Sodium Bicarbonate for COVID-19: real-time meta analysis of 7 studies
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, and recovery. 6 studies from 5 independent teams in 5 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
43% [24‑57%] lower risk. Results are similar for Randomized Controlled Trials and peer-reviewed studies.
Early treatment is more effective than late treatment.
SARS-CoV-2 requires acidic pH for fusion1. Alkalinization of the respiratory mucosa may reduce risk.
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.
We also present an analysis covering other alkalinization treatments2.
Sodium Bicarbonate may affect the natural microbiome, especially with prolonged use.
Sodium Bicarbonate for COVID-19 — Highlights
Sodium Bicarbonate reduces
risk with very high confidence for pooled analysis, high confidence for mortality, and low confidence for hospitalization and recovery.
37th treatment shown effective with ≥3 clinical studies in
May 2022, now with p = 0.00015 from 7 studies.
Outcome specific analyses and combined evidence from all
studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 77
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 sodium bicarbonate studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 5.7 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 5.7 months, compared to using pooled outcomes.
Naso/oropharyngeal treatments
AllCetylpyridin..
Chlorhexidine
Hydrogen Per..
Iota-carragee..
Nitric Oxide
Phthalocyanine
Povidone-Iod..
Sodium Bicarb..
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
cavity4.
Progression may lead to cytokine storm, pneumonia, ARDS, neurological
injury5-12 and cognitive
deficits7,12, cardiovascular
complications13, organ failure, and death.
Systemic treatments may be insufficient to prevent
neurological damage11.
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 may 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 factorsA,14-18, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 7,000 compounds may
reduce COVID-19 risk19, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Kreutzberger et al. showed that SARS-CoV-2 requires acidic pH for fusion. The mean pH of the airway-facing surface of the nasal cavity was 6.6, compatible with fusion, while pH is neutral in other parts of the nasopharyngeal cavity and in the lung20, suggesting no viral fusion in those locations prior to endocytic uptake. Liu et al. found that a more acidic pH significantly increased SARS-CoV-2 pseudovirus infection and cell surface ACE2 levels, mediated by pH-dependent inhibition of actin polymerization. Treatments that increase the pH of respiratory mucosa may inhibit fusion and reduce risk for COVID-19.
We analyze all significant
controlled studies of
sodium bicarbonate
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, and Randomized Controlled Trials (RCTs).
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, 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, and 9
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, hospitalization, progression, recovery, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 43% [24‑57%] *** | 7 | 1,092 | 69 |
| Peer-reviewed studiesPeer-reviewed | 48% [26‑63%] *** | 6 | 546 | 62 |
| Randomized Controlled TrialsRCTs | 35% [19‑48%] *** | 5 | 834 | 45 |
| Mortality | 41% [6‑63%] * | 4 | 898 | 43 |
| HospitalizationHosp. | 39% [19‑54%] *** | 2 | 134 | 25 |
| Recovery | 25% [18‑32%] **** | 2 | 728 | 14 |
| RCT mortality | 23% [0‑41%] * | 2 | 640 | 19 |
| Early treatment | Late treatment | |
|---|---|---|
| All studies | 65% [-727‑99%] | 43% [23‑58%] *** |
| Peer-reviewed studiesPeer-reviewed | 65% [-727‑99%] | 48% [25‑64%] *** |
| Randomized Controlled TrialsRCTs | 65% [-727‑99%] | 35% [17‑50%] *** |
| Mortality | 41% [6‑63%] * |
|
| HospitalizationHosp. | 65% [-727‑99%] | 39% [18‑54%] *** |
| Recovery | 25% [18‑32%] **** |
|
| RCT mortality | 23% [0‑41%] * |
Loading..
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.
Loading..
Loading..
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.
Loading..
Loading..
Figure 5. Random effects meta-analysis for mortality results.
Loading..
Figure 6. Random effects meta-analysis for hospitalization.
Loading..
Figure 7. Random effects meta-analysis for progression.
Loading..
Figure 8. Random effects meta-analysis for recovery.
Loading..
Figure 9. 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 10 shows a comparison of results for RCTs and non-RCT studies.
Figure 11 and 12
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.
Loading..
Figure 10. Results for RCTs and non-RCT studies.
Loading..
Figure 11. 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.
Loading..
Figure 12. 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 biases24, and
analysis of double-blind RCTs has identified extreme levels of bias25.
