Probiotics reduce COVID-19 risk: real-time meta analysis of 28 studies
Abstract
Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
28% [18‑36%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Better results are seen with early treatment.
Results are very robust — in exclusion sensitivity analysis 25 of
28 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Probiotic efficacy depends on the specific strains used. Specific microbes may decrease or increase COVID-19 risk1.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
The quality of non-prescription supplements varies widely2,3. Many probiotic supplements may not include labeled ingredients4.
All data and sources to reproduce this analysis are in the appendix.
Probiotics for COVID-19 — Highlights
Probiotics reduce
risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for cases, and low confidence for progression.
Probiotic efficacy depends on the specific strains used.
18th treatment shown effective in
March 2021, now with p = 0.0000011 from 28 studies.
Real-time updates
and corrections with a consistent protocol for 112 treatments. Outcome specific analysis and combined evidence from all
studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in probiotics 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 9.3 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 injury7-18 and
cognitive deficits10,15, cardiovascular
complications19-22, 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,23-29, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk30, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Efficacy with probiotics has been shown for the common cold31.
We analyze all significant
controlled studies of
Probiotics
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Table 1 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 | 28% [18‑36%] **** | 28 | 19,646 | 314 |
| After exclusions | 27% [16‑37%] **** | 25 | 4,339 | 267 |
| Peer-reviewed studiesPeer-reviewed | 27% [17‑36%] **** | 25 | 19,014 | 281 |
| Randomized Controlled TrialsRCTs | 32% [17‑44%] *** | 16 | 2,942 | 150 |
| Mortality | 59% [35‑74%] *** | 10 | 1,302 | 109 |
| VentilationVent. | 38% [-87‑79%] | 3 | 325 | 40 |
| ICU admissionICU | 21% [-20‑48%] | 5 | 599 | 51 |
| HospitalizationHosp. | 13% [5‑21%] ** | 5 | 890 | 38 |
| Recovery | 19% [10‑27%] *** | 13 | 2,075 | 129 |
| Cases | 36% [7‑55%] * | 9 | 17,164 | 130 |
| Viral | 4% [-43‑35%] | 3 | 641 | 27 |
| RCT mortality | 41% [-27‑73%] | 6 | 928 | 58 |
| RCT hospitalizationRCT hosp. | 13% [-2‑25%] | 4 | 590 | 24 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 36% [24‑46%] **** | 18% [2‑31%] * | 37% [12‑55%] ** |
| After exclusions | 36% [24‑47%] **** | 16% [1‑29%] * | 41% [11‑61%] * |
| Peer-reviewed studiesPeer-reviewed | 35% [24‑45%] **** | 18% [2‑31%] * | 39% [10‑59%] * |
| Randomized Controlled TrialsRCTs | 36% [24‑47%] **** | 12% [-4‑26%] | 49% [-10‑77%] |
| Mortality | 67% [-716‑99%] | 60% [35‑75%] *** | -2% [-1509‑94%] |
| VentilationVent. | 38% [-87‑79%] | ||
| ICU admissionICU | 21% [-20‑48%] | ||
| HospitalizationHosp. | 69% [-13‑91%] | 13% [5‑20%] ** | |
| Recovery | 30% [20‑39%] **** | 9% [2‑16%] * | 32% [2‑52%] * |
| Cases | 36% [7‑55%] * |
||
| Viral | 29% [-22‑59%] | -6% [-70‑34%] | |
| RCT mortality | 67% [-716‑99%] | 42% [-33‑75%] | -2% [-1509‑94%] |
| RCT hospitalizationRCT hosp. | 69% [-13‑91%] | 11% [-4‑24%] |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. Random effects meta-analysis for 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
32% improvement,
compared to 24% 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 biases34, and
analysis of double-blind RCTs has identified extreme levels of bias35.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 112 treatments we have analyzed,
66% 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 112 treatments37.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 112 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.92‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.82‑1.03].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see41,42.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to evaluate each trial on its own merits. RCTs for a
given medication and disease may be more reliable, however they may also be
less reliable. For off-patent medications, very high conflict of interest
trials may be more likely to be RCTs, and more likely to be large trials that
dominate meta analyses.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 18 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Di Pierro, unadjusted differences between groups.
