Hydrogen Peroxide for COVID-19: real-time meta analysis of 7 studies
Abstract
Statistically significant lower risk is seen for viral clearance. 2 studies from 2 independent teams in 2 countries show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
38% [5‑59%] lower risk. Results are better for Randomized Controlled Trials and higher quality studies and slightly worse for peer-reviewed studies.
Currently there is limited data, with only 921 patients and only 16 control events for the most serious outcome in trials to date.
4 RCTs
with 323 patients have not reported results (up to 2 years late).
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
may be more effective.
Hydrogen Peroxide may be detrimental to the natural microbiome, raising concern for side effects, especially with prolonged or excessive use.
All data to reproduce this paper and
sources are in the appendix.
Highlights
Hydrogen Peroxide reduces
risk for COVID-19 with very high confidence for viral clearance, high confidence for pooled analysis, low confidence for mortality, ventilation, and cases, and very low confidence for recovery.
19th treatment shown effective with ≥3 clinical studies in
May 2021, now with p = 0.029 from 7 studies.
We show outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary
confounding factor for COVID-19.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 69
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 hydrogen peroxide studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome. Efficacy based on specific outcomes was delayed by 5.8 months, compared to using pooled outcomes.
Naso/oropharyngeal treatments
AlkalinizationCetylpyridin..
Chlorhexidine
Hydrogen Per..
Iota-carragee..
Nitric Oxide
Phthalocyanine
Povidone-Iod..
SARS-CoV-2 infection typically starts in the upper respiratory tract, and
specifically the nasal respiratory epithelium. Entry via the eyes and
gastrointestinal tract is possible, but less common, and entry via other
routes is rare.
Infection may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems. The primary initial route for entry into
the central nervous system is thought to be the olfactory nerve in the nasal
cavity Dai.
Progression may lead to cytokine storm, pneumonia, ARDS, neurological issues
Duloquin, Hampshire, Scardua-Silva, Sodagar, Yang, cardiovascular complications
Eberhardt, organ failure, and death. Minimizing replication as early
as possible is recommended.
Logically, stopping replication in the upper respiratory tract should be
simpler and more effective.
Early or prophylactic nasopharyngeal/oropharyngeal treatment can avoid the
consequences of viral replication in other tissues, and avoid the requirement
for systemic treatments with greater potential for side effects.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, Niarakis, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 7,000 compounds may
reduce COVID-19 risk c19early.org, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
hydrogen peroxide
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, and 11
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 38% [5‑59%] * | 7 | 921 | 72 |
| After exclusions | 57% [21‑77%] ** | 6 | 864 | 66 |
| Peer-reviewed studiesPeer-reviewed | 28% [-22‑57%] | 5 | 817 | 44 |
| Randomized Controlled TrialsRCTs | 59% [-27‑87%] | 4 | 300 | 42 |
| Mortality | 75% [-42‑96%] | 2 | 132 | 24 |
| VentilationVent. | 75% [-42‑96%] | 2 | 132 | 24 |
| ICU admissionICU | 41% [-160‑87%] | 2 | 168 | 18 |
| Recovery | 19% [-10‑41%] | 3 | 177 | 26 |
| Viral | 33% [11‑50%] ** | 4 | 192 | 42 |
| RCT mortality | 75% [-42‑96%] | 2 | 132 | 24 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 34% [-14‑62%] | 51% [-68‑86%] | 93% [-13‑100%] |
| After exclusions | 54% [3‑78%] * | 51% [-68‑86%] | 93% [-13‑100%] |
| Peer-reviewed studiesPeer-reviewed | 12% [-58‑52%] | 51% [-68‑86%] | 93% [-13‑100%] |
| Randomized Controlled TrialsRCTs | 86% [-169‑99%] | 51% [-68‑86%] | |
| Mortality | 86% [-169‑99%] | 67% [-194‑96%] | |
| VentilationVent. | 86% [-169‑99%] | 67% [-194‑96%] | |
| ICU admissionICU | 41% [-160‑87%] | ||
| Recovery | 19% [-10‑41%] | ||
| Viral | 21% [0‑37%] * | 45% [33‑55%] **** | |
| RCT mortality | 86% [-169‑99%] | 67% [-194‑96%] |
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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 recovery.
