Metformin for COVID-19: real-time meta analysis of 97 studies
Abstract
Statistically significant lower risk is seen for mortality, ventilation, ICU admission, hospitalization, progression, and recovery. 62 studies from 59 independent teams in 20 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
30% [26‑34%] lower risk. Results are similar for higher quality and peer-reviewed studies and better for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 77 of
97 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Most studies analyze existing use with diabetic patients.
Prophylaxis results typically include continuing use after infection and
hospitalization, and greater benefit is seen for more serious
outcomes. The TOGETHER RCT shows
27% lower mortality. While not statistically significant, p = 0.53, this
is consistent with the mortality results from all studies,
35% [31‑39%].
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.
Metformin for COVID-19 — Highlights
Metformin reduces
risk with very high confidence for mortality, ventilation, ICU admission, hospitalization, progression, recovery, and in pooled analysis, and very low confidence for viral clearance.
3rd treatment shown effective with ≥3 clinical studies in
July 2020, now with p < 0.00000000001 from 97 studies.
Outcome specific analyses and combined evidence from all
studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in metformin 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 25.0 months, compared to using all studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury22-32 and
cognitive deficits24,29, cardiovascular
complications33-35, 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,36-41, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk42, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
A systematic review and meta-analysis of 15 non-COVID-19 preclinical studies showed that metformin inhibits pulmonary inflammation and oxidative stress, minimizes lung injury, and improves survival in animal models of acute respiratory distress syndrome (ARDS) or acute lung injury (ALI)43.
Metformin inhibits SARS-CoV-2 in vitro44,45, minimizes LPS-induced cytokine storm in a mouse model46, minimizes lung damage and fibrosis in a mouse model of LPS-induced ARDS47, may protect against SARS-CoV-2-induced neurological disorders22, may be beneficial via inhibitory effects on ORF3a-mediated inflammasome activation48, reduces UUO and FAN-induced kidney fibrosis47, increases mitochondrial function and decreases TGF-β-induced fibrosis, apoptosis, and inflammation markers in lung epithelial cells47, may reduce inflammation, oxidative stress, and thrombosis via regulating glucose metabolism49, attenuates spike protein S1-induced inflammatory response and α-synuclein aggregation50, and may improve outcomes via modulation of immune responses with increased anti-inflammatory T lymphocyte gene expression and via enhanced gut microbiota diversity51.
Efficacy with metformin has been shown for influenza A52.
We analyze all significant
controlled studies of
metformin
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.
A systematic review and meta-analysis of 15 non-COVID-19 preclinical studies showed that metformin inhibits pulmonary inflammation and oxidative stress, minimizes lung injury, and improves survival in animal models of acute respiratory distress syndrome (ARDS) or acute lung injury (ALI)43.
Metformin inhibits SARS-CoV-2 in vitro44,45, minimizes LPS-induced cytokine storm in a mouse model46, minimizes lung damage and fibrosis in a mouse model of LPS-induced ARDS47, may protect against SARS-CoV-2-induced neurological disorders22, may be beneficial via inhibitory effects on ORF3a-mediated inflammasome activation48, reduces UUO and FAN-induced kidney fibrosis47, increases mitochondrial function and decreases TGF-β-induced fibrosis, apoptosis, and inflammation markers in lung epithelial cells47, may reduce inflammation, oxidative stress, and thrombosis via regulating glucose metabolism49, and attenuates spike protein S1-induced inflammatory response and α-synuclein aggregation50.
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, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 30% [26‑34%] **** | 97 | 292,912 | 1,181 |
| After exclusions | 31% [27‑34%] **** | 89 | 274,582 | 1,064 |
| Peer-reviewed studiesPeer-reviewed | 29% [25‑33%] **** | 90 | 261,400 | 1,099 |
| Randomized Controlled TrialsRCTs | 45% [14‑64%] ** | 4 | 1,431 | 100 |
| Mortality | 35% [31‑39%] **** | 68 | 210,363 | 890 |
| VentilationVent. | 33% [17‑46%] *** | 13 | 60,369 | 150 |
| ICU admissionICU | 17% [6‑26%] ** | 13 | 84,555 | 151 |
| HospitalizationHosp. | 18% [11‑23%] **** | 24 | 94,290 | 251 |
| Recovery | 41% [13‑60%] ** | 4 | 4,176 | 78 |
| Cases | 5% [-4‑13%] | 8 | 77,443 | 96 |
| Viral | 26% [-9‑49%] | 3 | 1,437 | 74 |
| RCT mortality | 45% [-19‑74%] | 3 | 1,411 | 86 |
| RCT hospitalizationRCT hosp. | 7% [-6‑17%] | 3 | 627 | 63 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 58% [23‑77%] ** | 44% [37‑50%] **** | 27% [23‑31%] **** |
| After exclusions | 58% [23‑77%] ** | 44% [37‑50%] **** | 28% [24‑31%] **** |
| Peer-reviewed studiesPeer-reviewed | 58% [23‑77%] ** | 52% [30‑67%] *** | 26% [23‑30%] **** |
| Randomized Controlled TrialsRCTs | 24% [-89‑70%] | 49% [17‑69%] ** | |
| Mortality | 58% [23‑77%] ** | 44% [36‑51%] **** | 32% [27‑36%] **** |
| VentilationVent. | 79% [1‑96%] * | 31% [15‑45%] *** |
|
| ICU admissionICU | 63% [-9‑87%] | 16% [5‑25%] ** |
|
| HospitalizationHosp. | 6% [-61‑45%] | 7% [-6‑18%] | 19% [12‑25%] **** |
| Recovery | 41% [13‑60%] ** |
||
| Cases | 5% [-4‑13%] | ||
| Viral | 19% [-25‑48%] | 41% [5‑63%] * | |
| RCT mortality | 24% [-89‑70%] | 74% [-6‑94%] | |
| RCT hospitalizationRCT hosp. | 6% [-61‑45%] | 7% [-6‑18%] |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Figure 14 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
45% improvement,
compared to 30% for other studies.
Figure 15, 16, and 17
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 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
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Figure 17. 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 biases57, and
analysis of double-blind RCTs has identified extreme levels of bias58.
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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 109 treatments60.
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 (B) 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 see64,65.
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 18 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Akinosoglou, unadjusted results with no group details.
Al-kuraishy, unadjusted results with significant baseline differences.
Alieva, unadjusted results with no group details.
Bidari, unadjusted results with no group details.
Bliden, unadjusted results with minimal group details.
Farah, unadjusted results with no group details.
Holt, significant unadjusted confounding possible.
Ravindra, minimal details provided.
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Figure 18. 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 hours74,75.
Baloxavir marboxil studies for influenza also show that treatment delay is critical
— Ikematsu et al. report an 86% reduction in cases for post-exposure
prophylaxis, Hayden et al. show a 33 hour reduction in the time to
alleviation of symptoms for treatment within 24 hours and a reduction of 13
hours for treatment within 24-48 hours, and Kumar et al. report only 2.5
hours improvement for inpatient treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases76 |
| <24 hours | -33 hours symptoms77 |
| 24-48 hours | -13 hours symptoms77 |
| Inpatients | -2.5 hours to improvement78 |
Figure 19 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 19. 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 variants80, for
example the Gamma variant shows significantly different characteristics81-84. 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 variants85,86.
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 synergistic87-98, therefore
efficacy may depend strongly on combined treatments.
The
quality of medications may vary significantly between manufacturers and
production batches, which may significantly affect efficacy and safety.
Williams et al. analyze ivermectin from 11 different sources, showing
highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however note that pooled effects are no longer
required for metformin as of July 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 20 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 21 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 22 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 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. 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 23 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 23. 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.
Efficacy with metformin has also been shown for influenza A52.
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 results102-105.
For metformin, there is currently not
enough data to evaluate publication bias with high confidence.
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 24 shows a scatter plot of
results for prospective and retrospective studies.
64% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
57% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 37% improvement,
compared to 37% for prospective
studies, showing similar results.
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Figure 24. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 25 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 < 0.05106-113.
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 25. 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. Metformin for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 metformin 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 metformin 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 alone87-98.
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.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors36-41,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk42, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 26 shows an overview of the results for metformin
in the context of multiple COVID-19 treatments, and Figure 27 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 26.
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 efficacy120.
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Statistically significant lower risk is seen for mortality, ventilation, ICU admission, hospitalization, progression, and recovery. 62 studies from 59 independent teams in 20 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
30% [26‑34%] lower risk. Results are similar for higher quality and peer-reviewed studies and better for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 77 of
97 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Most studies analyze existing use with diabetic patients.
Prophylaxis results typically include continuing use after infection and
hospitalization, and greater benefit is seen for more serious
outcomes. The TOGETHER RCT shows
27% lower mortality. While not statistically significant, p = 0.53, this
is consistent with the mortality results from all studies,
35% [31‑39%].
22 other meta analyses show significant improvements with metformin for mortality1-21, hospitalization7,13, progression1, and severity8,9,13.
Efficacy with metformin has also been shown for influenza A52.
Abu-Jamous:
Retrospective diabetes patients in the UK, showing lower mortality for metformin treatment (administered within 21 days after a positive PCR test).
Akinosoglou:
Prospective multicenter study of 354 hospitalized type 2 diabetes patients with COVID-19 in Greece showing increased risk with DPP4 inhibitor use as part of chronic diabetes treatment. There was no significant difference with metformin use in unadjusted results. Results do not account for differences in the risk of hospitalization.
Al-kuraishy:
Prospective study of 60 hospitalized type 2 diabetes patients with COVID-19 on metformin monotherapy compared to 40 patients on other diabetes treatments, showing significantly lower inflammatory biomarkers, oxidative stress, and mortality, and improvements in radiological and clinical outcomes with metformin. Confounding due to differences in baseline characteristics may be significant.
