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N-acetylcysteine for COVID-19: real-time meta analysis of 24 studies

@CovidAnalysis, April 2024, Version 2V2
 
0 0.5 1 1.5+ All studies 25% 24 26,243 Improvement, Studies, Patients Relative Risk Mortality 31% 20 25,781 Ventilation 6% 6 1,008 ICU admission 7% 7 2,076 Hospitalization 11% 11 1,737 Recovery 41% 5 1,426 Cases 26% 1 0 RCTs 29% 11 1,302 RCT mortality 34% 9 951 Peer-reviewed 28% 22 23,775 Prophylaxis 26% 1 0 Early 21% 2 416 Late 27% 21 25,827 N-acetylcysteine for COVID-19 c19early.org April 2024 after exclusions FavorsN-acetylcysteine Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, and cases. 11 studies from 11 independent teams in 8 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 25% [14‑34%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 16 of 24 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence N-acetylcysteine p=0.000034 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org April 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
N-acetylcysteine reduces risk for COVID-19 with very high confidence for mortality, hospitalization, and in pooled analysis, and low confidence for recovery and cases.
14th treatment shown effective with ≥3 clinical studies in February 2021, now with p = 0.000034 from 24 studies, and recognized in 3 countries.
We show outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor for COVID-19.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 69 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Altay (DB RCT) 80% 0.20 [0.01-4.85] hosp. 0/229 1/76 CT​1 Improvement, RR [CI] Treatment Control Ignatova 20% 0.80 [0.64-1.00] hosp. time 56 (n) 55 (n) Tau​2 = 0.00, I​2 = 0.0%, p = 0.043 Early treatment 21% 0.79 [0.63-0.99] 0/285 1/131 21% lower risk de Alencar (DB RCT) -3% 1.03 [0.41-2.27] death 9/67 9/68 Improvement, RR [CI] Treatment Control Gaynitdinova (RCT) 15% 0.85 [0.77-0.93] hosp. time 24 (n) 22 (n) Pellegrini 52% 0.48 [0.33-0.70] death 138 (n) 726 (n) Pourhoseingholi 11% 0.89 [0.68-1.18] death 65/309 274/2,159 Taher (DB RCT) 18% 0.82 [0.43-1.58] death 12/47 14/45 Assimakopoulos 97% 0.03 [0.00-0.30] death 2/42 12/40 Avdeev 69% 0.31 [0.03-2.72] death 1/24 3/22 STORM Faverio (PSW) -19% 1.19 [0.85-1.66] death 91/572 44/329 Ramadhan -135% 2.35 [0.33-16.9] death 11/75 1/16 Izquierdo 26% 0.74 [0.63-0.88] death 136/2,071 1,935/17,137 Delić (RCT) 10% 0.90 [0.63-1.30] death 21/39 31/52 Intubated patients Fariña-González 39% 0.61 [0.34-1.09] death 10/38 44/102 Intubated patients Mousapour (DB RCT) 2% 0.98 [0.26-3.64] death 4/42 4/41 Rahimi (SB RCT) 33% 0.67 [0.40-1.11] death 10/20 15/20 ICU patients Çavuş (ICU) -13% 1.13 [0.85-1.50] death 52/97 44/93 ICU patients Panahi (RCT) 92% 0.08 [0.03-0.22] death 4/125 49/125 Inhaled Gamarra-Mo.. (RCT) 16% 0.84 [0.55-1.29] death 25/72 28/68 ICU patients Afaghi 29% 0.71 [0.33-1.52] death 10/217 16/245 Sherkawy (RCT) 0% 1.00 [0.07-15.3] death 1/30 1/30 Galindo-Andúgar 43% 0.57 [0.31-0.99] death 199 (n) 179 (n) Atefi (SB RCT) 67% 0.33 [0.04-3.03] death 1/30 3/30 Tau​2 = 0.08, I​2 = 70.5%, p = 0.00052 Late treatment 27% 0.73 [0.61-0.87] 465/4,278 2,527/21,549 27% lower risk Huh 26% 0.74 [0.68-0.80] cases population-based cohort Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 26% 0.74 [0.68-0.80] 26% lower risk All studies 25% 0.75 [0.66-0.86] 465/4,563 2,528/21,680 25% lower risk 24 N-acetylcysteine COVID-19 studies c19early.org April 2024 Tau​2 = 0.04, I​2 = 68.1%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors N-acetylcysteine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Altay (DB RCT) 80% hospitalization CT​1 Improvement Relative Risk [CI] Ignatova 20% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p = 0.043 Early treatment 21% 21% lower risk de Alen.. (DB RCT) -3% death Gaynitdinova (RCT) 15% hospitalization Pellegrini 52% death Pourhoseingholi 11% death Taher (DB RCT) 18% death Assimakopoulos 97% death Avdeev 69% death STORM Faverio (PSW) -19% death Ramadhan -135% death Izquierdo 26% death Delić (RCT) 10% death Intubated patients Fariña-González 39% death Intubated patients Mousapour (DB RCT) 2% death Rahimi (SB RCT) 33% death ICU patients Çavuş (ICU) -13% death ICU patients Panahi (RCT) 92% death Inhaled Gamarra-M.. (RCT) 16% death ICU patients Afaghi 29% death Sherkawy (RCT) 0% death Galindo-Andúgar 43% death Atefi (SB RCT) 67% death Tau​2 = 0.08, I​2 = 70.5%, p = 0.00052 Late treatment 27% 27% lower risk Huh 26% case Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 26% 26% lower risk All studies 25% 25% lower risk 24 N-acetylcysteine C19 studies c19early.org April 2024 Tau​2 = 0.04, I​2 = 68.1%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors N-acetylcysteine Favors control
