N-acetylcysteine reduces COVID-19 risk: real-time meta analysis of 24 studies
Abstract
Significantly lower risk is seen for mortality, hospitalization, and cases. 11 studies from 11 independent teams in 8 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
25% [14‑35%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are very robust — in exclusion sensitivity analysis 16 of
24 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
All data and sources to reproduce this analysis are in the appendix.
Alam et al. present another meta analysis for N-acetylcysteine, showing significant improvement for mortality.
N-acetylcysteine for COVID-19 — Highlights
N-acetylcysteine reduces risk with very high confidence for mortality, hospitalization, and in pooled analysis, and low confidence for recovery and cases.
14th treatment shown effective in February 2021, now with p = 0.000028 from 24 studies, recognized in 3 countries.
Real-time updates and corrections with a consistent protocol for 135 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in N-acetylcysteine 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, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 3.6 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 3.6 months, compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 29.3 months, compared to using pooled outcomes in RCTs.
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 injury2-14 and
cognitive deficits5,10, cardiovascular
complications15-19, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,20-27, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk28, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
N-acetylcysteine shows dose-dependent inhibition of SARS-CoV-229-31, shows anti-inflammatory and immunomodulatory effects against SARS-CoV-2-induced immune responses in combination with bromelain32, suppressed virus-induced reactive oxygen species and blocked viral replication in a humanized mouse model and in human lung cells33, may limit COVID-19 induced cardiac damage by boosting cellular antioxidant defenses and potentially mitigating the oxidative stress caused by spike protein-induced ROS production in cardiac fibroblasts16, and reduces disulfide bonds in proteins and exhibits antioxidant properties that may inhibit viral replication and modulate inflammatory responses34.
NAC may be beneficial for COVID-19 by replenishing glutathione stores and reinforcing the glutathione peroxidase-4 pathway to inhibit ferroptosis, an oxidative stress-induced cell death pathway implicated in COVID-1935.
NAC reinforces glutathione levels, reduces ROS, and minimizes ferroptosis and cytokine storm36.
Efficacy with N-acetylcysteine has been shown for influenza37.
We analyze all significant
controlled studies of
N-acetylcysteine
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.
N-acetylcysteine shows dose-dependent inhibition of SARS-CoV-229-31, shows anti-inflammatory and immunomodulatory effects against SARS-CoV-2-induced immune responses in combination with bromelain32, suppressed virus-induced reactive oxygen species and blocked viral replication in a humanized mouse model and in human lung cells33, may limit COVID-19 induced cardiac damage by boosting cellular antioxidant defenses and potentially mitigating the oxidative stress caused by spike protein-induced ROS production in cardiac fibroblasts16, and reduces disulfide bonds in proteins and exhibits antioxidant properties that may inhibit viral replication and modulate inflammatory responses34.
An In Silico study supports the efficacy of N-acetylcysteine38.
An In Vivo animal study supports the efficacy of N-acetylcysteine33.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, and 11
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 25% [14‑35%] **** | 24 | 26,243 | 257 |
| After exclusions | 24% [14‑33%] **** | 20 | 6,504 | 235 |
| Peer-reviewed studiesPeer-reviewed | 28% [14‑40%] *** | 22 | 23,775 | 236 |
| Randomized Controlled TrialsRCTs | 28% [6‑45%] * | 11 | 1,302 | 152 |
| Mortality | 31% [14‑44%] ** | 20 | 25,781 | 211 |
| VentilationVent. | 6% [-25‑29%] | 6 | 1,008 | 92 |
| ICU admissionICU | 7% [-16‑26%] | 7 | 2,076 | 104 |
| HospitalizationHosp. | 11% [6‑17%] *** | 11 | 1,737 | 145 |
| Recovery | 41% [-9‑68%] | 5 | 1,426 | 45 |
| RCT mortality | 34% [2‑56%] * | 9 | 951 | 128 |
| RCT hospitalizationRCT hosp. | 8% [-1‑17%] | 7 | 928 | 117 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 21% [1‑37%] * | 27% [13‑39%] *** | 28% [21‑34%] **** |
| After exclusions | 21% [1‑37%] * | 26% [11‑38%] ** | 28% [21‑34%] **** |
| Peer-reviewed studiesPeer-reviewed | 21% [1‑37%] * | 29% [14‑42%] *** | |
| Randomized Controlled TrialsRCTs | 80% [-385‑99%] | 28% [5‑45%] * | |
| Mortality | 31% [14‑44%] ** | ||
| VentilationVent. | 6% [-25‑29%] | ||
| ICU admissionICU | 7% [-16‑26%] | ||
| HospitalizationHosp. | 21% [1‑37%] * | 11% [5‑16%] *** | |
| Recovery | 83% [76‑87%] **** | 17% [-6‑35%] | |
| RCT mortality | 34% [2‑56%] * | ||
| RCT hospitalizationRCT hosp. | 80% [-385‑99%] | 8% [-2‑17%] |
Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for ICU admission.
