Nitric Oxide reduces COVID-19 risk: real-time meta-analysis of 14 studies

November 2026

Media covered none of the 6 studies showing significantly lower risk

No positive nitric oxide studies were covered

2021 2022 2023 2024 2025

Abstract

Significantly lower risk is seen for hospitalization, cases, and viral clearance. 7 studies from 6 independent teams in 6 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 35% [9‑54%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Early treatment is more effective than late treatment.

Emergent results for the efficacy gradient across stages (p < 0.0000001) that match the biological mechanisms confirm efficacy.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (14)35%

Mortality (8)12%

Ventilation (5)25%

ICU admission (1)39%

Hospitalization (3)36%

Progression (3)8%

Cases (1)75%

Viral clearance (3)43%

Nasal (3)63%

Oral (1)−1%

RCTs (9)39%

RCT mortality (4)25%

Prophylaxis (1)75%

Early (3)44%

Late (10)15%

after exclusions

00.511.5+

← Nitric Oxide
reduces riskIncreased
risk →

No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. Nitric Oxide may affect the natural microbiome, especially with prolonged use. All data and sources to reproduce this analysis are in the appendix.

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Nitric Oxide for COVID-19

Highlights

Nitric Oxide reduces risk with very high confidence for hospitalization, high confidence for viral clearance and in pooled analysis, low confidence for cases, and very low confidence for mortality and ICU admission.
Emergent results for the efficacy gradient across stages (p < 0.0000001) that match the biological mechanisms confirm efficacy.
43rd treatment shown effective in June 2022, now with p = 0.013 from 14 studies, recognized in 10 countries.
Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.

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Nitric oxide COVID-19 studies

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Chandel−54%1.54 · 0.72–2.78death12/6636/206 Moni (RCT)90%0.10 · 0.01–1.67death0/144/11ICU patients Strickland (RCT)−179%2.79 · 0.12–63.96ventilation1/190/15 Poonam14%0.86 · 0.72–1.04death32/4156/62Ventilated patients OT¹ Valsecchi58%0.42 · 0.02–9.86death0/201/51 Di Fenza (SB RCT)23%0.77 · 0.44–1.32death94 (n)99 (n)NOSARSCOVIDVentilated patients Wolak (RCT)64%0.36 · 0.14–0.91oxygen16 (n)19 (n) Friedrich (RCT)−50%1.50 · 0.27–8.28death3/272/27iNO-COVID-19 Kurnik (ICU)23%0.77 · 0.38–1.59death3/597/125ICU patients Berra (RCT)65%0.35 · 0.01–8.21death0/291/33NoCovid

Late treatment15%0.85 · 0.66–1.1151/331197/64815% lower risk

Tau² = 0.03, I² = 17.9%, p = 0.23

Immediate treatment recommended

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies35%0.65 · 0.46–0.9178/874341/1,42735% lower risk

Tau² = 0.17, I² = 59.7%, p = 0.01300.511.52+

¹ OT: comparison with other treatment

Rotate screen for more detailsIncrease width for more details

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 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 nitric oxide 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 specific outcomes in RCTs was delayed by 31.3 months, compared to using pooled outcomes in RCTs.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from1.

SARS-CoV-2 infection typically starts in the upper respiratory tract, and specifically the nasal respiratory epithelium. Entry via the eyes and gastrointestinal tract is possible, but less common, and entry via other routes is rare. Infection may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems. The primary initial route for entry into the central nervous system is thought to be the olfactory nerve in the nasal cavity2. Progression may lead to cytokine storm, pneumonia, ARDS, neurological injury3-19 and cognitive deficits6,11, cardiovascular complications20-26, DNA damage27-30, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits31—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Systemic treatments may be insufficient to prevent neurological damage10. Minimizing replication as early as possible is recommended.

Fig. 3. SARS-CoV-2 virions attached to cilia of nasal epithelial cells, from Chien-Ting Wu32,33.

