Covid Analysis, January 25, 2022, DRAFT
https://c19curcumin.com/meta.html
•Statistically significant improvements are seen for mortality and recovery. 5 studies from 5 independent teams in 2 different countries show statistically significant
improvements in isolation (3 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
49% [24‑66%] improvement. Results are slightly worse for Randomized Controlled Trials. Results are consistent with early treatment being more effective than late treatment.
•Currently there is limited data, with only 918 patients and only 47 control events for the most serious outcome in trials to date.
•Studies typically use advanced formulations for greatly improved bioavailability.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 40% of curcumin
studies show zero events in the treatment arm.
•Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•Elimination of COVID-19 is a race
against viral evolution. No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used, including treatments, as supported by Pfizer [Pfizer].
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
| Studies | Early treatment | Late treatment | Patients | Authors | ||
| All studies | 10 | 71% [26‑89%] | 47% [-6‑73%] | 918 | 90 | |
| Randomized Controlled TrialsRCTs | 9 | 60% [12‑82%] | 47% [-6‑73%] | 877 | 81 | |
| Percentage improvement with curcumin treatment | ||||||
Figure 1. A. Random effects
meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
B. Scatter plot showing the
distribution of effects reported in studies. C. History of all reported
effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of
curcumin 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.
Mechanisms of Action
| 3CLpro inhibitor | Curcumin inhibits SARS-CoV-2 3CLpro [Bahun, Guijarro-Real, Rehman]. |
| RdRp inhibitor | SARS-CoV-2 RNA‐dependent RNA polymerase (RdRp) inhibition [Singh]. |
| ACE2 inhibitor | Curcumin inhibits ACE2 activity. SARS-CoV-2 viral entry requires host cell surface proteins ACE2 and TMPRSS2 [Jena, Patel]. |
| TMPRSS2 downregulation | Curcumin downregulates transmembrane serine protease 2 (TMPRSS2). SARS-CoV-2 viral entry requires host cell surface proteins ACE2 and TMPRSS2 [Goc]. |
| Cathepsin L inhibitor | Curcumin inhibits cathepsin L activity. Cathepsin L plays a key role in viral entry [Goc]. |
| Anti‑inflammatory | Curcumin shows anti-inflammatory effects [Daily, Derosa, Gupta, Marín-Palma, Rattis, Sahebkar]. |
| Inhibition in Vero E6 cells demonstrated | In Vitro research shows curcumin inhibits SARS-CoV-2 in Vero E6 cells [Bormann, Marín-Palma]. |
| Inhibition in Calu-3 cells demonstrated | In Vitro research shows curcumin inhibits SARS-CoV-2 in Calu-3 cells [Bormann]. |
Table 1. Curcumin mechanisms of action.
Submit updates.
Results
Figure 3, 4, 5, 6, 7, 8, and 9
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, and viral clearance.
Table 2 summarizes the results by treatment stage.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Early treatment | 6 | 6 | 100% |
71% improvement RR 0.29 [0.11‑0.74] p = 0.0094 |
| Late treatment | 3 | 4 | 75.0% |
47% improvement RR 0.53 [0.27‑1.06] p = 0.072 |
| All studies | 9 | 10 | 90.0% |
49% improvement RR 0.51 [0.34‑0.76] p = 0.00089 |
Table 2. Results by treatment stage.
Figure 3. Random effects meta-analysis for all studies with pooled effects.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 4. Random effects meta-analysis for mortality results.
Figure 5. Random effects meta-analysis for ventilation.
Figure 6. Random effects meta-analysis for hospitalization.
Figure 7. Random effects meta-analysis for progression.
Figure 8. Random effects meta-analysis for recovery.
Figure 9. Random effects meta-analysis for viral clearance.
Randomized Controlled Trials (RCTs)
Figure 10 shows the distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 11 and 12
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 3 summarizes the 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 curcumin 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].
Figure 10. The distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 11. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects, discussion can be found in the heterogeneity section,
and results for specific outcomes can be found in the individual outcome analyses.
Effect extraction is pre-specified, using the most serious outcome reported.
For details of effect extraction see the appendix.
Figure 12. Random effects meta-analysis for RCT mortality results.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Randomized Controlled Trials | 8 | 9 | 88.9% |
37% improvement RR 0.63 [0.52‑0.76] p < 0.0001 |
| RCT mortality results | 5 | 5 | 100% |
59% improvement RR 0.41 [0.22‑0.75] p = 0.0042 |
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection
or the onset of symptoms and treatment may critically affect how well a
treatment works. For example an antiviral may be very effective when used
early but may not be effective in late stage disease, and may even be harmful.
