 of 42 (14.3%), NNT 18. |
| risk of death/ICU, 19.0% lower, OR 0.81, p = 0.24, treatment 104, control 42, adjusted per study, multivariable, RR approximated with OR. | |
| risk of ICU admission, 101.9% higher, RR 2.02, p = 0.51, treatment 10 of 104 (9.6%), control 2 of 42 (4.8%). | |
| hospitalization time, 25.0% lower, relative time 0.75, p = 0.16, treatment 104, control 42. | |
| Coskun, 3/21/2023, retrospective, Turkey, peer-reviewed, 1 author, study period March 2020 - June 2020, trial NCT04710329 (history), excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 25.4% lower, RR 0.75, p = 0.26, treatment 17 of 38 (44.7%), control 24 of 40 (60.0%), NNT 6.6. |
| risk of mechanical ventilation, 1.8% lower, RR 0.98, p = 1.00, treatment 28 of 38 (73.7%), control 30 of 40 (75.0%), NNT 76. | |
| relative SOFA score, 28.4% better, RR 0.72, p = 0.005, treatment 38, control 40, mean SOFA score, day 4. | |
| Darban, 12/15/2020, Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, study period 7 April, 2020 - 8 June, 2020, dosage 2000mg qid days 1-10, this trial uses multiple treatments in the treatment arm (combined with melatonin and zinc) - results of individual treatments may vary, trial IRCT20151228025732N52, excluded in exclusion analyses: very late stage, ICU patients. | risk of progression, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 10 (20.0%), control 3 of 10 (30.0%), NNT 10. |
| ICU time, 6.0% lower, relative time 0.94, p = 0.30, treatment 10, control 10. | |
| Doocy, 10/19/2022, prospective, multiple countries, peer-reviewed, 6 authors, study period December 2020 - June 2021, dosage not specified, trial NCT04568499 (history). | risk of death, 62.8% lower, RR 0.37, p = 0.22, treatment 2 of 64 (3.1%), control 22 of 80 (27.5%), NNT 4.1, adjusted per study, inverted to make RR<1 favor treatment, multivariable. |
| Elhadi, 4/30/2021, prospective, Libya, peer-reviewed, 21 authors, study period 29 May, 2020 - 30 December, 2020, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details; very late stage, ICU patients. | risk of death, 12.0% higher, RR 1.12, p = 0.15, treatment 175 of 277 (63.2%), control 106 of 188 (56.4%). |
| Fogleman, 7/27/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 52.0, 7 authors, study period 5 October, 2020 - 21 June, 2021, average treatment delay 6.0 days, dosage 1000mg days 1-14, trial NCT04530539 (history). | relative recovery, 4.4% better, RR 0.96, p = 0.83, treatment mean 17.59 (±13.1) n=32, control mean 16.82 (±15.7) n=34, mid-recovery, relative symptom improvement, day 9. |
| Fowler, 4/4/2024, Double Blind Randomized Controlled Trial, placebo-controlled, USA, preprint, 1 author, trial NCT04344184 (history) (SAFE EVICT CORONA-ALI), excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 18.8% lower, RR 0.81, p = 0.75, treatment 5 of 22 (22.7%), control 7 of 25 (28.0%), NNT 19. |
| relative WHO status, 1.7% worse, RR 1.02, p = 0.28, treatment mean 3.05 (±0.22) n=21, control mean 3.0 (±0.0) n=23, day 27. | |
| Gadhiya, 4/8/2021, retrospective, USA, peer-reviewed, 4 authors, dosage not specified, excluded in exclusion analyses: substantial unadjusted confounding by indication likely. | risk of death, 0.7% higher, RR 1.01, p = 0.98, treatment 19 of 55 (34.5%), control 36 of 226 (15.9%), adjusted per study, odds ratio converted to relative risk, multivariate logistic regression. | Galindo, 5/15/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Colombia, trial NCT05029037 (history). | Estimated 160 patient RCT with results unknown and over 2 years late. |