Targeted treatment to the primary location of initial infection

Logically, stopping replication in the upper respiratory tract should be simpler and more effective. Wu et al., using an airway organoid model incorporating many in vivo aspects, show that SARS-CoV-2 initially attaches to cilia—hair-like structures responsible for moving the mucus layer and where ACE2 is localized in nasal epithelial cells34. The mucus layer and the need for ciliary transport slow down infection, providing more time for localized treatments32,33. Early or prophylactic nasopharyngeal/oropharyngeal treatment may avoid the consequences of viral replication in other tissues, and avoid the requirement for systemic treatments with greater potential for side effects.

Many treatments are expected to modulate infection

SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,35-42, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk43, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

Analysis

We analyze all significant controlled studies of nitric oxide 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.

Treatment timing

Fig. 4 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.

Fig. 4. Treatment stages.

Evaluation Notes

Analyzing the data from first principles is always important, and may be especially important for COVID-19. Bias in the design, analysis, and reporting of clinical studies has long been reported, and may be increased for COVID-19 due to politicization. For example, Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."44 Social pressure to report politically acceptable results was very strong. There may therefore be unusually strong bias in design, analysis, and reporting. For example, Prof. Greenland reports that Hayward et al. applied an unjustified and undisclosed extremely tight, null-centered Bayesian prior that biases towards more politically acceptable results46. While the politicization focused on certain treatments, it was not limited to them. For example, media coverage was highly biased against positive results for low-cost treatments47 across the 227 treatments we cover. Studies should be evaluated from first principles, incorporating the treatment delay, treatment regimen, patient population, and other confounding factors. For COVID-19, there is no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]48—in both cases bias varies from minimal to extreme, and all studies must be evaluated individually.

US authorities claim only three high-profit drugs from companies with strong US lobbying are beneficial for early treatment (2 repurposed drugs - remdesivir and molnupiravir, and one novel drug - nirmatrelvir)B. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets49. Given the 12,000+ proposed treatments43, existing results for many treatments with overlapping secondary complications and therapeutic targets, and the combination of known safety and pharmocokinetics for certain treatments along with strong mechanistic evidence, the probability of only 3 high-profit drugs from top lobbying companies being beneficial is extremely low. This likely reflects a bias toward evaluation of drugs with strong financial resources and efforts devoted to passing the US approval process, and very limited or non-existing evaluation of low-cost treatments. Indeed, official recommendations varied widely between countries50. Some countries evaluated limited low-cost treatments early and 26 low-cost treatments were approved in one or more countries50.

Contrary to claims found online, we analyze both all studies and higher-quality studies (with evaluation focusing on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias), we analyze specific outcomes and pooled outcomes (with extensive analysis and validation of pooled outcomes), and we do not include preclinical studies or retracted studies in meta-analysis. Pooled outcomes can detect efficacy earlier but do not bias towards efficacy—the inclusion of less appropriate designs such as very late treatment may hide efficacy for appopriate use.

Preclinical Research

3 in vitro studies support the efficacy of nitric oxide51-53.

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.

Results

Table 1 summarizes the results for all studies, for Randomized Controlled Trials, for peer-reviewed studies, with different exclusions, for specific outcomes, and by treatment stage. Fig. 5 plots individual results by treatment stage. Fig. 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17 show forest plots for random-effects meta-analysis of all studies with pooled effects, early treatment and prophylaxis, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, peer reviewed studies, and nasopharyngeal/oropharyngeal administration (excluding late treatment).

Nitric Oxide for COVID-19: meta-analysis results

November 2026

All stages

Prophylaxis

Early treatment

Late treatment

All studies14 studies · 2,301 patients

All stages0.65^* 0.46–0.9135% lower risk

Prophylaxis0.25^*** 0.14–0.4375% lower risk

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment0.85 0.66–1.1115% lower risk

Exc. late treatment4 studies · 1,322 patients

All stages0.42^** 0.23–0.7558% lower risk

Prophylaxis0.25^*** 0.14–0.4375% lower risk

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment—

After exclusions13 studies · 2,171 patients

All stages0.63^* 0.43–0.9237% lower risk

Prophylaxis0.25^*** 0.14–0.4375% lower risk

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment0.85 0.62–1.1815% lower risk