Holt, significant unadjusted confounding possible.
Veterini, the observered difference in duration could be caused by the baseline difference in Ct values.
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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 hours46,47. 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 cases48 |
| <24 hours | -33 hours symptoms49 |
| 24-48 hours | -13 hours symptoms49 |
| Inpatients | -2.5 hours to improvement50 |
Figure 19 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 112 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 112 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
variants52, for example the Gamma variant shows significantly
different characteristics53-56. 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 variants57,58.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Non-prescription supplements may show very wide variations in quality2,3.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for probiotics as of March 2021. 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.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 112
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 = 0.00000032 to p = 0.000000011.
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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 5.0 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 probiotics has also been shown for the common cold31.
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 results74-77.
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.
70% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
33% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 52% improvement,
compared to 34% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
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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 < 0.0578-85.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 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. Probiotics for COVID-19
lack this because they are generally inexpensive and widely available.
In contrast, most COVID-19 probiotics 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 probiotics 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 alone61-72.
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
factors23-29, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk30, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 26 shows an overview of the results for probiotics
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.
0.5% of 8,000+ proposed treatments show efficacy100.
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
28% [18‑36%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Better results are seen with early treatment.
Results are very robust — in exclusion sensitivity analysis 25 of
28 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Probiotic efficacy depends on the specific strains used. Specific microbes may decrease or increase COVID-19 risk1.
Other meta analyses show significant improvements with probiotics for hospitalization5 and recovery5,6.
Efficacy with probiotics has also been shown for the common cold31.
Small RCT 60 healthcare workers in Iran, showing lower cases with treatment but without statistical significance. Once daily oral synbiotic capsule (Lactocare®) containing 1 billion CFU L. (Lactobacillus) casei, L. rhamnosus, Streptococcus thermophilus, Bifidobacterium breve, L. acidophilus, Bifidobacterium infantis, L. bulgaricus, and Fructooligosacharide.
Retrospective 60 patients in Romania taking probiotics and 60 matched controls, showing faster symptom resolution with the use of probiotics. Spore-based probiotic containing five strains of Bacillus.
Prospective analysis of 69 severe COVID-19 patients requiring non-invasive oxygen therapy, 40 treated with probiotic formulation SLAB51, showing lower oxygen requirements and higher blood levels of pO2, O2Hb and SaO2 with treatment. Authors suggest that enzymes in SLAB51 could reduce oxygen requirements in intestinal cells, resulting in more oxygen available for other organs.
Retrospective 200 severe condition hospitalized patients in Italy, 88 treated with probiotic Sivomixx, showing lower mortality with treatment.
Retrospective 70 hospitalized patients in Italy, 28 treated with probiotic Sivomixx, showing lower risk of respiratory failure and faster recovery with treatment.
Retrospective study of 287 nursery school children in Italy, 186 treated with S. salivarius K12 probiotic. The probiotic group had significantly lower rates of COVID-19, bronchitis, sinusitis, and laryngitis as well as lower antibiotic use. The study was registered retrospectively and details of COVID-19 diagnosis are not provided. Parents that administer the treatment may also use other treatments or take other actions that reduce risk for their children.
Interim report on an RCT for prophylactic treatment with S. salivarius K12, showing significantly lower cases with treatment. Only patients with symptoms or known positive contacts were tested. Trial identification/registration details are not provided.
RCT 50 hospitalized patients in Pakistan, 25 treated with S. salivarius K12, showing lower mortality with treatment, without statistical significance. There were more patients with higher oxygen requirements at baseline in the control group - 18 vs. 6 with O2 ≥ 8 L/min.
RCT 200 nursing home residents over 60 years old in Spain showing Loigolactobacillus coryniformis K8 probiotic administration enhanced IgG antibody response in subjects previously infected with SARS-CoV-2 and tended to improve IgA antibody response in those over 85 years old not previously infected, in the context of COVID-19 vaccination. There was no significant difference in incidence of COVID-19 infection between the probiotic and placebo groups during the study. The probiotic group had a higher percentage of asymptomatic COVID-19 cases compared to placebo, without statistical significance.
