Covid Analysis, January 22, 2022, DRAFT
https://c19bromhexine.com/meta.html
•Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 4 studies from 4 independent teams in 3 different countries show statistically significant
improvements in isolation (2 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
45% [23‑60%] improvement. Results are similar for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
•Currently there is limited data, with only 663 patients and only 32 control events for the most serious outcome in trials to date.
•Bromhexine may be less effective for omicron due to the entry
process moving towards TMPRSS2-independent fusion [Peacock, Willett].
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 50% of bromhexine
studies show zero events in the treatment arm.
•Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•Elimination of COVID-19 is a race
against viral evolution. No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used, including treatments, as supported by Pfizer [Pfizer].
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
| Studies | Early treatment | Late treatment | Prophylaxis | Patients | Authors | |
| All studies | 6 | 79% [28‑94%] | 39% [14‑57%] | 65% [-212‑96%] | 663 | 72 |
| Peer-reviewed | 4 | 79% [28‑94%] | 39% [14‑57%] | 241 | 48 | |
| Randomized Controlled TrialsRCTs | 6 | 79% [28‑94%] | 39% [14‑57%] | 65% [-212‑96%] | 663 | 72 |
| Percentage improvement with bromhexine treatment | ||||||
Figure 1. A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of
bromhexine 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, for studies within each treatment stage, for individual outcomes, for
peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after
exclusions.
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.
Results
Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, viral clearance, and peer reviewed studies.
Table 1 summarizes the results by treatment stage.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Early treatment | 2 | 2 | 100% |
79% improvement RR 0.21 [0.06‑0.72] p = 0.013 |
| Late treatment | 2 | 2 | 100% |
39% improvement RR 0.61 [0.43‑0.86] p = 0.0051 |
| Prophylaxis | 2 | 2 | 100% |
65% improvement RR 0.35 [0.04‑3.12] p = 0.35 |
| All studies | 6 | 6 | 100% |
45% improvement RR 0.55 [0.40‑0.77] p = 0.00055 |
Table 1. Results by treatment stage.
Figure 3. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 4. Random effects meta-analysis for mortality results.
Figure 5. Random effects meta-analysis for ventilation.
Figure 6. Random effects meta-analysis for ICU admission.
Figure 7. Random effects meta-analysis for hospitalization.
Figure 8. Random effects meta-analysis for recovery.
Figure 9. Random effects meta-analysis for cases.
Figure 10. Random effects meta-analysis for viral clearance.
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.
Randomized Controlled Trials (RCTs)
Figure 12 shows a chronological history of Randomized Controlled Trials.
Figure 13 and 14
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 2 summarizes the results.
Currently all studies are RCTs, so these are the same as for all studies.
Figure 12. Chronological history of Randomized Controlled Trials.
Figure 13. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 14. Random effects meta-analysis for RCT mortality results.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Randomized Controlled Trials | 6 | 6 | 100% |
45% improvement RR 0.55 [0.40‑0.77] p = 0.00055 |
| RCT mortality results | 3 | 3 | 100% |
77% improvement RR 0.23 [0.04‑1.39] p = 0.11 |
Table 2. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
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]. Other
medications might be beneficial for late stage complications, while early use
may not be effective or may even be harmful. Figure 15 shows
an example where efficacy declines as a function of treatment delay.
Figure 15. Effectiveness may depend critically
on treatment delay.
Patient demographics.
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 (as in [López-Medina]).Effect measured.
Efficacy may differ
significantly depending on the effect measured, for example a treatment may be
very effective at reducing mortality, but less effective at minimizing cases
or hospitalization. Or a treatment may have no effect on viral clearance while
still being effective at reducing mortality.Variants.
There are many different
variants of SARS-CoV-2 and efficacy may depend critically on the distribution
of variants encountered by the patients in a study. 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 viral entry
process for the omicron variant has moved towards TMPRSS2-independent fusion,
suggesting that TMPRSS2 inhibitors may be less effective
[Peacock, Willett].Regimen.
Effectiveness may depend
strongly on the dosage and treatment regimen.Treatments.
The use of other
treatments may significantly affect outcomes, including anything from
supplements, other medications, or other kinds of treatment such as prone
positioning.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 the treatment may be very effective when
used earlier.