B
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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 issues Duloquin, Hampshire, Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, Niarakis, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk c19early.org, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
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-19 Yuan. N-acetylcysteine shows dose-dependent inhibition of SARS-CoV-2 Akhter, La Maestra, shows anti-inflammatory and immunomodulatory effects against SARS-CoV-2-induced immune responses in combination with bromelain Ferreira, and suppressed virus-induced reactive oxygen species and blocked viral replication in a humanized mouse model and in human lung cells Frasson.
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-2 Akhter, La Maestra, shows anti-inflammatory and immunomodulatory effects against SARS-CoV-2-induced immune responses in combination with bromelain Ferreira, and suppressed virus-induced reactive oxygen species and blocked viral replication in a humanized mouse model and in human lung cells Frasson.
5 In Vitro studies support the efficacy of N-acetylcysteine Akhter, Ferreira, Frasson, Goc, La Maestra.
An In Vivo animal study supports the efficacy of N-acetylcysteine Frasson.
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.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies25% [14‑34%]
****
24 26,243 257
After exclusions24% [13‑33%]
****
20 6,504 235
Peer-reviewed studiesPeer-reviewed28% [14‑40%]
***
22 23,775 236
Randomized Controlled TrialsRCTs29% [5‑46%]
*
11 1,302 152
Mortality31% [13‑44%]
**
20 25,781 211
VentilationVent.6% [-25‑29%]6 1,008 92
ICU admissionICU7% [-16‑26%]7 2,076 104
HospitalizationHosp.11% [6‑17%]
***
11 1,737 145
Recovery41% [-9‑68%]5 1,426 45
RCT mortality34% [0‑57%]
*
9 951 128
RCT hospitalizationRCT hosp.8% [-1‑17%]7 928 117
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies21% [1‑37%]
*
27% [13‑39%]
***
26% [20‑32%]
****
After exclusions21% [1‑37%]
*
26% [11‑38%]
**
26% [20‑32%]
****
Peer-reviewed studiesPeer-reviewed21% [1‑37%]
*
29% [14‑42%]
***
Randomized Controlled TrialsRCTs80% [-385‑99%]28% [4‑46%]
*
Mortality31% [13‑44%]
**
VentilationVent.6% [-25‑29%]
ICU admissionICU7% [-16‑26%]
HospitalizationHosp.21% [1‑37%]
*
11% [5‑16%]
***
Recovery83% [76‑87%]
****
17% [-6‑35%]
RCT mortality34% [0‑57%]
*
RCT hospitalizationRCT hosp.80% [-385‑99%]8% [-2‑17%]
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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.
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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.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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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 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 29% 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.
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Figure 12. Results for RCTs and non-RCT studies.
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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.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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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 biases Jadad, and analysis of double-blind RCTs has identified extreme levels of bias Gøtzsche. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, 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 69 treatments we have analyzed, 63% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.
Evidence shows that non-RCT studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see Deaton, Nichol.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 28 have been confirmed in RCTs, with a mean delay of 5.7 months. When considering only low cost treatments, 23 have been confirmed with a delay of 6.9 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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.