Peer-reviewed13 studies · 2,239 patients

All stages0.65^* 0.46–0.9235% lower risk

Prophylaxis0.25^*** 0.14–0.4375% lower risk

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment0.85 0.64–1.1315% lower risk

RCTs9 studies · 1,100 patients

All stages0.61^** 0.44–0.8339% lower risk

Prophylaxis—

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment0.64 0.38–1.0836% lower risk

RCTs exc. late treatment3 studies · 697 patients

All stages0.56^** 0.36–0.8744% lower risk

Prophylaxis—

Early treatment0.56^** 0.36–0.8744% lower risk

Late treatment—

Mortality8 studies · 910 patients

All stages0.88 0.75–1.0412% lower risk

Prophylaxis—

Early treatment—

Late treatment0.88 0.75–1.0412% lower risk

Ventilation5 studies · 464 patients

All stages0.75 0.29–1.9425% lower risk

Prophylaxis—

Early treatment—

Late treatment0.75 0.29–1.9425% lower risk

Hospitalization3 studies · 123 patients

All stages0.64^*** 0.49–0.8336% lower risk

Prophylaxis—

Early treatment—

Late treatment0.64^*** 0.49–0.8336% lower risk

Viral3 studies · 238 patients

All stages0.57^* 0.35–0.9243% lower risk

Prophylaxis—

Early treatment0.65 0.40–1.0635% lower risk

Late treatment0.36^** 0.17–0.7464% lower risk

RCT mortality4 studies · 334 patients

All stages0.75 0.45–1.2425% lower risk

Prophylaxis—

Early treatment—

Late treatment0.75 0.45–1.2425% lower risk

RCT hospitalization3 studies · 123 patients

All stages0.64^*** 0.49–0.8336% lower risk

Prophylaxis—

Early treatment—

Late treatment0.64^*** 0.49–0.8336% lower risk

Random-effects meta-analysis. Relative risk with 95% CI. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 1. Random-effects meta-analysis results for nitric oxide. Results show the relative risk and 95% confidence interval.

Fig. 5. 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.

Nitric oxide COVID-19 studies

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Late treatment15%0.85 · 0.66–1.1151/331197/64815% lower risk

Tau² = 0.03, I² = 17.9%, p = 0.23

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies35%0.65 · 0.46–0.9178/874341/1,42735% lower risk

Tau² = 0.17, I² = 59.7%, p = 0.01300.511.52+

¹ OT: comparison with other treatment

Rotate screen for more detailsIncrease width for more details

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 6. 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.

Nitric Oxide COVID-19 early treatment and prophylaxis studies

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies58%0.42 · 0.23–0.7527/543144/77958% lower risk

Tau² = 0.17, I² = 53.1%, p = 0.003500.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 7. Random-effects meta-analysis for early treatment and prophylaxis.

Nitric oxide COVID-19 mortality results

November 2026

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Chandel−54%1.54 · 0.72–2.7812/6636/206 Moni (RCT)90%0.10 · 0.01–1.670/144/11ICU patients Poonam14%0.86 · 0.72–1.0432/4156/62Ventilated patients OT¹ Valsecchi58%0.42 · 0.02–9.860/201/51 Di Fenza (SB RCT)23%0.77 · 0.44–1.3294 (n)99 (n)NOSARSCOVIDVentilated patients Friedrich (RCT)−50%1.50 · 0.27–8.283/272/27iNO-COVID-19 Kurnik (ICU)23%0.77 · 0.38–1.593/597/125ICU patients Berra (RCT)65%0.35 · 0.01–8.210/291/33NoCovid

Late treatment12%0.88 · 0.75–1.0450/296197/61412% lower risk

Tau² = 0.00, I² = 0.7%, p = 0.15

All studies12%0.88 · 0.75–1.0450/296197/61412% lower risk

Tau² = 0.00, I² = 0.7%, p = 0.1500.511.52+

¹ OT: comparison with other treatment

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 8. Random-effects meta-analysis for mortality results.

