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Probiotics for COVID-19: real-time meta analysis of 20 studies
Covid Analysis, December 2022
https://c19early.org/kmeta.html
 
0 0.5 1 1.5+ All studies 22% 20 17,944 Improvement, Studies, Patients Relative Risk Mortality 61% 7 1,004 Ventilation 38% 3 325 ICU admission 29% 4 549 Hospitalization 13% 4 790 Recovery 16% 8 1,726 Cases 31% 6 15,852 Viral clearance 4% 3 641 RCTs 29% 10 1,647 RCT mortality 24% 3 630 Peer-reviewed 22% 18 17,412 Prophylaxis 29% 6 15,852 Early 34% 4 712 Late 17% 10 1,380 Probiotics for COVID-19 c19early.org/k Dec 2022 Favorsprobiotics Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, hospitalization, progression, and recovery. 10 studies from 10 independent teams in 7 different countries show statistically significant improvements in isolation (5 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 22% [12‑31%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Better results are seen with early treatment.
Results are robust — in exclusion sensitivity analysis 18 of 20 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 22% 20 17,944 Improvement, Studies, Patients Relative Risk Mortality 61% 7 1,004 Ventilation 38% 3 325 ICU admission 29% 4 549 Hospitalization 13% 4 790 Recovery 16% 8 1,726 Cases 31% 6 15,852 Viral clearance 4% 3 641 RCTs 29% 10 1,647 RCT mortality 24% 3 630 Peer-reviewed 22% 18 17,412 Prophylaxis 29% 6 15,852 Early 34% 4 712 Late 17% 10 1,380 Probiotics for COVID-19 c19early.org/k Dec 2022 Favorsprobiotics Favorscontrol after exclusions
The immune effects of probiotics are strain-specific and studies use different strains.
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 30% of probiotics studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix. [Neris Almeida Viana] present another meta analysis for probiotics, showing significant improvement for recovery.
Highlights
Probiotics reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, low confidence for progression and cases, and very low confidence for ICU admission. The immune effects of probiotics are strain-specific.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ 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 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Early treatment 34% 0.66 [0.56-0.78] 83/360 119/352 34% improvement 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 Tau​2 = 0.02, I​2 = 31.0%, p = 0.047 Late treatment 17% 0.83 [0.70-1.00] 45/644 89/736 17% improvement Louca 8% 0.92 [0.85-0.99] cases Improvement, RR [CI] Treatment Control Di Pierro (RCT) 98% 0.02 [0.00-0.33] cases 0/64 24/64 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 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 Tau​2 = 0.09, I​2 = 54.3%, p = 0.082 Prophylaxis 29% 0.71 [0.48-1.04] 39/1,220 480/14,632 29% improvement All studies 22% 0.78 [0.69-0.88] 167/2,224 688/15,720 22% improvement 20 probiotics COVID-19 studies c19early.org/k Dec 2022 Tau​2 = 0.02, I​2 = 45.4%, 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 Relative Risk [CI] Gutiérr.. (DB RCT) 35% recovery Veterini 29% viral- Navarro-L.. (RCT) 33% recovery Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Early treatment 34% 34% improvement 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 Tau​2 = 0.02, I​2 = 31.0%, p = 0.047 Late treatment 17% 17% improvement Louca 8% case Di Pierro (RCT) 98% case Holt 30% case Ahanchian (DB RCT) 73% symp. case Wischm.. (DB RCT) 33% mod./sev. case Rodrigu.. (DB RCT) 9% case Tau​2 = 0.09, I​2 = 54.3%, p = 0.082 Prophylaxis 29% 29% improvement All studies 22% 22% improvement 20 probiotics COVID-19 studies c19early.org/k Dec 2022 Tau​2 = 0.02, I​2 = 45.4%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors probiotics Favors control
B
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C
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D
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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. D. Timeline of results in probiotics studies.
We analyze all significant studies concerning the use 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
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.
Improvement Studies Patients Authors
All studies22% [12‑31%]20 17,944 245
After exclusions22% [10‑31%]18 2,687 205
Peer-reviewed studiesPeer-reviewed22% [11‑31%]18 17,412 218
Randomized Controlled TrialsRCTs29% [11‑43%]10 1,647 95
Mortality61% [34‑77%]7 1,004 77
VentilationVent.38% [-87‑79%]3 325 40
ICU admissionICU29% [-19‑57%]4 549 44
HospitalizationHosp.13% [5‑21%]4 790 32
Cases31% [-1‑53%]6 15,852 104
Viral4% [-43‑35%]3 641 27
RCT mortality24% [-142‑76%]3 630 26
RCT hospitalizationRCT hosp.12% [-3‑25%]3 490 18
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.