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Figure 9. Random effects meta-analysis for cases.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. 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 12 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
59% improvement,
compared to 42% for other studies.
Figure 13 and 14
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 1 and Table 2.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. 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 14. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases Jadad, and
analysis of double-blind RCTs has identified extreme levels of bias
Gøtzsche.
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 69 treatments we have analyzed,
63% 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.
Evidence shows that non-RCT 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. summarized reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates. 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 Internet 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 see
Deaton, Nichol.
Currently, 44 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, 28 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 23 have been confirmed with a delay of 8.4 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
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 15 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Pablo-Marcos, unadjusted results with no group details.
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Figure 15. 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 hours McLean, Treanor.
Baloxavir 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 cases Ikematsu |
| <24 hours | -33 hours symptoms Hayden |
| 24-48 hours | -13 hours symptoms Hayden |
| Inpatients | -2.5 hours to improvement Kumar |
Figure 16 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 69 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 69 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 variants Korves, for
example the Gamma variant shows significantly different characteristics
Faria, Karita, Nonaka, Zavascki. 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 variants
Peacock, Willett.
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 synergistic Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan, 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.
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 69
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 17 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 18 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 19 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 = 0.0000031 to p = 0.0000000067.
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Figure 17. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 17. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.7 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 5.5 months.
Figure 20 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 20. 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.
Analysis of short-term changes in viral load using PCR may not detect
effective treatments because PCR is unable to differentiate between intact
infectious virus and non-infectious or destroyed virus particles. For example
Alemany, Tarragó‐Gil perform RCTs with cetylpyridinium chloride
(CPC) mouthwash that show no difference in PCR viral load, however there was
significantly increased detection of SARS-CoV-2 nucleocapsid protein,
indicating viral lysis. CPC inactivates SARS-CoV-2 by degrading its membrane,
exposing the nucleocapsid of the virus. To better estimate changes in viral
load and infectivity, methods like viral culture or antigen detection that can
differentiate intact vs. degraded virus are preferred.
Studies to
date use a variety of administration methods to the respiratory tract,
including nasal and oral sprays, nasal irrigation, oral rinses, and
inhalation. Table 4 shows the relative efficacy for nasal, oral,
and combined administration. Combined administration shows the best results,
and nasal administration is more effective than oral. Precise efficacy depends
on the details of administration, e.g., mucoadhesion and sprayability for
sprays.
| Nasal/oral administration to the respiratory tract | Improvement | Studies |
| Oral spray/rinse | 38% [25‑49%] | 8 |
| Nasal spray/rinse | 56% [44‑65%] | 12 |
| Nasal & oral | 94% [74‑99%] | 6 |
Nasopharyngeal/oropharyngeal treatments may not be highly selective. In
addition to inhibiting or disabling SARS-CoV-2, they may also be harmful to
beneficial microbes, disrupting the natural microbiome in the oral cavity and
nasal passages that have important protective and metabolic roles
Brookes. This may be
especially important for prolonged use or overuse.
Table 5 summarizes the potential for common
nasopharyngeal/oropharyngeal treatments to affect the natural
microbiome.
| Treatment | Microbiome disruption potential | Notes |
|---|---|---|
| Iota-carrageenan | Low | Primarily antiviral, however extended use may mildly affect the microbiome |
| Nitric Oxide | Low to moderate | More selective towards pathogens, however excessive concentrations or prolonged use may disrupt the balance of bacteria |
| Alkalinization | Moderate | Increases pH, negatively impacting beneficial microbes that thrive in a slightly acidic environment |
| Cetylpyridinium Chloride | Moderate | Quaternary ammonium broad-spectrum antiseptic that can disrupt beneficial and harmful bacteria |
| Phthalocyanine | Moderate to high | Photodynamic compound with antimicrobial activity, likely to affect the microbiome |
| Chlorhexidine | High | Potent antiseptic with broad activity, significantly disrupts the microbiome |
| Hydrogen Peroxide | High | Strong oxidizer, harming both beneficial and harmful microbes |
| Povidone-Iodine | High | Potent broad-spectrum antiseptic harmful to beneficial microbes |
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 results
Boulware, Meeus, Meneguesso, twitter.com.