| Gao, 2/26/2021, retrospective, China, peer-reviewed, 14 authors, dosage 12000mg day 1, 6,000mg days 2-5. | risk of death, 86.0% lower, HR 0.14, p = 0.04, treatment 1 of 46 (2.2%), control 5 of 30 (16.7%), NNT 6.9, adjusted per study, KM. |
| Gavrielatou, 2/11/2022, retrospective, Greece, peer-reviewed, 10 authors, study period 21 October, 2020 - 8 March, 2021, average treatment delay 5.5 days, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 58.0% lower, RR 0.42, p = 0.11, treatment 2 of 10 (20.0%), control 49 of 103 (47.6%), NNT 3.6. |
| Hakamifard, 4/14/2021, Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, study period March 2020 - April 2020, dosage 1000mg daily, this trial uses multiple treatments in the treatment arm (combined with vitamin E) - results of individual treatments may vary. | risk of ICU admission, 46.3% lower, RR 0.54, p = 0.46, treatment 3 of 38 (7.9%), control 5 of 34 (14.7%), NNT 15. |
| hospitalization time, 1.0% lower, relative time 0.99, p = 0.82, treatment 38, control 34. | |
| Hamidi-Alamdari, 3/8/2021, Randomized Controlled Trial, Iran, peer-reviewed, 23 authors, study period 19 April, 2020 - 21 September, 2020, this trial uses multiple treatments in the treatment arm (combined with methylene blue and N-acetyl cysteine) - results of individual treatments may vary, trial NCT04370288 (history). | risk of death, 44.4% lower, RR 0.56, p = 0.38, treatment 5 of 40 (12.5%), control 9 of 40 (22.5%), NNT 10.0. |
| hospitalization time, 37.6% lower, relative time 0.62, p = 0.004, treatment 40, control 40. | He, 1/31/2021, Single Blind Randomized Controlled Trial, China, trial NCT04664010 (history). | 60 patient RCT with results unknown and over 3 years late. |
| Hess, 3/29/2022, retrospective, USA, peer-reviewed, 9 authors, study period March 2020 - July 2020. | risk of death, 20.0% lower, HR 0.80, p = 0.54, treatment 10 of 25 (40.0%), control 37 of 75 (49.3%), NNT 11, time to event analysis, propensity score weighting. |
| risk of mechanical ventilation, 39.5% lower, RR 0.60, p = 0.05, treatment 18 of 25 (72.0%), control 54 of 75 (72.0%), odds ratio converted to relative risk, propensity score weighting. | |
| risk of mechanical ventilation, 50.0% lower, HR 0.50, p = 0.03, treatment 18 of 25 (72.0%), control 54 of 75 (72.0%), time to event analysis, propensity score weighting. | |
| risk of ICU admission, 27.2% lower, RR 0.73, p = 0.10, treatment 22 of 25 (88.0%), control 63 of 75 (84.0%), odds ratio converted to relative risk, propensity score weighting. | |
| risk of ICU admission, 30.0% lower, HR 0.70, p = 0.19, treatment 22 of 25 (88.0%), control 63 of 75 (84.0%), time to event analysis, propensity score weighting. | |
| Izzo, 7/19/2022, prospective, Italy, peer-reviewed, 21 authors, 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 L-arginine) - results of individual treatments may vary, LINCOLN trial. | relative recovery, 41.4% better, RR 0.59, p < 0.001, treatment mean 8.15 (±1.3) n=869, control mean 13.9 (±2.3) n=521, relative symptom score. |
| relative recovery, 67.5% better, RR 0.33, p < 0.001, treatment 869, control 521, relative Borg score. | |
| JamaliMoghadamSiahkali, 1/9/2021, Randomized Controlled Trial, Iran, preprint, 17 authors, study period April 2020 - May 2020, dosage 1500mg qid days 1-5. | risk of death, no change, RR 1.00, p = 1.00, treatment 3 of 30 (10.0%), control 3 of 30 (10.0%). |
| risk of mechanical ventilation, 25.0% higher, RR 1.25, p = 1.00, treatment 5 of 30 (16.7%), control 4 of 30 (13.3%). | |
| hospitalization time, 30.8% higher, relative time 1.31, p = 0.03, treatment 30, control 30. | |
