Top
Overview
Introduction
Preclinical
Results
RCTs
Heterogeneity
Pooled Effects
Discussion
Perspective
Conclusion
 
Study Notes
Methods and Data
Supplementary
References
 
All studies
Mortality
Ventilation
ICU admission
Hospitalization
All RCTs
 
Feedback
Home
c19early.org COVID-19 treatment researchVitamin B12Vitamin B12 (more..)
Melatonin Meta
Metformin Meta
Azvudine Meta
Bromhexine Meta Molnupiravir Meta
Budesonide Meta
Colchicine Meta
Conv. Plasma Meta Nigella Sativa Meta
Curcumin Meta Nitazoxanide Meta
Famotidine Meta Paxlovid Meta
Favipiravir Meta Quercetin Meta
Fluvoxamine Meta Remdesivir Meta
Hydroxychlor.. Meta Thermotherapy Meta
Ivermectin Meta

Loading...
More

Vitamin B12 for COVID-19: real-time meta analysis of 4 studies

@CovidAnalysis, June 2024, Version 2V2
 
0 0.5 1 1.5+ All studies 30% 4 11,407 Improvement, Studies, Patients Relative Risk Mortality 34% 1 9,206 Ventilation 35% 1 9,229 ICU admission 30% 2 9,301 Hospitalization 11% 3 11,930 RCTs 75% 1 34 Prophylaxis 26% 2 11,354 Late 77% 2 53 Vitamin B12 for COVID-19 c19early.org June 2024 Favorsvitamin B12 Favorscontrol
Abstract
Meta analysis using the most serious outcome reported shows 30% [5‑48%] lower 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 are more effective. There has been no early treatment studies to date. There is very limited data to date, with only one RCT, 2 treatment studies, and 2 prophylaxis studies.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Vitamin B12 p=0.023 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org June 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Vitamin B12 for COVID-19 — Highlights
Vitamin B12 reduces risk with high confidence for pooled analysis and very low confidence for hospitalization.
34th treatment shown effective with ≥3 clinical studies in February 2022, now with p = 0.023 from 4 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 75 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Jang 78% 0.22 [0.05-0.94] no recov. 1/6 10/13 ECMO patients Improvement, RR [CI] Treatment Control Erfani (RCT) 75% 0.25 [0.03-2.01] ICU 1/17 4/17 Tau​2 = 0.00, I​2 = 0.0%, p = 0.035 Late treatment 77% 0.23 [0.06-0.91] 2/23 14/30 77% lower risk Bejan 34% 0.66 [0.30-1.44] death 617 (n) 8,589 (n) Improvement, RR [CI] Treatment Control Nimer 24% 0.76 [0.52-1.10] hosp. 35/395 184/1,753 Tau​2 = 0.00, I​2 = 0.0%, p = 0.065 Prophylaxis 26% 0.74 [0.54-1.02] 35/1,012 184/10,342 26% lower risk All studies 30% 0.70 [0.52-0.95] 37/1,035 198/10,372 30% lower risk 4 vitamin B12 COVID-19 studies c19early.org June 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.023 Effect extraction pre-specified(most serious outcome, see appendix) Favors vitamin B12 Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Jang 78% recovery ECMO patients Improvement Relative Risk [CI] Erfani (RCT) 75% ICU admission Tau​2 = 0.00, I​2 = 0.0%, p = 0.035 Late treatment 77% 77% lower risk Bejan 34% death Nimer 24% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p = 0.065 Prophylaxis 26% 26% lower risk All studies 30% 30% lower risk 4 vitamin B12 C19 studies c19early.org June 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.023 Effect extraction pre-specifiedRotate device for details Favors vitamin B12 Favors control
B
-100% -50% 0% 50% 100% Timeline of COVID-19 vitamin B12 studies (pooled effects) 2020 2021 2022 2023 Favorsvitamin B12 Favorscontrol c19early.org June 2024 February 2022: efficacy (pooled outcomes)
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 vitamin B12 studies. The marked date indicates the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes.
Introduction
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury1-8 and cognitive deficits3,8, cardiovascular complications9, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factorsA,10-14, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk15, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Vitamin B12 has been identified by the European Food Safety Authority (EFSA) as having sufficient evidence for a causal relationship between intake and optimal immune system function16-18.
We analyze all significant controlled studies of vitamin B12 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, 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.
Preclinical Research
2 In Silico studies support the efficacy of vitamin B1219,20.
An In Vitro study supports the efficacy of vitamin B1221.
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