For hydrogen peroxide, 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 21 shows a scatter plot of
results for prospective and retrospective studies.
The median effect size for
retrospective studies is 93% improvement,
compared to 50% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
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Figure 21. 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 22 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 < 0.05
Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley.
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 22. 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. Hydrogen Peroxide for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 hydrogen peroxide 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 hydrogen peroxide 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 alone Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
1 of 7 studies
combine treatments. The results of
hydrogen peroxide
alone may differ.
1 of 4 RCTs use combined treatment.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors Lui, Lv, Malone, Murigneux, Niarakis,
providing many therapeutic targets.
Over 7,000 compounds have been predicted to reduce COVID-19
risk c19early.org, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 23 shows an overview of the results for hydrogen peroxide
in the context of multiple COVID-19 treatments, and Figure 24 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 23.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 7,000+ proposed treatments show efficacy
c19early.org (B).
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Figure 24. Efficacy vs. cost for COVID-19 treatments.
SARS-CoV-2 infection typically starts in the upper respiratory tract.
Progression may lead to cytokine storm, pneumonia, ARDS, neurological issues,
organ failure, and death. Stopping replication in the upper respiratory tract,
via early or prophylactic nasopharyngeal/oropharyngeal treatment, can avoid
the consequences of progression to other tissues, and avoid the requirement
for systemic treatments with greater potential for side effects.
Statistically significant lower risk is seen for viral clearance. 2 studies from 2 independent teams in 2 countries show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
38% [5‑59%] lower risk. Results are better for Randomized Controlled Trials and higher quality studies and slightly worse for peer-reviewed studies.
Currently there is limited data, with only 921 patients and only 16 control events for the most serious outcome in trials to date.
Hydrogen Peroxide may be detrimental to the natural microbiome, raising concern for side effects, especially with prolonged or excessive use.
Agrawal:
RCT 40 patients in India, showing improved recovery with nebulized hydrogen peroxide.
Amoah:
Retrospective 458 healthcare workers in Ghana, showing lower COVID-19 cases with hydrogen peroxide prophylaxis (oral and nasal rinse), without statistical significance.
Di Domênico:
RCT very late treatment (>9 days from onset) comparing hydrogen peroxide + mint essence with water + mint essence, showing no significant differences.
Di Domênico (B):
RCT very late treatment (>10 days from onset) comparing hydrogen peroxide + mint essence with water + mint essence, showing no significant differences.
Gansky:
54 patient hydrogen peroxide early treatment RCT with results not reported over 1.5 years after completion.
Jacox:
129 patient hydrogen peroxide early treatment RCT with results not reported over 2 years after completion.
Jayaraman:
Study of SARS-CoV-2 burden in whole mouth fluid and respiratory droplets with povidone iodine, hydrogen peroxide, and chlorhexidine mouthwashes in 36 hospitalized COVID-19 patients using PCR and rapid antigen testing. There were significant reductions in SARS-CoV-2 burden with all treatments in both respiratory droplets and whole mouth fluid.
Analysis of short-term changes in viral load using PCR may not detect effective treatments because PCR is unable to differentiate between intact infectious virus and non-infectious or destroyed virus particles. For example Alemany, Tarragó‐Gil perform RCTs with cetylpyridinium chloride (CPC) mouthwash that show no difference in PCR viral load, however there was significantly increased detection of SARS-CoV-2 nucleocapsid protein, indicating viral lysis. CPC inactivates SARS-CoV-2 by degrading its membrane, exposing the nucleocapsid of the virus. To better estimate changes in viral load and infectivity, methods like viral culture or antigen detection that can differentiate intact vs. degraded virus are preferred. .
Authors perform antigen testing for 6 hydrogen peroxide patients, showing that 50% became negative after treatment.