| Jang, 12/16/2020, retrospective, South Korea, peer-reviewed, median age 63.0, 10 authors, study period February 2020 - April 2020, dosage not specified, excluded in exclusion analyses: very late stage, ECMO patients. | risk of no recovery, 51.4% lower, RR 0.49, p = 0.15, treatment 5 of 12 (41.7%), control 6 of 7 (85.7%), NNT 2.3, weaning from ECMO. |
| Krishnan, 7/20/2020, retrospective, USA, peer-reviewed, 13 authors, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 30.7% lower, RR 0.69, p = 0.04, treatment 40 of 79 (50.6%), control 52 of 73 (71.2%), NNT 4.9, odds ratio converted to relative risk. |
| Kumar (B), 8/30/2022, Double Blind Randomized Controlled Trial, placebo-controlled, India, peer-reviewed, mean age 57.0, 11 authors, average treatment delay 7.5 days, dosage 1000mg tid days 1-4, trial CTRI/2020/11/029230, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 23.1% lower, RR 0.77, p = 0.60, treatment 10 of 30 (33.3%), control 13 of 30 (43.3%), NNT 10.0. |
| risk of mechanical ventilation, 21.4% lower, RR 0.79, p = 0.60, treatment 11 of 30 (36.7%), control 14 of 30 (46.7%), NNT 10.0. | |
| Kumari, 11/30/2020, Randomized Controlled Trial, Pakistan, peer-reviewed, 10 authors, study period March 2020 - July 2020, dosage 50mg/kg daily. | risk of death, 36.4% lower, RR 0.64, p = 0.45, treatment 7 of 75 (9.3%), control 11 of 75 (14.7%), NNT 19. |
| risk of mechanical ventilation, 20.0% lower, RR 0.80, p = 0.67, treatment 12 of 75 (16.0%), control 15 of 75 (20.0%), NNT 25. | |
| recovery time, 26.0% lower, relative time 0.74, p < 0.001, treatment 75, control 75, days to symptom-free. | |
| hospitalization time, 24.3% lower, relative time 0.76, p < 0.001, treatment 75, control 75, days spent in hospital. | |
| Kyagambiddwa, 5/11/2023, retrospective, Uganda, peer-reviewed, mean age 39.0, 15 authors, study period May 2020 - August 2022, dosage not specified. | risk of death, 50.0% lower, HR 0.50, p = 0.06, adjusted per study, multivariable, Cox proportional hazards. |
| Labbani-Motlagh, 12/14/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 12 authors, study period 5 April, 2020 - 19 November, 2020, dosage 12000mg days 1-4, trial IRCT20190917044805N2. | risk of death, 33.3% lower, RR 0.67, p = 0.74, treatment 4 of 37 (10.8%), control 6 of 37 (16.2%), NNT 18, day 28. |
| hospitalization time, 12.8% higher, relative time 1.13, p = 0.49, treatment mean 9.24 (±7.5) n=37, control mean 8.19 (±5.34) n=37. | |
| risk of progression, 15.9% lower, RR 0.84, p = 0.12, treatment 37, control 37, SOFA, day 5. | |
| risk of progression, 9.3% higher, RR 1.09, p = 0.47, treatment 37, control 37, NEWS, day 5. | |
| risk of progression, 5.8% higher, RR 1.06, p = 0.38, treatment 37, control 37, WHO, day 5. | |
| risk of progression, 60.0% lower, RR 0.40, p = 0.14, treatment 4 of 37 (10.8%), control 10 of 37 (27.0%), NNT 6.2, AKI. | Lamontagne, 12/6/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Canada, trial NCT04401150 (history) (LOVIT-COVID). | 392 patient RCT with results unknown and over 1.5 years late. |
| Li, 6/8/2021, retrospective, propensity score matching, USA, peer-reviewed, 6 authors, excluded in exclusion analyses: very late stage, ICU patients; very late stage, ICU patients. | risk of death, 10.5% higher, RR 1.11, p = 1.00, treatment 7 of 8 (87.5%), control 19 of 24 (79.2%), PSM. | Liu, 6/1/2023, Single Blind Randomized Controlled Trial, China, trial NCT05694975 (history) (CEMVISCC). | Estimated 608 patient RCT with results unknown and over 1 year late. |
| Majidi, 12/15/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 16 authors, study period May 2020 - July 2020, dosage 500mg days 1-14, trial IRCT20151226025699N5, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 13.6% lower, RR 0.86, p = 0.03, treatment 26 of 31 (83.9%), control 67 of 69 (97.1%), NNT 7.6, day 28. |