Figure 8. Random effects meta-analysis for hospitalization.
Figure 9. Random effects meta-analysis for recovery.
Figure 10. Random effects meta-analysis for cases.
Figure 11. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
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 12 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
28% improvement,
compared to 25% for other studies.
Figure 13, 14, and 15
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.
Figure 12. Results for RCTs and non-RCT studies.
Figure 13. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 14. Random effects meta-analysis for RCT mortality results.
Figure 15. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases42, and
analysis of double-blind RCTs has identified extreme levels of bias43.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 135 treatments we have analyzed,
66% 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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.93‑1.08]
across 135 treatments45.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 135 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.93‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.93‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.93 [0.83‑1.04].
Details can be found in the
supplementary data.
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 remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see49,50.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.7 months (66% with 8.8 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
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.
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 can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 16 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Izquierdo, significant unadjusted confounding possible.
Panahi, large difference in mortality vs. ICU results, significant baseline differences.
Ramadhan, excessive unadjusted differences between groups.
Çavuş, unadjusted results with no group details.
Figure 16. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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 hours55,56. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases57 |
| <24 hours | -33 hours symptoms58 |
| 24-48 hours | -13 hours symptoms58 |
| Inpatients | -2.5 hours to improvement59 |
Figure 17 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 135 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 135 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants61, for example the Gamma variant shows significantly
different characteristics62-65. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants66,67.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for N-acetylcysteine as of June 2021. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes was delayed by 3.6 months compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 29.3 months compared to using pooled outcomes in RCTs.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 135
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 18 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 19 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 20 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000011 to p = 0.0000000048.
Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 52 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 these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 21 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 21. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Efficacy with N-acetylcysteine has also been shown for influenza37.
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 results88-91.
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 22 shows a scatter plot of
results for prospective and retrospective studies.
60% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
36% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 33% improvement,
compared to 16% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
Figure 22. 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 23 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 < 0.0592-99.
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 23. 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. N-acetylcysteine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 N-acetylcysteine 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 N-acetylcysteine trials
represent the optimal conditions for efficacy.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone70-86.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
1 of 24 studies
combine treatments. The results of
N-acetylcysteine
alone may differ.
1 of 11 RCTs use combined treatment.
Alam et al. present another meta analysis for N-acetylcysteine, showing significant improvement for mortality.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors20-27, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk28, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 24 shows an overview of the results for N-acetylcysteine
in the context of multiple COVID-19 treatments, and Figure 25 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 24.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy143.
Figure 25. Efficacy vs. cost for COVID-19 treatments.
N-acetylcysteine is
an effective treatment for COVID-19.
Significantly lower risk is seen for mortality, hospitalization, and cases. 11 studies from 11 independent teams in 8 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
25% [14‑35%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are very robust — in exclusion sensitivity analysis 16 of
24 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Alam et al. present another meta analysis for N-acetylcysteine, showing significant improvement for mortality.
Efficacy with N-acetylcysteine has also been shown for influenza37.
Prospective study of 217 patients treated with NAC and 245 matched controls, showing improved recovery with treatment. 1500mg intravenous NAC daily.
RCT 304 low-risk outpatients, 229 treated with N-acetylcysteine, l-carnitine tartrate, nicotinamide riboside chloride, and serine, showing significantly faster recovery with treatment. Plasma levels of proteins and metabolites associated with inflammation and antioxidant metabolism were significantly improved in treated patients.
Retrospective 42 hospitalized PCR+ COVID-19 pneumonia patients treated with NAC, and a matched control group of 40 patients, showing significantly lower severe respiratory failure and significantly lower mortality with treatment. NAC 600 mg bid orally for 14 days.
RCT 60 hospitalized COVID-19 patients evaluating the efficacy and safety of adding oral N-acetylcysteine (NAC) at 600mg three times daily to standard antiviral treatment regimens. The NAC group showed significantly greater reduction in C-reactive protein levels, indicating reduced inflammation. Authors conclude that oral NAC may provide benefits through reducing inflammation, increasing oxygen saturation, and potentially reducing mortality when combined with certain antiviral medications in hospitalized COVID-19 patients.