Al-Salameh:
Retrospective 140 diabetic patients in France, showing lower mortality for patients where metformin use was continued after hospitalization.
Alamgir:
In Silico study followed by PSM analysis of the National COVID Cohort Collaborative data in the USA, showing 27% lower mortality with metformin use.
Alieva:
Retrospective 763 COVID-19 patients with type 2 diabetes in Uzbekistan, showing lower hospitalization with metformin use in unadjusted results, without statistical significance.
Ando:
Retrospective 28,093 COVID+ patients in the USA, showing lower risk of hospitalization with metformin use.
Araldi:
UK Biobank retrospective including 43,610 type 2 diabetes patients, showing lower mortality with metformin use within matched type 2 diabetes patients.
Bidari:
Retrospective 406 COVID-19 patients in Iran, showing lower risk of severe cases with metformin use in unadjusted results, without statistical significance.
Blanc:
Retrospective 179 patients in France exposed to COVID-19 showing, without statistical significance, a higher risk of cases, and a lower risk of mortality among cases with existing metformin treatment.
Bliden:
Retrospective 75 diabetes patients, 34 on metformin, showing lower mortality with treatment in unadjusted results with minimal group details.
Boye:
Retrospective 9531 COVID+ diabetes patients in the USA, showing lower risk of hospitalization with existing biguanides treatment (defined as mainly metformin in the abstract and entirely metformin in the text).
Bramante:
Retrospective 17,396 PCR+ patients in the USA, showing lower mortality with metformin use.
Bramante (B):
Retrospective 6,256 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment, statistically significant only for women.
COVID-OUT remotely operated RCT, showing lower
combined ER/hospitalization/death with metformin. Results for other treatments are listed
separately -
ivermectin,
fluvoxamine.
The "control" group includes patients receiving active
treatments fluvoxamine and ivermectin.
Control arm results are very different between treatments, for
example considering hospitalization/death, this was 1.0% for ivermectin vs.
2.7% for overall control, however it was 1.3% for the ivermectin-specific
control. 394 control patients are shared. The rate for the non-shared 261
metformin control patients is 5%, compared to 1.3% for ivermectin control
patients. The metformin arm started earlier, however it is unclear why the
difference in outcomes is so large.
Results were delayed for 6 months with no explanation, with
followup ending Feb 14, 2022.
Adherence was very low, with 77% overall reporting 70+%
adherence. Numbers for 100% adherence are not provided.
Multiple outcomes are missing, for example time to recovery
(where ACTIV-6 showed superiority of ivermectin).
Treatment was 14 days for metformin and fluvoxamine, but only 3
days for ivermectin.
Medication delivery varied significantly over the trial. In
this presentation132, author indicates that
delivery was initially local, later via FedEx, was much slower in August,
there were delays due to team bandwidth issues, and they only realized they
could use FedEx same day delivery in September.
Cariou:
Analysis of 1,317 hospitalized COVID-19 patients with diabetes showing lower mortality with metformin use, without statistical significance.
Chan:
Retrospective 3,136 patients with prediabetes and 282 with PCOS, showing metformin associated with reduced COVID-19 severity.
Chen (B):
Retrospective 413 hospitalized COVID-19 patients with type 2 diabetes in China showing lower ICU admission, lower pneumonia incidence, and shorter hospital stay with metformin use.
Chen (C):
Retrospective 120 COVID-19 diabetes patients, showing non-statistically significantly lower mortality with existing metformin treatment.
Cheng:
Retrospective 1,213 hospitalized diabetic COVID-19 patients in China, showing no significant difference in mortality with pre-existing metformin use.
Choi:
Retrospective 293 patients in South Korea, showing higher risk of progression with metformin use, without statistical significance.
Cousins:
PSM retrospective 64,349 COVID-19 patients in the USA, showing metformin associated with lower ICU admission and mechanical ventilation.
Crouse:
Retrospective 219 COVID-19+ diabetes patients in the USA, showing lower mortality with existing metformin treatment.
Dimnjaković:
Retrospective 7,539 patients with diabetes mellitus type 2 and chronic kidney disease in Croatia showing lower risk of SARS-CoV-2 infection with SGLT-2 inhibitors, metformin, and repaglinide use, and lower risk of COVID-19 hospitalization with SGLT-2 inhibitors and metformin use.
Farah:
Retrospective 1,039 diabetes patients in Jordan, showing no significant difference in COVID-19 cases with metformin use in unadjusted results. Severity outcomes are not provided for metformin.
Fu:
Retrospective 108 T2D patients hospitalized with COVID-19, showing lower risk of unfavorable outcomes with metformin use vs. other diabetic medications.
Gao:
Retrospective 110 hospitalized COVID-19 patients with diabetes in China, showing increased risk of severity with metformin.
Ghany:
Retrospective 1,139 elderly COVID+ patients in the USA, 392 with pre-existing metformin use, showing significantly lower mortality, hospitalization, and ARDS with treatment.
Goodall:
Retrospective 981 hospitalized patients in the UK, showing no significant difference with metformin use.
Greco:
Retrospective 76,764 diabetes patients in Italy, showing that patients on metformin had lower rates of COVID-19 hospitalization compared to those on insulin/insulin secretagogues, GLP-1 receptor agonists, and DPP-4 inhibitors. Metformin vs. no metformin results are not provided. The most relevant result for COVID-19 and metformin may be the DPP-4i comparison, based on the DPP-4i group being the most similar to the metformin group in terms of baseline COVID-19 risk and confounders. Patients on insulin/secretagogues may have more severe or advanced diabetes.
Guo:
Retrospective 571 type 2 diabetes patients with COVID-19 in China, showing lower combined mortality/mechanical ventilation with metformin.
Gálvez-Barrón:
Analysis of 103 elderly hospitalized COVID-19 patients in Spain, showing higher mortality with metformin, without statistical significance.
Harmon:
Retrospective 11,993 hospitalized COVID-19 patients with diabetes mellitus but without chronic kidney disease or need for hemodialysis, showing lower mortality with metformin use.
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.
Huh:
Retrospective database analysis showing no significant differences with pre-existing metformin use.
Hunt:
Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including metformin. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment in some cases, and may be early or late treatment in other cases. Further reduction in mortality was seen with combinations of treatments.
Hussein:
Retrospective 545 hospitalized COVID-19 patients with diabetes showing high mortality (33%). Metformin, SGLT inhibitors, and DPP4 inhibitors were associated with lower mortality compared with insulin.
Jang:
Retrospective 556 diabetic patients in South Korea with COVID-19 showing lower risk of mechanical ventilation and death with metformin, lower risks of oxygen treatment and death with DPP-4 inhibitors, and increased risk of mechanical ventilation with sulfonylureas. The study used nationwide data to analyze the impact of common antidiabetic medications on COVID-19 outcomes. Authors note that South Korea had a policy early in the pandemic of hospitalizing nearly all confirmed COVID-19 patients regardless of severity.
Jiang:
Retrospective 328 COVID-19 patients with type 2 diabetes in China, showing significantly lower risk of ARDS with existing metformin use.
Johnson:
N3C/PCORnet retrospective adults with type 2 diabetes in the USA showing lower incidence of mortality or long COVID with metformin use.
Khunti:
Retrospective 2,851,465 people with type 2 diabetes in the UK, showing lower mortality with existing metformin use. Results are subject to confounding by indication because metformin is typically used early in the progression of type 2 diabetes.
Kim:
Retrospective 235 hospitalized diabetes patients in South Korea, showing lower mortality and lower progression to severe disease with metformin.
Lalau:
Retrospective 2,449 hospitalized COVID-19 diabetes patients in France, 1,496 with existing metformin use, showing lower mortality with treatment. Statistical significance was reached in model 1 but not in models 2-4 which also adjust for HbA1c, eGFR, and diabetes duration, but have a lower number of patients. CORONADO (Coronavirus SARS-CoV-2 and Diabetes Outcomes).
Lally:
Retrospective 775 nursing home residents in the USA, showing lower mortality with existing metformin use.
Lewandowski:
Retrospective 430 hospitalized COVID-19 patients with type 2 diabetes in Poland showing lower mortality with metformin and higher mortality with remdesivir, convalescent plasma, and aspirin in univariable analysis. These results were not statistically significant except for aspirin, and no baseline information per treatment is provided to assess confounding.
Li (B):
Retrospective 131 type II diabetes patients with COVID pneumonia, showing lower mortality with existing metformin use. Acarbose (commonly used in China as an initial therapy for diabetes) did not have a similar association with mortality, suggesting that the result may not be explained by metformin being used early in type II diabetes.
Li (C):
Retrospective 131 hospitalized COVID-19 patients with type 2 diabetes, showing lower mortality with metformin treatment and acarbose treatment.
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.
Luo:
Retrospective 283 COVID-19+ diabetes patients in China, showing lower mortality with existing metformin treatment.
Ma (B):
PSM/IPTW retrospective 1,356 hospitalized COVID-19 patients with type 2 diabetes in China, showing lower mortality/hospice with metformin use.
MacFadden:
Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant difference in cases with chronic use of metformin.
Mamari:
Retrospective 109 hospitalized COVID-19 patients in Syria, 68 with diabetes, showing significantly lower mortality with metformin vs. sulfonylureas, and significantly higher mortality with discontinuation of metformin.
Mannucci:
Retrospective 54,009 diabetes patients in Italy, showing lower mortality with metformin use.