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 77 treatments we have analyzed,
64% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
RCTs have a bias against finding an
effect for interventions that are widely available — patients that
believe they need the intervention are more likely to decline participation
and take the intervention. RCTs for sodium bicarbonate are more likely to
enroll low-risk participants that do not need treatment to recover, making the
results less applicable to clinical practice. This bias is likely to be
greater for widely known treatments, and may be greater when the risk of a
serious outcome is overstated. This bias does not apply to the typical
pharmaceutical trial of a new drug that is otherwise unavailable.
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 see30,31.
Currently, 46 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, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 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.
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 hours32,33.
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 cases34 |
| <24 hours | -33 hours symptoms35 |
| 24-48 hours | -13 hours symptoms35 |
| Inpatients | -2.5 hours to improvement36 |
Figure 13 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 77 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Loading..
Figure 13. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 77 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 variants38, for
example the Gamma variant shows significantly different characteristics39-42. 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 variants43,44.
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 synergistic45-55, therefore
efficacy may depend strongly on combined treatments.
The
quality of medications may vary significantly between manufacturers and
production batches, which may significantly affect efficacy and safety.
Williams et al. analyze ivermectin from 11 different sources, showing
highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however note that pooled effects are no longer
required for sodium bicarbonate as of November 2022.
Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 5.7 months, compared to using pooled outcomes.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes"
is not a good reason for disregarding results.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 77
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 14 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 15 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 16 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 = 0.0000014 to p = 0.000000005.
Loading..
Figure 14. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 15. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 14. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 46 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 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 6.4 months.
Figure 17 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Loading..
Loading..
Figure 17. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Studies 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 roles59. 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 results60-63.
For sodium bicarbonate, there is currently not
enough data to evaluate publication bias with high confidence.
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 18 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 < 0.0564-71.
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 18. 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. Sodium Bicarbonate for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 sodium bicarbonate 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 sodium bicarbonate 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 alone45-55.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
1 of the 7
studies compare against other treatments, which may reduce the effect
seen.
Shafiee present another meta analysis for sodium bicarbonate, showing significant improvements for mortality and recovery.
Rashedi et al. present a review covering sodium bicarbonate for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors14-18,
providing many therapeutic targets.
Over 7,000 compounds have been predicted to reduce COVID-19
risk19, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 19 shows an overview of the results for sodium bicarbonate
in the context of multiple COVID-19 treatments, and Figure 20 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Loading..
Figure 19.
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 efficacy73.
Loading..
Loading..
Figure 20. 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.
Studies to date show that sodium bicarbonate is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, hospitalization, and recovery. 6 studies from 5 independent teams in 5 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
43% [24‑57%] lower risk. Results are similar for Randomized Controlled Trials and peer-reviewed studies.
Early treatment is more effective than late treatment.
SARS-CoV-2 requires acidic pH for fusion1. Alkalinization of the respiratory mucosa may reduce risk.
Shafiee present another meta analysis for sodium bicarbonate, showing significant improvements for mortality and recovery.
We also present an analysis covering other alkalinization treatments2.
Sodium Bicarbonate may affect the natural microbiome, especially with prolonged use.
Baxter:
Small RCT 79 PCR+ patients 55+ comparing pressure-based nasal irrigation with povidone-iodine and sodium bicarbonate, showing significantly lower hospitalization when compared with CDC data.
Delić:
RCT mechanically ventilated patients in Croatia, 42 treated with sodium bicarbonate inhalation, and 52 control patients, showing no significant difference in mortality with treatment. Treated patients showed a lower incidence of gram-positive or MRSA-caused ventilator-associated pneumonia. ICU mortality results are from76.
El-Badrawy:
RCT 546 patients showing significantly faster recovery and lower mortality with sodium bicarbonate (inhaled and nasal drops). The reduction in mortality is only statistically significant when excluding baseline critical cases.
Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
El-Badrawy (B):
Prospective study of 182 COVID-19 pneumonia patients, 127 treated with sodium bicarbonate inhalation and nasal drops, showing significantly faster recovery and improved CT scores with treatment.
Authors note that contacts of index cases also received sodium bicarbonate treatment, with none reporting COVID-19.
Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
Authors note that contacts of index cases also received sodium bicarbonate treatment, with none reporting COVID-19.
Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
Mody:
RCT 60 hospitalized patients in India, showing significantly greater clinical improvement with inhaled sodium bicarbonate.
Nasal and oral inhalation of nebulized 50ml 8.4% sodium bicarbonate for 5 minutes twice daily for 5 days.
Nasal and oral inhalation of nebulized 50ml 8.4% sodium bicarbonate for 5 minutes twice daily for 5 days.
