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Bromhexine for COVID-19: real-time meta analysis of 6 studies
Covid Analysis, October 6, 2022, DRAFT
https://c19early.com/bmeta.html
 
0 0.5 1 1.5+ All studies 50% 6 684 Improvement, Studies, Patients Relative Risk Mortality 77% 3 550 Ventilation 89% 1 78 ICU admission 82% 1 78 Hospitalization 14% 3 488 Recovery 43% 2 84 Cases 33% 3 522 Viral clearance 76% 2 80 RCTs 50% 6 684 RCT mortality 77% 3 550 Peer-reviewed 57% 4 262 Prophylaxis 65% 2 422 Early 79% 2 96 Late 12% 2 166 Bromhexine for COVID-19 c19early.com/b Oct 2022 Favorsbromhexine Favorscontrol
Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 3 studies from 3 independent teams in 2 different countries show statistically significant improvements in isolation (not for the most serious outcome).
Meta analysis using the most serious outcome reported shows 50% [-8‑77%] improvement, without reaching statistical significance. Results are similar for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
0 0.5 1 1.5+ All studies 50% 6 684 Improvement, Studies, Patients Relative Risk Mortality 77% 3 550 Ventilation 89% 1 78 ICU admission 82% 1 78 Hospitalization 14% 3 488 Recovery 43% 2 84 Cases 33% 3 522 Viral clearance 76% 2 80 RCTs 50% 6 684 RCT mortality 77% 3 550 Peer-reviewed 57% 4 262 Prophylaxis 65% 2 422 Early 79% 2 96 Late 12% 2 166 Bromhexine for COVID-19 c19early.com/b Oct 2022 Favorsbromhexine Favorscontrol
Currently there is limited data, with only 684 patients and only 12 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].
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 50% of bromhexine studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Bromhexine reduces risk for COVID-19 with low confidence for mortality, ventilation, ICU admission, viral clearance, and in pooled analysis. Bromhexine may be less effective for omicron due to the entry process moving towards TMPRSS2-independent fusion.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 treatments.
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] 24mg death 0/39 5/39 Improvement, RR [CI] Dose (1d) Treatment Control Li (RCT) 75% 0.25 [0.05-1.35] 96mg no disch. 2/12 4/6 Tau​2 = 0.00, I​2 = 0.0%, p = 0.013 Early treatment 79% 0.21 [0.06-0.72] 2/51 9/45 79% improvement Mareev (RCT) 11% 0.89 [0.65-1.22] 32mg no recov. 33 (n) 33 (n) CT​1 Improvement, RR [CI] Dose (1d) Treatment Control Tolouian (RCT) 76% 0.24 [0.01-8.03] 32mg death 48 (n) 52 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.42 Late treatment 12% 0.88 [0.64-1.20] 0/81 0/85 12% improvement Mikhaylov (RCT) 80% 0.20 [0.01-3.97] 24mg hosp. 0/25 2/25 Improvement, RR [CI] Dose (1d) Treatment Control Tolouian (DB RCT) 33% 0.67 [0.04-10.5] 24mg death 0/187 1/185 Tau​2 = 0.00, I​2 = 0.0%, p = 0.35 Prophylaxis 65% 0.35 [0.04-3.12] 0/212 3/210 65% improvement All studies 50% 0.50 [0.23-1.08] 2/344 12/340 50% improvement 6 bromhexine COVID-19 studies c19early.com/b Oct 2022 Tau​2 = 0.24, I​2 = 24.6%, p = 0.077 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors bromhexine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ansarin (RCT) 91% death Improvement Relative Risk [CI] Li (RCT) 75% discharge Tau​2 = 0.00, I​2 = 0.0%, p = 0.013 Early treatment 79% 79% improvement Mareev (RCT) 11% recovery CT​1 Tolouian (RCT) 76% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.42 Late treatment 12% 12% improvement Mikhaylov (RCT) 80% hospitalization Tolouian (DB RCT) 33% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.35 Prophylaxis 65% 65% improvement All studies 50% 50% improvement 6 bromhexine COVID-19 studies c19early.com/b Oct 2022 Tau​2 = 0.24, I​2 = 24.6%, p = 0.077 Protocol pre-specified/rotate for details1 CT: study uses combined treatment 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.
Preclinical Research
An In Silico study supports the efficacy of bromhexine [Sgrignani].
2 In Vitro studies support the efficacy of bromhexine [Carpinteiro, Hoffman].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, 11, and 12 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.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION RECOVERY CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS RCT MORTALITY PEER-REVIEWED All Prophylaxis Early Late Bromhexine for COVID-19 C19EARLY.COM/B OCT 2022
Figure 3. Overview of results.
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% 12% improvement
RR 0.88 [0.64‑1.20]
p = 0.42
Prophylaxis 2 2 100% 65% improvement
RR 0.35 [0.04‑3.12]
p = 0.35
All studies 6 6 100% 50% improvement
RR 0.50 [0.23‑1.08]
p = 0.077
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 679% [28‑94%]12% [-20‑36%]65% [-212‑96%] 684 72
Peer-reviewed 479% [28‑94%]12% [-20‑36%] 262 48
Randomized Controlled TrialsRCTs 679% [28‑94%]12% [-20‑36%]65% [-212‑96%] 684 72
Table 2. Results by treatment stage for all studies and with different exclusions.