Mehrizi:
Retrospective study of 917,198 hospitalized COVID-19 cases covered by the Iran Health Insurance Organization over 26 months showing that antithrombotics, corticosteroids, and antivirals reduced mortality while diuretics, antibiotics, and antidiabetics increased it. Confounding makes some results very unreliable. For example, diuretics like furosemide are often used to treat fluid overload, which is more likely in ICU or advanced disease requiring aggressive fluid resuscitation. Hospitalization length has increased risk of significant confounding, for example longer hospitalization increases the chance of receiving a medication, and death may result in shorter hospitalization. Mortality results may be more reliable.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
Miao:
Retrospective 4,462 COVID+ diabetes patients in the USA, showing no significant difference in outcomes with metformin use.
Miguel:
Mouse models showing reduced lung and kidney injury with metformin. Metformin minimized lung damage and fibrosis in a mouse model of LPS-induced ARDS, and reduced UUO and FAN-induced kidney fibrosis. In Vitro study showing that metformin increased mitochondrial function and decreased TGF-β-induced fibrosis, apoptosis, and inflammation markers in lung epithelial cells.
Authors also include a retrospective study showing lower ICU admission with metformin without statistical significance.
Authors also include a retrospective study showing lower ICU admission with metformin without statistical significance.
Milosavljevic:
Retrospective 733 hospitalized COVID-19 patients with diabetes in the USA, showing lower risk of severity with metformin use.
Mirani:
Retrospective 90 hospitalized COVID-19 patients with diabetes in Italy, showing lower mortality with metformin use, without statistical significance.
Morrison:
Retrospective 13,585 COVID+ patients in the USA, showing lower mortality with metformin use, but no significant difference for ventilation, ICU admission, and hospitalization.
Obiri-Yeboah:
Retrospective 529 hospitalized COVID-19 patients with type 2 diabetes, showing no significant difference in outcomes with metformin use. This does not account for the different risk of being hospitalized based on metformin use.
Authors note that "there is a lower-than-expected proportion of metformin prescription in our population (28%) compared to the general US population", without noting that this may reflect the lower risk of being hospitalized for metformin patients, as shown in other studies175.
Authors note that "there is a lower-than-expected proportion of metformin prescription in our population (28%) compared to the general US population", without noting that this may reflect the lower risk of being hospitalized for metformin patients, as shown in other studies175.
Oh:
Retrospective 27,493 type II diabetes patients in the USA, 7,204 on metformin, showing significantly lower COVID-19 cases, but no significant difference in mortality.
Ojeda-Fernández:
Retrospective 31,966 COVID+ patients using anti-hyperglycemic drugs in Italy, showing lower mortality and ICU admission with metformin use.
Olawore:
Retrospective 7,047 outpatients with type 2 diabetes showing a lower risk of PASC (long COVID) with metformin compared to sulfonylurea or DPP-4 inhibitor use, without statistical significance.
Ong:
Retrospective 355 diabetic hospitalized COVID-19 patients in the Philippines, showing lower mortality with metformin use.
Ouchi:
Retrospective 31,006 diabetic COVID-19 patients in Spain, showing lower mortality with metformin treatment, without statistical significance. Authors provide results for metformin compared with untreated patients rather than all non-metformin patients, which may increase confounding due to higher prevalence for treatment of patients with more severe disease.
Piarulli:
Retrospective diabetic COVID-19 patients in Italy, showing lower risk of hospitalization with metformin use.
Pinchera:
Retrospective 43 diabetes patients hospitalized for COVID-19 in Italy, showing lower risk of severe cases with metformin vs. insulin.
Pérez-Belmonte:
Retrospective 2,666 type 2 diabetes COVID-19 patients in Spain, showing higher mortality with existing metformin use (not statistically significant).
Ramos-Rincón:
Retrospective 790 hospitalized type 2 diabetes patients ≥80 years old in Spain, showing no significant difference in mortality with existing metformin use.
Ravindra:
Retrospective 1,035 hospitalized patients in India. Of 366 diabetic patients, there was lower mortality for the 53 that were on metformin.
See also
RCT with 215 patients treated with metformin and 203 controls, showing no significant difference with treatment.
For multiple major issues with this trial see186,187. An expression of concern was posted in 2024188.
The hospitalization risk for off-protocol patients was several times higher in both arms, resulting in Simpson's paradox when combining per-protocol and off-protocol patients189.
750mg twice daily for 10 days.
The TOGETHER trial has extreme COI, impossible data,
blinding failure, randomization failure, uncorrected errors, and many
protocol violations. Authors do not respond to these issues and they
have refused to release the data as promised. Some issues may apply only
to specific arms. For more details see190-194.
Sakamaki:
Retrospective 650,317 COVID-19 patients in Japan showing lower risk of severe COVID-19 with metformin use.
Sandhu:
Retrospective 3,974,272 COVID-19 patients in the USA, showing 3% lower risk of hospitalization with pre-existing metformin use.
Saygili:
Retrospective 586 diabetic hospitalized COVID-19 patients in Turkey, showing lower mortality with existing metformin use.
Servais:
Retrospective 375 hospitalized diabetes patients in Belgium, showing lower risk of COVID-19 mortality with metformin use.
Shaseb:
RCT 189 hospitalized patients showing lower mortality, ICU admission, and intubation with metformin, statistically significant only for intubation. Treatment patients may have also taken metformin prior to admission. Authors note that patients receiving metformin prior to the study were not matched, and diabetes and hyperlipidemia differed between groups.
Shestakova:
Retrospective 224,190 type 2 diabetes patients in Russia, showing lower mortality with metformin use.
Silverii:
Retrospective 524 hospitalized COVID-19 patients with diabetes in Italy, showing lower risk of mortality with metformin use, without statistical significance. The results adjusted only for COVID-19 MRS differ between the text and Figure 2.
Somasundaram:
Retrospective 421 hospitalized COVID-19 patients with type 2 diabetes in India, showing significantly lower mortality with metformin use compared to other antidiabetic medications.
Sourij:
Retrospective 247 hospitalized COVID-19 diabetes patients, showing lower mortality with metformin use in unadjusted results.
Sugimoto:
Retrospective 168,370 hospitalized COVID-19 patients with diabetes in Japan showing lower mortality and reduced risk of acute kidney injury with biguanide (likely primarily or only metformin) use. Authors hypothesize that metformin's activation of AMPK in renal tubular epithelium may provide a protective effect against COVID-19-induced kidney damage.
Tamura:
Retrospective 188 hospitalized patients in Brazil, showing lower risk of mortality with metformin use. Authors note that, although pre-hospital metformin use improved clinical parameters at admission, continuous use during hospitalization is essential. Patients that used pre-hospital metformin therapy but interrupted the treatment during hospitalization showed higher mortality than those that continued metformin therapy.
Usman:
Retrospective 75 diabetes patients, 34 on metformin, showing improved clinical outcomes with treatment, without statistical significance.
Ventura-López:
RCT 20 hospitalized COVID-19 patients showing faster viral load reduction and lower oxygen use with metformin glycinate 620mg twice daily for 14 days compared to placebo. The in vitro portion demonstrated inhibition of viral replication and cytopathic effects with metformin glycinate pretreatment.
Wallace:
Retrospective 9,532 hospitalized COVID+ veterans in the USA, showing lower mortality with metformin use. The study provides results for use before, after, and before+after. Before+after should more accurately represent prophylaxis up to COVID-19 infection (and continued use). Before included use up to 2 years before, and after included use up to 60 days later.
Wander:
Retrospective 64,892 veterans with diabetes in the USA, showing lower mortality with existing metformin use.
Wang (B):
Retrospective 16,504 COVID-19 type 2 diabetes patients, showing lower risk of ICU admission with existing metformin use.
Wang (C):
Retrospective 58 multiple myeloma COVID-19 patients in the USA, showing non-statistically significant lower mortality with metformin treatment.
Wargny:
Retrospective 2,796 hospitalized diabetes patients with COVID-19 in France, showing lower mortality with metformin use.
Wong:
Retrospective 1,214 COVID+ type 2 diabetes patients in Hong Kong, showing lower mortality and improved recovery with metformin use.
Wong (B):
N3C retrospective 39,616 COVID-19 patients with diabetes in the USA, showing lower mortality, ventilation, and hospitalization with metformin use.
Xu (B):
Retrospective 4,922 COVID-19 patients with type 2 diabetes in China, showing lower mortality with metformin and alpha-glucosidase inhibitor treatment and higher mortality with insulin treatment.
Yeh:
Retrospective 4,944 COVID-19 patients with type 2 diabetes in the USA, showing lower risk of hospitalization and combined ICU/intubation/death with metformin use.
Yen:
TriNetX retrospective 123,709 vaccinated patients with type 2 diabetes, showing significantly lower risk of COVID-19 mortality, mechanical ventilation, and hospitalization with metformin use. There was no significant difference for cases. The increasing benefit for more serious outcomes matches the results of studies to date.
Yip:
Retrospective 12,331 diabetes patients in Hong Kong, showing no significant difference in outcomes with metformin use.
Zaccardi:
Retrospective 624,771 people with type 2 diabetes in the UK, showing lower COVID-19 mortality and hospitalization with metformin use.
Zihono:
Retrospective 137 hospitalized mild to moderate COVID-19 patients with type 2 diabetes in Indonesia, showing a significantly lower mortality with metformin treatment.
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 metformin 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 metformin 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 to220.
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 1223.
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 PythonMeta224
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 effective74,75.