RCT 52 acute COVID-19 inpatients in Italy showing a multistrain synbiotic formula prevented a decrease in gut microbiota diversity and prevented decreases in lymphocyte count and hemoglobin levels compared to placebo. The probiotic group also had enrichment of beneficial bacteria and fewer neurological/neurocognitive symptoms at 6 months, although not statistically significant. Authors suggest modulating gut microbiota in acute COVID-19 through probiotics could be a useful supportive strategy.
RCT 293 outpatients in Mexico, 147 treated with a probiotic composed of three L. plantarum strains (KABP022, KABP023 and KABP033) and one P. acidilacti strain (KABP021), showing improved recovery with treatment. There were no hospitalizations or deaths.
RCT 350 COVID+ outpatients in the USA, 174 treated with prebiotic KB109 (a microbiome metabolic therapy candidate), showing lower combined hospitalization, ER, and urgent care visits with treatment. NCT04414124.
RCT 150 patients in Egypt showing no significant difference in outcomes with probiotic lactobacillus acidophilus, although hospitalization was 2% versus 10% for control. SOC included vitamin C, D, and zinc.
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.
RCT 200 patients, 99 treated with a probiotic (Lacticaseibacillus rhamnosus PDV 1705, Bifidobacterium bifidum PDV 0903, Bifidobacterium longum subsp. infantis PDV 1911, and Bifidobacterium longum subsp. longum PDV 2301). There was no significant difference in mortality or recovery time, however benefits were seen for diarrhea. NCT04854941.
RCT 73 outpatients with mild COVID-19 showing improved recovery and increased RBD/spike antibody response with 28 days of a multi-strain probiotic (Bifidobacterium (B.) lactis BI040, B. longum BL020, Lactobacillus (L) rhamnosus LR110, L. casei LC130, L. acidophilus LA120, 5 billion CFU total).
Retrospective 311 severe condition hospitalized patients in China, 123 treated with probiotics, showing slower viral clearance and recovery with treatment. Authors note that probiotics were able to moderate immunity and decrease the incidence of secondary infections.
Survey analysis of dietary supplements showing probiotic usage associated with lower incidence of COVID-19. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
RCT with 24 probiotics and 15 control patients in Spain, showing lower overall symptoms and lower digestive symptoms with treatment. Kluyveromyces marxianus B0399 plus lactobacillus rhamnosus CECT 30579.
Prophylaxis RCT with 127 probiotics and 128 control healthcare workers in Spain, showing no significant difference in cases. There were only 4 cases. Severity information by arm is not provided. L. coryniformis K8 CECT 5711.
Treatment may help sustain the immune response to vaccination - in the subgroup of subjects for whom more than 81 days had passed since they received the first dose, IgG levels were significantly higher in the treatment group. Patients that started probiotic consumption before the first vaccine dose also reported significantly fewer side effects.
Treatment may help sustain the immune response to vaccination - in the subgroup of subjects for whom more than 81 days had passed since they received the first dose, IgG levels were significantly higher in the treatment group. Patients that started probiotic consumption before the first vaccine dose also reported significantly fewer side effects.
RCT 827 children aged 1-6 years in daycare in Finland analyzing the effectiveness of daily Streptococcus salivarius K12 oral probiotic use for 6 months in preventing acute otitis media (AOM). The probiotic group did not have a significantly lower rate of AOM requiring antibiotics compared to placebo. A secondary outcome shows no significant difference in COVID-19, with only 2 and 3 cases in the treatment and placebo groups.
RCT 80 COVID-19 interstitial pneumonia patients in Italy, 40 treated with probiotics, showing significantly reduced gut inflammatory markers with treatment, and lower ICU admission and mortality, without statistical significance. Bifidobacterium lactis LA 304, lactobacillus salivarius LA 302, and lactobacillus acidophilus LA 201 bid for 10 days.
Small RCT 60 patients in India, 30 treated with ImmunoSEB and ProbioSEB CSC3, showing faster recovery with treatment. CTRI/2020/09/027685, CTRI/2020/08/027168.
Retrospective COVID-19 patients requiring CPAP, 21 treated with SLAB51 probiotics and 15 control patients, showing improved outcomes with treatment, despite significantly lower blood oxygenation at baseline in the treatment group.