| Mousaviasl, 7/22/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 13 authors, study period November 2020 - May 2021, dosage 500mg bid days 1-5. | risk of death, 20.4% lower, RR 0.80, p = 0.64, treatment 8 of 201 (4.0%), control 10 of 200 (5.0%), NNT 98, day 28. |
| risk of death, 99.0% higher, RR 1.99, p = 1.00, treatment 2 of 201 (1.0%), control 1 of 200 (0.5%), ICU mortality. | |
| risk of mechanical ventilation, 199.5% higher, RR 3.00, p = 1.00, treatment 1 of 201 (0.5%), control 0 of 200 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). | |
| risk of ICU admission, 32.7% higher, RR 1.33, p = 0.79, treatment 8 of 201 (4.0%), control 6 of 200 (3.0%). | |
| Mulhem, 4/7/2021, retrospective, database analysis, USA, peer-reviewed, 3 authors, dosage not specified, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically. | risk of death, 32.2% higher, RR 1.32, p = 0.01, treatment 157 of 794 (19.8%), control 359 of 2,425 (14.8%), adjusted per study, odds ratio converted to relative risk, logistic regression. |
| Patel, 10/1/2020, retrospective, USA, peer-reviewed, 8 authors, dosage 1000mg days 1-4, 500mg to 1500mg daily. | risk of death, 29.5% lower, RR 0.71, p = 0.18, treatment 22 of 96 (22.9%), control 26 of 80 (32.5%), NNT 10. |
| risk of death, 15.6% lower, RR 0.84, p = 0.60, treatment 15 of 30 (50.0%), control 16 of 27 (59.3%), NNT 11, ICU patients. | |
| Pourhoseingholi, 5/26/2021, prospective, Iran, preprint, mean age 57.9, 11 authors, study period 2 February, 2020 - 20 July, 2020, average treatment delay 7.4 days, dosage not specified. | risk of death, 13.0% lower, HR 0.87, p = 0.38, treatment 54 of 199 (27.1%), control 285 of 2,269 (12.6%), adjusted per study, multivariable, Cox proportional hazards. |
| Rana, 6/28/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Pakistan, peer-reviewed, 10 authors, study period 28 December, 2020 - 10 April, 2022, dosage 30000mg days 1-4, trial NCT04682574 (history), excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 54.5% lower, RR 0.45, p = 0.20, treatment 5 of 139 (3.6%), control 11 of 139 (7.9%), NNT 23. |
| risk of mechanical ventilation, 44.4% lower, RR 0.56, p = 0.41, treatment 5 of 139 (3.6%), control 9 of 139 (6.5%), NNT 35. | |
| hospitalization time, 36.8% lower, relative time 0.63, p = 0.91, treatment 139, control 139. | |
| Salehi, 3/11/2022, retrospective, Iran, preprint, mean age 62.0, 11 authors, study period April 2021 - September 2021, excluded in exclusion analyses: unadjusted results with no group details; very late stage, ICU patients. | risk of death, 10.1% lower, RR 0.90, p = 0.56, treatment 22 of 40 (55.0%), control 52 of 85 (61.2%), NNT 16. |
| Seely, 9/22/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Canada, peer-reviewed, mean age 39.9, 10 authors, study period September 2021 - April 2022, this trial uses multiple treatments in the treatment arm (combined with vitamin C, D, K2, and zinc) - results of individual treatments may vary, trial NCT04780061 (history). | ER visit, 47.6% lower, RR 0.52, p = 0.68, treatment 2 of 42 (4.8%), control 4 of 44 (9.1%), NNT 23. |
| relative mean cumulative symptom score, 13.8% better, RR 0.86, p = 0.41, treatment mean 166.3 (±92.3) n=34, control mean 192.9 (±153.6) n=24. | |
| EQ-VAS average score <80, 29.4% lower, RR 0.71, p = 0.54, treatment 7 of 34 (20.6%), control 7 of 24 (29.2%), NNT 12, average daily EQ-VAS score <80. | |
| relative EQ5D improvement, 28.6% better, RR 0.71, p = 0.44, treatment 32, control 31, relative improvement in EQ5D, week 1. | |