In general, by combining heterogeneous studies, as all meta
analyses do, we run the risk of obscuring an effect by including studies where
the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we
expect the estimated effect size to be lower than that for the optimal case.
We do not a priori expect that pooling all studies will create a
positive result for an effective treatment. 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.
Discussion
Publication bias.
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].
For bromhexine, there is currently not
enough data to evaluate publication bias with high confidence.Conflicts of interest.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Bromhexine for COVID-19
lacks this because it is
off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 bromhexine 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 bromhexine trials
represent the optimal conditions for efficacy.Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do
here), while others distinguish between mild/moderate/severe cases. We note
that 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.Conclusion
Bromhexine is an effective treatment for COVID-19.
Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 4 studies from 4 independent teams in 3 different countries show statistically significant
improvements in isolation (2 for the most serious outcome).
Meta analysis using the most serious outcome reported shows
45% [23‑60%] improvement. Results are similar for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
Currently there is limited data, with only 663 patients and only 32 control events for the most serious outcome in trials to date.
Study Notes
[Ansarin]
RCT with 39 bromhexine and 39 control patients showing lower mortality, intubation, and ICU admission with treatment. The treatment group received bromhexine hydrochloride 8 mg three times a day for two weeks. All patients received SOC including HCQ.
[Li]
Tiny RCT with 12 bromhexine and 6 control patients showing non-statistically significant improvements in chest CT, need for oxygen therapy, and discharge rate within 20 days. Authors recommend a larger scale trial.
[Mareev]
Prospective 103 PCR+ patients in Russia, 33 treated with bromexhine+spironolactone. The odds ratio of having a positive PCR or hospitalization for ≥10 days was 0.07 [0.008–0.61] with treatment. Dosing was bromhexine 8mg 4 times daily, spironolactone 25-50 mg/day for 10 days.
[Mikhaylov]
Small prophylaxis RCT with 25 treatment and 25 control health care worker, showing lower PCR+, symptomatic cases, and hospitalization with treatment, although not statistically significant with the small sample size.
[Tolouian (B)]
PEP RCT with 372 close contacts of COVID+ patients, 187 treated with bromhexine, showing significantly lower cases with treatment. IRCT20120703010178N22.
[Tolouian]
Small RCT with 100 patients, 48 with bromhexine added to SOC, showing slower viral- conversion but lower mortality and greater clinical improvement with bromhexine (not statistically significant with few deaths and very high recovery). The very large difference between unadjusted and adjusted results is due to much higher risk for patients with renal disease and the much higher prevalence of renal disease in the bromhexine group.
The study also shows 90% of patients in the control group had BMI>=30 compared to 0% in the treatment group, suggesting a possible problem with randomization. Due to the imbalance between groups, results were adjusted for BMI>30, smoking, and renal disease.
11 patients were lost to followup in the treatment group compared to zero in the control group, perhaps in part due to faster recovery in the treatment group. 9 patients were excluded from the treatment group because they did not want to take bromhexine after discharge. Therefore up to 29% of treatment patients may have been excluded because they recovered quickly.
The study also shows 90% of patients in the control group had BMI>=30 compared to 0% in the treatment group, suggesting a possible problem with randomization. Due to the imbalance between groups, results were adjusted for BMI>30, smoking, and renal disease.
11 patients were lost to followup in the treatment group compared to zero in the control group, perhaps in part due to faster recovery in the treatment group. 9 patients were excluded from the treatment group because they did not want to take bromhexine after discharge. Therefore up to 29% of treatment patients may have been excluded because they recovered quickly.
We performed ongoing searches of PubMed, medRxiv,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19bromhexine.com. Search terms were bromhexine, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of bromhexine for COVID-19 that report
a comparison with a control group are included in the main analysis.
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 are used. Mortality
alone is preferred over combined outcomes.
Outcomes with zero events in both arms were not used (the next most serious
outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room
for an effective treatment to do better).
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 computed the relative risk when
possible, or converted 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 including propensity score matching (PSM), the PSM results are used.
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
(3.9.10) with
scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.14.0), and plotly (5.4.0).
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.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment, 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].