Soares:
Analysis of 76 ICU patients in Brazil, 44 treated with bronchoalveolar lavage using 3% sodium bicarbonate, showing significantly lower mortality with treatment.
Bronchoalveolar lavage with 10ml of sodium bicarbonate solution directly into the tube (closed circuit), 500μl for each lung segment, followed by aspiration of the solution, performed every 6 hours for 7 days.
Bronchoalveolar lavage with 10ml of sodium bicarbonate solution directly into the tube (closed circuit), 500μl for each lung segment, followed by aspiration of the solution, performed every 6 hours for 7 days.
Wang:
RCT 55 mild/moderate patients in China, showing shorter hospitalization with sodium bicarbonate nasal irrigation and oral rinsing. Oral rinse with 5% sodium bicarbonate solution three times daily. Nasal irrigation two times with the solution entering through one nostril and exiting from the other. 30–40mL of solution was used every time and irrigation was performed for at least 30s. Details of randomization are not provided.
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 sodium bicarbonate 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 sodium bicarbonate for COVID-19 that report
a comparison with a control group are included in the main analysis.
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 to82.
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 185.
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
(3.12.4) with
scipy (1.13.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta86
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 effective32,33.
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/sbmeta.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.
| Baxter, 8/25/2022, Randomized Controlled Trial, USA, peer-reviewed, 12 authors, study period 24 September, 2020 - 21 December, 2020, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04559035 (history). | risk of hospitalization, 65.3% lower, RR 0.35, p = 1.00, treatment 0 of 37 (0.0%), control 1 of 42 (2.4%), NNT 42, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), vs. PVP-I. |
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.
| Delić, 5/28/2022, Randomized Controlled Trial, Croatia, peer-reviewed, 12 authors, study period October 2020 - June 2021, trial NCT04755972 (history). | risk of death, 23.0% lower, RR 0.77, p = 0.13, treatment 23 of 42 (54.8%), control 37 of 52 (71.2%), NNT 6.1, ICU mortality. |
| risk of death, 20.1% lower, RR 0.80, p = 0.30, treatment 20 of 42 (47.6%), control 31 of 52 (59.6%), NNT 8.3, 28 day mortality. | |
| El-Badrawy, 11/18/2022, Randomized Controlled Trial, Egypt, preprint, 7 authors, study period 1 September, 2021 - 30 April, 2022, trial NCT05035524 (history). | risk of death, 23.2% lower, RR 0.77, p = 0.26, treatment 32 of 272 (11.8%), control 42 of 274 (15.3%), NNT 28, all cases. |
| risk of death, 54.8% lower, RR 0.45, p = 0.02, treatment 12 of 247 (4.9%), control 27 of 251 (10.8%), NNT 17, mild/moderate/severe cases. | |
| risk of death, 79.2% lower, RR 0.21, p = 0.21, treatment 1 of 125 (0.8%), control 5 of 130 (3.8%), NNT 33, moderate cases. | |
| risk of death, 53.2% lower, RR 0.47, p = 0.02, treatment 11 of 63 (17.5%), control 22 of 59 (37.3%), NNT 5.0, severe cases. | |
| risk of death, 22.7% higher, RR 1.23, p = 0.33, treatment 20 of 25 (80.0%), control 15 of 23 (65.2%), critical cases. | |
| recovery time, 27.6% lower, relative time 0.72, p < 0.001, treatment mean 4.2 (±2.5) n=272, control mean 5.8 (±3.1) n=274, time to clinical improvement. | |
| CT score, 33.3% lower, RR 0.67, p = 0.001, treatment 238, control 229, CT score, day 30. | |
| El-Badrawy (B), 6/12/2022, prospective, Egypt, peer-reviewed, 7 authors, study period 15 April, 2020 - 31 August, 2020, trial NCT04374591 (history). | risk of death, 56.7% lower, RR 0.43, p = 0.37, treatment 3 of 127 (2.4%), control 3 of 55 (5.5%), NNT 32. |
| risk of progression, 39.4% lower, RR 0.61, p = 0.52, treatment 7 of 127 (5.5%), control 5 of 55 (9.1%), NNT 28, deterioration or death, day 30. | |
| risk of no recovery, 19.2% lower, RR 0.81, p = 0.03, treatment 84 of 127 (66.1%), control 45 of 55 (81.8%), NNT 6.4, day 30. | |
| relative CT score, 72.7% better, RR 0.27, p < 0.001, treatment 127, control 55, day 30. | |
| recovery time, 66.2% lower, relative time 0.34, p < 0.001, treatment mean 3.31 (±0.99) n=127, control mean 9.79 (±6.288) n=55, time to clinical improvement. | |
| Mody, 3/19/2021, Randomized Controlled Trial, India, peer-reviewed, 1 author, study period July 2020 - September 2020, trial CTRI/2020/07/026535. | risk of no improvement, 63.6% lower, RR 0.36, p < 0.001, treatment 8 of 30 (26.7%), control 22 of 30 (73.3%), NNT 2.1. |
| Soares, 12/29/2021, prospective, Brazil, peer-reviewed, 17 authors, study period December 2020 - May 2021. | risk of death, 75.8% lower, RR 0.24, p < 0.001, treatment 6 of 44 (13.6%), control 18 of 32 (56.2%), NNT 2.3. |
| Wang, 3/15/2023, Randomized Controlled Trial, China, peer-reviewed, 13 authors. | hospitalization time, 38.5% lower, relative time 0.61, p < 0.001, treatment mean 7.7 (±4.15) n=23, control mean 12.53 (±5.56) n=32. |
Kreutzberger et al., SARS-CoV-2 requires acidic pH to infect cells, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.2209514119.