Nitric oxide COVID-19 mechanical ventilation results

November 2026

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Chandel−27%1.27 · 0.82–1.7329/6679/206 Moni (RCT)90%0.10 · 0.01–1.670/144/11ICU patients Strickland (RCT)−179%2.79 · 0.12–63.961/190/15 Valsecchi68%0.32 · 0.08–1.262/2016/51 Berra (RCT)−14%1.14 · 0.07–17.391/291/33NoCovid

Late treatment25%0.75 · 0.29–1.9433/148100/31625% lower risk

Tau² = 0.45, I² = 42.2%, p = 0.56

All studies25%0.75 · 0.29–1.9433/148100/31625% lower risk

Tau² = 0.45, I² = 42.2%, p = 0.5600.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 9. Random-effects meta-analysis for ventilation.

Nitric oxide COVID-19 ICU results

November 2026

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Valsecchi39%0.61 · 0.27–1.395/2021/51

Late treatment39%0.61 · 0.27–1.395/2021/5139% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.24

All studies39%0.61 · 0.27–1.395/2021/5139% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2400.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 10. Random-effects meta-analysis for ICU admission.

Nitric oxide COVID-19 hospitalization results

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Strickland (RCT)21%0.79 · 0.05–11.61hosp.1/191/15 Wolak (RCT)41%0.59 · 0.24–1.43hosp.16 (n)19 (n) Friedrich (RCT)36%0.64 · 0.49–0.84hosp. time27 (n)27 (n)iNO-COVID-19

Late treatment36%0.64 · 0.49–0.831/621/6136% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.00066

All studies36%0.64 · 0.49–0.831/621/6136% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0006600.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 11. Random-effects meta-analysis for hospitalization.

Nitric oxide COVID-19 progression results

November 2026

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Bryan (DB RCT)−1%1.01 · 0.21–4.953/2613/263

Early treatment−1%1.01 · 0.21–4.953/2613/2631% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.99

Di Fenza (SB RCT)30%0.70 · 0.14–3.3994 (n)99 (n)NOSARSCOVIDVentilated patients Friedrich (RCT)0%1.00 · 0.33–3.065/275/27iNO-COVID-19

Late treatment11%0.89 · 0.36–2.225/1215/12611% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.81

All studies8%0.92 · 0.41–2.038/3828/3898% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.8400.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 12. Random-effects meta-analysis for progression.

Nitric oxide COVID-19 recovery results

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Bryan (DB RCT)11%0.89 · 0.71–1.11recov. time261 (n)263 (n)

Early treatment11%0.89 · 0.71–1.11261 (n)263 (n)11% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.3

All studies11%0.89 · 0.71–1.11261 (n)263 (n)11% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.300.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 13. Random-effects meta-analysis for recovery.

Nitric oxide COVID-19 case results

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 14. Random-effects meta-analysis for cases.

Nitric oxide COVID-19 viral clearance results

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)51%0.49 · 0.32–0.75viral load40 (n)40 (n) Tandon (DB RCT)20%0.80 · 0.75–0.86viral load64 (n)69 (n)

Early treatment35%0.65 · 0.40–1.06104 (n)109 (n)35% lower risk

Tau² = 0.10, I² = 80.5%, p = 0.083

Moni (RCT)64%0.36 · 0.17–0.74viral load14 (n)11 (n)ICU patients

Late treatment64%0.36 · 0.17–0.7314 (n)11 (n)64% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0053

All studies43%0.57 · 0.35–0.92118 (n)120 (n)43% lower risk

Tau² = 0.14, I² = 79.5%, p = 0.02200.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 15. Random-effects meta-analysis for viral clearance.

Nitric oxide COVID-19 peer reviewed studies

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Late treatment15%0.85 · 0.64–1.1351/302196/61515% lower risk

Tau² = 0.04, I² = 24.8%, p = 0.28

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies35%0.65 · 0.46–0.9278/845340/1,39435% lower risk

Tau² = 0.18, I² = 62.5%, p = 0.01600.511.52+

¹ OT: comparison with other treatment

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 16. Random-effects meta-analysis for peer reviewed studies. 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. 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.