Analysis of short-term changes in viral load using PCR may not detect effective treatments because PCR is unable to differentiate between intact infectious virus and non-infectious or destroyed virus particles. For example Alemany, Tarragó‐Gil perform RCTs with cetylpyridinium chloride (CPC) mouthwash that show no difference in PCR viral load, however there was significantly increased detection of SARS-CoV-2 nucleocapsid protein, indicating viral lysis. CPC inactivates SARS-CoV-2 by degrading its membrane, exposing the nucleocapsid of the virus. To better estimate changes in viral load and infectivity, methods like viral culture or antigen detection that can differentiate intact vs. degraded virus are preferred. .
Authors perform antigen testing for 6 hydrogen peroxide patients, showing that 50% became negative after treatment.
Khan:
Estimated 50 patient hydrogen peroxide early treatment RCT with results not reported over 1.5 years after estimated completion.
Mukhtar:
RCT for mouthwash containing hydrogen peroxide 2% and chlorhexidine gluconate, showing higher discharge, shorter hospital stay, less intubation, and lower mortality with treatment.
Pablo-Marcos:
Small prospective study with 31 patients gargling povidone-iodine, 17 hydrogen peroxide, and 40 control patients, showing lower viral load mid-recovery with povidone-iodine, without reaching statistical significance. Oropharyngeal only, and only every 8 hours for two days. Results may be better with the addition of nasopharyngeal use, more frequent use, and without the two day limit.
Authors report only one of the 7 previous trials for PVP-I and COVID-19. Non-randomized study with no adjustments or group details. Some results in Figure 1 appear to be switched compared to the text and the labels in the figure. The viral clearance figures do not match the group sizes - for example authors report 62% PCR- for PVP-I at the 3rd test, however there is no number of 31 patients that rounds to 62%.
Authors report only one of the 7 previous trials for PVP-I and COVID-19. Non-randomized study with no adjustments or group details. Some results in Figure 1 appear to be switched compared to the text and the labels in the figure. The viral clearance figures do not match the group sizes - for example authors report 62% PCR- for PVP-I at the 3rd test, however there is no number of 31 patients that rounds to 62%.
Xie:
Estimated 90 patient hydrogen peroxide early treatment RCT with results not reported over 2 years after estimated completion.
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 hydrogen peroxide 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 hydrogen peroxide 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 to Zhang.
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 1 Sweeting.
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
(3.12.3) with
scipy (1.13.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.21.0).
Forest plots are computed using PythonMeta Deng
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 (4.1.2) using the metafor
(3.0-2) and rms (6.2-0) 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 effective
McLean, Treanor.
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/hpmeta.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.
| Gansky, 9/10/2022, Double Blind Randomized Controlled Trial, USA, trial NCT04409873 (history) (AMPoL). | 54 patient RCT with results unknown and over 1.5 years late. | Jacox, 10/20/2021, Double Blind Randomized Controlled Trial, USA, trial NCT04584684 (history) (MOR). | 129 patient RCT with results unknown and over 2 years late. |
| Jayaraman, 3/1/2021, prospective, India, preprint, 12 authors. | risk of no viral clearance, 50.0% lower, RR 0.50, p = 0.18, treatment 3 of 6 (50.0%), control 6 of 6 (100.0%), NNT 2.0, antigen results. | Khan, 7/31/2022, Double Blind Randomized Controlled Trial, Pakistan, trial NCT04341688 (history) (GARGLES). | Estimated 50 patient RCT with results unknown and over 1.5 years late. |
| Mukhtar, 11/30/2020, Randomized Controlled Trial, Qatar, preprint, 16 authors, this trial uses multiple treatments in the treatment arm (combined with chlorhexidine gluconate) - results of individual treatments may vary, trial ISRCTN10197987. | risk of death, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 46 (0.0%), control 3 of 46 (6.5%), NNT 15, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), including third control death on day 54. |
| risk of mechanical ventilation, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 46 (0.0%), control 3 of 46 (6.5%), NNT 15, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of no viral clearance, 18.1% lower, RR 0.82, p = 0.16, treatment 28 of 43 (65.1%), control 35 of 44 (79.5%), NNT 6.9, day 15. | |
| risk of no viral clearance, 14.0% lower, RR 0.86, p = 0.01, treatment 37 of 43 (86.0%), control 44 of 44 (100.0%), NNT 7.2, day 5. | |
| Pablo-Marcos, 10/25/2021, prospective, Spain, peer-reviewed, mean age 43.0, 6 authors, study period May 2020 - November 2020, excluded in exclusion analyses: unadjusted results with no group details. | relative viral load, 12.5% better, RR 0.88, p = 0.67, treatment mean 2.1 (±2.5) n=17, control mean 2.4 (±2.4) n=40, 3rd PCR (mid-recovery). |
| relative viral load, 63.6% worse, RR 1.64, p = 0.16, treatment mean 1.8 (±2.5) n=31, control mean 1.1 (±1.6) n=40, 4th PCR (most patients recovered). | Xie, 2/28/2022, Double Blind Randomized Controlled Trial, placebo-controlled, trial NCT04931004 (history). | Estimated 90 patient RCT with results unknown and over 2 years late. |
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.