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3 plots individual results by treatment stage. Figure 4, 5, 6, 7, and 8 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, and hospitalization.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05.
Improvement Studies Patients Authors
All studies30% [5‑48%]
*
4 11,407 24
Randomized Controlled TrialsRCTs75% [-101‑97%]1 34 4
ICU admissionICU30% [-64‑70%]2 9,301 10
HospitalizationHosp.11% [-9‑28%]3 11,930 14
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05.
Late treatment Prophylaxis
All studies77% [9‑94%]
*
26% [-2‑46%]
Randomized Controlled TrialsRCTs75% [-101‑97%]
ICU admissionICU75% [-101‑97%]16% [-60‑56%]
HospitalizationHosp.-1% [-41‑28%]18% [-6‑37%]
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Prophylaxis Efficacy in COVID-19 vitamin B12 studies (pooled effects) Favors vitamin B12 Favors control c19early.org June 2024
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 ventilation.
Loading..
Figure 7. Random effects meta-analysis for ICU admission.
Loading..
Figure 8. Random effects meta-analysis for hospitalization.
Randomized Controlled Trials (RCTs)
Figure 9 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 1 and Table 2. Currently there is only one RCT.
Loading..
Figure 9. 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.
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases22, and analysis of double-blind RCTs has identified extreme levels of bias23. 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 75 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.
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 see28,29.
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
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 hours30,31. 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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases32
<24 hours-33 hours symptoms33
24-48 hours-13 hours symptoms33
Inpatients-2.5 hours to improvement34
Figure 10 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 75 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Loading..
Figure 10. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 75 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 variants36, for example the Gamma variant shows significantly different characteristics37-40. 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 variants41,42.
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 synergistic43-53, 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.
Pooled Effects
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 75 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 11 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 12 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 13 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 11. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 12. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 11. 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 14 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Loading..
Loading..
Figure 14. 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.
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 results57-60. For vitamin B12, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 15 shows a scatter plot of results for prospective and retrospective studies.
Loading..
Figure 15. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Vitamin B12 for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 vitamin B12 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 vitamin B12 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 alone43-53. 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.
Batista et al. present a review covering vitamin B12 for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors10-14, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk15, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 16 shows an overview of the results for vitamin B12 in the context of multiple COVID-19 treatments, and Figure 17 shows a plot of efficacy vs. cost for COVID-19 treatments.
Loading..
Figure 16. 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 efficacy62.
Loading..
Loading..
Figure 17. Efficacy vs. cost for COVID-19 treatments.
Meta analysis using the most serious outcome reported shows 30% [5‑48%] lower risk.
0 0.5 1 1.5 2+ Mortality 34% Improvement Relative Risk Ventilation 35% ICU admission 16% Hospitalization 10% Vitamin B12 for COVID-19  Bejan et al.  Prophylaxis Is prophylaxis with vitamin B12 beneficial for COVID-19? Retrospective 9,748 patients in the USA Lower mortality (p=0.3) and ventilation (p=0.26), not sig. c19early.org Bejan et al., Clinical Pharmacology & .., Feb 2021 Favors vitamin B12 Favors control