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/mfmeta.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.
| Bramante (C), 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 37 authors, average treatment delay 4.8 days, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04510194 (history) (COVID-OUT). | risk of death, 2.9% lower, RR 0.97, p = 1.00, treatment 1 of 408 (0.2%), control 1 of 396 (0.3%), NNT 13464, day 28. |
| risk of death, 197.1% higher, RR 2.97, p = 1.00, treatment 1 of 408 (0.2%), control 0 of 396 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 14. | |
| risk of death/hospitalization, 52.3% lower, RR 0.48, p = 0.09, treatment 8 of 652 (1.2%), control 18 of 655 (2.7%), NNT 66, odds ratio converted to relative risk. | |
| risk of progression, 40.2% lower, RR 0.60, p = 0.03, treatment 27 of 652 (4.1%), control 48 of 655 (7.3%), NNT 31, odds ratio converted to relative risk, combined ER, hospitalization, death. | |
| risk of progression, 12.1% lower, RR 0.88, p = 0.18, treatment 154 of 652 (23.6%), control 179 of 653 (27.4%), NNT 26, odds ratio converted to relative risk, combined hypoxemia, ER, hospitalization, death, primary outcome. | |
| risk of no viral clearance, 36.9% lower, RR 0.63, p < 0.001, treatment 72 of 504 (14.3%), control 112 of 495 (22.6%), NNT 12, day 10. | |
| risk of no viral clearance, 8.7% lower, RR 0.91, p = 0.15, treatment 251 of 504 (49.8%), control 270 of 495 (54.5%), NNT 21, day 5. | |
| Hunt, 6/29/2022, retrospective, USA, peer-reviewed, 8 authors, study period 1 March, 2020 - 10 September, 2020. | risk of death, 67.0% lower, RR 0.33, p < 0.001, treatment 73 of 3,956 (1.8%), control 1,539 of 22,552 (6.8%), NNT 20, adjusted per study, day 30. |
| Reis, 8/31/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 23 authors, study period 15 January, 2021 - 3 April, 2021, impossible data, see notes, trial NCT04727424 (history) (TOGETHER). | risk of death, 26.6% lower, RR 0.73, p = 0.53, treatment 7 of 215 (3.3%), control 9 of 203 (4.4%), NNT 85, day 28. |
| risk of hospitalization, 5.6% lower, RR 0.94, p = 0.88, treatment 24 of 215 (11.2%), control 24 of 203 (11.8%), NNT 152, ITT. | |
| risk of hospitalization, 39.1% lower, RR 0.61, p = 0.28, treatment 8 of 168 (4.8%), control 14 of 179 (7.8%), NNT 33, PP. | |
| risk of extended ER observation or hospitalization, 14.0% higher, RR 1.14, p = 0.58, treatment 34 of 215 (15.8%), control 28 of 203 (13.8%), ITT, primary outcome. | |
| risk of extended ER observation or hospitalization, 12.0% lower, RR 0.88, p = 0.72, treatment 14 of 168 (8.3%), control 17 of 179 (9.5%), NNT 86, PP. | |
| risk of ER visit, 31.0% lower, RR 0.69, p = 0.48, treatment 8 of 216 (3.7%), control 11 of 205 (5.4%), NNT 60, ITT. | |
| risk of ER visit, 25.9% lower, RR 0.74, p = 0.62, treatment 7 of 171 (4.1%), control 10 of 181 (5.5%), NNT 70, PP. | |
| risk of no viral clearance, 1.0% lower, RR 0.99, p = 0.85, treatment 215, control 203, adjusted per study. |
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.
| Abu-Jamous, 8/23/2020, retrospective, United Kingdom, preprint, 7 authors, study period 1 January, 2020 - 27 May, 2020. | risk of death, 65.3% lower, RR 0.35, p = 0.04, treatment 4 of 23 (17.4%), control 94 of 168 (56.0%), NNT 2.6, odds ratio converted to relative risk. |
| Li (C), 9/29/2021, retrospective, China, peer-reviewed, 13 authors. | risk of death, 75.8% lower, RR 0.24, p = 0.02, treatment 2 of 37 (5.4%), control 21 of 94 (22.3%), NNT 5.9. |
| Mehrizi, 12/18/2023, retrospective, Iran, peer-reviewed, 10 authors, study period 1 February, 2020 - 20 March, 2022. | risk of death, 44.0% lower, OR 0.56, p < 0.001, RR approximated with OR. |
| Shaseb, 7/2/2022, Randomized Controlled Trial, Iran, peer-reviewed, 26 authors, study period 20 March, 2020 - 5 April, 2020, trial IRCT20160310026998N10. | risk of death, 74.0% lower, OR 0.26, p = 0.06, treatment 85, control 104, RR approximated with OR. |
| risk of mechanical ventilation, 79.0% lower, OR 0.21, p = 0.048, treatment 85, control 104, RR approximated with OR. | |
| risk of ICU admission, 63.0% lower, OR 0.37, p = 0.07, treatment 85, control 104, RR approximated with OR. | |
| hospitalization time, 5.0% lower, relative time 0.95, p = 0.52, treatment 85, control 104. | |
| Sugimoto, 7/21/2024, retrospective, Japan, preprint, 12 authors, study period September 2021 - March 2023. | risk of death, 40.0% lower, HR 0.60, p < 0.001, treatment 30,908, control 137,642, adjusted per study, multivariable, day 100, model 2. |
| AKI, 41.0% lower, HR 0.59, p < 0.001, treatment 30,908, control 137,642, adjusted per study, multivariable, model 2. | |
| Tamura, 7/13/2021, retrospective, Brazil, peer-reviewed, 4 authors, study period 10 March, 2020 - 13 November, 2020. | risk of death, 96.6% lower, OR 0.03, p = 0.02, treatment 115, control 73, adjusted per study, in-hospital use, multivariable, RR approximated with OR. |
| Ventura-López, 8/31/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, mean age 47.5, 14 authors, study period January 2020 - August 2021. | oxygen time, 44.3% lower, relative time 0.56, p = 0.03, treatment mean 5.9 (±4.6) n=10, control mean 10.6 (±6.2) n=10. |
| hospitalization time, 10.2% lower, relative time 0.90, p = 0.35, treatment mean 8.8 (±6.1) n=10, control mean 9.8 (±5.4) n=10. | |
| time to viral-, 41.1% lower, relative time 0.59, p = 0.03, treatment mean 3.3 (±2.16) n=10, control mean 5.6 (±0.89) n=10. |
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.
| Akinosoglou, 5/27/2023, prospective, Greece, peer-reviewed, median age 70.0, 23 authors, study period February 2021 - June 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 36.8% lower, OR 0.63, p = 0.12, treatment 147, control 207, RR approximated with OR. |
| risk of ICU admission, 38.7% higher, OR 1.39, p = 0.26, treatment 147, control 207, RR approximated with OR. | |
| risk of ARDS, 2.7% higher, OR 1.03, p = 0.92, treatment 147, control 207, RR approximated with OR. | |
| Al-kuraishy, 12/1/2023, prospective, Iraq, peer-reviewed, 10 authors, study period March 2020 - June 2020, excluded in exclusion analyses: unadjusted results with significant baseline differences. | risk of death, 77.8% lower, RR 0.22, p = 0.01, treatment 3 of 60 (5.0%), control 9 of 40 (22.5%), NNT 5.7. |
| relative clinical score, 40.8% better, RR 0.59, p < 0.001, treatment 57, control 31. | |
| relative CT score, 84.0% better, RR 0.16, p < 0.001, treatment 57, control 31. | |
| Al-Salameh, 11/30/2021, retrospective, France, peer-reviewed, 4 authors. | risk of death/ICU, 55.5% lower, RR 0.45, p = 0.04, treatment 9 of 47 (19.1%), control 22 of 50 (44.0%), NNT 4.0, adjusted per study, odds ratio converted to relative risk, metformin continued, multivariable. |
| risk of death/ICU, 68.4% higher, RR 1.68, p = 0.02, treatment 34 of 43 (79.1%), control 22 of 50 (44.0%), adjusted per study, odds ratio converted to relative risk, metformin discontinued, multivariable. | |
| Alamgir, 4/6/2021, retrospective, database analysis, USA, preprint, 11 authors. | risk of death, 27.0% lower, OR 0.73, p < 0.001, treatment 11,062, control 11,062, all patients, RR approximated with OR. |
| risk of death, 34.0% lower, OR 0.66, p = 0.007, treatment 5,369, control 5,369, diabetic patients with CCI≤3, RR approximated with OR. | |
| risk of death, 30.0% lower, OR 0.70, p = 0.02, treatment 2,525, control 2,525, non-diabetic patients with CCI≤3, RR approximated with OR. | |
| Alieva, 6/6/2023, retrospective, Uzbekistan, peer-reviewed, 9 authors, study period April 2020 - December 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 15.3% lower, OR 0.85, p = 0.56, treatment 375, control 388, RR approximated with OR. |
| Ando, 9/9/2021, retrospective, USA, peer-reviewed, 6 authors, study period 1 January, 2020 - 30 November, 2020. | risk of hospitalization, 39.0% lower, HR 0.61, p = 0.04, treatment 19 of 663 (2.9%), control 1,056 of 27,430 (3.8%), adjusted per study, multivariable, Cox proportional hazards. |
| Araldi, 5/19/2023, retrospective, United Kingdom, preprint, 3 authors. | risk of death, 60.0% lower, HR 0.40, p < 0.001, treatment 107 of 2,598 (4.1%), control 263 of 2,598 (10.1%), NNT 17, adjusted per study, type 2 diabetes patients, matched cohort, multivariable, Cox proportional hazards. |
| Bidari, 10/19/2023, retrospective, Iran, peer-reviewed, 8 authors, study period February 2020 - April 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of severe case, 10.5% lower, RR 0.90, p = 0.53, treatment 29 of 80 (36.2%), control 132 of 326 (40.5%), NNT 24. |
