Top
Overview
Introduction
Results
RCTs
Exclusions
Heterogeneity
Discussion
Conclusion
 
Study Notes
Methods and Data
Supplementary
References
 
All studies
Mortality
Ventilation
ICU admission
Hospitalization
Progression
Recovery
COVID‑19 cases
Viral clearance
Peer reviewed
Exclusions
All RCTs
RCT mortality
RCT hospitalization
 
Feedback
Home
c19early.org COVID-19 treatment researchProbioticsProbiotics (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

Probiotics for COVID-19: real-time meta analysis of 26 studies

@CovidAnalysis, February 2024, Version 28V28
 
0 0.5 1 1.5+ All studies 28% 26 19,398 Improvement, Studies, Patients Relative Risk Mortality 61% 8 1,054 Ventilation 38% 3 325 ICU admission 21% 5 599 Hospitalization 13% 5 890 Recovery 19% 11 2,016 Cases 40% 8 16,966 Viral clearance 4% 3 641 RCTs 34% 14 2,694 RCT mortality 46% 4 680 Peer-reviewed 28% 23 18,766 Prophylaxis 38% 9 17,086 Early 36% 6 882 Late 19% 11 1,430 Probiotics for COVID-19 c19early.org February 2024 after exclusions Favorsprobiotics Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 28% [18‑37%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Better results are seen with early treatment.
Results are robust — in exclusion sensitivity analysis 24 of 26 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
The immune effects of probiotics are strain-specific and studies use different strains.
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. The quality of non-prescription supplements can vary widely Crawford, Crighton. Many probiotic supplements may not include labeled ingredients Hazan.
All data to reproduce this paper and sources are in the appendix. Other meta analyses show significant improvements with probiotics for hospitalization Tian and recovery Neris Almeida Viana, Tian.
Evolution of COVID-19 clinical evidence Probiotics p=0.0000013 Acetaminophen p=0.00000029 2020 2021 2022 2023 2024 Effective Harmful c19early.org February 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Probiotics reduce risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for cases, and low confidence for progression. The immune effects of probiotics are strain-specific.
Probiotics were the 18th treatment shown effective with ≥3 clinical studies in March 2021, now known with p = 0.0000013 from 26 studies.
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 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Haran (RCT) 67% 0.33 [0.01-8.16] death 0/174 1/176 Improvement, RR [CI] Treatment Control Gutiérre.. (DB RCT) 35% 0.65 [0.53-0.80] no recov. 69/147 105/146 Veterini 29% 0.71 [0.41-1.22] viral time 15 (n) 15 (n) Navarro-Ló.. (RCT) 33% 0.67 [0.43-1.05] no recov. 14/24 13/15 Hassan (RCT) 80% 0.20 [0.02-1.65] hosp. 1/50 5/50 Kolesnyk (DB RCT) 60% 0.40 [0.18-0.90] no recov. 6/34 16/36 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Early treatment 36% 0.64 [0.54-0.76] 90/444 140/438 36% lower risk d'Ettorre 87% 0.13 [0.01-2.33] death 0/28 4/42 Improvement, RR [CI] Treatment Control Ceccarelli 64% 0.36 [0.18-0.68] death 10/88 34/112 Shah (RCT) 11% 0.89 [0.75-1.06] recov. time 30 (n) 30 (n) CT​1 Li -12% 1.12 [0.74-1.69] no disch. 30/123 41/188 Zhang 14% 0.86 [0.77-0.96] hosp. time 150 (n) 150 (n) Ceccarelli 70% 0.30 [0.01-7.02] death 0/40 1/29 Ivashkin (RCT) -2% 1.02 [0.26-3.97] death 4/99 4/101 Zhang 65% 0.35 [0.02-8.30] ventilation 0/25 1/30 Saviano (RCT) 67% 0.33 [0.01-7.95] death 0/40 1/40 Trinchieri 78% 0.22 [0.03-1.93] death 1/21 3/14 Di Pierro (RCT) 62% 0.38 [0.11-1.25] death 3/25 8/25 Tau​2 = 0.02, I​2 = 32.8%, p = 0.031 Late treatment 19% 0.81 [0.67-0.98] 48/669 97/761 19% lower risk Louca 8% 0.92 [0.85-0.99] cases population-based cohort Improvement, RR [CI] Treatment Control Di Pierro (RCT) 98% 0.02 [0.00-0.33] cases 0/64 24/64 COVIDENCE UK Holt 30% 0.70 [0.45-1.10] cases 20/909 426/14,318 Ahanchian (DB RCT) 73% 0.27 [0.03-2.25] symp. case 1/29 4/31 PROTECT-EHC Wischme.. (DB RCT) 33% 0.67 [0.38-1.17] m/s case 16/91 24/91 Rodrigue.. (DB RCT) 9% 0.91 [0.12-6.70] cases 2/127 2/128 Catinean 40% 0.60 [0.41-0.88] recovery 60 (n) 60 (n) Di Pierro 78% 0.22 [0.07-0.67] cases 186 (n) 101 (n) Sarlin (DB RCT) 33% 0.67 [0.11-3.98] cases 2/413 3/414 Tau​2 = 0.11, I​2 = 62.4%, p = 0.0065 Prophylaxis 38% 0.62 [0.44-0.88] 41/1,879 483/15,207 38% lower risk All studies 28% 0.72 [0.63-0.82] 179/2,992 720/16,406 28% lower risk 26 probiotics COVID-19 studies c19early.org February 2024 Tau​2 = 0.03, I​2 = 50.7%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors probiotics Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Haran (RCT) 67% death Improvement Relative Risk [CI] Gutiérr.. (DB RCT) 35% recovery Veterini 29% viral- Navarro-L.. (RCT) 33% recovery Hassan (RCT) 80% hospitalization Kolesnyk (DB RCT) 60% recovery Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Early treatment 36% 36% lower risk d'Ettorre 87% death Ceccarelli 64% death Shah (RCT) 11% recovery CT​1 Li -12% discharge Zhang 14% hospitalization Ceccarelli 70% death Ivashkin (RCT) -2% death Zhang 65% ventilation Saviano (RCT) 67% death Trinchieri 78% death Di Pierro (RCT) 62% death Tau​2 = 0.02, I​2 = 32.8%, p = 0.031 Late treatment 19% 19% lower risk Louca 8% case Di Pierro (RCT) 98% case COVIDENCE UK Holt 30% case Ahanchian (DB RCT) 73% symp. case PROTECT-EHC Wischm.. (DB RCT) 33% mod./sev. case Rodrigu.. (DB RCT) 9% case Catinean 40% recovery Di Pierro 78% case Sarlin (DB RCT) 33% case Tau​2 = 0.11, I​2 = 62.4%, p = 0.0065 Prophylaxis 38% 38% lower risk All studies 28% 28% lower risk 26 probiotics C19 studies c19early.org February 2024 Tau​2 = 0.03, I​2 = 50.7%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors probiotics Favors control
B
Loading..