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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 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 69 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 69 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 variants Korves, 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 degree to which TMPRSS2 contributes to viral entry can differ across variants Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan, therefore efficacy may depend strongly on combined treatments.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results.
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 69 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 = 0.0000045 to p = 0.0000000067.
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Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 3.7 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 5.8 months. Figure 21 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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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.
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, twitter.com.
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.
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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 < 0.05 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 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 alone Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan. 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.
Multiple reviews cover N-acetylcysteine for COVID-19, presenting additional background on mechanisms and related results, including Schloss, Yuan (B).
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors Lui, Lv, Malone, Murigneux, Niarakis, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk c19early.org, 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.
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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 7,000+ proposed treatments show efficacy c19early.org (B).
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Figure 25. Efficacy vs. cost for COVID-19 treatments.
N-acetylcysteine is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, hospitalization, and cases. 11 studies from 11 independent teams in 8 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 25% [14‑34%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 16 of 24 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Ventilation 41% ICU admission 20% Hospitalization time 14% ΔSpO2 57% no CI N-acetylcysteine  Afaghi et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Prospective study of 462 patients in Iran (June 2020 - March 2021) Shorter hospitalization with N-acetylcysteine (p=0.002) c19early.org Afaghi et al., Caspian J Intern Med, Jun 2023 Favors N-acetylcysteine Favors control
Afaghi: Prospective study of 217 patients treated with NAC and 245 matched controls, showing improved recovery with treatment. 1500mg intravenous NAC daily.
0 0.5 1 1.5 2+ Hospitalization 80% Improvement Relative Risk Recovery 83% N-acetylcysteine  Altay et al.  EARLY TREATMENT  DB RCT Is early treatment with N-acetylcysteine + combined treatments beneficial for COVID-19? Double-blind RCT 305 patients in Turkey Improved recovery with N-acetylcysteine + combined treatments (p<0.000001) c19early.org Altay et al., Advanced Science, June 2021 Favors N-acetylcysteine Favors control
Altay: 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.
0 0.5 1 1.5 2+ Mortality 97% Improvement Relative Risk N-acetylcysteine  Assimakopoulos et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 82 patients in Greece (February - April 2021) Lower mortality with N-acetylcysteine (p=0.006) c19early.org Assimakopoulos et al., Infectious Dise.., Jun 2021 Favors N-acetylcysteine Favors control
Assimakopoulos: 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.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk N-acetylcysteine  Atefi et al.  LATE TREATMENT  RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? RCT 60 patients in Iran Trial underpowered to detect differences c19early.org Atefi et al., Immunity, Inflammation a.., Nov 2023 Favors N-acetylcysteine Favors control
Atefi: 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.
0 0.5 1 1.5 2+ Mortality 69% Improvement Relative Risk Ventilation 77% ICU admission 77% Hospitalization time 15% N-acetylcysteine  Avdeev et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 46 patients in Russia (April - June 2020) Shorter hospitalization with N-acetylcysteine (p=0.01) c19early.org Avdeev et al., J. Infection, July 2021 Favors N-acetylcysteine Favors control
Avdeev: 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.
0 0.5 1 1.5 2+ Mortality -3% Improvement Relative Risk Ventilation -16% ICU admission 9% ICU time -12% Hospitalization time -10% N-acetylcysteine  de Alencar et al.  LATE TREATMENT  DB RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? Double-blind RCT 135 patients in Brazil (April - May 2020) No significant difference in outcomes seen c19early.org de Alencar et al., Clinical Infectious.., Sep 2020 Favors N-acetylcysteine Favors control
de Alencar: RCT 135 severe stage patients in Brazil, showing no significant differences. NAC 21g (~300mg/kg) for 20 hours. U1111-1250-356 ensaiosclinicos.gov.br.
0 0.5 1 1.5 2+ Mortality 10% Improvement Relative Risk N-acetylcysteine  Delić et al.  INTUBATED PATIENTS  RCT Is very late treatment with N-acetylcysteine beneficial for COVID-19? RCT 91 patients in Croatia (October 2020 - June 2021) No significant difference in mortality c19early.org Delić et al., Microorganisms, May 2022 Favors N-acetylcysteine Favors control
Delić: 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.