| relative EQ5D improvement, 14.3% better, RR 0.86, p = 0.73, treatment 33, control 30, relative improvement in EQ5D, week 2. | |
| relative EQ5D improvement, 50.0% better, RR 0.50, p = 0.17, treatment 32, control 33, relative improvement in EQ5D, week 3. | |
| relative EQ5D improvement, 12.5% worse, RR 1.12, p = 0.47, treatment 30, control 25, relative improvement in EQ5D, week 4. | |
| recovery time, 4.0% higher, relative time 1.04, p = 0.81, treatment 34, control 24. | |
| risk of PASC, 12.1% lower, RR 0.88, p = 1.00, treatment 3 of 33 (9.1%), control 3 of 29 (10.3%), NNT 80, 12 weeks. | |
| risk of PASC, 35.7% lower, RR 0.64, p = 0.69, treatment 3 of 35 (8.6%), control 4 of 30 (13.3%), NNT 21, 8 weeks. | |
| risk of PASC, 0.6% lower, RR 0.99, p = 1.00, treatment 6 of 35 (17.1%), control 5 of 29 (17.2%), NNT 1015, 4 weeks. | Sharmin, 9/1/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Bangladesh, trial NCT04558424 (history). | Estimated 50 patient RCT with results unknown and over 3 years late. |
| Simsek, 9/27/2021, retrospective, Turkey, peer-reviewed, 16 authors, dosage 25000mg days 1-7. | risk of death, 44.1% lower, RR 0.56, p = 0.18, treatment 6 of 58 (10.3%), control 15 of 81 (18.5%), NNT 12. |
| risk of ICU admission, 10.2% lower, RR 0.90, p = 0.66, treatment 18 of 58 (31.0%), control 28 of 81 (34.6%), NNT 28. | |
| Suna, 5/11/2021, retrospective, Turkey, peer-reviewed, 5 authors, dosage 2000mg daily, excluded in exclusion analyses: substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically. | risk of death, 21.3% lower, RR 0.79, p = 0.52, treatment 17 of 153 (11.1%), control 24 of 170 (14.1%), NNT 33. |
| risk of ICU admission, 1.9% higher, RR 1.02, p = 1.00, treatment 11 of 153 (7.2%), control 12 of 170 (7.1%). | |
| Tan, 7/26/2021, retrospective, China, peer-reviewed, 7 authors, dosage 500mg tid days 1-7, this trial uses multiple treatments in the treatment arm (combined with diammonium glycyrrhizinate) - results of individual treatments may vary. | risk of death/intubation, 24.5% lower, RR 0.75, p = 0.74, treatment 1 of 46 (2.2%), control 14 of 115 (12.2%), NNT 10.0, odds ratio converted to relative risk, primary outcome. |
| risk of ARDS, 73.3% lower, RR 0.27, p = 0.002, treatment 7 of 46 (15.2%), control 41 of 115 (35.7%), NNT 4.9, odds ratio converted to relative risk. | |
| Tehrani, 11/8/2021, Randomized Controlled Trial, Iran, peer-reviewed, 10 authors, study period March 2020 - May 2020, average treatment delay 9.0 days, dosage 2000mg qid days 1-5. | risk of death, 87.1% lower, RR 0.13, p = 0.13, treatment 0 of 18 (0.0%), control 4 of 26 (15.4%), NNT 6.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| hospitalization time, 17.6% lower, relative time 0.82, p = 0.23, treatment 18, control 26. | |
| Tu, 1/13/2022, retrospective, Sierra Leone, peer-reviewed, 11 authors, study period 31 March, 2020 - 11 August, 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 83.0% lower, RR 0.17, p < 0.001, treatment 8 of 116 (6.9%), control 26 of 64 (40.6%), NNT 3.0. |
| Vishnuram, 6/30/2021, retrospective, India, peer-reviewed, 5 authors, dosage 4000mg days 1-10, excluded in exclusion analyses: unadjusted results with no group details; minimal details of groups provided. | risk of death, 54.2% lower, RR 0.46, p = 0.03, treatment 164 of 8,634 (1.9%), control 10 of 241 (4.1%), NNT 44. |