A summary of study results is below. Please submit
updates and corrections at https://c19bromhexine.com/meta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in pooled analysis, which may differ from the effect a paper
focuses on. Other outcomes are used in outcome specific analyses.
| [Ansarin], 7/19/2020, Randomized Controlled Trial, Iran, Middle East, peer-reviewed, 11 authors. | risk of death, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 39 (0.0%), control 5 of 39 (12.8%), NNT 7.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of mechanical ventilation, 88.9% lower, RR 0.11, p = 0.01, treatment 1 of 39 (2.6%), control 9 of 39 (23.1%), NNT 4.9. | |
| risk of ICU admission, 81.8% lower, RR 0.18, p = 0.01, treatment 2 of 39 (5.1%), control 11 of 39 (28.2%), NNT 4.3. | |
| [Li], 9/3/2020, Randomized Controlled Trial, China, Asia, peer-reviewed, 10 authors. | risk of no hospital discharge, 75.0% lower, RR 0.25, p = 0.11, treatment 2 of 12 (16.7%), control 4 of 6 (66.7%), NNT 2.0. |
| risk of oxygen therapy, 50.0% lower, RR 0.50, p = 0.57, treatment 2 of 12 (16.7%), control 2 of 6 (33.3%), NNT 6.0. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in pooled analysis, which may differ from the effect a paper
focuses on. Other outcomes are used in outcome specific analyses.
| [Mareev], 12/3/2020, Randomized Controlled Trial, Russia, Europe, peer-reviewed, 20 authors. | risk of PCR+ on day 10 and hospitalization > 10 days, 38.8% lower, RR 0.61, p = 0.02, treatment 14 of 24 (58.3%), control 20 of 21 (95.2%), NNT 2.7, odds ratio converted to relative risk. |
| risk of no virological cure, 87.4% lower, RR 0.13, p = 0.08, treatment 0 of 17 (0.0%), control 3 of 13 (23.1%), NNT 4.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 10. | |
| [Tolouian], 3/15/2021, Randomized Controlled Trial, Iran, Middle East, peer-reviewed, 7 authors. | risk of death, 76.0% lower, RR 0.24, p = 0.43, treatment 48, control 52, Table 3, adjusted, RR approximated with OR. |
| risk of no improvement, 75.9% lower, RR 0.24, p = 0.43, treatment 48, control 52, Table 2, adjusted, RR approximated with OR. | |
| risk of case, 74.5% higher, RR 1.75, p = 0.02, treatment 29 of 48 (60.4%), control 18 of 52 (34.6%), mid-recovery day 7. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in pooled analysis, which may differ from the effect a paper
focuses on. Other outcomes are used in outcome specific analyses.
| [Mikhaylov], 3/8/2021, Randomized Controlled Trial, Russia, Europe, preprint, 8 authors. | risk of hospitalization, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 25 (0.0%), control 2 of 25 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of symptomatic case, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 25 (0.0%), control 5 of 25 (20.0%), NNT 5.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of no virological cure, 71.4% lower, RR 0.29, p = 0.14, treatment 2 of 25 (8.0%), control 7 of 25 (28.0%), NNT 5.0. | |
| [Tolouian (B)], 12/20/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, Middle East, preprint, 16 authors. | risk of death, 32.9% lower, RR 0.67, p = 0.76, treatment 0 of 187 (0.0%), control 1 of 185 (0.5%), NNT 185, odds ratio converted to relative risk, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 70.3% lower, RR 0.30, p = 0.14, treatment 1 of 187 (0.5%), control 6 of 185 (3.2%), NNT 37, adjusted per study, odds ratio converted to relative risk. | |
| risk of symptomatic case, 53.0% lower, RR 0.47, p = 0.007, treatment 16 of 187 (8.6%), control 34 of 185 (18.4%), NNT 10, odds ratio converted to relative risk. | |
| risk of case, 50.2% lower, RR 0.50, p = 0.03, treatment 13 of 187 (7.0%), control 26 of 185 (14.1%), NNT 14, odds ratio converted to relative risk. |
Supplementary Data
References
Altman, D., BMJ, doi:10.1136/bmj.d2304,
How to obtain the P value from a confidence interval,
https://www.bmj.com/content/343/bmj.d2304.
