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Bromhexine for COVID-19: real-time meta analysis of 5 studies
DRAFT
PLEASE SUBMIT FEEDBACK
Covid Analysis, November 18, 2021
https://c19bromhexine.com/meta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 45% 5 291 Improvement, Studies, Patients Relative Risk, 95% CI Mortality 87% 2 178 Ventilation 89% 1 78 ICU admission 82% 1 78 Hospitalization 80% 1 50 Recovery 49% 2 63 Cases -75% 1 100 Viral clearance 76% 2 80 RCTs 45% 5 291 Peer-reviewed 50% 4 241 Prophylaxis 80% 1 50 Early 79% 2 96 Late 39% 2 145 Bromhexine for COVID-19 c19bromhexine.com Nov 18, 2021 Favors bromhexine Favors control
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.
Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 3 studies show statistically significant improvements in isolation (2 for the most serious outcome).
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 45% 5 291 Improvement, Studies, Patients Relative Risk, 95% CI Mortality 87% 2 178 Ventilation 89% 1 78 ICU admission 82% 1 78 Hospitalization 80% 1 50 Recovery 49% 2 63 Cases -75% 1 100 Viral clearance 76% 2 80 RCTs 45% 5 291 Peer-reviewed 50% 4 241 Prophylaxis 80% 1 50 Early 79% 2 96 Late 39% 2 145 Bromhexine for COVID-19 c19bromhexine.com Nov 18, 2021 Favors bromhexine Favors control
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 40% 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 the risk of COVID-19 becoming endemic; and increases mortality, morbidity, and collateral damage.
All data to reproduce this paper and sources are in the appendix.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 579% [28‑94%]39% [14‑57%]80% [-297‑99%] 291 56
Peer-reviewed 479% [28‑94%]39% [14‑57%] 241 48
Percentage improvement with bromhexine treatment
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 91% 0.09 [0.01-1.59] death 0/39 5/39 Improvement, RR [CI] Treatment Control Li (RCT) 75% 0.25 [0.06-1.00] no disch. 2/12 4/6 Tau​2 = 0.00; I​2 = 0.0% Early treatment 79% 0.21 [0.06-0.72] 2/51 9/45 79% improvement Mareev (RCT) 39% 0.61 [0.14-0.97] no disch. 14/24 20/21 Improvement, RR [CI] Treatment Control Tolouian (RCT) 76% 0.24 [0.01-8.03] death 48 (n) 52 (n) Tau​2 = 0.00; I​2 = 0.0% Late treatment 39% 0.61 [0.43-0.86] 14/72 20/73 39% improvement Mikhaylov (RCT) 80% 0.20 [0.01-3.97] hosp. 0/25 2/25 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis 80% 0.20 [0.01-3.97] 0/25 2/25 80% improvement All studies 45% 0.55 [0.40-0.77] 16/148 31/143 45% improvement 5 bromhexine COVID-19 studies c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 3.46 Effect extraction pre-specified, see appendix Favors bromhexine Favors control
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 timeNumber of studies reporting positive effects Total number of studiesPercentage 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 1 1 100% 80% improvement
RR 0.20 [0.01‑3.97]
p = 0.29
All studies 5 5 100% 45% improvement
RR 0.55 [0.40‑0.77]
p = 0.00057
Table 1. Results by treatment stage.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 91% 0.09 [0.01-1.59] death 0/39 5/39 Improvement, RR [CI] Treatment Control Li (RCT) 75% 0.25 [0.06-1.00] no disch. 2/12 4/6 Tau​2 = 0.00; I​2 = 0.0% Early treatment 79% 0.21 [0.06-0.72] 2/51 9/45 79% improvement Mareev (RCT) 39% 0.61 [0.14-0.97] no disch. 14/24 20/21 Improvement, RR [CI] Treatment Control Tolouian (RCT) 76% 0.24 [0.01-8.03] death 48 (n) 52 (n) Tau​2 = 0.00; I​2 = 0.0% Late treatment 39% 0.61 [0.43-0.86] 14/72 20/73 39% improvement Mikhaylov (RCT) 80% 0.20 [0.01-3.97] hosp. 0/25 2/25 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis 80% 0.20 [0.01-3.97] 0/25 2/25 80% improvement All studies 45% 0.55 [0.40-0.77] 16/148 31/143 45% improvement 5 bromhexine COVID-19 studies c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 3.46 Effect extraction pre-specified, see appendix Favors bromhexine Favors control
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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 91% 0.09 [0.01-1.59] 0/39 5/39 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 91% 0.09 [0.01-1.59] 0/39 5/39 91% improvement Tolouian (RCT) 76% 0.24 [0.01-8.03] 48 (n) 52 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 76% 0.24 [0.01-8.03] 0/48 0/52 76% improvement All studies 87% 0.13 [0.01-1.23] 0/87 5/91 87% improvement 2 bromhexine COVID-19 mortality results c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 1.78 Favors bromhexine Favors control
Figure 4. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 89% 0.11 [0.01-0.84] 1/39 9/39 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 89% 0.11 [0.01-0.84] 1/39 9/39 89% improvement All studies 89% 0.11 [0.01-0.84] 1/39 9/39 89% improvement 1 bromhexine COVID-19 mechanical ventilation result c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 2.13 Favors bromhexine Favors control
Figure 5. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 82% 0.18 [0.04-0.77] 2/39 11/39 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 82% 0.18 [0.04-0.77] 2/39 11/39 82% improvement All studies 82% 0.18 [0.04-0.77] 2/39 11/39 82% improvement 1 bromhexine COVID-19 ICU result c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 2.32 Favors bromhexine Favors control
Figure 6. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mikhaylov (RCT) 80% 0.20 [0.01-3.97] hosp. 0/25 2/25 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis 80% 0.20 [0.01-3.97] 0/25 2/25 80% improvement All studies 80% 0.20 [0.01-3.97] 0/25 2/25 80% improvement 1 bromhexine COVID-19 hospitalization result c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 1.06 Favors bromhexine Favors control