0 0.5 1 1.5 2+ Mortality 39% Improvement Relative Risk N-acetylcysteine  Fariña-González et al.  INTUBATED PATIENTS Is very late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 140 patients in Spain (March - April 2020) Lower mortality with N-acetylcysteine (not stat. sig., p=0.081) c19early.org Fariña-González et al., J. Intensive C.., May 2022 Favors N-acetylcysteine Favors control
Fariña-González: Retrospective 140 mechanically ventilated patients in Spain, showing lower mortality with acetylcysteine treatment in unadjusted results, not reaching statistical significance.
0 0.5 1 1.5 2+ Mortality -19% Improvement Relative Risk ICU admission -34% Discharge 1% N-acetylcysteine for COVID-19  STORM  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 901 patients in Italy (February 2020 - April 2021) Higher mortality (p=0.33) and ICU admission (p=0.08), not sig. c19early.org Faverio et al., ERJ Open Research, Dec 2021 Favors N-acetylcysteine Favors control
Faverio: 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.
0 0.5 1 1.5 2+ Mortality 43% Improvement Relative Risk N-acetylcysteine  Galindo-Andúgar et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 378 patients in Spain (March - April 2020) Lower mortality with N-acetylcysteine (p=0.05) c19early.org Galindo-Andúgar et al., Revista Clínic.., Jul 2023 Favors N-acetylcysteine Favors control
Galindo-Andúgar: Retrospective 378 hospitalized patients in Spain, showing lower mortality with N-acetylcysteine treatment.
0 0.5 1 1.5 2+ Mortality 16% Improvement Relative Risk N-acetylcysteine  Gamarra-Morales et al.  ICU PATIENTS  RCT Is very late treatment with N-acetylcysteine beneficial for COVID-19? RCT 140 patients in Spain (March - June 2020) Lower mortality with N-acetylcysteine (not stat. sig., p=0.49) c19early.org Gamarra-Morales et al., Nutrients, May 2023 Favors N-acetylcysteine Favors control
Gamarra-Morales: 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.
0 0.5 1 1.5 2+ Hospitalization time 15% Improvement Relative Risk Improvement in lung Ct 51% N-acetylcysteine  Gaynitdinova et al.  LATE TREATMENT  RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? RCT 46 patients in Russia Shorter hospitalization (p=0.001) and improved recovery (p=0.001) c19early.org Gaynitdinova et al., Pulmonologiya, Feb 2021 Favors N-acetylcysteine Favors control
Gaynitdinova: 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.
0 0.5 1 1.5 2+ Case 26% Improvement Relative Risk N-acetylcysteine for COVID-19  Huh et al.  Prophylaxis Does N-acetylcysteine reduce COVID-19 infections? Retrospective 65,149 patients in South Korea Fewer cases with N-acetylcysteine (p<0.000001) c19early.org Huh et al., medRxiv, May 2020 Favors N-acetylcysteine Favors control
Huh: 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.
0 0.5 1 1.5 2+ Hospitalization time 20% Improvement Relative Risk N-acetylcysteine  Ignatova et al.  EARLY TREATMENT Is early treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 111 patients in Russia Shorter hospitalization with N-acetylcysteine (not stat. sig., p=0.05) c19early.org Ignatova et al., Russian Medical Inquiry, Nov 2021 Favors N-acetylcysteine Favors control
Ignatova: 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.
0 0.5 1 1.5 2+ Mortality 26% Improvement Relative Risk N-acetylcysteine  Izquierdo et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 19,208 patients in Spain (March 2020 - January 2021) Lower mortality with N-acetylcysteine (p=0.00067) c19early.org Izquierdo et al., Science Progress, Jan 2022 Favors N-acetylcysteine Favors control
Izquierdo: 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.
0 0.5 1 1.5 2+ Mortality 2% Improvement Relative Risk Ventilation 27% Improvement 6% Discharge 5% N-acetylcysteine  Mousapour et al.  LATE TREATMENT  DB RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? Double-blind RCT 83 patients in Iran Trial underpowered for serious outcomes c19early.org Mousapour et al., Gastroenterology and.., Jun 2022 Favors N-acetylcysteine Favors control
Mousapour: 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.
0 0.5 1 1.5 2+ Mortality 92% Inhaled Improvement Relative Risk ICU admission 36% Inhaled Hospitalization time 1% Inhaled N-acetylcysteine  Panahi et al.  LATE TREATMENT  RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? RCT 250 patients in Iran (May - August 2021) Lower mortality with N-acetylcysteine (p<0.000001) c19early.org Panahi et al., J. Medical Virology, Dec 2022 Favors N-acetylcysteine Favors control
Panahi: 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.