| Blanc, 7/17/2021, retrospective, France, peer-reviewed, 22 authors. | risk of death, 78.6% lower, RR 0.21, p = 0.06, treatment 1 of 14 (7.1%), control 25 of 75 (33.3%), NNT 3.8, COVID+. |
| risk of case, 43.7% higher, RR 1.44, p = 0.12, treatment 11 of 16 (68.8%), control 78 of 163 (47.9%). | |
| Bliden, 11/8/2021, retrospective, USA, preprint, 9 authors, excluded in exclusion analyses: unadjusted results with minimal group details. | risk of death, 59.8% lower, RR 0.40, p = 0.21, treatment 3 of 34 (8.8%), control 9 of 41 (22.0%), NNT 7.6. |
| risk of mechanical ventilation, 75.9% lower, RR 0.24, p = 0.05, treatment 2 of 34 (5.9%), control 10 of 41 (24.4%), NNT 5.4. | |
| Boye, 7/18/2021, retrospective, USA, peer-reviewed, 14 authors. | risk of hospitalization, 10.0% lower, RR 0.90, p < 0.001, treatment 2,067 of 4,250 (48.6%), control 3,196 of 5,281 (60.5%), NNT 8.4, odds ratio converted to relative risk. |
| Bramante, 3/23/2021, retrospective, USA, peer-reviewed, 18 authors, study period 4 March, 2020 - 4 December, 2020. | risk of death, 62.0% lower, OR 0.38, p = 0.03, treatment 342, control 342, propensity score matching, RR approximated with OR. |
| risk of death, 68.0% lower, OR 0.32, p = 0.003, treatment 676, control 8,879, adjusted per study, multivariable, RR approximated with OR. | |
| risk of ICU admission, 9.0% higher, OR 1.09, p = 0.78, treatment 342, control 342, propensity score matching, RR approximated with OR. | |
| risk of ICU admission, 32.0% lower, OR 0.68, p = 0.06, treatment 676, control 8,879, adjusted per study, multivariable, RR approximated with OR. | |
| risk of hospitalization, 22.0% lower, OR 0.78, p = 0.10, treatment 676, control 8,879, adjusted per study, multivariable, RR approximated with OR. | |
| Bramante (B), 12/3/2020, retrospective, database analysis, USA, peer-reviewed, 17 authors. | risk of death, 11.6% lower, HR 0.88, p = 0.65, treatment 394 of 2,333 (16.9%), control 791 of 3,923 (20.2%), NNT 31, adjusted per study, multivariable, Cox proportional hazards. |
| risk of death, 21.5% lower, HR 0.79, p = 0.01, treatment 1,129, control 2,173, adjusted per study, women, multivariable, Cox proportional hazards. | |
| risk of death, 4.3% lower, HR 0.96, p = 0.69, treatment 1,204, control 1,750, adjusted per study, men, multivariable, Cox proportional hazards. | |
| Cariou, 5/29/2020, retrospective, France, peer-reviewed, mean age 69.8, 41 authors, study period 10 March, 2020 - 10 April, 2020, trial NCT04324736 (history) (CORONADO). | risk of death, 20.0% lower, OR 0.80, p = 0.46, treatment 746, control 571, adjusted per study, multivariable, RR approximated with OR. |
| Chan, 8/30/2022, retrospective, USA, preprint, 15 authors. | risk of death, 58.6% lower, OR 0.41, p = 0.66, treatment 400, control 2,736, adjusted per study, mortality/hospice, multivariable, prediabeties, RR approximated with OR. |
| risk of severe case, 54.1% lower, OR 0.46, p = 0.37, treatment 400, control 2,736, adjusted per study, multivariable, prediabeties, RR approximated with OR. | |
| risk of progression, 42.4% lower, RR 0.58, p = 0.37, treatment 51 of 400 (12.8%), control 798 of 2,736 (29.2%), NNT 6.1, adjusted per study, odds ratio converted to relative risk, moderate, multivariable, prediabeties. | |
| risk of progression, 37.0% lower, OR 0.63, p = 0.37, treatment 400, control 2,736, adjusted per study, mild ER, multivariable, prediabeties, RR approximated with OR. | |
| risk of progression, 40.7% lower, OR 0.59, p = 0.22, treatment 196, control 86, adjusted per study, moderate, multivariable, PCOS, RR approximated with OR. | |
| risk of progression, 34.5% lower, OR 0.66, p = 0.20, treatment 196, control 86, adjusted per study, mild ER, multivariable, PCOS, RR approximated with OR. | |
| Chen (B), 6/8/2024, retrospective, China, peer-reviewed, mean age 66.3, 11 authors, study period 20 March, 2022 - 18 June, 2022. | risk of ICU admission, 80.7% lower, RR 0.19, p = 0.008, treatment 2 of 121 (1.7%), control 25 of 292 (8.6%), NNT 14. |
| pneumonia, 39.1% lower, RR 0.61, p = 0.009, treatment 25 of 121 (20.7%), control 99 of 292 (33.9%), NNT 7.6. | |
| hospitalization time, 16.6% lower, relative time 0.83, p = 0.001, treatment 121, control 292. | |
| Chen (C), 7/31/2020, retrospective, China, peer-reviewed, 12 authors. | risk of death, 33.0% lower, RR 0.67, p = 0.46, treatment 4 of 43 (9.3%), control 15 of 77 (19.5%), NNT 9.8, adjusted per study, odds ratio converted to relative risk. |
| Cheng, 8/20/2021, retrospective, propensity score matching, China, peer-reviewed, 35 authors. | risk of death, 65.0% higher, HR 1.65, p = 0.25, treatment 678, control 535, after PSM. |
| Choi, 6/23/2020, retrospective, South Korea, peer-reviewed, median age 29.0, 8 authors, study period 5 March, 2020 - 18 March, 2020. | risk of progression, 120.0% higher, OR 2.20, p = 0.26, treatment 6 of 36 (16.7%) cases, 3 of 36 (8.3%) controls, case control OR, propensity score matching. |
| Cousins, 7/6/2022, retrospective, propensity score matching, USA, peer-reviewed, 10 authors. | risk of mechanical ventilation, 50.0% lower, OR 0.50, p = 0.01, treatment 2,463, control 2,463, propensity score matching, RR approximated with OR. |
| risk of ICU admission, 51.0% lower, OR 0.49, p < 0.001, treatment 2,463, control 2,463, propensity score matching, RR approximated with OR. | |
| Crouse, 1/13/2021, retrospective, USA, peer-reviewed, 6 authors. | risk of death, 60.8% lower, RR 0.39, p = 0.02, treatment 8 of 76 (10.5%), control 34 of 144 (23.6%), NNT 7.6, adjusted per study, odds ratio converted to relative risk, multiple logistic regression. |
| Dimnjaković, 3/27/2024, retrospective, Croatia, peer-reviewed, 7 authors. | risk of hospitalization, 23.1% lower, OR 0.77, p = 0.004, treatment 2,843, control 4,475, adjusted per study, multivariable, RR approximated with OR. |
| risk of case, 12.5% lower, OR 0.88, p = 0.04, treatment 2,843, control 4,475, adjusted per study, multivariable, RR approximated with OR. | |
| Farah, 9/20/2023, retrospective, Jordan, peer-reviewed, mean age 59.5, 10 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of case, 2.7% higher, RR 1.03, p = 0.87, treatment 267 of 821 (32.5%), control 69 of 218 (31.7%). |
| Fu, 1/17/2022, retrospective, China, peer-reviewed, median age 63.0, 14 authors, study period 8 January, 2020 - 7 March, 2020, this trial compares with another treatment - results may be better when compared to placebo. | risk of unfavorable outcome, 71.9% lower, RR 0.28, p = 0.03, treatment 4 of 49 (8.2%), control 9 of 31 (29.0%), NNT 4.8, unfavorable outcome, metformin vs. other treatments. |
| Gao, 10/19/2020, retrospective, China, peer-reviewed, 7 authors, study period 31 January, 2020 - 20 March, 2020. | risk of progression, 225.0% higher, RR 3.25, p = 0.045, treatment 16 of 56 (28.6%), control 4 of 54 (7.4%), odds ratio converted to relative risk, progression to life threatening complications. |
| Ghany, 3/31/2021, retrospective, USA, peer-reviewed, 8 authors. | risk of death, 66.0% lower, HR 0.34, p < 0.001, treatment 392, control 747, adjusted per study, multivariable, Cox proportional hazards. |
| risk of hospitalization, 29.0% lower, HR 0.71, p = 0.008, treatment 392, control 747, adjusted per study, multivariable, Cox proportional hazards. | |
| risk of ARDS, 68.0% lower, HR 0.32, p < 0.001, treatment 392, control 747, adjusted per study, multivariable, Cox proportional hazards. | |
| Goodall, 10/13/2020, retrospective, United Kingdom, peer-reviewed, 7 authors, study period 12 March, 2020 - 15 April, 2020. | risk of death, 3.0% lower, HR 0.97, p = 0.81, treatment 74 of 210 (35.2%), control 280 of 771 (36.3%), NNT 93. |
| Greco, 8/18/2023, retrospective, Italy, peer-reviewed, 8 authors, study period January 2020 - December 2021, this trial compares with another treatment - results may be better when compared to placebo. | risk of hospitalization, 22.0% lower, OR 0.78, p = 0.11, treatment 30,238, control 2,264, DPP-4is, RR approximated with OR. |
| risk of hospitalization, 26.0% lower, OR 0.74, p = 0.006, treatment 30,238, control 14,739, insulin or insulin secretagogues, RR approximated with OR. | |
| risk of hospitalization, 17.0% lower, OR 0.83, p = 0.54, treatment 30,238, control 317, GLP-1 RAs, RR approximated with OR. | |