Bejan: Retrospective 9,748 COVID-19 patients in the USA showing lower risk with vitamin B12 use, without statistical significance.
0 0.5 1 1.5 2+ ICU admission 75% Improvement Relative Risk Hospitalization time -1% Vitamin B12  Erfani et al.  LATE TREATMENT  RCT Is late treatment with vitamin B12 beneficial for COVID-19? RCT 34 patients in Iran Lower ICU admission with vitamin B12 (not stat. sig., p=0.34) c19early.org Erfani et al., Health Science Reports, Sep 2023 Favors vitamin B12 Favors control
Erfani: Small RCT 34 hospitalized patients in Iran showing improved imflammatory markers and lower ICU admission with vitamin B12 treatment, without statistical significance. There was no mortality.
0 0.5 1 1.5 2+ Recovery 78% Improvement Relative Risk Vitamin B12 for COVID-19  Jang et al.  ECMO PATIENTS Is late treatment with vitamin B12 beneficial for COVID-19? Retrospective 19 patients in South Korea (February - April 2020) Improved recovery with vitamin B12 (p=0.041) c19early.org Jang et al., Heart & Lung, December 2020 Favors vitamin B12 Favors control
Jang: Retrospective 19 COVID-19 ECMO patients in South Korea, showing a higher rate of weaning from ECMO with vitamin B12 treatment, without statistical significance. Authors perform multivariate analysis but do not provide full results, only reporting p > 0.05.
0 0.5 1 1.5 2+ Hospitalization 24% Improvement Relative Risk Severe case 27% Vitamin B12 for COVID-19  Nimer et al.  Prophylaxis Is prophylaxis with vitamin B12 beneficial for COVID-19? Retrospective 2,148 patients in Jordan (March - July 2021) Lower hospitalization (p=0.15) and severe cases (p=0.06), not sig. c19early.org Nimer et al., Bosnian J. Basic Medical.., Feb 2022 Favors vitamin B12 Favors control
Nimer: Retrospective 2,148 COVID-19 recovered patients in Jordan, showing lower risk of severity and hospitalization with vitamin B12 prophylaxis, without statistical significance.
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 vitamin B12 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 vitamin B12 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 to67. 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 170. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.12.3) with scipy (1.13.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta71 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 effective30,31.
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 the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/b12meta.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.
Erfani, 9/14/2023, Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, median age 52.6, 4 authors. risk of ICU admission, 75.0% lower, RR 0.25, p = 0.34, treatment 1 of 17 (5.9%), control 4 of 17 (23.5%), NNT 5.7.
hospitalization time, 0.8% higher, relative time 1.01, p = 0.97, treatment mean 7.47 (±2.93) n=17, control mean 7.41 (±4.88) n=17.
Jang, 12/16/2020, retrospective, South Korea, peer-reviewed, median age 63.0, 10 authors, study period February 2020 - April 2020. risk of no recovery, 78.3% lower, RR 0.22, p = 0.04, treatment 1 of 6 (16.7%), control 10 of 13 (76.9%), NNT 1.7, weaning from ECMO.
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.
Bejan, 2/28/2021, retrospective, USA, peer-reviewed, mean age 42.0, 6 authors. risk of death, 34.0% lower, OR 0.66, p = 0.30, treatment 617, control 8,589, adjusted per study, RR approximated with OR.
risk of mechanical ventilation, 35.0% lower, OR 0.65, p = 0.26, treatment 618, control 8,611, adjusted per study, RR approximated with OR.
risk of ICU admission, 16.0% lower, OR 0.84, p = 0.61, treatment 625, control 8,642, adjusted per study, RR approximated with OR.
risk of hospitalization, 10.0% lower, OR 0.90, p = 0.60, treatment 678, control 9,070, adjusted per study, RR approximated with OR.
Nimer, 2/28/2022, retrospective, Jordan, peer-reviewed, survey, 4 authors, study period March 2021 - July 2021. risk of hospitalization, 23.9% lower, RR 0.76, p = 0.15, treatment 35 of 395 (8.9%), control 184 of 1,753 (10.5%), NNT 61, adjusted per study, odds ratio converted to relative risk, multivariable.
risk of severe case, 27.3% lower, RR 0.73, p = 0.06, treatment 40 of 395 (10.1%), control 220 of 1,753 (12.5%), NNT 41, adjusted per study, odds ratio converted to relative risk, multivariable.
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.
  or use drag and drop   
Submit