Oseltamivir, for example, is generally only considered effective for influenza
when used within 0-36 or 0-48 hours [McLean, Treanor]. Other
medications might be beneficial for late stage complications, while early use
may not be effective or may even be harmful. Figure 13 shows
an example where efficacy declines as a function of treatment delay.
Figure 13. Effectiveness may depend critically
on treatment delay.
Patient demographics.
Details of the
patient population including age and comorbidities may critically affect how
well a treatment works. For example, many COVID-19 studies with relatively
young low-comorbidity patients show all patients recovering quickly with or
without treatment. In such cases, there is little room for an effective
treatment to improve results (as in [López-Medina]).Effect measured.
Efficacy may differ
significantly depending on the effect measured, for example a treatment may be
very effective at reducing mortality, but less effective at minimizing cases
or hospitalization. Or a treatment may have no effect on viral clearance while
still being effective at reducing mortality.Variants.
There are many different
variants of SARS-CoV-2 and efficacy may depend critically on the distribution
of variants encountered by the patients in a study. For example, the Gamma
variant shows significantly different characteristics
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be
more or less effective depending on variants, for example the viral entry
process for the omicron variant has moved towards TMPRSS2-independent fusion,
suggesting that TMPRSS2 inhibitors may be less effective
[Peacock, Willett].Regimen.
Effectiveness may depend
strongly on the dosage and treatment regimen.Treatments.
The use of other
treatments may significantly affect outcomes, including anything from
supplements, other medications, or other kinds of treatment such as prone
positioning.The distribution of studies will alter the outcome of a meta
analysis. Consider a simplified example where everything is equal except for
the treatment delay, and effectiveness decreases to zero or below with
increasing delay. If there are many studies using very late treatment, the
outcome may be negative, even though the treatment may be very effective when
used earlier.
In general, by combining heterogeneous studies, as all meta
analyses do, we run the risk of obscuring an effect by including studies where
the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we
expect the estimated effect size to be lower than that for the optimal case.
We do not a priori expect that pooling all studies will create a
positive result for an effective treatment. Looking at all studies is valuable
for providing an overview of all research, important to avoid cherry-picking,
and informative when a positive result is found despite combining less-optimal
situations. However, the resulting estimate does not apply to specific cases
such as early treatment in high-risk populations.
Discussion
Publication bias.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results
[Boulware, Meeus, Meneguesso].
For curcumin, there is currently not
enough data to evaluate publication bias with high confidence.Conflicts of interest.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Curcumin for COVID-19
lacks this because it is
off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 curcumin 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 curcumin trials
represent the optimal conditions for efficacy.Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do
here), while others distinguish between mild/moderate/severe cases. We note
that viral load does not indicate degree of symptoms — for example
patients may have a high viral load while being asymptomatic. With regard to
treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.Notes.
3 of 10 studies
combine treatments. The results of curcumin alone may differ.
3 of 9 RCTs use combined treatment.
[Vahedian-Azimi] present another meta analysis for curcumin, showing significant improvements for mortality, hospitalization, and symptoms.
Conclusion
Curcumin is an effective treatment for COVID-19.
Statistically significant improvements are seen for mortality, and recovery. 5 studies from 5 independent teams in 2 different countries show statistically significant
improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows
49% [24‑66%] improvement. Results are slightly worse for Randomized Controlled Trials. Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 918 patients and only 47 control events for the most serious outcome in trials to date.
Studies typically use advanced formulations for greatly improved bioavailability.
Study Notes
[Ahmadi]
RCT 60 outpatients in Iran, 30 treated with nano-curcumin showing lower hospitalization and faster recovery with treatment.
[Asadirad]
RCT 60 hospitalized patients in Iran, 30 treated with nano-curcumin, showing significant improvements in inflammatory cytokines, and improvements in clinical outcomes without statistical significance. 240 mg/day nano-curcumin for 7 days.
[Dound]
RCT 200 COVID-19 positive patients in India, 100 treated with Curcumin, Vitamin C, Vitamin K2-7, and L-Selenomethionine, showing faster recovery with treatment.
[Hassaniazad]
Small RCT with 40 low risk patients in Iran, 20 treated with nano-curcumin, showing no significant difference in outcomes with treatment. Authors note that treatment can improve peripheral blood inflammatory indices and modulate immune response by decreasing Th1 and Th17 responses, increasing T regulatory responses, further reducing IL-17 and IFN-γ, and increasing suppressive cytokines TGF-β and IL-4.
[Majeed]
RCT 100 patients in India, 50 treated with ImmuActive (curcumin, andrographolides, resveratrol, zinc, selenium, and piperine), showing improved recovery with treatment.