| Guo, 8/24/2023, retrospective, China, peer-reviewed, median age 65.0, 8 authors, study period 4 February, 2020 - 11 April, 2020. | risk of death/intubation, 62.4% lower, HR 0.38, p = 0.03, treatment 241, control 330, adjusted per study, multivariable, Cox proportional hazards. |
| risk of progression, 81.1% lower, HR 0.19, p = 0.003, treatment 241, control 330, adjusted per study, severe respiratory failure, multivariable, Cox proportional hazards. | |
| risk of progression, 80.1% lower, HR 0.20, p = 0.05, treatment 241, control 330, adjusted per study, ARDS, multivariable, Cox proportional hazards. | |
| Gálvez-Barrón, 4/14/2021, retrospective, Spain, peer-reviewed, mean age 86.8, 13 authors, study period 12 March, 2020 - 2 May, 2020. | risk of death, 16.1% higher, RR 1.16, p = 0.46, treatment 20, control 83, odds ratio converted to relative risk, control prevalance approximated with overall prevalence. |
| risk of severe case, 16.1% higher, RR 1.16, p = 0.46, treatment 20, control 83, odds ratio converted to relative risk, control prevalance approximated with overall prevalence. | |
| Harmon, 9/19/2024, retrospective, USA, peer-reviewed, 6 authors, study period 25 January, 2020 - 9 February, 2022. | risk of death, 18.0% lower, RR 0.82, p < 0.001, treatment 4,667, control 5,745, propensity score weighting. |
| 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, 27.0% higher, RR 1.27, p = 0.42, treatment 12 of 429 (2.8%), control 434 of 14,798 (2.9%), adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated. |
| Huh, 12/19/2020, retrospective, database analysis, South Korea, peer-reviewed, 8 authors. | risk of progression, 0.7% higher, RR 1.01, p = 0.11, treatment 104 of 272 (38.2%), control 774 of 2,533 (30.6%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of case, 4.0% lower, OR 0.96, p = 0.82, treatment 329 of 7,341 (4.5%) cases, 1,545 of 36,705 (4.2%) controls, adjusted per study, case control OR, multivariable. | |
| Hussein, 6/30/2024, retrospective, Iraq, peer-reviewed, 4 authors, this trial compares with another treatment - results may be better when compared to placebo. | risk of death, 63.8% lower, RR 0.36, p = 0.048, treatment 30 of 158 (19.0%), control 60 of 110 (54.5%), NNT 2.8, adjusted per study, odds ratio converted to relative risk, multivariable. |
| Jang, 1/29/2024, retrospective, South Korea, peer-reviewed, 6 authors. | risk of death, 60.5% lower, OR 0.40, p = 0.02, treatment 461, control 95, adjusted per study, multivariable, RR approximated with OR. |
| risk of mechanical ventilation, 71.9% lower, OR 0.28, p = 0.008, treatment 461, control 95, adjusted per study, multivariable, RR approximated with OR. | |
| risk of ICU admission, 38.8% lower, OR 0.61, p = 0.12, treatment 461, control 95, adjusted per study, multivariable, RR approximated with OR. | |
| risk of oxygen therapy, 29.7% lower, OR 0.70, p = 0.23, treatment 461, control 95, adjusted per study, multivariable, RR approximated with OR. | |
| risk of hospitalization, 27.1% higher, OR 1.27, p = 0.42, treatment 461, control 95, adjusted per study, multivariable, RR approximated with OR. | |
| Jiang, 3/31/2021, retrospective, China, peer-reviewed, 12 authors. | risk of death, 46.0% lower, HR 0.54, p = 0.40, treatment 3 of 74 (4.1%), control 10 of 74 (13.5%), adjusted per study, mixed effect Cox, propensity score matching. |
| risk of ARDS, 80.2% lower, RR 0.20, p = 0.02, treatment 8 of 74 (10.8%), control 17 of 74 (23.0%), NNT 8.2, adjusted per study, odds ratio converted to relative risk, mixed effect Cox, propensity score matching. | |
| Johnson, 9/17/2024, retrospective, USA, peer-reviewed, 19 authors. | death or long COVID, 11.3% lower, HR 0.89, p = 0.11, treatment 42,275, control 6,713, combined. |
| death or long COVID, 21.0% lower, HR 0.79, p < 0.001, treatment 42,275, control 6,713, N3C, EHR code. | |
| death or long COVID, 15.0% lower, HR 0.85, p < 0.001, treatment 42,275, control 6,713, N3C, phenotype. | |
| death or long COVID, 13.0% lower, HR 0.87, p = 0.32, treatment 30,748, control 5,933, PCORnet, EHR code. | |
| death or long COVID, 4.0% higher, HR 1.04, p = 0.26, treatment 30,748, control 5,933, PCORnet, phenotype. | |
| Khunti, 3/30/2021, retrospective, population-based cohort, United Kingdom, peer-reviewed, 15 authors. | risk of death, 23.0% lower, HR 0.77, p < 0.001, adjusted per study. |
| Kim, 8/12/2020, retrospective, South Korea, peer-reviewed, 32 authors. | risk of death, 64.0% lower, OR 0.36, p = 0.10, treatment 113, control 122, adjusted per study, multivariable, RR approximated with OR. |
| risk of progression, 52.0% lower, OR 0.48, p = 0.13, treatment 113, control 122, adjusted per study, multivariable, RR approximated with OR. | |
| Lalau, 12/10/2020, retrospective, France, peer-reviewed, 33 authors, study period 10 March, 2020 - 10 April, 2020. | risk of death, 22.2% lower, OR 0.78, p = 0.16, treatment 671, control 419, day 28, model 2, propensity score matching, RR approximated with OR. |
| risk of death/intubation, 17.8% lower, OR 0.82, p = 0.21, treatment 671, control 419, day 28, model 2, propensity score matching, primary outcome, RR approximated with OR. | |
| risk of mechanical ventilation, 6.8% lower, OR 0.93, p = 0.72, treatment 671, control 419, day 28, model 2, propensity score matching, RR approximated with OR. | |
| Lally, 1/31/2021, retrospective, USA, peer-reviewed, 6 authors. | risk of death, 52.0% lower, HR 0.48, p = 0.009, treatment 16 of 127 (12.6%), control 144 of 648 (22.2%), NNT 10, adjusted per study, multivariable regression. |
| Lewandowski, 3/7/2024, retrospective, Poland, peer-reviewed, 15 authors. | risk of death, 22.9% lower, RR 0.77, p = 0.15, treatment 14 of 101 (13.9%), control 83 of 329 (25.2%), NNT 8.8, odds ratio converted to relative risk. |
| Li (B), 10/1/2020, retrospective, China, peer-reviewed, 16 authors, study period 23 January, 2020 - 19 March, 2020. | risk of death, 77.7% lower, HR 0.22, p = 0.02, treatment 2 of 37 (5.4%), control 21 of 94 (22.3%), NNT 5.9, adjusted per study, multivariable. |
| risk of mechanical ventilation, 27.0% higher, RR 1.27, p = 1.00, treatment 1 of 37 (2.7%), control 2 of 94 (2.1%). | |
| Loucera, 8/16/2022, retrospective, Spain, peer-reviewed, 8 authors, study period January 2020 - November 2020. | risk of death, 30.0% lower, HR 0.70, p < 0.001, treatment 1,896, control 14,072, Cox proportional hazards, day 30. |
| Luo, 5/21/2020, retrospective, China, peer-reviewed, 9 authors. | risk of death, 74.7% lower, RR 0.25, p = 0.02, treatment 3 of 104 (2.9%), control 22 of 179 (12.3%), NNT 11, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariate. |
| Ma (B), 4/1/2022, retrospective, USA, peer-reviewed, 4 authors, study period 16 March, 2020 - 15 February, 2021. | risk of death, 74.2% lower, RR 0.26, p = 0.03, treatment 3 of 361 (0.8%), control 40 of 995 (4.0%), NNT 31, odds ratio converted to relative risk, in-hospital death or hospice, propensity score weighting. |
| risk of mechanical ventilation, 25.0% lower, RR 0.75, p = 0.44, treatment 12 of 360 (3.3%), control 16 of 360 (4.4%), NNT 90, propensity score matching. | |
| MacFadden, 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. | risk of case, 1.0% lower, OR 0.99, p = 0.45, RR approximated with OR. |
| Mamari, 11/30/2023, retrospective, Syria, peer-reviewed, 2 authors, this trial compares with another treatment - results may be better when compared to placebo. | risk of death, 50.0% lower, RR 0.50, p = 0.01, treatment 11 of 34 (32.4%), control 22 of 34 (64.7%), NNT 3.1. |
| Mannucci, 10/31/2022, retrospective, Italy, peer-reviewed, 10 authors, study period 1 March, 2020 - 31 December, 2020. | risk of death, 38.0% lower, OR 0.62, p = 0.02, RR approximated with OR. |
| risk of hospitalization, 15.0% lower, OR 0.85, p = 0.25, RR approximated with OR. | |
| Miao, 11/9/2022, retrospective, USA, peer-reviewed, 6 authors, study period 1 January, 2020 - 7 May, 2020. | risk of death, 1.3% lower, RR 0.99, p = 0.91, treatment 233 of 796 (29.3%), control 236 of 796 (29.6%), NNT 265, propensity score matching. |
| hospitalization time, 4.9% lower, relative time 0.95, p = 0.23, treatment 796, control 796, propensity score matching. | |
| Miguel, 11/17/2023, retrospective, Spain, peer-reviewed, 19 authors, study period March 2020 - June 2020. | risk of ICU admission, 37.4% lower, RR 0.63, p = 0.24, treatment 64, control 68, both cohorts combined. |
| risk of ICU admission, 42.9% lower, RR 0.57, p = 0.34, treatment 3 of 15 (20.0%), control 14 of 40 (35.0%), NNT 6.7. | |