Prospective study of 24 hospitalized COVID-19 patients in Russia treated with NAC, and 22 matched controls, showing significantly improved SpO2/FiO2, and significantly shorter hospitalization with treatment.
RCT 135 severe stage patients in Brazil, showing no significant differences. NAC 21g (~300mg/kg) for 20 hours. U1111-1250-356144.
RCT mechanically ventilated patients in Croatia, 39 treated with N-acetylcysteine and 52 control patients, showing no significant difference in mortality with treatment. Treated patients showed a lower incidence of gram-positive or MRSA-caused ventilator-associated pneumonia. ICU mortality results are from145.
Retrospective 140 mechanically ventilated patients in Spain, showing lower mortality with acetylcysteine treatment in unadjusted results, not reaching statistical significance.
Retrospective 1,083 consecutive hospitalized COVID patients in Italy, showing no significant differences with NAC treatment. The number of patients transferred to another facility exceeds the number of deaths, which may significantly affect results.
Retrospective 378 hospitalized patients in Spain, showing lower mortality with N-acetylcysteine treatment.
RCT 140 ICU patients in Spain, 72 treated with N-acetylcysteine (NAC). NAC patients showed improved PaO2/FiO2, CRP, D-dimer, and LDH, and there were associations between glutathione and clinical outcomes and severity biomarkers in NAC-treated patients. There was no significant difference in mortality.
RCT 46 hospitalized patients with moderate COVID-19 pneumonia, 24 treated with N-acetylcysteine, showing significantly shorter hospitalization with treatment. NAC 1,200 – 1,500mg/day intravenously.
Retrospective database analysis of 65,149 in South Korea, showing significantly lower cases with existing N-acetylcysteine treatment. The journal version of this paper does not present the N-acetylcysteine results.
Retrospective 111 patients with moderate COVID-19 pneumonia, 56 treated with NAC, showing shorter hospitalization time with treatment. NAC 1200mg daily intravenous, divided into two doses.
Retrospective 19,208 COVID+ hospitalized patients in Spain, 2,071 treated with high dose NAC, showing lower mortality with treatment. In multivariable analysis, authors adjust for corticosteroids, but do not adjust for HCQ use which was also significantly more common in the NAC group. NAC 600mg every 8 hours.
RCT 83 severe COVID-19 pnuemonia patients in Iran, 42 treated with acetylcysteine, showing no significant difference in clinical outcomes. All patients received remdesivir, famotidine, and vitamin C. More patients were at baseline category 4+ in the treatment group - 18 vs. 12. The trial focused on preventing liver injury in patients treated with remdesivir, showing improved AST/ALT levels with acetylcysteine.
RCT 250 hospitalized COVID-19 patients showing reduced mortality rate and inflammatory markers with N-acetylcysteine (NAC) 400μg inhaled spray twice daily for 7 days as adjunctive treatment. There was no significant difference in hospital length of stay or ICU admission. The NAC group was older on average, while the control group had significantly lower SpO2 at baseline. 400 μg/day NAC inhaler spray for 7 days.
Retrospective 864 hospitalized late stage COVID-19 patients in the USA, 138 receiving NAC treatment for acute hepatitis, showing lower mortality with treatment. Results are adjusted for confounders, however details are not provided.
Prospective study of 2,468 hospitalized COVID-19 patients in Iran, showing no significant difference with NAC treatment. IR.MUQ.REC.1399.013.
RCT 40 ICU patients in Iran, showing lower mortality with NAC treatment, without statistical significance. Single dose intravenous NAC 300 mg/kg.
Prospective study with 75 NAC patients and 16 control patients, showing no significant difference in mortality.
RCT 60 hospitalized patients showing that oral N-acetylcysteine (NAC) at 1800mg daily significantly decreased plasma TNF-α levels and increased glutathione peroxidase levels. The NAC group had a shorter duration of oxygen support, while there were no significant difference for length of hospital stay, need for oxygen support, or mortality. Overall, the addition of high-dose NAC reduced inflammatory markers and oxidative stress in moderate COVID-19.
Limitations include the small sample size, late treatment, lack of blinding, potential overlap of treatment effect with SOC, clinical significance of biomarker results, and limited adverse event reporting.
Limitations include the small sample size, late treatment, lack of blinding, potential overlap of treatment effect with SOC, clinical significance of biomarker results, and limited adverse event reporting.