[Pawar]
RCT 140 patients, 70 treated with curcumin and piperine (for absorption), showing faster recovery, lower progression, and lower mortality with treatment. Control group partients also received probiotics. CTRI/2020/05/025482.
[Saber-Moghaddam]
Small prospective nonrandomized trial with 41 patients, 21 treated with curcumin, showing lower disease progression and faster recovery with treatment. IRCT20200408046990N1.
[Sankhe]
RCT 174 patients in India, 87 treated with AyurCoro-3 (turmeric, gomutra, potassium alum, khadisakhar, bos indicus milk, ghee), showing faster recovery with treatment. EC/NEW/INST/2019/245.
[Tahmasebi]
RCT 40 hospitalized, 40 ICU, and 40 control patients in Iran, showing lower mortality and improved regulatory T cell responses with nanocurcumin treatment (SinaCurcumin).
[Valizadeh]
Small RCT with 40 nano-curcumin patients and 40 control patients showing lower mortality with treatment. Authors conclude that nano-curcumin may be able to modulate the increased rate of inflammatory cytokines especially IL-1β and IL-6 mRNA expression and cytokine secretion in COVID-19 patients, which may improve clinical outcomes.
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 c19curcumin.com. Search terms were curcumin, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of curcumin for COVID-19 that report
a comparison with a control group are included in the main analysis.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days are used. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms were not used (the next most serious
outcome is used — no studies were excluded). For example, in low-risk
populations with no mortality, a reduction in mortality with treatment is not
possible, however a reduction in hospitalization, for example, is still
valuable.
Clinical outcome is considered more important than PCR testing status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available (after most or all patients have recovered there is no room
for an effective treatment to do better).
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we computed the relative risk when
possible, or converted to a relative risk according to [Zhang].
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported including propensity score matching (PSM), the PSM results are used.
Adjusted primary outcome results have preference over unadjusted results for a more
serious outcome when the adjustments significantly alter results.
When needed, conversion between reported p-values and confidence
intervals followed [Altman, Altman (B)], and Fisher's exact test was
used to calculate p-values for event data. If continuity correction for
zero values is required, we use the reciprocal of the opposite arm with the
sum of the correction factors equal to 1 [Sweeting].
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.9.10) with
scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.14.0), and plotly (5.4.0).
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.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment, and treatment
started within 5 days of the onset of symptoms. If studies contain a mix of
early treatment and late treatment patients, we consider the treatment time of
patients contributing most to the events (for example, consider a study where
most patients are treated early but late treatment patients are included, and
all mortality events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective
[McLean, Treanor].
A summary of study results is below. Please submit
updates and corrections at https://c19curcumin.com/meta.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.
| [Ahmadi], 6/19/2021, Double Blind Randomized Controlled Trial, Iran, Middle East, peer-reviewed, 11 authors. | risk of hospitalization, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 30 (0.0%), control 3 of 30 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| recovery time, 20.6% lower, relative time 0.79, p = 0.37, treatment 30, control 30. | |
| [Dound], 11/16/2020, Randomized Controlled Trial, India, South Asia, peer-reviewed, 5 authors, this trial uses multiple treatments in the treatment arm (combined with vitamin C, vitamin K2-7, and l-selenomethionine) - results of individual treatments may vary. | relative improvement on 6-point scale, 33.3% better, RR 0.67, p < 0.001, treatment 100, control 100. |