| risk of ICU admission, 31.4% lower, RR 0.69, p = 0.52, treatment 6 of 49 (12.2%), control 5 of 28 (17.9%), NNT 18. | |
| Milosavljevic, 11/9/2022, retrospective, USA, peer-reviewed, mean age 67.4, 7 authors, study period 1 March, 2020 - 31 December, 2020. | risk of severe case, 33.0% lower, OR 0.67, p = 0.03, treatment 377, control 356, RR approximated with OR. |
| Mirani, 10/6/2020, retrospective, Italy, peer-reviewed, median age 66.0, 8 authors, study period 20 February, 2020 - 9 April, 2020. | risk of death, 45.0% lower, HR 0.55, p = 0.10, treatment 25 of 69 (36.2%), control 13 of 21 (61.9%), NNT 3.9, adjusted per study, Cox proportional hazards. |
| Morrison, 10/10/2022, retrospective, USA, peer-reviewed, mean age 62.5, 3 authors, study period March 2020 - March 2021. | risk of death, 41.1% lower, OR 0.59, p = 0.003, treatment 2,684, control 2,684, propensity score matching, RR approximated with OR. |
| risk of mechanical ventilation, 15.7% higher, OR 1.16, p = 0.49, treatment 2,684, control 2,684, propensity score matching, RR approximated with OR. | |
| risk of ICU admission, 2.8% lower, OR 0.97, p = 0.85, treatment 2,684, control 2,684, propensity score matching, RR approximated with OR. | |
| risk of hospitalization, 3.9% higher, OR 1.04, p = 0.72, treatment 2,684, control 2,684, propensity score matching, RR approximated with OR. | |
| Obiri-Yeboah, 6/8/2023, retrospective, USA, peer-reviewed, mean age 67.0, 8 authors. | risk of death, 1.0% higher, OR 1.01, p = 0.98, treatment 148, control 381, RR approximated with OR. |
| risk of mechanical ventilation, 4.0% higher, OR 1.04, p = 0.87, treatment 148, control 381, RR approximated with OR. | |
| risk of ICU admission, 8.0% lower, OR 0.92, p = 0.72, treatment 148, control 381, RR approximated with OR. | |
| Oh, 2/13/2021, retrospective, USA, peer-reviewed, 2 authors. | risk of death, 26.0% higher, OR 1.26, p = 0.30, treatment 5,946, control 5,946, adjusted per study, multivariable, RR approximated with OR. |
| risk of case, 28.0% lower, RR 0.72, p < 0.001, treatment 390 of 5,946 (6.6%), control 541 of 5,946 (9.1%), NNT 39, adjusted per study, odds ratio converted to relative risk, propensity score matching. | |
| Ojeda-Fernández, 1/10/2022, retrospective, Italy, peer-reviewed, 11 authors. | risk of death, 16.2% lower, RR 0.84, p < 0.001, treatment 1,476 of 6,556 (22.5%), control 1,787 of 6,556 (27.3%), NNT 21, odds ratio converted to relative risk, propensity score matching. |
| risk of death, 22.1% lower, RR 0.78, p < 0.001, treatment 968 of 6,556 (14.8%), control 1,261 of 6,556 (19.2%), NNT 22, odds ratio converted to relative risk, in-hospital mortality, propensity score matching. | |
| risk of ICU admission, 22.4% lower, RR 0.78, p = 0.01, treatment 166 of 6,556 (2.5%), control 212 of 6,556 (3.2%), NNT 143, odds ratio converted to relative risk, propensity score matching. | |
| risk of hospitalization, 2.7% lower, RR 0.97, p = 0.11, treatment 3,551 of 6,556 (54.2%), control 3,670 of 6,556 (56.0%), NNT 55, odds ratio converted to relative risk, propensity score matching. | |
| risk of death, 8.3% lower, RR 0.92, p = 0.06, treatment 793 of 3,297 (24.1%), control 876 of 3,297 (26.6%), NNT 40, odds ratio converted to relative risk, excluding patients previously treated with insulin, propensity score matching. | |
| risk of death, 16.0% lower, RR 0.84, p = 0.003, treatment 512 of 3,297 (15.5%), control 618 of 3,297 (18.7%), NNT 31, odds ratio converted to relative risk, excluding patients previously treated with insulin, in-hospital mortality, propensity score matching. | |
| risk of ICU admission, 39.2% lower, RR 0.61, p = 0.002, treatment 64 of 3,297 (1.9%), control 102 of 3,297 (3.1%), NNT 87, odds ratio converted to relative risk, excluding patients previously treated with insulin, propensity score matching. | |
| risk of hospitalization, 2.2% higher, RR 1.02, p = 0.36, treatment 1,822 of 3,297 (55.3%), control 1,792 of 3,297 (54.4%), odds ratio converted to relative risk, excluding patients previously treated with insulin, propensity score matching. | |
| Olawore, 5/31/2024, retrospective, USA, peer-reviewed, 8 authors, study period October 2021 - April 2023. | risk of PASC, 19.0% lower, RR 0.81, p = 0.29, treatment 5,596, control 1,451, 6 months. |
| risk of PASC, 14.0% lower, RR 0.86, p = 0.50, treatment 5,596, control 1,451, 3 months. | |
| Ong, 10/30/2021, retrospective, Philippines, peer-reviewed, 6 authors, study period 1 March, 2020 - 30 September, 2020. | risk of death, 46.8% lower, RR 0.53, p = 0.02, treatment 33 of 186 (17.7%), control 57 of 169 (33.7%), NNT 6.3, adjusted per study, odds ratio converted to relative risk, combined pre-existing and in-hospital use. |
| risk of death, 23.9% lower, RR 0.76, p = 0.16, treatment 28 of 109 (25.7%), control 57 of 169 (33.7%), NNT 12, odds ratio converted to relative risk, pre-existing use, unadjusted. | |
| risk of death, 85.2% lower, RR 0.15, p = 0.002, treatment 2 of 40 (5.0%), control 57 of 169 (33.7%), NNT 3.5, odds ratio converted to relative risk, in-hospital use, unadjusted. | |
| risk of death, 76.0% lower, RR 0.24, p = 0.005, treatment 3 of 37 (8.1%), control 57 of 169 (33.7%), NNT 3.9, odds ratio converted to relative risk, mixed pre-existing/in-hospital use, unadjusted. | |
| Ouchi, 10/4/2022, retrospective, Spain, peer-reviewed, mean age 71.5, 5 authors, study period March 2020 - June 2020. | risk of death, 9.9% lower, OR 0.90, p = 0.19, treatment 6,168, control 9,875, inverted to make OR<1 favor treatment, metformin monotherapy vs. untreated, RR approximated with OR. |
| risk of death/hospitalization, 8.3% lower, OR 0.92, p = 0.12, treatment 6,168, control 9,875, inverted to make OR<1 favor treatment, metformin monotherapy vs. untreated, RR approximated with OR. | |
| Piarulli, 6/24/2023, retrospective, Italy, peer-reviewed, 7 authors, study period February 2020 - February 2021. | risk of death/ICU, 53.0% lower, OR 0.47, p = 0.08, treatment 1,444, control 1,009, adjusted per study, for all patients, combined odds of hospitalization and ICU/death for hospitalized patients, multivariable, RR approximated with OR. |
| risk of death/ICU, 15.0% lower, OR 0.85, p = 0.68, treatment 209, control 180, adjusted per study, among hospitalized patients, multivariable, RR approximated with OR. | |
| risk of hospitalization, 45.0% lower, OR 0.55, p < 0.001, treatment 1,444, control 1,009, adjusted per study, multivariable, RR approximated with OR. | |
| Pinchera, 1/6/2023, retrospective, Italy, peer-reviewed, 9 authors, study period November 2021 - May 2022, this trial compares with another treatment - results may be better when compared to placebo. | risk of severe case, 15.2% lower, RR 0.85, p = 0.048, treatment 5 of 19 (26.3%), control 14 of 24 (58.3%), NNT 3.1, adjusted per study, odds ratio converted to relative risk, multivariable. |
| Pérez-Belmonte, 11/16/2020, retrospective, propensity score matching, Spain, peer-reviewed, 26 authors. | risk of death, 10.4% higher, RR 1.10, p = 0.48, treatment 79 of 249 (31.7%), control 79 of 249 (31.7%), adjusted per study, odds ratio converted to relative risk, mixed effect logistic regression, propensity score matching. |
| Ramos-Rincón, 12/28/2020, retrospective, Spain, preprint, 25 authors, study period 1 March, 2020 - 29 May, 2020. | risk of death, 1.3% lower, RR 0.99, p = 0.78, treatment 206 of 420 (49.0%), control 179 of 370 (48.4%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Ravindra, 5/5/2021, retrospective, India, peer-reviewed, 14 authors, excluded in exclusion analyses: minimal details provided. | risk of death, 29.6% lower, RR 0.70, p = 0.42, treatment 5 of 53 (9.4%), control 57 of 313 (18.2%), adjusted per study, odds ratio converted to relative risk. |
| Sakamaki, 9/27/2024, retrospective, Japan, peer-reviewed, mean age 52.1, 3 authors, study period 15 January, 2020 - 31 December, 2022. | risk of severe case, 23.0% lower, OR 0.77, p < 0.001, adjusted per study, multivariable, RR approximated with OR. |
| Sandhu, 3/31/2023, retrospective, USA, peer-reviewed, mean age 50.7, 7 authors, study period 1 January, 2020 - 31 December, 2020. | risk of hospitalization, 2.8% lower, OR 0.97, p = 0.004, RR approximated with OR. |
| Saygili, 10/29/2021, retrospective, Turkey, peer-reviewed, 5 authors. | risk of death, 41.5% lower, RR 0.58, p = 0.02, treatment 120, control 120, overall mortality, Cox regression in matched group, propensity score matching. |