Small case control analysis with 15 probiotics patients and 15 contol patients, showing no significant differences. PCR tests were only done weekly. Dosage is unknown. 115/LOE/301.4.2/IX/2020.
RCT 182 COVID-19 exposed patients, 91 treated with daily probiotic Lactobacillus rhamnosus GG starting a median of 3 days from exposure, showing lower symptomatic COVID-19 with treatment. There were no hospitalizations or deaths.
Pilot study of probiotic SIM01 with 25 consecutive COVID-19 patients in Hong Kong and 30 control patients treated by a different team during the same time period, showing improved antibody formation, reduced viral load and pro-inflammatory responses, and improvements for gut dysbiosis. SIM01 contains bifidobacteria strains, galactooligosaccharides, xylooligosaccharide, and resistant dextrin (derived from metagenomic databases of COVID-19 patients and healthy patients).
Retrospective 375 patients in China, 179 treated with probiotics (Bifidobacterium, Lactobacillus, and Enterococcus), showing improved clinical outcomes with 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 probiotics 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 probiotics 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 to101.
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 1104.
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
(3.13.1) 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 PythonMeta105
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 effective46,47.
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/kmeta.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.
| Gutiérrez-Castrellón, 5/24/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 9 authors, study period 19 August, 2020 - 2 February, 2021, average treatment delay 4.0 days, trial NCT04517422 (history). | risk of no recovery, 34.7% lower, RR 0.65, p < 0.001, treatment 69 of 147 (46.9%), control 105 of 146 (71.9%), NNT 4.0. |
| Haran, 3/29/2021, Randomized Controlled Trial, USA, preprint, 6 authors, study period 2 July, 2020 - 23 December, 2020, trial NCT04414124 (history). | risk of death, 66.5% lower, RR 0.33, p = 1.00, treatment 0 of 174 (0.0%), control 1 of 176 (0.6%), NNT 176, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), death two weeks after study withdrawal. |
| risk of hospitalization, 59.5% lower, RR 0.40, p = 0.45, treatment 2 of 174 (1.1%), control 5 of 176 (2.8%), NNT 59, including treatment period. | |
| risk of hospitalization/ER/urgent care, 50.0% lower, RR 0.50, p = 0.13, treatment 7 of 169 (4.1%), control 15 of 181 (8.3%), NNT 24. | |
| time to resolution of symptoms, 20.3% lower, relative time 0.80, p = 0.10, treatment 169, control 172, inverted to make RR<1 favor treatment. | |
| Hassan, 6/13/2023, Randomized Controlled Trial, Egypt, preprint, 6 authors, study period July 2021 - August 2022. | risk of hospitalization, 80.0% lower, RR 0.20, p = 0.20, treatment 1 of 50 (2.0%), control 5 of 50 (10.0%), NNT 12. |
| risk of no recovery, 17.9% lower, RR 0.82, p = 0.42, treatment 23 of 50 (46.0%), control 28 of 50 (56.0%), NNT 10.0. | |
| Kolesnyk, 1/4/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Ukraine, peer-reviewed, 10 authors, study period November 2021 - June 2022, trial NCT04907877 (history). | WHO score >1, 60.3% lower, RR 0.40, p = 0.02, treatment 6 of 34 (17.6%), control 16 of 36 (44.4%), NNT 3.7. |
| recovery time, 21.4% lower, relative time 0.79, p = 0.04, treatment 34, control 36. | |
| PCFS ≥1, 67.8% lower, RR 0.32, p = 0.008, treatment 5 of 34 (14.7%), control 16 of 35 (45.7%), NNT 3.2, long COVID, Supplementary Table 1. | |
| Navarro-López, 8/24/2022, Randomized Controlled Trial, Spain, peer-reviewed, 13 authors, study period December 2020 - February 2021, trial NCT04390477 (history). | risk of no recovery, 32.7% lower, RR 0.67, p = 0.08, treatment 14 of 24 (58.3%), control 13 of 15 (86.7%), NNT 3.5, day 30. |
| risk of no recovery, 53.1% lower, RR 0.47, p = 0.10, treatment 6 of 24 (25.0%), control 8 of 15 (53.3%), NNT 3.5, digestive symptoms, day 30. | |
| relative recovery, 20.0% better, RR 0.80, p = 0.03, treatment 24, control 15, relative symptom improvement, day 30. | |
| relative recovery, 26.1% better, RR 0.74, p = 0.06, treatment 24, control 15, relative improvement for digestive symptoms, day 30. | |
| Veterini, 6/30/2021, retrospective, Indonesia, peer-reviewed, 6 authors, excluded in exclusion analyses: the observered difference in duration could be caused by the baseline difference in Ct values. | time to viral-, 29.0% lower, relative time 0.71, p = 0.22, treatment 15, control 15. |
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.