RCT 92 hospitalized patients, 47 treated with NAC, showing non-significant improvements in outcomes. IRCT20120215009014N355. NAC 40mg/kg/day intravenous for 3 days.
Retrospective 190 critical COVID-19 patients in Turkey, showing no significant differences with N-acetylcysteine treatment in unadjusted results with no baseline details. NAC 2400mg/day.
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 N-acetylcysteine 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 N-acetylcysteine 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 to146.
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 1149.
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
(3.13.3) with
scipy (1.15.2), pythonmeta (1.26), numpy (2.2.5), statsmodels (0.14.4), and plotly (6.0.1).
Forest plots are computed using PythonMeta150
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective55,56.
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 https://c19early.org/nacmeta.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.
| Altay, 6/28/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Turkey, peer-reviewed, 18 authors, this trial uses multiple treatments in the treatment arm (combined with l-carnitine tartrate, nicotinamide riboside chloride, serine) - results of individual treatments may vary. | risk of hospitalization, 80.1% lower, RR 0.20, p = 0.25, treatment 0 of 229 (0.0%), control 1 of 76 (1.3%), NNT 76, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of no recovery, 82.7% lower, RR 0.17, p < 0.001, treatment 229, control 75, inverted to make RR<1 favor treatment, multivariate Cox regression. | |
| Ignatova, 11/10/2021, retrospective, Russia, peer-reviewed, median age 49.2, 12 authors, average treatment delay 4.6 days. | hospitalization time, 20.3% lower, relative time 0.80, p < 0.05, treatment 56, control 55. |
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.
| Afaghi, 6/1/2023, prospective, Iran, peer-reviewed, 10 authors, study period 1 June, 2020 - 13 March, 2021, average treatment delay 6.0 days. | risk of death, 29.4% lower, RR 0.71, p = 0.42, treatment 10 of 217 (4.6%), control 16 of 245 (6.5%), NNT 52. |
| risk of mechanical ventilation, 41.1% lower, RR 0.59, p = 0.16, treatment 12 of 217 (5.5%), control 23 of 245 (9.4%), NNT 26. | |
| risk of ICU admission, 20.1% lower, RR 0.80, p = 0.36, treatment 29 of 217 (13.4%), control 41 of 245 (16.7%), NNT 30. | |
| hospitalization time, 13.6% lower, relative time 0.86, p = 0.002, treatment 217, control 245. | |
| Assimakopoulos, 6/29/2021, retrospective, Greece, peer-reviewed, 9 authors, study period 1 February, 2021 - 30 April, 2021. | risk of death, 97.1% lower, RR 0.03, p = 0.006, treatment 2 of 42 (4.8%), control 12 of 40 (30.0%), NNT 4.0, inverted to make RR<1 favor treatment, odds ratio converted to relative risk. |
| Atefi, 11/20/2023, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 10 authors, trial IRCT20200623047897N1. | risk of death, 66.7% lower, RR 0.33, p = 0.61, treatment 1 of 30 (3.3%), control 3 of 30 (10.0%), NNT 15. |
| Avdeev, 7/9/2021, retrospective, Russia, peer-reviewed, 4 authors, study period 12 April, 2020 - 20 June, 2020, average treatment delay 7.2 days. | risk of death, 69.4% lower, RR 0.31, p = 0.34, treatment 1 of 24 (4.2%), control 3 of 22 (13.6%), NNT 11. |
| risk of mechanical ventilation, 77.1% lower, RR 0.23, p = 0.18, treatment 1 of 24 (4.2%), control 4 of 22 (18.2%), NNT 7.1. | |
| risk of ICU admission, 77.1% lower, RR 0.23, p = 0.18, treatment 1 of 24 (4.2%), control 4 of 22 (18.2%), NNT 7.1. | |
| hospitalization time, 15.4% lower, relative time 0.85, p = 0.01, treatment 24, control 22. | |
| de Alencar, 9/23/2020, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, median age 59.0, 65 authors, study period 10 April, 2020 - 25 May, 2020, average treatment delay 7.0 days. | risk of death, 2.6% higher, RR 1.03, p = 0.94, treatment 9 of 67 (13.4%), control 9 of 68 (13.2%), odds ratio converted to relative risk. |