| Servais, 12/7/2022, retrospective, Belgium, peer-reviewed, median age 73.0, 21 authors, study period 1 March, 2020 - 6 May, 2020. | risk of death, 49.0% lower, HR 0.51, p = 0.002, adjusted per study, multivariable. |
| Shestakova, 8/9/2022, retrospective, Russia, peer-reviewed, 6 authors, study period 20 March, 2020 - 25 November, 2021. | risk of death, 21.6% lower, RR 0.78, p = 0.001, treatment 21,471 of 139,637 (15.4%), control 12,721 of 50,361 (25.3%), adjusted per study, odds ratio converted to relative risk, Table S2, multivariable. |
| Silverii, 3/24/2024, retrospective, Italy, peer-reviewed, 6 authors. | risk of death, 29.0% lower, OR 0.71, p = 0.50, treatment 220, control 304, adjusted for COVID-19 MRS, antivirals, heart disease, RR approximated with OR. |
| risk of death, 20.5% lower, OR 0.80, p = 0.34, treatment 220, control 304, adjusted for COVID-19 MRS only, RR approximated with OR. | |
| Somasundaram, 11/9/2024, retrospective, India, peer-reviewed, mean age 53.3, 13 authors, study period 1 April, 2020 - 31 March, 2022, trial CTRI/2022/02/040064. | risk of death, 89.4% lower, OR 0.11, p < 0.001, treatment 221, control 200, adjusted per study, multivariable, RR approximated with OR. |
| Sourij, 12/4/2020, retrospective, Austria, peer-reviewed, mean age 71.1, 24 authors. | risk of death, 37.3% lower, RR 0.63, p = 0.13, treatment 14 of 77 (18.2%), control 44 of 161 (27.3%), NNT 11, odds ratio converted to relative risk. |
| Usman, 1/18/2022, retrospective, USA, peer-reviewed, 10 authors. | risk of death, 59.8% lower, RR 0.40, p = 0.21, treatment 3 of 34 (8.8%), control 9 of 41 (22.0%), NNT 7.6. |
| risk of mechanical ventilation, 75.9% lower, RR 0.24, p = 0.05, treatment 2 of 34 (5.9%), control 10 of 41 (24.4%), NNT 5.4. | |
| hospitalization time, 33.7% lower, relative time 0.66, p = 0.13, treatment 34, control 41. | |
| Wallace, 12/31/2021, retrospective, database analysis, USA, peer-reviewed, 6 authors. | risk of death, 72.0% lower, HR 0.28, p < 0.001, treatment 103 of 1,203 (8.6%), control 1,536 of 6,970 (22.0%), NNT 7.4, adjusted per study, before+after, propensity score weighting, Cox proportional hazards. |
| Wander, 10/6/2021, retrospective, database analysis, USA, peer-reviewed, 8 authors. | risk of death, 15.0% lower, RR 0.85, p < 0.001, treatment 29,685, control 35,207, odds ratio converted to relative risk, logistic regression, within 30 days of diagnosis, control prevalance approximated with overall prevalence. |
| risk of ICU admission, 1.9% lower, RR 0.98, p = 0.62, treatment 29,685, control 35,207, odds ratio converted to relative risk, logistic regression, within 30 days of diagnosis, control prevalance approximated with overall prevalence. | |
| risk of hospitalization, 3.2% lower, RR 0.97, p = 0.09, treatment 29,685, control 35,207, odds ratio converted to relative risk, logistic regression, within 30 days of diagnosis, control prevalance approximated with overall prevalence. | |
| Wang (B), 9/7/2021, retrospective, USA, peer-reviewed, 4 authors. | risk of ICU admission, 12.0% lower, RR 0.88, p = 0.005, treatment 6,504, control 10,000, Cox proportional hazards. |
| Wang (C), 7/14/2020, retrospective, USA, peer-reviewed, 13 authors. | risk of death, 57.7% lower, RR 0.42, p = 0.43, treatment 1 of 9 (11.1%), control 13 of 49 (26.5%), NNT 6.5, odds ratio converted to relative risk. |
| Wargny, 2/17/2021, retrospective, France, peer-reviewed, 43 authors, study period 10 March, 2020 - 10 April, 2020, trial NCT04324736 (history) (CORONADO). | risk of death, 28.3% lower, RR 0.72, p = 0.03, treatment 247 of 1,553 (15.9%), control 330 of 1,241 (26.6%), NNT 9.4, adjusted per study, odds ratio converted to relative risk, multivariable, day 28. |
| risk of no hospital discharge, 14.8% lower, RR 0.85, p = 0.02, treatment 690 of 1,553 (44.4%), control 702 of 1,241 (56.6%), NNT 8.2, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable, day 28. | |
| Wong, 3/7/2022, retrospective, China, peer-reviewed, 11 authors, study period 21 January, 2020 - 31 January, 2021. | risk of death, 59.0% lower, OR 0.41, p = 0.01, treatment 786, control 428, adjusted per study, propensity score weighting, multivariable, RR approximated with OR. |
| risk of no recovery, 60.6% lower, OR 0.39, p = 0.005, treatment 786, control 428, adjusted per study, inverted to make OR<1 favor treatment, propensity score weighting, multivariable, RR approximated with OR. | |
| clinical improvement, 63.5% better, OR 0.36, p = 0.009, treatment 786, control 428, adjusted per study, inverted to make OR<1 favor treatment, propensity score weighting, multivariable, RR approximated with OR. | |
| risk of no hospital discharge, 55.8% lower, OR 0.44, p = 0.009, treatment 786, control 428, adjusted per study, inverted to make OR<1 favor treatment, propensity score weighting, multivariable, RR approximated with OR. | |
| Wong (B), 2/24/2022, retrospective, USA, peer-reviewed, 15 authors. | risk of death, 51.0% lower, HR 0.49, p < 0.001, treatment 10,408, control 29,208, Cox proportional hazards. |
| risk of mechanical ventilation, 41.0% lower, OR 0.59, p < 0.001, treatment 10,408, control 29,208, adjusted per study, multivariable, RR approximated with OR. | |
| risk of hospitalization, 40.0% lower, OR 0.60, p < 0.001, treatment 10,408, control 29,208, adjusted per study, multivariable, RR approximated with OR. | |
| Xu (B), 5/17/2024, retrospective, China, peer-reviewed, 6 authors. | risk of death, 52.0% lower, HR 0.48, p = 0.01, treatment 405, control 405, adjusted per study, propensity score matching, multivariable, Cox proportional hazards. |
| risk of death, 59.0% lower, HR 0.41, p = 0.001, treatment 466, control 4,456, adjusted per study, multivariable, Cox proportional hazards. | |
| risk of mechanical ventilation, 54.0% lower, HR 0.46, p = 0.007, treatment 466, control 4,456, adjusted per study, multivariable, Cox proportional hazards, Table S7. | |
| risk of ARDS, 72.0% lower, HR 0.28, p = 0.04, treatment 466, control 4,456, adjusted per study, multivariable, Cox proportional hazards, Table S7. | |
| Yeh, 6/9/2022, retrospective, USA, peer-reviewed, mean age 62.3, 9 authors, study period 1 March, 2020 - 28 February, 2021, trial NCT02788903 (history). | ICU/intubation/death, 44.0% lower, OR 0.56, p < 0.001, RR approximated with OR. |
| risk of hospitalization, 37.0% lower, OR 0.63, p < 0.001, RR approximated with OR. | |
| Yen, 5/6/2023, retrospective, multiple countries, peer-reviewed, 4 authors, study period 1 January, 2020 - 22 November, 2022. | risk of death, 25.0% lower, HR 0.75, p = 0.001, treatment 232 of 20,894 (1.1%), control 295 of 20,894 (1.4%), NNT 332, propensity score matching, Kaplan–Meier. |
| risk of mechanical ventilation, 25.0% lower, HR 0.75, p = 0.01, treatment 133 of 20,894 (0.6%), control 168 of 20,894 (0.8%), NNT 597, propensity score matching, Kaplan–Meier. | |
| risk of ICU admission, 19.0% lower, HR 0.81, p = 0.005, treatment 332 of 20,894 (1.6%), control 390 of 20,894 (1.9%), NNT 360, propensity score matching, Kaplan–Meier. | |
| risk of hospitalization, 15.0% lower, HR 0.85, p < 0.001, treatment 2,820 of 20,894 (13.5%), control 3,139 of 20,894 (15.0%), NNT 65, propensity score matching, Kaplan–Meier. | |
| risk of case, 2.0% higher, HR 1.02, p = 0.63, treatment 1,467 of 20,894 (7.0%), control 1,364 of 20,894 (6.5%), propensity score matching, Kaplan–Meier. | |
| Yip, 9/21/2022, retrospective, China, peer-reviewed, mean age 69.0, 10 authors, study period 16 February, 2022 - 31 March, 2022. | risk of death/hospitalization, 7.0% lower, HR 0.93, p = 0.61, treatment 8,604, control 3,727, propensity score matching, Cox proportional hazards. |
| risk of progression, 15.0% lower, HR 0.85, p = 0.16, treatment 8,604, control 3,727, ER/hosp./death, propensity score matching, Cox proportional hazards. | |
| risk of progression, 15.0% lower, HR 0.85, p = 0.13, treatment 8,604, control 3,727, hypoxemia/ER/hosp./death, propensity score matching, Cox proportional hazards. | |
| Zaccardi, 9/13/2022, retrospective, United Kingdom, peer-reviewed, 11 authors. | risk of death, 34.3% lower, RR 0.66, p < 0.001, meta analysis of 6 groups reported. |
| risk of hospitalization, 31.2% lower, RR 0.69, p < 0.001, meta analysis of 6 groups reported. | |
| Zihono, 9/10/2023, retrospective, Indonesia, peer-reviewed, 6 authors. | risk of death, 48.7% lower, RR 0.51, p = 0.02, treatment 11 of 56 (19.6%), control 31 of 81 (38.3%), NNT 5.4. |
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