| Ceccarelli, 8/23/2021, prospective, Italy, peer-reviewed, 10 authors. | risk of death, 70.4% lower, RR 0.30, p = 0.42, treatment 0 of 40 (0.0%), control 1 of 29 (3.4%), NNT 29, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 81.9% lower, RR 0.18, p = 0.15, treatment 1 of 40 (2.5%), control 4 of 29 (13.8%), NNT 8.9. | |
| Ceccarelli (B), 1/11/2021, retrospective, Italy, peer-reviewed, 14 authors. | risk of death, 64.2% lower, RR 0.36, p = 0.003, treatment 10 of 88 (11.4%), control 34 of 112 (30.4%), NNT 5.3, adjusted per study, odds ratio converted to relative risk. |
| risk of ICU admission, 15.2% lower, RR 0.85, p = 0.60, treatment 16 of 88 (18.2%), control 24 of 112 (21.4%), NNT 31. | |
| d'Ettorre, 7/7/2020, retrospective, Italy, peer-reviewed, 17 authors. | risk of death, 87.0% lower, RR 0.13, p = 0.14, treatment 0 of 28 (0.0%), control 4 of 42 (9.5%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of mechanical ventilation, 76.9% lower, RR 0.23, p = 0.51, treatment 0 of 28 (0.0%), control 2 of 42 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| respiratory failure, 88.4% lower, OR 0.12, p = 0.01, treatment 28, control 42, inverted to make OR<1 favor treatment, RR approximated with OR. | |
| Di Pierro, 9/28/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 48.5, 7 authors, study period 11 August, 2021 - 18 November, 2021, trial NCT05043376 (history), excluded in exclusion analyses: unadjusted differences between groups. | risk of death, 62.5% lower, RR 0.38, p = 0.17, treatment 3 of 25 (12.0%), control 8 of 25 (32.0%), NNT 5.0. |
| risk of ICU admission, no change, RR 1.00, p = 1.00, treatment 8 of 25 (32.0%), control 8 of 25 (32.0%). | |
| Giancola, 7/16/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Italy, peer-reviewed, 16 authors, study period 18 January, 2022 - 21 March, 2023. | risk of death, 14.8% lower, RR 0.85, p = 1.00, treatment 1 of 27 (3.7%), control 1 of 23 (4.3%), NNT 155. |
| risk of moderate/severe symptoms, 33.2% lower, RR 0.67, p = 0.32, treatment 22, control 20, combined. | |
| risk of moderate/severe symptoms, 21.2% higher, RR 1.21, p = 1.00, treatment 4 of 22 (18.2%), control 3 of 20 (15.0%), moderate/severe symptoms, day 180, cardio-respiratory symptoms. | |
| risk of moderate/severe symptoms, 69.7% lower, RR 0.30, p = 0.12, treatment 2 of 22 (9.1%), control 6 of 20 (30.0%), NNT 4.8, moderate/severe symptoms, day 180, digestive symptoms. | |
| risk of moderate/severe symptoms, 74.0% lower, RR 0.26, p = 0.06, treatment 2 of 22 (9.1%), control 7 of 20 (35.0%), NNT 3.9, moderate/severe symptoms, day 180, neurological/neurocognitive symptoms. | |
| risk of moderate/severe symptoms, 13.6% higher, RR 1.14, p = 0.76, treatment 10 of 22 (45.5%), control 8 of 20 (40.0%), moderate/severe symptoms, day 180, systemic symptoms. | |
| Ivashkin, 10/13/2021, Randomized Controlled Trial, Russia, peer-reviewed, 11 authors, study period December 2020 - March 2021, average treatment delay 8.0 days, trial NCT04854941 (history). | risk of death, 2.0% higher, RR 1.02, p = 1.00, treatment 4 of 99 (4.0%), control 4 of 101 (4.0%). |
| risk of mechanical ventilation, 18.4% lower, RR 0.82, p = 1.00, treatment 4 of 99 (4.0%), control 5 of 101 (5.0%), NNT 110. | |
| risk of ICU admission, 27.1% lower, RR 0.73, p = 0.77, treatment 5 of 99 (5.1%), control 7 of 101 (6.9%), NNT 53. | |