C
Loading..
D
Loading..
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. 0.6% of 6,662 proposed treatments show efficacy c19early.org. D. Timeline of results in probiotics studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 9.3 months, compared to using all studies.
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 issues Scardua-Silva, Yang, cardiovascular complications Eberhardt, 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 factors Note A, Malone, Murigneux, Lv, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 6,000 compounds may reduce COVID-19 risk c19early.org (B), either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
Efficacy with probiotics has been shown for the common cold Kobatake.
We analyze all significant controlled studies of Probiotics for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies28% [18‑37%]
****
26 19,398 289
After exclusions28% [17‑37%]
****
23 4,091 242
Peer-reviewed studiesPeer-reviewed28% [17‑37%]
****
23 18,766 256
Randomized Controlled TrialsRCTs34% [17‑47%]
***
14 2,694 125
Mortality61% [37‑76%]
***
8 1,054 84
VentilationVent.38% [-87‑79%]3 325 40
ICU admissionICU21% [-20‑48%]5 599 51
HospitalizationHosp.13% [5‑21%]
**
5 890 38
Recovery19% [10‑28%]
***
11 2,016 104
Cases40% [10‑60%]
*
8 16,966 121
Viral4% [-43‑35%]3 641 27
RCT mortality46% [-25‑77%]4 680 33
RCT hospitalizationRCT hosp.13% [-2‑25%]4 590 24
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  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies36% [24‑46%]
****
19% [2‑33%]
*
38% [12‑56%]
**
After exclusions36% [24‑47%]
****
17% [0‑30%]
*
42% [11‑62%]
*
Peer-reviewed studiesPeer-reviewed35% [24‑45%]
****
19% [2‑33%]
*
40% [11‑60%]
*
Randomized Controlled TrialsRCTs36% [24‑47%]
****
12% [-4‑26%]55% [-13‑82%]
Mortality67% [-716‑99%]61% [36‑76%]
***
VentilationVent.38% [-87‑79%]
ICU admissionICU21% [-20‑48%]
HospitalizationHosp.69% [-13‑91%]13% [5‑20%]
**
Recovery30% [20‑39%]
****
9% [2‑16%]
*
38% [13‑55%]
**
Cases40% [10‑60%]
*
Viral29% [-22‑59%]-6% [-70‑34%]
RCT mortality67% [-716‑99%]44% [-33‑77%]
RCT hospitalizationRCT hosp.69% [-13‑91%]11% [-4‑24%]
Loading..
Loading..
Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Loading..
Loading..
Figure 4. Random effects meta-analysis for mortality results.
Loading..
Figure 5. Random effects meta-analysis for ventilation.
Loading..
Figure 6. Random effects meta-analysis for ICU admission.
Loading..
Figure 7. Random effects meta-analysis for hospitalization.
Loading..
Figure 8. Random effects meta-analysis for progression.
Loading..
Figure 9. Random effects meta-analysis for recovery.
Loading..
Figure 10. Random effects meta-analysis for cases.
Loading..
Figure 11. Random effects meta-analysis for viral clearance.
Loading..
Figure 12. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta analyses including 114 trials.
Figure 13 shows a comparison of results for RCTs and non-RCT studies. Figure 14, 15, and 16 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases Jadad, and analysis of double-blind RCTs has identified extreme levels of bias Gøtzsche. 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, 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 66 treatments we have analyzed, 63% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. RCTs for probiotics are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials 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 could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see Deaton, Nichol.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 44 treatments with statistically significant efficacy/harm, 28 have been confirmed in RCTs, with a mean delay of 5.8 months. When considering only low cost treatments, 23 have been confirmed with a delay of 7.0 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.
Loading..
Figure 13. Results for RCTs and non-RCT studies.
Loading..
Figure 14. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Loading..
Figure 15. Random effects meta-analysis for RCT mortality results.
Loading..
Figure 16. Random effects meta-analysis for RCT hospitalization results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), and can be easily influenced by potential bias.
The studies excluded are as below. Figure 17 shows a forest plot for random effects meta-analysis of all studies after exclusions.
Di Pierro, unadjusted differences between groups.
Holt, significant unadjusted confounding possible.