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Figure 4. 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.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during a pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 13 shows a chronological history of Randomized Controlled Trials. Figure 14 and 15 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results. Currently all studies are RCTs, so these are the same as for all studies.
Figure 13. Chronological history of Randomized Controlled Trials.
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Figure 14. 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.
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Figure 15. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 6 6 100% 50% improvement
RR 0.50 [0.23‑1.08]
p = 0.077
RCT mortality results 3 3 100% 77% improvement
RR 0.23 [0.04‑1.39]
p = 0.11
Table 3. 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]. Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 16. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
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.
Other 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.
Medication quality.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Meta analysis.
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. While we present pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant for specific use cases.
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.
Funnel plot analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 17 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 17. Example funnel plot analysis for simulated perfect trials.
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.
Notes.
1 of 6 studies combine treatments. The results of bromhexine alone may differ. 1 of 6 RCTs use combined treatment.
Conclusion
Statistically significant improvements are seen for ventilation, ICU admission, and viral clearance. 3 studies from 3 independent teams in 2 different countries show statistically significant improvements in isolation (not for the most serious outcome). Meta analysis using the most serious outcome reported shows 50% [-8‑77%] improvement, without reaching statistical significance. 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 684 patients and only 12 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].
Study Notes
0 0.5 1 1.5 2+ Mortality 91% Improvement Relative Risk Ventilation 89% ICU admission 82% c19early.com/b Ansarin et al. Bromhexine for COVID-19 RCT EARLY TREATMENT 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.
0 0.5 1 1.5 2+ Discharge 75% Improvement Relative Risk Oxygen therapy 50% Recovery time -3% no CI c19early.com/b Li et al. Bromhexine for COVID-19 RCT EARLY TREATMENT 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.
0 0.5 1 1.5 2+ SHOKS-COVID score 11% Improvement Relative Risk PCR+ on day 10 or hospi.. 39% Hospitalization time 8% Viral clearance 87% c19early.com/b Mareev et al. Bromhexine for COVID-19 RCT LATE TREATMENT Favors bromhexine Favors control
[Mareev] Prospective 103 PCR+ patients in Russia, 33 treated with bromexhine+spironolactone, showing lower PCR+ at day 10 or hospitalization >10 days with treatment. Bromhexine 8mg 4 times daily, spironolactone 25-50 mg/day for 10 days.
0 0.5 1 1.5 2+ Hospitalization 80% Improvement Relative Risk Symptomatic case 91% Viral clearance 71% primary c19early.com/b Mikhaylov et al. Bromhexine for COVID-19 RCT Prophylaxis 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.
0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk Hospitalization 70% Symptomatic case 53% Case 50% c19early.com/b Tolouian et al. Bromhexine for COVID-19 RCT Prophylaxis Favors bromhexine Favors control
[Tolouian (B)] PEP RCT with 372 close contacts of COVID+ patients, 187 treated with bromhexine, showing significantly lower cases with treatment. IRCT20120703010178N22.
0 0.5 1 1.5 2+ Mortality 76% Improvement Relative Risk Improvement 76% Case -75% c19early.com/b Tolouian et al. Bromhexine for COVID-19 RCT LATE 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.
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 c19early.com. Search terms were bromhexine, filtered for papers containing the terms COVID-19 or SARS-CoV-2. 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. 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 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. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.7) with scipy (1.9.1), pythonmeta (1.26), numpy (1.23.3), statsmodels (0.13.2), and plotly (5.10.0).
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. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
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 (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
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://c19early.com/bmeta.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.
[Ansarin], 7/19/2020, Randomized Controlled Trial, Iran, 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, 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. 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.
[Mareev], 12/3/2020, Randomized Controlled Trial, Russia, peer-reviewed, 20 authors, this trial uses multiple treatments in the treatment arm (combined with spironolactone) - results of individual treatments may vary. relative SHOKS-COVID score, 11.3% better, RR 0.89, p = 0.47, treatment mean 2.12 (±1.39) n=33, control mean 2.39 (±1.59) n=33.
risk of PCR+ on day 10 or 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.
hospitalization time, 8.2% lower, relative time 0.92, p = 0.35, treatment 33, control 33.
risk of no viral clearance, 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, peer-reviewed, 7 authors. risk of death, 76.0% lower, OR 0.24, p = 0.43, treatment 48, control 52, Table 3, adjusted, RR approximated with OR.
risk of no improvement, 75.9% lower, OR 0.24, p = 0.43, treatment 48, control 52, inverted to make OR<1 favor treatment, 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. 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.
[Mikhaylov], 3/8/2021, Randomized Controlled Trial, Russia, 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 viral clearance, 71.4% lower, RR 0.29, p = 0.14, treatment 2 of 25 (8.0%), control 7 of 25 (28.0%), NNT 5.0, primary outcome.
[Tolouian (B)], 12/20/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, 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%), 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%), 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
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