| Yang (B), 1/15/2022, Randomized Controlled Trial, China, peer-reviewed, 11 authors, study period 1 February, 2020 - 29 February, 2020, this trial uses multiple treatments in the treatment arm (combined with TCM) - results of individual treatments may vary, trial ChiCTR2000032717, excluded in exclusion analyses: combined treatments may contribute significantly to the effect seen. | recovery time, 32.9% lower, relative time 0.67, p < 0.001, treatment mean 10.2 (±1.75) n=10, control mean 15.2 (±2.49) n=10, symptom disappearance, severe patients, group C vs. group A. |
| recovery time, 44.6% lower, relative time 0.55, p < 0.001, treatment mean 4.1 (±0.88) n=10, control mean 7.4 (±1.26) n=10, symptom disappearance, non-severe patients, group C vs. group A. | |
| recovery time, 23.9% lower, relative time 0.76, p = 0.006, treatment mean 10.2 (±1.75) n=10, control mean 13.4 (±2.76) n=10, symptom disappearance, severe patients, group C vs. group B (high vs. low dose). | |
| recovery time, 28.1% lower, relative time 0.72, p = 0.003, treatment mean 4.1 (±0.88) n=10, control mean 5.7 (±1.16) n=10, symptom disappearance, non-severe patients, group C vs. group B (high vs. low dose). | |
| recovery time, 27.1% lower, relative time 0.73, p = 0.002, treatment mean 13.45 (±3.11) n=10, control mean 18.45 (±3.12) n=10, disease recovery, severe patients, group C vs. group A. | |
| recovery time, 23.2% lower, relative time 0.77, p < 0.001, treatment mean 7.0 (±0.94) n=10, control mean 9.11 (±1.25) n=10, disease recovery, non-severe patients, group C vs. group A. | |
| recovery time, 15.4% lower, relative time 0.85, p = 0.15, treatment mean 13.45 (±3.11) n=10, control mean 15.89 (±4.06) n=10, disease recovery, severe patients, group C vs. group B (high vs. low dose). | |
| recovery time, 14.6% lower, relative time 0.85, p = 0.02, treatment mean 7.0 (±0.94) n=10, control mean 8.2 (±1.14) n=10, disease recovery, non-severe patients, group C vs. group B (high vs. low dose). | |
| Yüksel, 9/20/2020, retrospective, Turkey, preprint, 13 authors, dosage 200mg/kg days 1-4, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 18.8% lower, RR 0.81, p = 0.04, treatment 31 of 42 (73.8%), control 40 of 44 (90.9%), NNT 5.8, propensity score matching. |
| Zangeneh, 5/13/2022, retrospective, Iran, peer-reviewed, 3 authors, dosage not specified, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 4.0% lower, HR 0.96, p = 0.86, Cox proportional hazards. |
| Zhang, 8/10/2020, Randomized Controlled Trial, China, peer-reviewed, 11 authors, study period 14 February, 2020 - 29 March, 2020, dosage 12000mg bid days 1-7, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 50.0% lower, RR 0.50, p = 0.20, treatment 6 of 27 (22.2%), control 11 of 29 (37.9%), NNT 6.4, adjusted per study, ICU mortality. |
| risk of death, 80.0% lower, RR 0.20, p = 0.04, treatment 5 of 27 (18.5%), control 11 of 29 (37.9%), NNT 5.2, adjusted per study, ICU mortality for SOFA>=3. | |
| risk of death, 50.0% lower, RR 0.50, p = 0.31, treatment 6 of 27 (22.2%), control 10 of 29 (34.5%), NNT 8.2, adjusted per study, 28 day mortality. | |
| risk of death, 70.0% lower, RR 0.30, p = 0.07, treatment 5 of 27 (18.5%), control 10 of 29 (34.5%), NNT 6.3, adjusted per study, 28 day mortality for SOFA>=3. | |
| Zheng, 9/22/2021, retrospective, China, peer-reviewed, 10 authors, study period 13 February, 2020 - 29 February, 2020, dosage 3000mg days 1-5, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; immortal time bias may significantly affect results; treatment start times unknown, treatment may not have started at baseline. | risk of death, 157.0% higher, HR 2.57, p = 0.33, treatment 12 of 70 (17.1%), control 7 of 327 (2.1%), adjusted per study, propensity score matching. |
| risk of death, 169.0% higher, HR 2.69, p = 0.07, treatment 12 of 70 (17.1%), control 7 of 327 (2.1%), adjusted per study, IPTW. | |