Veterini, the observered difference in duration could be caused by the baseline difference in Ct values.
Loading..
Figure 17. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu report an 86% reduction in cases for post-exposure prophylaxis, Hayden 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 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 cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 18 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Loading..
Figure 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 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 (as in López-Medina).
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.
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.
Effectiveness may depend strongly on the dosage and treatment regimen.
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 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. Non-prescription supplements may show very wide variations in quality Crawford, Crighton.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 19. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.7 months. When restricting to RCTs only, 46% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
Loading..
Loading..
Figure 19. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
Efficacy with probiotics has also been shown for the common cold Kobatake.
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.
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 20 shows a scatter plot of results for prospective and retrospective studies. 70% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 38% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 52% improvement, compared to 48% for prospective studies, showing similar results.
Loading..
Figure 20. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-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 21 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 21. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Probiotics for COVID-19 lack this because they are generally inexpensive and widely available. In contrast, most COVID-19 probiotics 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 probiotics 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 by 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 affiliated with special interests 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 alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. 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, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
1 of 26 studies combine treatments. The results of probiotics alone may differ. 1 of 14 RCTs use combined treatment. Other meta analyses show significant improvements with probiotics for hospitalization Tian and recovery Neris Almeida Viana, Tian.
Many reviews cover probiotics for COVID-19, presenting additional background on mechanisms and related results, including Baud, Di Pierro (B), Kurian, Olaimat, Petrariu, Righi, Singh, Stavropoulou, Taufer, Taufer (B).
Statistically significant lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 13 studies from 12 independent teams in 9 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 28% [18‑37%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Better results are seen with early treatment. Results are robust — in exclusion sensitivity analysis 24 of 26 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
The immune effects of probiotics are strain-specific and studies use different strains.
Other meta analyses show significant improvements with probiotics for hospitalization Tian and recovery Neris Almeida Viana, Tian.
0 0.5 1 1.5 2+ Respiratory symptoms 73% Improvement Relative Risk Case 85% Probiotics  Ahanchian et al.  Prophylaxis  DB RCT Is prophylaxis with probiotics beneficial for COVID-19? Double-blind RCT 60 patients in Iran (July - August 2020) Fewer symptomatic cases (p=0.35) and cases (p=0.24), not sig. c19early.org Ahanchian et al., Open J. Nursing, May 2021 Favors probiotics Favors control
Ahanchian: Small RCT 60 healthcare workers in Iran, showing lower cases with treatment but without statistical significance. Once daily oral synbiotic capsule (Lactocare®) containing 1 billion CFU L. (Lactobacillus) casei, L. rhamnosus, Streptococcus thermophilus, Bifidobacterium breve, L. acidophilus, Bifidobacterium infantis, L. bulgaricus, and Fructooligosacharide.
0 0.5 1 1.5 2+ Symptom resolution 40% Improvement Relative Risk Resolution of fever 38% Probiotics for COVID-19  Catinean et al.  Prophylaxis Is prophylaxis with probiotics beneficial for COVID-19? Retrospective 120 patients in Romania (September 2020 - February 2021) Improved recovery with probiotics (p=0.0082) c19early.org Catinean et al., Nutrients, January 2023 Favors probiotics Favors control
Catinean: Retrospective 60 patients in Romania taking probiotics and 60 matched controls, showing faster symptom resolution with the use of probiotics. Spore-based probiotic containing five strains of Bacillus.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk ICU admission 82% Probiotics  Ceccarelli et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Prospective study of 69 patients in Italy Lower mortality (p=0.42) and ICU admission (p=0.15), not sig. c19early.org Ceccarelli et al., Nutrients, August 2021 Favors probiotics Favors control
Ceccarelli: Prospective analysis of 69 severe COVID-19 patients requiring non-invasive oxygen therapy, 40 treated with probiotic formulation SLAB51, showing lower oxygen requirements and higher blood levels of pO2, O2Hb and SaO2 with treatment. Authors suggest that enzymes in SLAB51 could reduce oxygen requirements in intestinal cells, resulting in more oxygen available for other organs.
0 0.5 1 1.5 2+ Mortality 64% Improvement Relative Risk ICU admission 15% Probiotics  Ceccarelli et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 200 patients in Italy Lower mortality with probiotics (p=0.003) c19early.org Ceccarelli et al., Frontiers in Medicine, Jan 2021 Favors probiotics Favors control
Ceccarelli (B): Retrospective 200 severe condition hospitalized patients in Italy, 88 treated with probiotic Sivomixx, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 87% Improvement Relative Risk Ventilation 77% Respiratory failure 88% Probiotics  d'Ettorre et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 70 patients in Italy Lower progression with probiotics (p=0.011) c19early.org d'Ettorre et al., Frontiers in Medicine, Jul 2020 Favors probiotics Favors control
d'Ettorre: Retrospective 70 hospitalized patients in Italy, 28 treated with probiotic Sivomixx, showing lower risk of respiratory failure and faster recovery with treatment.
0 0.5 1 1.5 2+ Case 78% Improvement Relative Risk Probiotics for COVID-19  Di Pierro et al.  Prophylaxis Do probiotics reduce COVID-19 infections? Retrospective 287 patients in Italy (January - March 2022) Fewer cases with probiotics (p=0.0074) c19early.org Di Pierro et al., Minerva Medica, September 2023 Favors probiotics Favors control
Di Pierro (C): Retrospective study of 287 nursery school children in Italy, 186 treated with S. salivarius K12 probiotic. The probiotic group had significantly lower rates of COVID-19, bronchitis, sinusitis, and laryngitis as well as lower antibiotic use. The study was registered retrospectively and details of COVID-19 diagnosis are not provided. Parents that administer the treatment may also use other treatments or take other actions that reduce risk for their children.