| recovery time, 4.8% lower, relative time 0.95, p = 0.47, treatment 99, control 101. | |
| Li (B), 3/5/2021, retrospective, China, peer-reviewed, 7 authors, average treatment delay 13.0 days. | risk of no hospital discharge, 11.8% higher, RR 1.12, p = 0.68, treatment 30 of 123 (24.4%), control 41 of 188 (21.8%). |
| time to discharge, 60.0% higher, relative time 1.60, p < 0.001, treatment 123, control 188. | |
| time to viral-, 35.3% higher, relative time 1.35, p < 0.001, treatment 123, control 188. | |
| Saviano, 6/28/2022, Randomized Controlled Trial, Italy, peer-reviewed, mean age 59.8, 9 authors. | risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 40 (0.0%), control 1 of 40 (2.5%), NNT 40, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 40 (0.0%), control 3 of 40 (7.5%), NNT 13, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| hospitalization time, 26.3% lower, relative time 0.74, p = 0.52, treatment mean 14.0 (±6.0) n=40, control mean 19.0 (±10.0) n=40. | |
| Shah, 2/2/2021, Randomized Controlled Trial, India, peer-reviewed, 3 authors, this trial uses multiple treatments in the treatment arm (combined with multi-enzyme formulation) - results of individual treatments may vary. | time to clinical improvement, 10.8% lower, relative time 0.89, p = 0.19, treatment 30, control 30. |
| hospitalization time, 10.6% lower, relative time 0.89, p = 0.18, treatment 30, control 30. | |
| risk of no clinical improvement, 83.3% lower, RR 0.17, p = 0.005, treatment 2 of 30 (6.7%), control 12 of 30 (40.0%), NNT 3.0, day 10 mid-recovery. | |
| risk of no clinical improvement, 3.7% lower, RR 0.96, p = 1.00, treatment 26 of 30 (86.7%), control 27 of 30 (90.0%), NNT 30, day 7. | |
| Trinchieri, 8/1/2022, retrospective, Italy, peer-reviewed, 10 authors, study period November 2020 - March 2021. | risk of death, 77.8% lower, RR 0.22, p = 0.28, treatment 1 of 21 (4.8%), control 3 of 14 (21.4%), NNT 6.0. |
| risk of miscellaneous, 77.8% lower, RR 0.22, p < 0.001, treatment 4 of 21 (19.0%), control 12 of 14 (85.7%), NNT 1.5, CPAP, day 7. | |
| risk of miscellaneous, 9.5% lower, RR 0.90, p = 0.51, treatment 19 of 21 (90.5%), control 14 of 14 (100.0%), NNT 10, CPAP, day 3. | |
| Zhang (B), 3/2/2022, retrospective, China, peer-reviewed, 12 authors, trial NCT04581018 (history). | risk of mechanical ventilation, 64.7% lower, RR 0.35, p = 1.00, treatment 0 of 25 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of no antibody formation, 67.3% lower, RR 0.33, p = 0.06, treatment 3 of 25 (12.0%), control 11 of 30 (36.7%), NNT 4.1. | |
| Zhang (C), 8/4/2021, retrospective, China, peer-reviewed, 14 authors. | hospitalization time, 13.6% lower, relative time 0.86, p = 0.009, treatment 150, control 150, PSM. |
| time to clinical improvement, 14.3% lower, relative time 0.86, p = 0.02, treatment 150, control 150, PSM. | |
| time to viral-, 16.7% lower, relative time 0.83, p < 0.001, treatment 150, control 150, PSM. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Ahanchian, 5/31/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 14 authors, study period July 2020 - August 2020, trial IRCT20101020004976N6. | respiratory symptoms, 73.3% lower, RR 0.27, p = 0.35, treatment 1 of 29 (3.4%), control 4 of 31 (12.9%), NNT 11. |