| [Majeed], 10/11/2021, Double Blind Randomized Controlled Trial, India, South Asia, peer-reviewed, 4 authors, this trial uses multiple treatments in the treatment arm (combined with andrographolides, resveratrol, zinc, selenium, and piperine) - results of individual treatments may vary. | risk of mechanical ventilation, 66.2% lower, RR 0.34, p = 1.00, treatment 0 of 45 (0.0%), control 1 of 47 (2.1%), NNT 47, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 79.7% lower, RR 0.20, p = 0.49, treatment 0 of 45 (0.0%), control 2 of 47 (4.3%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| relative ordinal scale, 43.0% better, RR 0.57, p = 0.004, treatment 45, control 47, day 28. | |
| risk of no recovery, 24.6% lower, RR 0.75, p = 0.08, treatment 26 of 45 (57.8%), control 36 of 47 (76.6%), NNT 5.3, day 28. | |
| time to viral-, 5.8% lower, relative time 0.94, p = 0.47, treatment 45, control 47. | |
| [Pawar], 5/28/2021, Double Blind Randomized Controlled Trial, India, South Asia, peer-reviewed, 8 authors. | risk of death, 81.8% lower, RR 0.18, p = 0.02, treatment 2 of 70 (2.9%), control 11 of 70 (15.7%), NNT 7.8. |
| risk of death, 60.0% lower, RR 0.40, p = 0.39, treatment 2 of 15 (13.3%), control 5 of 15 (33.3%), NNT 5.0, severe group. | |
| risk of death, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 25 (0.0%), control 5 of 25 (20.0%), NNT 5.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), moderate group. | |
| risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (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), mild group. | |
| [Saber-Moghaddam], 1/3/2021, prospective, Iran, Middle East, peer-reviewed, 9 authors. | risk of progression, 94.3% lower, RR 0.06, p = 0.001, treatment 0 of 21 (0.0%), control 8 of 20 (40.0%), NNT 2.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of no recovery, 38.4% lower, RR 0.62, p = 0.04, treatment 11 of 21 (52.4%), control 17 of 20 (85.0%), NNT 3.1. | |
| hospitalization time, 44.8% lower, relative time 0.55, p < 0.001, treatment 21, control 20. | |
| [Sankhe], 8/10/2021, Randomized Controlled Trial, India, South Asia, peer-reviewed, 8 authors, this trial uses multiple treatments in the treatment arm (combined with gomutra, potassium alum, khadisakhar, bos indicus milk, ghee) - results of individual treatments may vary. | risk of death, 88.9% lower, RR 0.11, p = 0.12, treatment 0 of 87 (0.0%), control 4 of 87 (4.6%), NNT 22, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of mechanical ventilation, 75.0% lower, RR 0.25, p = 0.37, treatment 1 of 87 (1.1%), control 4 of 87 (4.6%), NNT 29. | |
| risk of no 2-point improvement, 46.5% lower, RR 0.54, p = 0.002, treatment 29 of 87 (33.3%), control 60 of 87 (69.0%), NNT 2.8, odds ratio converted to relative risk, day 7 mid-recovery. | |
| hospitalization time, 10.0% lower, relative time 0.90, p = 0.40, treatment 87, control 87. |
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.
| [Asadirad], 1/17/2022, Randomized Controlled Trial, placebo-controlled, Iran, Middle East, peer-reviewed, 7 authors. | risk of death, 25.9% lower, RR 0.74, p = 0.74, treatment 5 of 27 (18.5%), control 6 of 24 (25.0%), NNT 15, excluding patients that stopped treatment due to progression - 3 for curcumin and 6 for control. |
| risk of progression, 50.0% lower, RR 0.50, p = 0.47, treatment 3 of 30 (10.0%), control 6 of 30 (20.0%), NNT 10.0. | |
| risk of unresolved fever, 45.3% lower, RR 0.55, p = 0.09, treatment 8 of 27 (29.6%), control 13 of 24 (54.2%), NNT 4.1. | |
| risk of unresolved dyspnea, 28.9% lower, RR 0.71, p = 0.72, treatment 4 of 27 (14.8%), control 5 of 24 (20.8%), NNT 17. | |
| risk of unresolved cough, 40.7% lower, RR 0.59, p = 0.36, treatment 6 of 27 (22.2%), control 9 of 24 (37.5%), NNT 6.5. | |
| risk of O2 <92%, 36.5% lower, RR 0.63, p = 0.51, treatment 5 of 27 (18.5%), control 7 of 24 (29.2%), NNT 9.4. | |
| risk of O2 <97%, 20.0% lower, RR 0.80, p = 0.21, treatment 18 of 27 (66.7%), control 20 of 24 (83.3%), NNT 6.0. | |
| [Hassaniazad], 9/19/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, Middle East, peer-reviewed, 12 authors. | relative improvement in SpO2, 45.7% worse, RR 1.46, p = 0.90, treatment 20, control 20. |
| [Tahmasebi], 3/28/2021, Double Blind Randomized Controlled Trial, Iran, Middle East, peer-reviewed, 14 authors. | risk of death, 83.3% lower, RR 0.17, p = 0.11, treatment 1 of 40 (2.5%), control 6 of 40 (15.0%), NNT 8.0. |
| risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 20 (0.0%), control 1 of 20 (5.0%), NNT 20, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), non-ICU patients. | |
| risk of death, 80.0% lower, RR 0.20, p = 0.18, treatment 1 of 20 (5.0%), control 5 of 20 (25.0%), NNT 5.0, ICU patients. | |
| [Valizadeh], 10/20/2020, Double Blind Randomized Controlled Trial, Iran, Middle East, peer-reviewed, 12 authors. | risk of death, 50.0% lower, RR 0.50, p = 0.30, treatment 4 of 20 (20.0%), control 8 of 20 (40.0%), NNT 5.0. |
Supplementary Data
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