Nitric oxide COVID-19 studies

Randomized Controlled Trials (RCTs)

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Oral spray/rinse−1%1.01 · 0.21–4.953/2613/2631% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.99

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Miller75%0.25 · 0.14–0.43cases13/203108/422 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69

Nasal spray/rinse63%0.37 · 0.20–0.7024/282141/51663% lower risk

Tau² = 0.18, I² = 61.0%, p = 0.0021

All studies58%0.42 · 0.23–0.7527/543144/77958% lower risk

Tau² = 0.17, I² = 53.1%, p = 0.003500.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 17. Random-effects meta-analysis for nasopharyngeal/oropharyngeal administration (excluding late treatment). 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.

Fig. 18 shows a comparison of results for RCTs and observational studies. Random-effects meta-analysis of RCTs shows 39% improvement, compared to 30% for other studies. Fig. 19, 20, 21, and 22 show forest plots for random-effects meta-analysis of all Randomized Controlled Trials, all early treatment and prophylaxis RCTs, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1.

Fig. 18. Results for RCTs and observational studies.

RCTs have many potential biases

RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases56, and analysis of double-blind RCTs has identified extreme levels of bias57. 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.

Conflicts of interest for COVID-19 RCTs

RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. 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. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.

RCTs for novel acute diseases requiring rapid treatment

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 227 treatments we have analyzed, 67% 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.

COVID-19 RCT vs. observational results from 6,000+ studies

Observational studies have been shown to be reliable

November 2026

RR · CI

Low-cost0.98 · 0.90–1.06 High-profit0.92 · 0.84–1.02

All treatments0.97 · 0.91–1.03

00.511.52

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RCTs show
higher efficacyRCTs show
lower efficacy

Fig. 23. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments61.

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 227 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]48. Similar results are found for all low-cost treatments, RR 0.98 [0.90‑1.06]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.92 [0.84‑1.02]. Details can be found in the supplementary data.

Lee (B) 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 see65,66.

RCTs may be less reliable

Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.

Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.

Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)

Currently, 59 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, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.

All studies must be carefully analyzed

Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.

All studies must be evaluated individually. 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.

Nitric oxide COVID-19 Randomized Controlled Trials

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Moni (RCT)90%0.10 · 0.01–1.67death0/144/11ICU patients Strickland (RCT)−179%2.79 · 0.12–63.96ventilation1/190/15 Di Fenza (SB RCT)23%0.77 · 0.44–1.32death94 (n)99 (n)NOSARSCOVIDVentilated patients Wolak (RCT)64%0.36 · 0.14–0.91oxygen16 (n)19 (n) Friedrich (RCT)−50%1.50 · 0.27–8.28death3/272/27iNO-COVID-19 Berra (RCT)65%0.35 · 0.01–8.21death0/291/33NoCovid

Late treatment36%0.64 · 0.38–1.084/1997/20436% lower risk

Tau² = 0.05, I² = 8.9%, p = 0.092

All studies39%0.61 · 0.44–0.8318/53943/56139% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.001700.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 19. 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.

Nitric Oxide COVID-19 early treatment and prophylaxis RCTs

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

All studies44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.009700.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 20. Random-effects meta-analysis for all early treatment and prophylaxis RCTs. 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.

Nitric oxide COVID-19 RCT mortality results

November 2026

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Moni (RCT)90%0.10 · 0.01–1.670/144/11ICU patients Di Fenza (SB RCT)23%0.77 · 0.44–1.3294 (n)99 (n)NOSARSCOVIDVentilated patients Friedrich (RCT)−50%1.50 · 0.27–8.283/272/27iNO-COVID-19 Berra (RCT)65%0.35 · 0.01–8.210/291/33NoCovid

Late treatment25%0.75 · 0.45–1.243/1647/17025% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.27

All studies25%0.75 · 0.45–1.243/1647/17025% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2700.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 21. Random-effects meta-analysis for RCT mortality results.