0 0.5 1 1.5 2+ Mortality 52% Improvement Relative Risk N-acetylcysteine  Pellegrini et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 864 patients in the USA (March - May 2020) Lower mortality with N-acetylcysteine (p=0.00014) c19early.org Pellegrini et al., Gastroenterology, May 2021 Favors N-acetylcysteine Favors control
Pellegrini: 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.
0 0.5 1 1.5 2+ Mortality 11% Improvement Relative Risk N-acetylcysteine  Pourhoseingholi et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Prospective study of 2,468 patients in Iran (Feb - Jul 2020) Lower mortality with N-acetylcysteine (not stat. sig., p=0.43) c19early.org Pourhoseingholi et al., Research Square, May 2021 Favors N-acetylcysteine Favors control
Pourhoseingholi: Prospective study of 2,468 hospitalized COVID-19 patients in Iran, showing no significant difference with NAC treatment. IR.MUQ.REC.1399.013.
0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk Hospitalization time 8% N-acetylcysteine  Rahimi et al.  ICU PATIENTS  RCT Is very late treatment with N-acetylcysteine beneficial for COVID-19? RCT 40 patients in Iran Lower mortality with N-acetylcysteine (not stat. sig., p=0.19) c19early.org Rahimi et al., Jundishapur J. Natural .., Oct 2022 Favors N-acetylcysteine Favors control
Rahimi: RCT 40 ICU patients in Iran, showing lower mortality with NAC treatment, without statistical significance. Single dose intravenous NAC 300 mg/kg.
0 0.5 1 1.5 2+ Mortality -135% unadjusted Improvement Relative Risk N-acetylcysteine  Ramadhan et al.  LATE TREATMENT Is late treatment with N-acetylcysteine beneficial for COVID-19? Prospective study of 91 patients in Indonesia (Jun 2020 - Jul 2021) Study underpowered to detect differences c19early.org Ramadhan et al., Indonesian J. Tropica.., Dec 2021 Favors N-acetylcysteine Favors control
Ramadhan: Prospective study with 75 NAC patients and 16 control patients, showing no significant difference in mortality.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Oxygen therapy 15% Oxygen time 33% Hospitalization time 12% N-acetylcysteine  Sherkawy et al.  LATE TREATMENT  RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? RCT 60 patients in Egypt (March 2021 - April 2022) Lower need for oxygen therapy (p=0.6) and shorter hospitalization (p=0.45), not sig. c19early.org Sherkawy et al., Archives of Pharmaceu.., Jun 2023 Favors N-acetylcysteine Favors control
Sherkawy: 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.
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk Ventilation 14% ICU time 20% Hospitalization time 33% Recovery 15% N-acetylcysteine  Taher et al.  LATE TREATMENT  DB RCT Is late treatment with N-acetylcysteine beneficial for COVID-19? Double-blind RCT 92 patients in Iran (June 2020 - February 2021) Shorter ICU admission (p=0.48) and hospitalization (p=0.31), not sig. c19early.org Taher et al., Pharmacological Reports, Jun 2021 Favors N-acetylcysteine Favors control
Taher: RCT 92 hospitalized patients, 47 treated with NAC, showing non-significant improvements in outcomes. IRCT20120215009014N355. NAC 40mg/kg/day intravenous for 3 days.
0 0.5 1 1.5 2+ Mortality -13% Improvement Relative Risk Ventilation -20% ICU time 12% Hospitalization time 13% N-acetylcysteine  Çavuş et al.  ICU PATIENTS Is very late treatment with N-acetylcysteine beneficial for COVID-19? Retrospective 190 patients in Turkey (April - September 2020) Higher ventilation (p=0.25) and shorter ICU admission (p=0.58), not sig. c19early.org Çavuş et al., Aksaray Üniversitesi Tıp.., Oct 2022 Favors N-acetylcysteine Favors control
Çavuş: 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 to Zhang. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 Sweeting. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.12.2) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.20.0).
Forest plots are computed using PythonMeta Deng with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective McLean, Treanor.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/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, 9.7% lower, RR 0.90, p = 0.67, treatment 21 of 39 (53.8%), control 31 of 52 (59.6%), NNT 17.
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, 26.2% lower, RR 0.74, p < 0.001, treatment 710 of 13,788 (5.1%), control 4,462 of 51,361 (8.7%), adjusted per study, odds ratio converted to relative risk, multivariable.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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