0 0.5 1 1.5 2+ Case 98% Improvement Relative Risk Probiotics  Di Pierro et al.  Prophylaxis  RCT Do probiotics reduce COVID-19 infections? RCT 128 patients in Italy (September - December 2020) Fewer cases with probiotics (p<0.000001) c19early.org Di Pierro et al., Minerva Medica, March 2021 Favors probiotics Favors control
Di Pierro (D): Interim report on an RCT for prophylactic treatment with S. salivarius K12, showing significantly lower cases with treatment. Only patients with symptoms or known positive contacts were tested. Trial identification/registration details are not provided.
0 0.5 1 1.5 2+ Mortality 62% Improvement Relative Risk ICU admission 0% Probiotics  Di Pierro et al.  LATE TREATMENT  RCT Is late treatment with probiotics beneficial for COVID-19? RCT 50 patients in Pakistan (August - November 2021) Lower mortality with probiotics (not stat. sig., p=0.17) c19early.org Di Pierro et al., Microorganisms, September 2022 Favors probiotics Favors control
Di Pierro: RCT 50 hospitalized patients in Pakistan, 25 treated with S. salivarius K12, showing lower mortality with treatment, without statistical significance. There were more patients with higher oxygen requirements at baseline in the control group - 18 vs. 6 with O2 ≥ 8 L/min.
0 0.5 1 1.5 2+ Recovery 35% Improvement Relative Risk Probiotics  Gutiérrez-Castrellón et al.  EARLY TREATMENT  RCT Is early treatment with probiotics beneficial for COVID-19? Double-blind RCT 293 patients in Mexico (August 2020 - February 2021) Improved recovery with probiotics (p=0.000017) c19early.org Gutiérrez-Castrellón et al., Gut Micro.., May 2021 Favors probiotics Favors control
Gutiérrez-Castrellón: RCT 293 outpatients in Mexico, 147 treated with a probiotic composed of three L. plantarum strains (KABP022, KABP023 and KABP033) and one P. acidilacti strain (KABP021), showing improved recovery with treatment. There were no hospitalizations or deaths.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Hospitalization 60% Hospitalization/ER/urgent.. 50% Time to resolution of sym.. 20% Probiotics  Haran et al.  EARLY TREATMENT  RCT Is early treatment with probiotics beneficial for COVID-19? RCT 350 patients in the USA (July - December 2020) Lower hospitalization (p=0.45) and fewer hosp./ER visits (p=0.13), not sig. c19early.org Haran et al., medRxiv, March 2021 Favors probiotics Favors control
Haran: RCT 350 COVID+ outpatients in the USA, 174 treated with prebiotic KB109 (a microbiome metabolic therapy candidate), showing lower combined hospitalization, ER, and urgent care visits with treatment. NCT04414124.
0 0.5 1 1.5 2+ Hospitalization 80% Improvement Relative Risk Recovery 18% Recovery time 8% no CI Probiotics  Hassan et al.  EARLY TREATMENT  RCT Is early treatment with probiotics beneficial for COVID-19? RCT 100 patients in Egypt (July 2021 - August 2022) Lower hospitalization (p=0.2) and improved recovery (p=0.42), not sig. c19early.org Hassan et al., Research Square, June 2023 Favors probiotics Favors control
Hassan: RCT 150 patients in Egypt showing no significant difference in outcomes with probiotic lactobacillus acidophilus, although hospitalization was 2% versus 10% for control. SOC included vitamin C, D, and zinc.
0 0.5 1 1.5 2+ Case 30% Improvement Relative Risk Probiotics for COVID-19  COVIDENCE UK  Prophylaxis Do probiotics reduce COVID-19 infections? Prospective study of 15,227 patients in the United Kingdom (May 2020 - Feb 2021) Fewer cases with probiotics (not stat. sig., p=0.11) c19early.org Holt et al., Thorax, March 2021 Favors probiotics Favors control
Holt: Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ Mortality -2% Improvement Relative Risk Ventilation 18% ICU admission 27% Recovery time 5% Probiotics  Ivashkin et al.  LATE TREATMENT  RCT Is late treatment with probiotics beneficial for COVID-19? RCT 200 patients in Russia (December 2020 - March 2021) Trial underpowered for serious outcomes c19early.org Ivashkin et al., Probiotics Antimicrob.., Oct 2021 Favors probiotics Favors control
Ivashkin: RCT 200 patients, 99 treated with a probiotic (Lacticaseibacillus rhamnosus PDV 1705, Bifidobacterium bifidum PDV 0903, Bifidobacterium longum subsp. infantis PDV 1911, and Bifidobacterium longum subsp. longum PDV 2301). There was no significant difference in mortality or recovery time, however benefits were seen for diarrhea. NCT04854941.
0 0.5 1 1.5 2+ WHO score >1 60% Improvement Relative Risk Recovery time 21% PCFS ≥1, long COVID 68% Probiotics  Kolesnyk et al.  EARLY TREATMENT  DB RCT Is early treatment with probiotics beneficial for COVID-19? Double-blind RCT 73 patients in Ukraine (November 2021 - June 2022) Improved recovery (p=0.021) and lower PASC (p=0.0082) c19early.org Kolesnyk et al., BMC Nutrition, January 2024 Favors probiotics Favors control
Kolesnyk: RCT 73 outpatients with mild COVID-19 showing improved recovery and increased RBD/spike antibody response with 28 days of a multi-strain probiotic (Bifidobacterium (B.) lactis BI040, B. longum BL020, Lactobacillus (L) rhamnosus LR110, L. casei LC130, L. acidophilus LA120, 5 billion CFU total).