Shafiee et al., Alkalinization Using Sodium Bicarbonate for COVID-19 Treatment: A Systematic Review and Meta-Analysis, Journal of Evidence-Based Integrative Medicine, doi:10.1177/2515690x241258403.
Dai et al., Neurological complications of COVID-19, QJM: An International Journal of Medicine, doi:10.1093/qjmed/hcac272.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Effros et al., The in vivo pH of the extravascular space of the lung, Journal of Clinical Investigation, doi:10.1172/JCI106164.
Liu et al., Nanoantidote for repression of acidosis pH promoting COVID‐19 infection, VIEW, doi:10.1002/VIW.20220004.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Brookes et al., Mouthwash Effects on the Oral Microbiome: Are They Good, Bad, or Balanced?, International Dental Journal, doi:10.1016/j.identj.2023.08.010.
Boulware, D., Comments regarding paper rejection, twitter.com/boulware_dr/status/1311331372884205570.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Rashedi et al., Sodium Bicarbonate Nebulized Therapy in Patients with Confirmed COVID-19, Advanced Pharmaceutical Bulletin, doi:10.34172/apb.2021.047.
Baxter et al., Rapid initiation of nasal saline irrigation to reduce severity in high-risk COVID+ outpatients, Ear, Nose & Throat Journal, doi:10.1177/01455613221123737.
Delić et al., Effects of Different Inhalation Therapy on Ventilator-Associated Pneumonia in Ventilated COVID-19 Patients: A Randomized Controlled Trial, Microorganisms, doi:10.3390/microorganisms10061118.
El-Badrawy et al., A randomized controlled trial of adjuvant inhalable sodium bicarbonate role in treatment of COVID-19, Research Square, doi:10.21203/rs.3.rs-2214180/v1.
El-Badrawy (B) et al., Role of Sodium Bicarbonate as Adjuvant Treatment of Nonsevere Computed Tomography-identified COVID-19 Pneumonia: A Preliminary Report, Indian Journal of Respiratory Care, doi:10.4103/ijrc.ijrc_48_21.
Mody, K., Effect of 8.4% Soda-Bicarbonate Steam Inhalation on the Course of Disease in Mild to Moderate Cases of Covid-19, Acta Scientific Orthopaedics, 4:4, actascientific.com/ASOR/ASOR-04-0290.php.
Soares et al., Preliminary observation of the use of sodium bicarbonate solution as an adjunct in the treatment of coronavirus 2019 disease (COVID-19): prognosis improvement in patients requiring intensive care, Brazilian Journal of Development, doi:10.34117/bjdv7n12-039.
Wang et al., Efficacy of nasal irrigation and oral rinse with sodium bicarbonate solution on virus clearance for COVID-19 patients, Frontiers in Public Health, doi:10.3389/fpubh.2023.1145669.
Zhang et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Please send us corrections, updates, or comments.
c19early involves the extraction of 100,000+ datapoints from
thousands of papers. Community updates
help ensure high accuracy.
Treatments and other interventions are complementary.
All practical, effective, and safe
means should be used based on risk/benefit analysis.
No treatment or intervention is 100% available and effective for all current
and future variants.
We do not provide medical advice. Before taking any medication,
consult a qualified physician who can provide personalized advice and details
of risks and benefits based on your medical history and situation. FLCCC and WCH
provide treatment protocols.