Nitric oxide COVID-19 RCT hospitalization results

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Strickland (RCT)21%0.79 · 0.05–11.61hosp.1/191/15 Wolak (RCT)41%0.59 · 0.24–1.43hosp.16 (n)19 (n) Friedrich (RCT)36%0.64 · 0.49–0.84hosp. time27 (n)27 (n)iNO-COVID-19

Late treatment36%0.64 · 0.49–0.831/621/6136% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.00066

All studies36%0.64 · 0.49–0.831/621/6136% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0006600.511.52+

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 22. Random-effects meta-analysis for RCT hospitalization results.

Fig. 24. Optimal spray angle may increase nasopharyngeal drug delivery 100x for nasal sprays, adapted from Akash et al.

Application

In addition to the dosage and frequency of administration, efficacy for nasopharyngeal/oropharyngeal treatments may depend on many other details. For example considering sprays, viscosity, mucoadhesion, sprayability, droplet size67,68, dispersion68, and application angle67 are important.

Akash et al. performed a computational fluid dynamics study of nasal spray administration showing 100x improvement in nasopharyngeal drug delivery using a new spray placement protocol, which involves holding the spay nozzle close to horizontal at the nostril, with a slight tilt towards the cheeks. The study also found the optimal droplet size range for nasopharyngeal deposition was ~7-17µm.

Exclusions

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. Fig. 25 shows a forest plot for random-effects meta-analysis of all studies after exclusions.

Kurnik, unadjusted results with no group details.

Nitric oxide COVID-19 studies after exclusions

November 2026

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Winchester (DB RCT)42%0.58 · 0.36–0.94no improv.8/1523/25 Tandon (DB RCT)68%0.32 · 0.09–1.12no improv.3/6410/69 Bryan (DB RCT)−1%1.01 · 0.21–4.95progression3/2613/263

Early treatment44%0.56 · 0.36–0.8714/34036/35744% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0097

Chandel−54%1.54 · 0.72–2.78death12/6636/206 Moni (RCT)90%0.10 · 0.01–1.67death0/144/11ICU patients Strickland (RCT)−179%2.79 · 0.12–63.96ventilation1/190/15 Poonam14%0.86 · 0.72–1.04death32/4156/62Ventilated patients OT¹ Valsecchi58%0.42 · 0.02–9.86death0/201/51 Di Fenza (SB RCT)23%0.77 · 0.44–1.32death94 (n)99 (n)NOSARSCOVIDVentilated patients Wolak (RCT)64%0.36 · 0.14–0.91oxygen16 (n)19 (n) Friedrich (RCT)−50%1.50 · 0.27–8.28death3/272/27iNO-COVID-19 Berra (RCT)65%0.35 · 0.01–8.21death0/291/33NoCovid

Late treatment15%0.85 · 0.62–1.1848/326100/52315% lower risk

Tau² = 0.05, I² = 26.4%, p = 0.34

Efficacy Gradient By Stage

Prophylaxis75%0.25 · 0.14–0.4313/203108/42275% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies37%0.63 · 0.43–0.9275/869244/1,30237% lower risk

Tau² = 0.20, I² = 62.8%, p = 0.01700.511.52+

¹ OT: comparison with other treatment

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← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fig. 25. 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.

Preclinical studies show mechanisms relevant to all stages: blocking initial infection, minimizing viral replication, and mitigating secondary complications. Specifically, for blocking initial infection: inhibition of Spike protein palmitoylation, reduction of receptor binding affinity, and improved mucociliary clearance; for minimizing viral replication: S-nitrosylation of 3CLpro and RdRp, and RNA synthesis inhibition; and for mitigating secondary complications: reversing endothelial dysfunction, antithrombotic effects, and anti-inflammatory effects.

If there are beneficial mechanisms relevant to all stages, we would expect to see benefits combine and increase for earlier treatment, and again for prophylactic treatment—as treatment is delayed the benefit of specific mechanisms is reduced or lost. Random-effects meta-regression shows a significant association with increasing efficacy for early treatment and prophylaxis. For every step towards earlier intervention (late treatment → early treatment → prophylaxis), meta-regression shows that the Risk Ratio decreases by a factor of 1.73 (slope β = -0.55 [-0.73 to -0.37]; p < 0.0000001). In other words, the protective effect becomes stronger as intervention is done earlier.