| Agrawal, 4/5/2022, Double Blind Randomized Controlled Trial, placebo-controlled, India, peer-reviewed, mean age 47.0, 8 authors, trial CTRI/2020/08/027038 (HOPE in COVID-19). | risk of death, 66.7% lower, RR 0.33, p = 0.60, treatment 1 of 20 (5.0%), control 3 of 20 (15.0%), NNT 10.0. |
| risk of mechanical ventilation, 66.7% lower, RR 0.33, p = 0.60, treatment 1 of 20 (5.0%), control 3 of 20 (15.0%), NNT 10.0. | |
| oxygen time, 46.3% lower, relative time 0.54, p < 0.001, treatment mean 4.74 (±1.62) n=19, control mean 8.82 (±1.59) n=17. | |
| recovery time, 35.7% lower, relative time 0.64, p < 0.001, treatment mean 4.58 (±1.12) n=19, control mean 7.12 (±1.05) n=17, dyspnea. | |
| recovery time, 38.9% lower, relative time 0.61, p < 0.001, treatment mean 2.84 (±1.01) n=19, control mean 4.65 (±1.22) n=17, fever. | |
| recovery time, 29.8% lower, relative time 0.70, p = 0.001, treatment mean 4.79 (±1.84) n=19, control mean 6.82 (±1.51) n=17, cough. | |
| time to viral-, 45.2% lower, relative time 0.55, p < 0.001, treatment mean 5.16 (±1.21) n=19, control mean 9.41 (±1.97) n=17. | |
| Di Domênico, 8/3/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, survey, 9 authors. | risk of ICU admission, 33.8% lower, RR 0.66, p = 1.00, treatment 2 of 77 (2.6%), control 2 of 51 (3.9%), NNT 76. |
| risk of no recovery, 1.0% higher, HR 1.01, p = 0.97, treatment 63, control 43, inverted to make HR<1 favor treatment. | |
| risk of PASC, 31.4% lower, RR 0.69, p = 0.54, treatment 6 of 51 (11.8%), control 6 of 35 (17.1%), NNT 19, antibody positive. | |
| Di Domênico (B), 5/1/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, survey, 9 authors, average treatment delay 10.72 days. | risk of ICU admission, 50.0% lower, RR 0.50, p = 1.00, treatment 1 of 20 (5.0%), control 2 of 20 (10.0%), NNT 20. |
| improvement, 5.7% lower, HR 0.94, p = 0.91, treatment 18, control 17, inverted to make HR<1 favor treatment. | |
| time to discharge, 7.0% lower, relative time 0.93, p = 0.61, treatment mean 3.86 (±1.6) n=18, control mean 4.15 (±1.77) n=17. |
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.
| Amoah, 8/31/2022, retrospective, Ghana, peer-reviewed, 12 authors, study period May 2020 - December 2021. | risk of case, 93.0% lower, RR 0.07, p = 0.06, treatment 94, control 458, both periods combined. |
| risk of case, 92.6% lower, RR 0.07, p = 0.22, treatment 0 of 94 (0.0%), control 10 of 372 (2.7%), NNT 37, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), Jan - Mar 2021. | |
| risk of case, 98.4% lower, RR 0.02, p = 0.60, treatment 0 of 8 (0.0%), control 62 of 458 (13.5%), NNT 7.4, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), May - Dec 2020. |
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