Figure 7. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Li (RCT) 75% 0.25 [0.06-1.00] no disch. 2/12 4/6 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 75% 0.25 [0.06-1.00] 2/12 4/6 75% improvement Mareev (RCT) 39% 0.61 [0.14-0.97] no disch. 14/24 20/21 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 39% 0.61 [0.14-0.97] 14/24 20/21 39% improvement All studies 49% 0.51 [0.25-1.04] 16/36 24/27 49% improvement 2 bromhexine COVID-19 recovery results c19bromhexine.com Nov 18, 2021 Tau​2 = 0.14; I​2 = 33.7%; Z = 1.86 Favors bromhexine Favors control
Figure 8. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Tolouian (RCT) -75% 1.75 [1.13-2.71] 29/48 18/52 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment -75% 1.75 [1.13-2.71] 29/48 18/52 -75% improvement All studies -75% 1.75 [1.13-2.71] 29/48 18/52 -75% improvement 1 bromhexine COVID-19 case result c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 2.49 Favors bromhexine Favors control
Figure 9. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mareev (RCT) 87% 0.13 [0.01-2.25] viral+ 0/17 3/13 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 87% 0.13 [0.01-2.25] 0/17 3/13 87% improvement Mikhaylov (RCT) 71% 0.29 [0.07-1.24] viral+ 2/25 7/25 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis 71% 0.29 [0.07-1.24] 2/25 7/25 71% improvement All studies 76% 0.24 [0.07-0.89] 2/42 10/38 76% improvement 2 bromhexine COVID-19 viral clearance results c19bromhexine.com Nov 18, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 2.13 Favors bromhexine Favors control
Figure 10. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 91% 0.09 [0.01-1.59] death 0/39 5/39 Improvement, RR [CI] Treatment Control Li (RCT) 75% 0.25 [0.06-1.00] no disch. 2/12 4/6 Tau​2 = 0.00; I​2 = 0.0% Early treatment 79% 0.21 [0.06-0.72] 2/51 9/45 79% improvement Mareev (RCT) 39% 0.61 [0.14-0.97] no disch. 14/24 20/21 Improvement, RR [CI] Treatment Control Tolouian (RCT) 76% 0.24 [0.01-8.03] death 48 (n) 52 (n) Tau​2 = 0.00; I​2 = 0.0% Late treatment 39% 0.61 [0.43-0.86] 14/72 20/73 39% improvement All studies 50% 0.50 [0.29-0.85] 16/123 29/118 50% improvement 4 bromhexine COVID-19 peer reviewed trials c19bromhexine.com Nov 18, 2021 Tau​2 = 0.06; I​2 = 9.9%; Z = 2.53 Effect extraction pre-specified, see appendix Favors bromhexine Favors control
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.
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 12 shows an example where efficacy declines as a function of treatment delay.
Figure 12. 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 thousands of different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study.
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. 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. Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 3 studies show statistically significant improvements in isolation (2 for the most serious outcome).
Study Notes
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 91% Imp. Relative Risk, 95% CI Mechanical ventilation 89% ICU admission 82% Ansarin: Effect of bromhexine on clinical outcomes and .. c19bromhexine.com/ansarin.html Favors bromhexine Favors control
[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. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospital discharge 75% Imp. Relative Risk, 95% CI Oxygen therapy 50% Recovery time -3% no CI Li: Bromhexine Hydrochloride Tablets for the Treat.. c19bromhexine.com/li5.html Favors bromhexine Favors control
[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. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ PCR+ on day 10 and hos.. 39% Imp. Relative Risk, 95% CI Virological cure 87% Mareev: Results of Open-Label non-Randomized Comparati.. c19bromhexine.com/mareev.html Favors bromhexine Favors control
[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. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization 80% Imp. Relative Risk, 95% CI Symptomatic case 91% Virological cure 71% Mikhaylov: Bromhexine Hydrochloride Prophylaxis of COVID-.. c19bromhexine.com/mikhaylov.html Favors bromhexine Favors control
[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. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 76% Imp. Relative Risk, 95% CI Improvement 76% Case -75% Tolouian: Effect of bromhexine in hospitalized patients .. c19bromhexine.com/tolouian.html Favors bromhexine Favors control
[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. Submit Corrections or Updates.
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. 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 calculations for that study. 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 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 (3.9.7) with scipy (1.7.1), pythonmeta (1.23), numpy (1.21.2), statsmodels (0.13.0), and plotly (5.3.1).
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. None
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 the bottom of this page.
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 calculations, which may differ from the effect a paper focuses on.
[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%), 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%).
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%).
[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%).
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%).
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 calculations, which may differ from the effect a paper focuses on.
[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%), 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%), 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 calculations, which may differ from the effect a paper focuses on.
[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%), 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%), 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%).
References
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