| risk of case, 85.3% lower, RR 0.15, p = 0.24, treatment 0 of 29 (0.0%), control 3 of 31 (9.7%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| Catinean, 1/17/2023, retrospective, Romania, peer-reviewed, 4 authors, study period 15 September, 2020 - 15 February, 2021. | symptom resolution, 40.5% lower, HR 0.60, p = 0.008, treatment 60, control 60, inverted to make HR<1 favor treatment. |
| resolution of fever, 37.5% lower, HR 0.62, p = 0.02, treatment 60, control 60, inverted to make HR<1 favor treatment, fever. | |
| Di Pierro (C), 9/30/2023, retrospective, Italy, peer-reviewed, 10 authors, study period January 2022 - March 2022, trial NCT05840926 (history). | risk of case, 77.8% lower, RR 0.22, p = 0.007, treatment mean 0.02 (±0.15) n=186, control mean 0.09 (±0.29) n=101. |
| Di Pierro (D), 3/12/2021, Randomized Controlled Trial, Italy, peer-reviewed, 2 authors, study period September 2020 - December 2020. | risk of case, 98.0% lower, RR 0.02, p < 0.001, treatment 0 of 64 (0.0%), control 24 of 64 (37.5%), NNT 2.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| Fernández-Ferreiro, 1/5/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, mean age 83.1, 9 authors, study period January 2021 - April 2021, trial NCT04756466 (history). | risk of death, 2.0% higher, RR 1.02, p = 1.00, treatment 1 of 98 (1.0%), control 1 of 100 (1.0%). |
| recovery time, 37.8% higher, relative time 1.38, p = 0.56, treatment mean 8.45 (±9.69) n=10, control mean 6.13 (±4.22) n=7. | |
| risk of severe case, 27.6% higher, RR 1.28, p = 0.75, treatment 5 of 98 (5.1%), control 4 of 100 (4.0%). | |
| risk of symptomatic case, 2.0% higher, RR 1.02, p = 1.00, treatment 7 of 98 (7.1%), control 7 of 100 (7.0%). | |
| risk of case, 35.1% higher, RR 1.35, p = 0.53, treatment 11 of 98 (11.2%), control 8 of 100 (8.0%), adjusted per study. | |
| 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, 30.4% lower, RR 0.70, p = 0.11, treatment 20 of 909 (2.2%), control 426 of 14,318 (3.0%), NNT 129, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated. |
| Louca, 11/30/2020, retrospective, United Kingdom, peer-reviewed, 26 authors. | risk of case, 8.5% lower, RR 0.92, p = 0.03, odds ratio converted to relative risk, United Kingdom, all adjustment model. |
| Rodriguez-Blanque, 8/3/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, 7 authors, study period 24 April, 2020 - 20 July, 2020, trial NCT04366180 (history). | risk of case, 9.3% lower, RR 0.91, p = 0.92, treatment 2 of 127 (1.6%), control 2 of 128 (1.6%), adjusted per study, multivariable. |
| Sarlin, 11/2/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Finland, peer-reviewed, 7 authors, study period 1 August, 2020 - 31 May, 2021. | risk of case, 33.2% lower, RR 0.67, p = 1.00, treatment 2 of 413 (0.5%), control 3 of 414 (0.7%), NNT 416. |
| Wischmeyer, 1/5/2022, Double Blind Randomized Controlled Trial, USA, preprint, 21 authors, study period 24 June, 2020 - 8 July, 2021, trial NCT04399252 (history) (PROTECT-EHC). | risk of moderate/severe case, 33.3% lower, RR 0.67, p = 0.15, treatment 16 of 91 (17.6%), control 24 of 91 (26.4%), NNT 11. |
| risk of symptomatic case, 38.5% lower, RR 0.62, p = 0.02, treatment 24 of 91 (26.4%), control 39 of 91 (42.9%), NNT 6.1, primary outcome. | |
| recovery time, 27.3% lower, relative time 0.73, p = 0.37, treatment 91, control 91. | |
| risk of case, 42.9% lower, RR 0.57, p = 0.17, treatment 8 of 91 (8.8%), control 14 of 91 (15.4%), NNT 15. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
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