0 0.5 1 1.5 2+ Discharge -12% Improvement Relative Risk Time to discharge -60% Time to viral- -35% Probiotics for COVID-19  Li et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 311 patients in China Slower viral clearance with probiotics (p=0.001) c19early.org Li et al., Int. Immunopharmacology, Mar 2021 Favors probiotics Favors control
Li: Retrospective 311 severe condition hospitalized patients in China, 123 treated with probiotics, showing slower viral clearance and recovery with treatment. Authors note that probiotics were able to moderate immunity and decrease the incidence of secondary infections.
0 0.5 1 1.5 2+ Case 8% Improvement Relative Risk Probiotics for COVID-19  Louca et al.  Prophylaxis Do probiotics reduce COVID-19 infections? Retrospective 372,720 patients in the United Kingdom Fewer cases with probiotics (p=0.03) c19early.org Louca et al., BMJ Nutrition, Preventio.., Nov 2020 Favors probiotics Favors control
Louca: Survey analysis of dietary supplements showing probiotic usage associated with lower incidence of COVID-19. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
0 0.5 1 1.5 2+ Recovery 33% Improvement Relative Risk Recovery (b) 53% Recovery (c) 20% Recovery (d) 26% Probiotics  Navarro-López et al.  EARLY TREATMENT  RCT Is early treatment with probiotics beneficial for COVID-19? RCT 39 patients in Spain (December 2020 - February 2021) Improved recovery with probiotics (not stat. sig., p=0.083) c19early.org Navarro-López et al., Medicine in Micr.., Aug 2022 Favors probiotics Favors control
Navarro-López: RCT with 24 probiotics and 15 control patients in Spain, showing lower overall symptoms and lower digestive symptoms with treatment. Kluyveromyces marxianus B0399 plus lactobacillus rhamnosus CECT 30579.
0 0.5 1 1.5 2+ Case 9% Improvement Relative Risk Probiotics  Rodriguez-Blanque et al.  Prophylaxis  DB RCT Do probiotics reduce COVID-19 infections? Double-blind RCT 255 patients in Spain (April - July 2020) Trial underpowered to detect differences c19early.org Rodriguez-Blanque et al., Frontiers in.., Aug 2022 Favors probiotics Favors control
Rodriguez-Blanque: Prophylaxis RCT with 127 probiotics and 128 control healthcare workers in Spain, showing no significant difference in cases. There were only 4 cases. Severity information by arm is not provided. L. coryniformis K8 CECT 5711.

Treatment may help sustain the immune response to vaccination - in the subgroup of subjects for whom more than 81 days had passed since they received the first dose, IgG levels were significantly higher in the treatment group. Patients that started probiotic consumption before the first vaccine dose also reported significantly fewer side effects.
0 0.5 1 1.5 2+ Case 33% Improvement Relative Risk Probiotics  Sarlin et al.  Prophylaxis  DB RCT Do probiotics reduce COVID-19 infections? Double-blind RCT 827 patients in Finland (August 2020 - May 2021) Trial underpowered to detect differences c19early.org Sarlin et al., JAMA Network Open, November 2023 Favors probiotics Favors control
Sarlin: RCT 827 children aged 1-6 years in daycare in Finland analyzing the effectiveness of daily Streptococcus salivarius K12 oral probiotic use for 6 months in preventing acute otitis media (AOM). The probiotic group did not have a significantly lower rate of AOM requiring antibiotics compared to placebo. A secondary outcome shows no significant difference in COVID-19, with only 2 and 3 cases in the treatment and placebo groups.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk ICU admission 86% Hospitalization time 26% Probiotics  Saviano et al.  LATE TREATMENT  RCT Is late treatment with probiotics beneficial for COVID-19? RCT 80 patients in Italy Lower ICU admission (p=0.24) and shorter hospitalization (p=0.52), not sig. c19early.org Saviano et al., J. Clinical Medicine, Jun 2022 Favors probiotics Favors control
Saviano: RCT 80 COVID-19 interstitial pneumonia patients in Italy, 40 treated with probiotics, showing significantly reduced gut inflammatory markers with treatment, and lower ICU admission and mortality, without statistical significance. Bifidobacterium lactis LA 304, lactobacillus salivarius LA 302, and lactobacillus acidophilus LA 201 bid for 10 days.

0 0.5 1 1.5 2+ Time to clinical improvem.. 11% Improvement Relative Risk Hospitalization time 11% Clinical improvement 83% Clinical improvement (b) 4% Probiotics  Shah et al.  LATE TREATMENT  RCT Is late treatment with probiotics + multi-enzyme formulation beneficial for COVID-19? RCT 60 patients in India Improved recovery with probiotics + multi-enzyme formulation (p=0.0048) c19early.org Shah et al., Advances in Clinical Toxi.., Feb 2021 Favors probiotics Favors control
Shah: Small RCT 60 patients in India, 30 treated with ImmunoSEB and ProbioSEB CSC3, showing faster recovery with treatment. CTRI/2020/09/027685, CTRI/2020/08/027168.
0 0.5 1 1.5 2+ Mortality 78% Improvement Relative Risk CPAP, day 7 78% CPAP, day 3 10% Probiotics  Trinchieri et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 35 patients in Italy (November 2020 - March 2021) Lower mortality with probiotics (not stat. sig., p=0.28) c19early.org Trinchieri et al., Biomedicines, August 2022 Favors probiotics Favors control
Trinchieri: Retrospective COVID-19 patients requiring CPAP, 21 treated with SLAB51 probiotics and 15 control patients, showing improved outcomes with treatment, despite significantly lower blood oxygenation at baseline in the treatment group.