Early treatment Late treatment Prophylaxis
All studies34% [22‑44%] 417% [0‑30%] 1029% [-4‑52%] 6
After exclusions34% [22‑45%] 317% [0‑30%] 1036% [-17‑65%] 5
Peer-reviewed studiesPeer-reviewed34% [22‑44%] 317% [0‑30%] 1031% [-15‑59%] 5
Randomized Controlled TrialsRCTs34% [22‑45%] 311% [-6‑25%] 364% [-26‑90%] 4
Mortality67% [-716‑99%] 161% [33‑77%] 6-
VentilationVent.-38% [-87‑79%] 3-
ICU admissionICU-29% [-19‑57%] 4-
HospitalizationHosp.60% [-106‑92%] 113% [5‑20%] 3-
Cases--31% [-1‑53%] 6
Viral29% [-22‑59%] 1-6% [-70‑34%] 2-
RCT mortality67% [-716‑99%] 114% [-199‑75%] 2-
RCT hospitalizationRCT hosp.60% [-106‑92%] 111% [-4‑24%] 2-
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.
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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.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
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. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. 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. Note that 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] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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Figure 13. Results for RCTs and non-RCT studies.
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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.
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Figure 15. Random effects meta-analysis for RCT mortality results.
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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), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 17 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Holt], significant unadjusted confounding possible.
[Veterini], the observered difference in duration could be caused by the baseline difference in Ct values.
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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.
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]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 18 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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.
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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.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For probiotics, 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.
62% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 42% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 47% improvement, compared to 34% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 20 shows a scatter plot of results for prospective and retrospective studies.
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Figure 20. Prospective vs. retrospective studies.
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.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
1 of 20 studies combine treatments. The results of probiotics alone may differ. 1 of 10 RCTs use combined treatment. [Neris Almeida Viana] present another meta analysis for probiotics, showing significant improvement for recovery.
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.
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.
Statistically significant improvements are seen for mortality, hospitalization, progression, and recovery. 10 studies from 10 independent teams in 7 different countries show statistically significant improvements in isolation (5 for the most serious outcome). Meta analysis using the most serious outcome reported shows 22% [12‑31%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Better results are seen with early treatment. Results are robust — in exclusion sensitivity analysis 18 of 20 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.
0 0.5 1 1.5 2+ Respiratory symptoms 73% Improvement Relative Risk Case 85% c19early.org/k Ahanchian et al. Probiotics for COVID-19 RCT Prophylaxis 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. IRCT-20101020004976N6.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk ICU admission 82% c19early.org/k Ceccarelli et al. Probiotics for COVID-19 LATE 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% c19early.org/k Ceccarelli et al. Probiotics for COVID-19 LATE 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% c19early.org/k d'Ettorre et al. Probiotics for COVID-19 LATE 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 98% Improvement Relative Risk c19early.org/k Di Pierro et al. Probiotics for COVID-19 RCT Prophylaxis Favors probiotics Favors control
[Di Pierro] 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+ Recovery 35% Improvement Relative Risk c19early.org/k Gutiérrez-Castrellón et al. NCT04517422 Probiotics RCT EARLY 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. NCT04517422.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Hospitalization 60% Hospitalization/ER/urgent.. 50% Time to resolution of sy.. 20% c19early.org/k Haran et al. NCT04414124 Probiotics RCT EARLY TREATMENT 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+ Case 30% Improvement Relative Risk c19early.org/k Holt et al. NCT04330599 COVIDENCE UK Probiotics Prophylaxis 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% c19early.org/k Ivashkin et al. NCT04854941 Probiotics RCT LATE TREATMENT 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+ Discharge -12% Improvement Relative Risk Time to discharge -60% Time to viral- -35% c19early.org/k Li et al. Probiotics for COVID-19 LATE TREATMENT 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 c19early.org/k Louca et al. Probiotics for COVID-19 Prophylaxis 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% c19early.org/k Navarro-López et al. NCT04390477 Probiotics RCT EARLY 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 c19early.org/k Rodriguez-Blanque et al. NCT04366180 Probiotics RCT Prophylaxis 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+ Mortality 67% Improvement Relative Risk ICU admission 86% Hospitalization time 26% c19early.org/k Saviano et al. Probiotics for COVID-19 RCT LATE TREATMENT 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 improve.. 11% Improvement Relative Risk Hospitalization time 11% Clinical improvement 83% Clinical improvement (b) 4% c19early.org/k Shah et al. Probiotics for COVID-19 RCT LATE TREATMENT 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% c19early.org/k Trinchieri et al. Probiotics for COVID-19 LATE 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 c19early.org/k Veterini et al. Probiotics for COVID-19 EARLY 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 Symptomatic case 38% primary Recovery time 27% Case 43% c19early.org/k Wischmeyer et al. NCT04399252 PROTECT-EHC Probiotics RCT Prophylaxis 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% c19early.org/k Zhang et al. NCT04581018 Probiotics LATE TREATMENT 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 improve.. 14% Time to viral- 17% c19early.org/k Zhang et al. Probiotics for COVID-19 LATE TREATMENT 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 performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were probiotics, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.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, 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.
[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.
[Ivashkin], 10/13/2021, Randomized Controlled Trial, Russia, peer-reviewed, 11 authors, 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. 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).
[Di Pierro], 3/12/2021, Randomized Controlled Trial, Italy, peer-reviewed, 2 authors. 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.
[Wischmeyer], 1/5/2022, Double Blind Randomized Controlled Trial, USA, preprint, 21 authors, 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. 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.
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