| risk of mechanical ventilation, 16.0% higher, RR 1.16, p = 0.64, treatment 16 of 67 (23.9%), control 14 of 68 (20.6%), odds ratio converted to relative risk. | |
| risk of ICU admission, 8.5% lower, RR 0.91, p = 0.65, treatment 29 of 67 (43.3%), control 32 of 68 (47.1%), NNT 26, odds ratio converted to relative risk. | |
| ICU time, 12.5% higher, relative time 1.12, p = 0.56, treatment 67, control 68. | |
| hospitalization time, 10.0% higher, relative time 1.10, p = 0.87, treatment 67, control 68. | |
| Delić, 5/28/2022, Randomized Controlled Trial, Croatia, peer-reviewed, 12 authors, study period October 2020 - June 2021, trial NCT04755972 (history). | risk of death, 13.5% lower, RR 0.86, p = 0.37, treatment 24 of 39 (61.5%), control 37 of 52 (71.2%), NNT 10, ICU mortality. |
| risk of death, 9.7% lower, RR 0.90, p = 0.67, treatment 21 of 39 (53.8%), control 31 of 52 (59.6%), NNT 17, 28 day mortality. | |
| Fariña-González, 5/31/2022, retrospective, Spain, peer-reviewed, 8 authors, study period 5 March, 2020 - 30 April, 2020. | risk of death, 39.0% lower, RR 0.61, p = 0.08, treatment 10 of 38 (26.3%), control 44 of 102 (43.1%), NNT 5.9. |
| Faverio, 12/2/2021, retrospective, Italy, peer-reviewed, 10 authors, study period February 2020 - April 2021, trial NCT04424992 (history) (STORM). | risk of death, 19.0% higher, RR 1.19, p = 0.33, treatment 91 of 572 (15.9%), control 44 of 329 (13.4%), propensity score weighting. |
| risk of ICU admission, 33.8% higher, RR 1.34, p = 0.08, treatment 107 of 572 (18.7%), control 46 of 329 (14.0%), propensity score weighting. | |
| risk of no hospital discharge, 1.4% lower, RR 0.99, p = 0.94, treatment 180 of 572 (31.5%), control 105 of 329 (31.9%), NNT 224, propensity score weighting. | |
| Galindo-Andúgar, 7/21/2023, retrospective, Spain, peer-reviewed, median age 73.3, 6 authors, study period March 2020 - April 2020. | risk of death, 43.0% lower, OR 0.57, p = 0.05, treatment 199, control 179, adjusted per study, multivariable, RR approximated with OR. |
| Gamarra-Morales, 5/8/2023, Randomized Controlled Trial, Spain, peer-reviewed, 8 authors, study period 1 March, 2020 - 1 June, 2020. | risk of death, 15.7% lower, RR 0.84, p = 0.49, treatment 25 of 72 (34.7%), control 28 of 68 (41.2%), NNT 15. |
| Gaynitdinova, 2/19/2021, Randomized Controlled Trial, Russia, peer-reviewed, 6 authors, average treatment delay 7.0 days. | hospitalization time, 15.4% lower, relative time 0.85, p < 0.001, treatment 24, control 22. |
| relative improvement in lung Ct, 50.7% better, RR 0.49, p < 0.001, treatment 24, control 22. | |
| Izquierdo, 1/27/2022, retrospective, Spain, peer-reviewed, 7 authors, study period 1 March, 2020 - 24 January, 2021, excluded in exclusion analyses: significant unadjusted confounding possible. | risk of death, 25.6% lower, RR 0.74, p < 0.001, treatment 136 of 2,071 (6.6%), control 1,935 of 17,137 (11.3%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Mousapour, 6/20/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 62.1, 5 authors, trial IRCT20210726051995N1. | risk of death, 2.4% lower, RR 0.98, p = 1.00, treatment 4 of 42 (9.5%), control 4 of 41 (9.8%), NNT 430, day 14. |
| risk of mechanical ventilation, 26.8% lower, RR 0.73, p = 0.57, treatment 6 of 42 (14.3%), control 8 of 41 (19.5%), NNT 19, day 14. | |
| risk of no improvement, 6.1% lower, RR 0.94, p = 0.82, treatment 25 of 42 (59.5%), control 26 of 41 (63.4%), NNT 26, day 14. | |
| risk of no hospital discharge, 5.4% lower, RR 0.95, p = 0.80, treatment 31 of 42 (73.8%), control 32 of 41 (78.0%), NNT 24, day 14. | |