0 0.5 1 1.5 2+ Time to viral- 29% Improvement Relative Risk Probiotics  Veterini et al.  EARLY TREATMENT Is early treatment with probiotics beneficial for COVID-19? Retrospective 30 patients in Indonesia Faster viral clearance with probiotics (not stat. sig., p=0.22) c19early.org Veterini et al., Indian J. Forensic Me.., Jun 2021 Favors probiotics Favors control
Veterini: Small case control analysis with 15 probiotics patients and 15 contol patients, showing no significant differences. PCR tests were only done weekly. Dosage is unknown. 115/LOE/301.4.2/IX/2020.
0 0.5 1 1.5 2+ Moderate/severe case 33% Improvement Relative Risk Symp. case 38% primary Recovery time 27% Case 43% Probiotics  PROTECT-EHC  Prophylaxis  DB RCT Is prophylaxis with probiotics beneficial for COVID-19? Double-blind RCT 182 patients in the USA (June 2020 - July 2021) Fewer symptomatic cases with probiotics (p=0.02) c19early.org Wischmeyer et al., medRxiv, January 2022 Favors probiotics Favors control
Wischmeyer: RCT 182 COVID-19 exposed patients, 91 treated with daily probiotic Lactobacillus rhamnosus GG starting a median of 3 days from exposure, showing lower symptomatic COVID-19 with treatment. There were no hospitalizations or deaths.
0 0.5 1 1.5 2+ Ventilation 65% Improvement Relative Risk Antibody formation 67% Probiotics for COVID-19  Zhang et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 55 patients in China No significant difference in outcomes c19early.org Zhang et al., J. Gastroenterology and .., Mar 2022 Favors probiotics Favors control
Zhang (B): Pilot study of probiotic SIM01 with 25 consecutive COVID-19 patients in Hong Kong and 30 control patients treated by a different team during the same time period, showing improved antibody formation, reduced viral load and pro-inflammatory responses, and improvements for gut dysbiosis. SIM01 contains bifidobacteria strains, galactooligosaccharides, xylooligosaccharide, and resistant dextrin (derived from metagenomic databases of COVID-19 patients and healthy patients).
0 0.5 1 1.5 2+ Hospitalization time 14% Improvement Relative Risk Time to clinical improvem.. 14% Time to viral- 17% Probiotics for COVID-19  Zhang et al.  LATE TREATMENT Is late treatment with probiotics beneficial for COVID-19? Retrospective 300 patients in China Shorter hospitalization (p=0.009) and faster recovery (p=0.022) c19early.org Zhang et al., Therapeutic Advances in .., Aug 2021 Favors probiotics Favors control
Zhang (C): Retrospective 375 patients in China, 179 treated with probiotics (Bifidobacterium, Lactobacillus, and Enterococcus), showing improved clinical outcomes with treatment.
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 probiotics 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 probiotics 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 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 to Zhang. 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 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 (3.12.2) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
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. 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 (4.1.2) using the metafor (3.0-2) and rms (6.2-0) 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 effective McLean, Treanor.
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/kmeta.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.
Gutiérrez-Castrellón, 5/24/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 9 authors, study period 19 August, 2020 - 2 February, 2021, average treatment delay 4.0 days, trial NCT04517422 (history). risk of no recovery, 34.7% lower, RR 0.65, p < 0.001, treatment 69 of 147 (46.9%), control 105 of 146 (71.9%), NNT 4.0.
Haran, 3/29/2021, Randomized Controlled Trial, USA, preprint, 6 authors, study period 2 July, 2020 - 23 December, 2020, trial NCT04414124 (history). risk of death, 66.5% lower, RR 0.33, p = 1.00, treatment 0 of 174 (0.0%), control 1 of 176 (0.6%), NNT 176, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), death two weeks after study withdrawal.
risk of hospitalization, 59.5% lower, RR 0.40, p = 0.45, treatment 2 of 174 (1.1%), control 5 of 176 (2.8%), NNT 59, including treatment period.
risk of hospitalization/ER/urgent care, 50.0% lower, RR 0.50, p = 0.13, treatment 7 of 169 (4.1%), control 15 of 181 (8.3%), NNT 24.
time to resolution of symptoms, 20.3% lower, relative time 0.80, p = 0.10, treatment 169, control 172, inverted to make RR<1 favor treatment.
Hassan, 6/13/2023, Randomized Controlled Trial, Egypt, preprint, 6 authors, study period July 2021 - August 2022. risk of hospitalization, 80.0% lower, RR 0.20, p = 0.20, treatment 1 of 50 (2.0%), control 5 of 50 (10.0%), NNT 12.
risk of no recovery, 17.9% lower, RR 0.82, p = 0.42, treatment 23 of 50 (46.0%), control 28 of 50 (56.0%), NNT 10.0.
Kolesnyk, 1/4/2024, Double Blind Randomized Controlled Trial, placebo-controlled, Ukraine, peer-reviewed, 10 authors, study period November 2021 - June 2022, trial NCT04907877 (history). WHO score >1, 60.3% lower, RR 0.40, p = 0.02, treatment 6 of 34 (17.6%), control 16 of 36 (44.4%), NNT 3.7.
recovery time, 21.4% lower, relative time 0.79, p = 0.04, treatment 34, control 36.