| clinical improvement ≥ 2 points, 35.1% worse, HR 1.35, p = 0.17, treatment 18 of 70 (25.7%), control 16 of 327 (4.9%), adjusted per study, inverted to make HR<1 favor treatment, propensity score matching. | |
| clinical improvement ≥ 2 points, 31.6% worse, HR 1.32, p = 0.11, treatment 18 of 70 (25.7%), control 16 of 327 (4.9%), adjusted per study, inverted to make HR<1 favor treatment, IPTW. | |
| Özgülteki̇n, 9/22/2022, retrospective, Turkey, peer-reviewed, 4 authors, study period March 2020 - June 2020, excluded in exclusion analyses: very late stage, ICU patients. | risk of death, 4.8% higher, RR 1.05, p = 1.00, treatment 18 of 21 (85.7%), control 18 of 22 (81.8%). |
| Özgünay, 7/4/2021, retrospective, Turkey, peer-reviewed, 7 authors, dosage 2000mg tid days 1-10, excluded in exclusion analyses: substantial unadjusted confounding by indication likely; very late stage, ICU patients. | risk of death, 9.3% lower, RR 0.91, p = 0.69, treatment 17 of 32 (53.1%), control 75 of 128 (58.6%), NNT 18. |
| risk of mechanical ventilation, 1.1% higher, RR 1.01, p = 1.00, treatment 23 of 32 (71.9%), control 91 of 128 (71.1%). |
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.
| Abdulateef, 4/8/2021, retrospective, Iraq, peer-reviewed, 7 authors, study period July 2020 - August 2020, dosage varies, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 18.7% lower, RR 0.81, p = 0.69, treatment 8 of 132 (6.1%), control 22 of 295 (7.5%), NNT 72, unadjusted. |
| Akbar, 11/7/2023, retrospective, Qatar, peer-reviewed, mean age 40.3, 9 authors, study period March 2020 - September 2020, dosage not specified. | risk of case, 14.0% lower, OR 0.86, p = 0.29, treatment 665, control 9,335, adjusted per study, multivariable, model 2, RR approximated with OR. |
| Aldwihi, 5/11/2021, retrospective, Saudi Arabia, peer-reviewed, survey, mean age 36.5, 8 authors, study period August 2020 - October 2020, dosage not specified. | risk of hospitalization, 36.3% lower, RR 0.64, p = 0.006, treatment 142 of 505 (28.1%), control 95 of 233 (40.8%), NNT 7.9, adjusted per study, odds ratio converted to relative risk, multivariable. |
| Asoudeh, 3/21/2023, retrospective, Iran, peer-reviewed, 10 authors, study period June 2021 - September 2021. | risk of severe case, 69.0% lower, OR 0.31, p = 0.003, adjusted per study, T3 vs. T1, multivariable, model 3, RR approximated with OR. |
| Behera, 11/3/2020, retrospective, India, peer-reviewed, 13 authors. | risk of case, 18.0% lower, OR 0.82, p = 0.58, treatment 29 of 67 (43.3%) cases, 38 of 148 (25.7%) controls, adjusted per study, case control OR, model 2 conditional logistic regression. |
| risk of case, 29.0% lower, OR 0.71, p = 0.24, treatment 29 of 67 (43.3%) cases, 38 of 148 (25.7%) controls, adjusted per study, case control OR, matched pair analysis. | |
| Bejan, 2/28/2021, retrospective, USA, peer-reviewed, mean age 42.0, 6 authors. | risk of death, 34.0% lower, OR 0.66, p = 0.33, treatment 569, control 8,637, adjusted per study, RR approximated with OR. |
| risk of mechanical ventilation, 25.0% lower, OR 0.75, p = 0.47, treatment 572, control 8,657, adjusted per study, RR approximated with OR. | |
| risk of ICU admission, 15.0% lower, OR 0.85, p = 0.65, treatment 577, control 8,690, adjusted per study, RR approximated with OR. | |
| risk of hospitalization, no change, OR 1.00, p = 1.00, treatment 626, control 9,122, adjusted per study, RR approximated with OR. | |
| Guan, 5/22/2024, retrospective, China, peer-reviewed, 5 authors, study period December 2022 - January 2023. | risk of symptomatic case, 31.4% lower, RR 0.69, p = 0.007, treatment 28 of 46 (60.9%), control 2,017 of 2,454 (82.2%), NNT 4.7, adjusted per study, odds ratio converted to relative risk, high dose, multivariable. |