| Panahi, 12/19/2022, Randomized Controlled Trial, Iran, peer-reviewed, 7 authors, study period May 2021 - August 2021, trial IRCT20080901001165N55, excluded in exclusion analyses: large difference in mortality vs. ICU results, significant baseline differences. | risk of death, 91.8% lower, RR 0.08, p < 0.001, treatment 4 of 125 (3.2%), control 49 of 125 (39.2%), NNT 2.8, Inhaled. |
| risk of ICU admission, 35.7% lower, RR 0.64, p = 0.38, treatment 9 of 125 (7.2%), control 14 of 125 (11.2%), NNT 25, Inhaled. | |
| hospitalization time, 0.8% lower, relative time 0.99, p = 0.81, treatment 125, control 125, Inhaled. | |
| Pellegrini, 5/23/2021, retrospective, USA, peer-reviewed, 10 authors, study period March 2020 - May 2020. | risk of death, 51.7% lower, OR 0.48, p < 0.001, treatment 138, control 726, adjusted per study, inverted to make OR<1 favor treatment, multivariable, RR approximated with OR. |
| Pourhoseingholi, 5/26/2021, prospective, Iran, preprint, mean age 57.9, 11 authors, study period 2 February, 2020 - 20 July, 2020, average treatment delay 7.4 days. | risk of death, 11.0% lower, HR 0.89, p = 0.43, treatment 65 of 309 (21.0%), control 274 of 2,159 (12.7%), adjusted per study, multivariable, Cox proportional hazards. |
| Rahimi, 10/8/2022, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 10 authors. | risk of death, 33.3% lower, RR 0.67, p = 0.19, treatment 10 of 20 (50.0%), control 15 of 20 (75.0%), NNT 4.0. |
| hospitalization time, 7.5% lower, relative time 0.92, p = 0.63, treatment 20, control 20. | |
| Ramadhan, 12/27/2021, prospective, Indonesia, peer-reviewed, 6 authors, study period June 2020 - July 2021, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of death, 134.7% higher, RR 2.35, p = 0.68, treatment 11 of 75 (14.7%), control 1 of 16 (6.2%), unadjusted. |
| Sherkawy, 6/1/2023, Randomized Controlled Trial, Egypt, peer-reviewed, 5 authors, study period March 2021 - April 2022, trial NCT04792021 (history). | risk of death, no change, RR 1.00, p = 1.00, treatment 1 of 30 (3.3%), control 1 of 30 (3.3%). |
| risk of oxygen therapy, 15.0% lower, RR 0.85, p = 0.60, treatment 17 of 30 (56.7%), control 20 of 30 (66.7%), NNT 10. | |
| oxygen time, 33.3% lower, relative time 0.67, p = 0.005, treatment 30, control 30. | |
| hospitalization time, 12.5% lower, relative time 0.88, p = 0.45, treatment 30, control 30. | |
| Taher, 6/10/2021, Double Blind Randomized Controlled Trial, Iran, peer-reviewed, 6 authors, study period June 2020 - February 2021, average treatment delay 7.0 days. | risk of death, 17.9% lower, RR 0.82, p = 0.65, treatment 12 of 47 (25.5%), control 14 of 45 (31.1%), NNT 18. |
| risk of mechanical ventilation, 13.8% lower, RR 0.86, p = 0.67, treatment 18 of 47 (38.3%), control 20 of 45 (44.4%), NNT 16. | |
| ICU time, 20.0% lower, relative time 0.80, p = 0.48, treatment 47, control 45. | |
| hospitalization time, 33.3% lower, relative time 0.67, p = 0.31, treatment 47, control 45. | |
| risk of no recovery, 14.5% lower, RR 0.85, p = 0.41, treatment 25 of 47 (53.2%), control 28 of 45 (62.2%), NNT 11. | |
| Çavuş, 10/25/2022, retrospective, Turkey, peer-reviewed, 2 authors, study period April 2020 - September 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 13.3% higher, RR 1.13, p = 0.47, treatment 52 of 97 (53.6%), control 44 of 93 (47.3%). |
| risk of mechanical ventilation, 19.8% higher, RR 1.20, p = 0.25, treatment 55 of 97 (56.7%), control 44 of 93 (47.3%). | |
| ICU time, 12.5% lower, relative time 0.88, p = 0.58, treatment 97, control 93. | |
| hospitalization time, 13.3% lower, relative time 0.87, p = 0.09, treatment 97, control 93. |
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.
| Huh, 5/4/2020, retrospective, database analysis, South Korea, preprint, 10 authors. | risk of case, 28.0% lower, OR 0.72, p < 0.001, treatment 710 of 5,172 (13.7%) cases, 13,078 of 59,977 (21.8%) controls, adjusted per study, case control OR, multivariable. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
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