PCFS ≥1, 67.8% lower, RR 0.32, p = 0.008, treatment 5 of 34 (14.7%), control 16 of 35 (45.7%), NNT 3.2, long COVID, Supplementary Table 1.
Navarro-López, 8/24/2022, Randomized Controlled Trial, Spain, peer-reviewed, 13 authors, study period December 2020 - February 2021, trial NCT04390477 (history). risk of no recovery, 32.7% lower, RR 0.67, p = 0.08, treatment 14 of 24 (58.3%), control 13 of 15 (86.7%), NNT 3.5, day 30.
risk of no recovery, 53.1% lower, RR 0.47, p = 0.10, treatment 6 of 24 (25.0%), control 8 of 15 (53.3%), NNT 3.5, digestive symptoms, day 30.
relative recovery, 20.0% better, RR 0.80, p = 0.03, treatment 24, control 15, relative symptom improvement, day 30.
relative recovery, 26.1% better, RR 0.74, p = 0.06, treatment 24, control 15, relative improvement for digestive symptoms, day 30.
Veterini, 6/30/2021, retrospective, Indonesia, peer-reviewed, 6 authors, excluded in exclusion analyses: the observered difference in duration could be caused by the baseline difference in Ct values. time to viral-, 29.0% lower, relative time 0.71, p = 0.22, treatment 15, control 15.
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.
Ceccarelli, 8/23/2021, prospective, Italy, peer-reviewed, 10 authors. risk of death, 70.4% lower, RR 0.30, p = 0.42, treatment 0 of 40 (0.0%), control 1 of 29 (3.4%), NNT 29, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 81.9% lower, RR 0.18, p = 0.15, treatment 1 of 40 (2.5%), control 4 of 29 (13.8%), NNT 8.9.
Ceccarelli (B), 1/11/2021, retrospective, Italy, peer-reviewed, 14 authors. risk of death, 64.2% lower, RR 0.36, p = 0.003, treatment 10 of 88 (11.4%), control 34 of 112 (30.4%), NNT 5.3, adjusted per study, odds ratio converted to relative risk.
risk of ICU admission, 15.2% lower, RR 0.85, p = 0.60, treatment 16 of 88 (18.2%), control 24 of 112 (21.4%), NNT 31.
d'Ettorre, 7/7/2020, retrospective, Italy, peer-reviewed, 17 authors. risk of death, 87.0% lower, RR 0.13, p = 0.14, treatment 0 of 28 (0.0%), control 4 of 42 (9.5%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 76.9% lower, RR 0.23, p = 0.51, treatment 0 of 28 (0.0%), control 2 of 42 (4.8%), NNT 21, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
respiratory failure, 88.4% lower, OR 0.12, p = 0.01, treatment 28, control 42, inverted to make OR<1 favor treatment, RR approximated with OR.
Di Pierro, 9/28/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 48.5, 7 authors, study period 11 August, 2021 - 18 November, 2021, trial NCT05043376 (history), excluded in exclusion analyses: unadjusted differences between groups. risk of death, 62.5% lower, RR 0.38, p = 0.17, treatment 3 of 25 (12.0%), control 8 of 25 (32.0%), NNT 5.0.
risk of ICU admission, no change, RR 1.00, p = 1.00, treatment 8 of 25 (32.0%), control 8 of 25 (32.0%).
Ivashkin, 10/13/2021, Randomized Controlled Trial, Russia, peer-reviewed, 11 authors, study period December 2020 - March 2021, average treatment delay 8.0 days, trial NCT04854941 (history). risk of death, 2.0% higher, RR 1.02, p = 1.00, treatment 4 of 99 (4.0%), control 4 of 101 (4.0%).
risk of mechanical ventilation, 18.4% lower, RR 0.82, p = 1.00, treatment 4 of 99 (4.0%), control 5 of 101 (5.0%), NNT 110.
risk of ICU admission, 27.1% lower, RR 0.73, p = 0.77, treatment 5 of 99 (5.1%), control 7 of 101 (6.9%), NNT 53.
recovery time, 4.8% lower, relative time 0.95, p = 0.47, treatment 99, control 101.
Li, 3/5/2021, retrospective, China, peer-reviewed, 7 authors, average treatment delay 13.0 days. risk of no hospital discharge, 11.8% higher, RR 1.12, p = 0.68, treatment 30 of 123 (24.4%), control 41 of 188 (21.8%).
time to discharge, 60.0% higher, relative time 1.60, p < 0.001, treatment 123, control 188.
time to viral-, 35.3% higher, relative time 1.35, p < 0.001, treatment 123, control 188.
Saviano, 6/28/2022, Randomized Controlled Trial, Italy, peer-reviewed, mean age 59.8, 9 authors. risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 40 (0.0%), control 1 of 40 (2.5%), NNT 40, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of ICU admission, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 40 (0.0%), control 3 of 40 (7.5%), NNT 13, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
hospitalization time, 26.3% lower, relative time 0.74, p = 0.52, treatment mean 14.0 (±6.0) n=40, control mean 19.0 (±10.0) n=40.
Shah, 2/2/2021, Randomized Controlled Trial, India, peer-reviewed, 3 authors, this trial uses multiple treatments in the treatment arm (combined with multi-enzyme formulation) - results of individual treatments may vary. time to clinical improvement, 10.8% lower, relative time 0.89, p = 0.19, treatment 30, control 30.
hospitalization time, 10.6% lower, relative time 0.89, p = 0.18, treatment 30, control 30.
risk of no clinical improvement, 83.3% lower, RR 0.17, p = 0.005, treatment 2 of 30 (6.7%), control 12 of 30 (40.0%), NNT 3.0, day 10 mid-recovery.
risk of no clinical improvement, 3.7% lower, RR 0.96, p = 1.00, treatment 26 of 30 (86.7%), control 27 of 30 (90.0%), NNT 30, day 7.