| risk of symptomatic case, 18.5% lower, RR 0.82, p = 0.02, treatment 55 of 79 (69.6%), control 2,017 of 2,454 (82.2%), NNT 8.0, adjusted per study, odds ratio converted to relative risk, medium dose, multivariable. | |
| risk of symptomatic case, 6.8% lower, RR 0.93, p = 0.13, treatment 129 of 167 (77.2%), control 2,017 of 2,454 (82.2%), NNT 20, adjusted per study, odds ratio converted to relative risk, low dose, multivariable. | |
| Guldemir, 11/16/2022, retrospective, Turkey, peer-reviewed, 3 authors, study period 30 March, 2020 - 23 September, 2020, dosage not specified, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 31.0% lower, RR 0.69, p = 0.046 (Fisher's exact test), treatment 33 of 173 (19.1%), control 84 of 304 (27.6%), NNT 12. |
| Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible. | risk of case, 2.9% higher, RR 1.03, p = 0.86, treatment 49 of 1,580 (3.1%), control 397 of 13,647 (2.9%), adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated. |
| Louca, 11/30/2020, retrospective, United Kingdom, peer-reviewed, 26 authors, dosage not specified. | risk of case, no change, RR 1.00, p = 1.00, odds ratio converted to relative risk, United Kingdom, all adjustment model. |
| Loucera, 8/16/2022, retrospective, Spain, peer-reviewed, 8 authors, study period January 2020 - November 2020. | risk of death, 28.3% lower, HR 0.72, p = 0.002, treatment 840, control 15,128, Cox proportional hazards, day 30. |
| Mahto, 2/15/2021, retrospective, India, peer-reviewed, 6 authors. | risk of IgG positive, 25.9% higher, RR 1.26, p = 0.49, treatment 34 of 140 (24.3%), control 59 of 549 (10.7%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Mohseni, 8/4/2021, retrospective, Iran, peer-reviewed, 4 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of case, 44.2% higher, RR 1.44, p = 0.002, treatment 34 of 43 (79.1%), control 307 of 560 (54.8%). |
| Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021, dosage not specified. | risk of hospitalization, 24.7% lower, RR 0.75, p = 0.08, treatment 52 of 651 (8.0%), control 167 of 1,497 (11.2%), NNT 32, adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of severe case, 17.0% lower, RR 0.83, p = 0.18, treatment 66 of 651 (10.1%), control 194 of 1,497 (13.0%), NNT 35, adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Sharif, 11/26/2022, retrospective, Bangladesh, peer-reviewed, 14 authors, study period 13 December, 2020 - 4 February, 2021. | risk of severe case, 46.0% lower, OR 0.54, p = 0.001, adjusted per study, multivariable, RR approximated with OR. |
| risk of severe case, 97.0% lower, OR 0.03, p = 0.005, adjusted per study, combined use of vitamin C, vitamin D, and zinc, multivariable, RR approximated with OR. | |
| Shehab, 2/28/2022, retrospective, multiple countries, peer-reviewed, survey, 7 authors, study period September 2020 - March 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of severe case, 4.3% lower, RR 0.96, p = 1.00, treatment 14 of 139 (10.1%), control 12 of 114 (10.5%), NNT 220, unadjusted, severe vs. mild cases. |
| Vaisi, 5/11/2023, retrospective, Iran, peer-reviewed, 5 authors. | risk of hospitalization, 37.9% lower, HR 0.62, p = 0.17, treatment 2,818, control 1,137, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards. |
| risk of symptomatic case, 9.6% lower, HR 0.90, p = 0.71, treatment 2,818, control 1,137, adjusted per study, inverted to make HR<1 favor treatment, sufficient vs. insufficient intake, multivariable, Cox proportional hazards. |
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