Altman (B) et al., BMJ, doi:10.1136/bmj.d2090,
How to obtain the confidence interval from a P value,
https://www.bmj.com/content/343/bmj.d2090.
Ansarin et al., Bioimpacts, doi:10.34172/bi.2020.27,
Effect of bromhexine on clinical outcomes and mortality in COVID-19 patients: A randomized clinical trial,
https://bi.tbzmed.ac.ir/Article/bi-23240.
Boulware, D.,
Comments regarding paper rejection,
https://twitter.com/boulware_dr/status/1311331372884205570.
Faria et al., Science, doi:10.1126/science.abh2644,
Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil,
https://www.science.org/lookup/doi/10.1126/science.abh2644.
Karita et al., medRxiv, doi:10.1101/2021.08.27.21262754,
Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection,
https://www.medrxiv.org/content/10.1101/2021.08.27.21262754v1.
Li et al., Clinical and Translational Science, doi:10.1111/cts.12881,
Bromhexine Hydrochloride Tablets for the Treatment of Moderate COVID‐19: An Open‐Label Randomized Controlled Pilot Study,
https://ascpt.onlinelibrary.wiley.com/doi/10.1111/cts.12881.
López-Medina et al., JAMA, doi:10.1001/jama.2021.3071,
Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial,
https://jamanetwork.com/journals/jama/fullarticle/2777389.
Mareev et al., Кардиология, doi:10.18087/cardio.2020.11.n1440,
Results of Open-Label non-Randomized Comparative Clinical Trial: “BromhexIne and Spironolactone for CoronаvirUs Infection requiring hospiTalization (BISCUIT),
https://lib.ossn.ru/jour/article/view/1440.
McLean et al., Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100,
Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525010/.
Meneguesso, A.,
Médica defende tratamento precoce da Covid-19,
https://www.youtube.com/watch?v=X5FCrIm_19U.
Mikhaylov et al., medRxiv, doi:10.1101/2021.03.03.21252855,
Bromhexine Hydrochloride Prophylaxis of COVID-19 for Medical Personnel: A Randomized Open-Label Study,
https://www.medrxiv.org/content/10.1101/2021.03.03.21252855v1.
Nonaka et al., International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003,
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,
https://www.sciencedirect.com/science/article/pii/S1201971221006354.
Peacock et al., bioRxiv, doi:10.1101/2021.12.31.474653,
The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry,
https://www.biorxiv.org/content/10.1101/2021.12.31.474653.
Pfizer,
Pfizer Second Quarter 2021 Earnings Teleconference,
https://s21.q4cdn.com/317678438/fi../Q2-2021-Earnings-Charts-FINAL.pdf.
Sweeting et al., Statistics in Medicine, doi:10.1002/sim.1761,
What to add to nothing? Use and avoidance of continuity corrections in meta‐analysis of sparse data,
https://onlinelibrary.wiley.com/doi/10.1002/sim.1761.
Tolouian et al., J. Investig. Med., doi:10.1136/jim-2020-001747,
Effect of bromhexine in hospitalized patients with COVID-19,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970656/.
Tolouian (B) et al., SSRN, doi:10.2139/ssrn.3989,
Bromhexine, for Post Exposure COVID-19 Prophylaxis: A Randomized, Double-Blind, Placebo Control Trial,
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3989849.
Treanor et al., JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016,
Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial,
https://jamanetwork.com/journals/jama/fullarticle/192425.
Willett et al., medRxiv, doi:10.1101/2022.01.03.21268111,
The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism,
https://www.medrxiv.org/content/10.1101/2022.01.03.21268111.
Zavascki et al., Research Square, doi:10.21203/rs.3.rs-910467/v1,
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,
https://www.researchsquare.com/article/rs-910467/v1.
Please send us
corrections, updates, or comments. Vaccines and treatments are both
valuable and complementary. All practical, effective, and safe means
should be used. Elimination of COVID-19 is a race against viral evolution. No
treatment, vaccine, or intervention is 100% available and effective for all
current and future variants. Denying the efficacy of any method increases
mortality, morbidity, collateral damage, and the risk of endemic status. 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. WCH
and FLCCC provide
treatment protocols.