Trinchieri, 8/1/2022, retrospective, Italy, peer-reviewed, 10 authors, study period November 2020 - March 2021. risk of death, 77.8% lower, RR 0.22, p = 0.28, treatment 1 of 21 (4.8%), control 3 of 14 (21.4%), NNT 6.0.
risk of miscellaneous, 77.8% lower, RR 0.22, p < 0.001, treatment 4 of 21 (19.0%), control 12 of 14 (85.7%), NNT 1.5, CPAP, day 7.
risk of miscellaneous, 9.5% lower, RR 0.90, p = 0.51, treatment 19 of 21 (90.5%), control 14 of 14 (100.0%), NNT 10, CPAP, day 3.
Zhang (B), 3/2/2022, retrospective, China, peer-reviewed, 12 authors, trial NCT04581018 (history). risk of mechanical ventilation, 64.7% lower, RR 0.35, p = 1.00, treatment 0 of 25 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no antibody formation, 67.3% lower, RR 0.33, p = 0.06, treatment 3 of 25 (12.0%), control 11 of 30 (36.7%), NNT 4.1.
Zhang (C), 8/4/2021, retrospective, China, peer-reviewed, 14 authors. hospitalization time, 13.6% lower, relative time 0.86, p = 0.009, treatment 150, control 150, PSM.
time to clinical improvement, 14.3% lower, relative time 0.86, p = 0.02, treatment 150, control 150, PSM.
time to viral-, 16.7% lower, relative time 0.83, p < 0.001, treatment 150, control 150, PSM.
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.
Ahanchian, 5/31/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, 14 authors, study period July 2020 - August 2020, trial IRCT20101020004976N6. respiratory symptoms, 73.3% lower, RR 0.27, p = 0.35, treatment 1 of 29 (3.4%), control 4 of 31 (12.9%), NNT 11.
risk of case, 85.3% lower, RR 0.15, p = 0.24, treatment 0 of 29 (0.0%), control 3 of 31 (9.7%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Catinean, 1/17/2023, retrospective, Romania, peer-reviewed, 4 authors, study period 15 September, 2020 - 15 February, 2021. symptom resolution, 40.5% lower, HR 0.60, p = 0.008, treatment 60, control 60, inverted to make HR<1 favor treatment.
resolution of fever, 37.5% lower, HR 0.62, p = 0.02, treatment 60, control 60, inverted to make HR<1 favor treatment, fever.
Di Pierro (C), 9/30/2023, retrospective, Italy, peer-reviewed, 10 authors, study period January 2022 - March 2022, trial NCT05840926 (history). risk of case, 77.8% lower, RR 0.22, p = 0.007, treatment mean 0.02 (±0.15) n=186, control mean 0.09 (±0.29) n=101.
Di Pierro (D), 3/12/2021, Randomized Controlled Trial, Italy, peer-reviewed, 2 authors, study period September 2020 - December 2020. risk of case, 98.0% lower, RR 0.02, p < 0.001, treatment 0 of 64 (0.0%), control 24 of 64 (37.5%), NNT 2.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Holt, 3/30/2021, prospective, United Kingdom, peer-reviewed, 34 authors, study period 1 May, 2020 - 5 February, 2021, trial NCT04330599 (history) (COVIDENCE UK), excluded in exclusion analyses: significant unadjusted confounding possible. risk of case, 30.4% lower, RR 0.70, p = 0.11, treatment 20 of 909 (2.2%), control 426 of 14,318 (3.0%), NNT 129, adjusted per study, odds ratio converted to relative risk, minimally adjusted, group sizes approximated.
Louca, 11/30/2020, retrospective, United Kingdom, peer-reviewed, 26 authors. risk of case, 8.5% lower, RR 0.92, p = 0.03, odds ratio converted to relative risk, United Kingdom, all adjustment model.
Rodriguez-Blanque, 8/3/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Spain, peer-reviewed, 7 authors, study period 24 April, 2020 - 20 July, 2020, trial NCT04366180 (history). risk of case, 9.3% lower, RR 0.91, p = 0.92, treatment 2 of 127 (1.6%), control 2 of 128 (1.6%), adjusted per study, multivariable.
Sarlin, 11/2/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Finland, peer-reviewed, 7 authors, study period 1 August, 2020 - 31 May, 2021. risk of case, 33.2% lower, RR 0.67, p = 1.00, treatment 2 of 413 (0.5%), control 3 of 414 (0.7%), NNT 416.
Wischmeyer, 1/5/2022, Double Blind Randomized Controlled Trial, USA, preprint, 21 authors, study period 24 June, 2020 - 8 July, 2021, trial NCT04399252 (history) (PROTECT-EHC). risk of moderate/severe case, 33.3% lower, RR 0.67, p = 0.15, treatment 16 of 91 (17.6%), control 24 of 91 (26.4%), NNT 11.
risk of symptomatic case, 38.5% lower, RR 0.62, p = 0.02, treatment 24 of 91 (26.4%), control 39 of 91 (42.9%), NNT 6.1, primary outcome.
recovery time, 27.3% lower, relative time 0.73, p = 0.37, treatment 91, control 91.
risk of case, 42.9% lower, RR 0.57, p = 0.17, treatment 8 of 91 (8.8%), control 14 of 91 (15.4%), NNT 15.
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. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, 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