This gradient reinforces the reliability of the overall finding that nitric oxide reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect to find a trend across stages that matches the biological mechanisms.

Interaction Matching Biological Mechanisms

With 14 studies for nitric oxide, we can analyze trends across studies. A significant interaction that matches the biological mechanisms provides further confirmation of efficacy. Analysis of the efficacy gradient across stages shows a significant interaction, with p < 0.0000001.

This reinforces the reliability of the overall finding that nitric oxide reduces risk for COVID-19. If the observed efficacy was due to a systematic bias increasing efficacy in results, we would not expect analysis across studies to find this significant interaction that matches the biological mechanisms.

Media Bias

Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."44.

We analyze media coverage for the 227 treatments we cover using Altmetric70 and PlumX Metrics71. Altmetric covers ~14,000 news outlets72 and PlumX covers ~55,000 news sources73. The two services are complementary with coverage of different news sources and different methods for identifying articles not containing a DOI. We consider studies to have received significant media coverage if they were covered by at least 100 articles in either Altmetric of PlumX73,74. The most widespread study was covered by 1,047 outlets. Studies covered by major syndicated sources shows hundreds of news articles, while studies covered by few outlets typically appear in niche medical news sources or press releases.

Fig. 26 shows that the mainstream media did not cover any of the positive studies for nitric oxide. Fig. 27 and 28 analyze coverage for all low-cost treatments and all high-profit treatments, showing significant bias. For low-cost treatments media coverage favored negative results 5.4× more than positive results, whereas for high-profit treatments coverage favored positive results 3.8× more than negative results.

The media bias may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity. High-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

Media coverage of nitric oxide for COVID‑19

November 2026

Media covered none of the 6 studies showing significantly lower risk

No positive nitric oxide studies were covered

2021 2022 2023 2024 2025

Fig. 26. Mainstream media did not cover any of the studies for nitric oxide.

Media bias for low-cost COVID‑19 treatments

November 2026

Media covered only 0.8% of studies showing significantly lower COVID-19 risk

Only 5 positive studies were covered

2020 2021 2022 2023

0.8%

one each: budesonide, cannabidiol, fluvoxamine, HCQ, vitamin D

34 negative studies were covered

2020 2021 2022 2023

HCQ 12, ivermectin 6, lopinavir/ritonavir 4, azithromycin 3, vitamin D 3, fluvoxamine 2, one each: ibuprofen, metformin, vitamin C, zinc

5.4×

Negative bias for low-cost treatments

negative studies were 5.4 times more likely to be covered

Fig. 27. Mainstream media coverage was biased against positive results for low-cost treatments. When restricting to RCTs, the bias was even larger, with negative RCTs 6.9× more likely to be covered.

Media bias for high-profit COVID‑19 treatments

November 2026

Media was 3.8× more likely to cover positive studies

19 positive studies were covered

2020 2021 2022 2023

11%

paxlovid 5, remdesivir 3, casirivimab/imdevimab 2, convalescent plasma 2, molnupiravir 2, one each: antiandrogens, peginterferon lambda, sargramostim, tixagevimab/cilgavimab, tocilizumab

Only 6 negative studies were covered

2020 2021 2022 2023

remdesivir 3, one each: bamlanivimab/etesevimab, bebtelovimab, sotrovimab

3.8×

Positive bias for high-profit treatments

positive studies were 3.8 times more likely to be covered

Fig. 28. For high-profit treatments, mainstream media coverage was biased towards positive results. When restricting to RCTs, the bias was even larger, with positive RCTs 12.0× more likely to be covered.

A combination of factors may have led to the very low media coverage of low-cost treatments:

•

Politicization

led to a media environment where coverage was often framed to support a political narrative rather than to provide objective scientific information. As Scott Alexander said:

"if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."

There was strong social pressure to discredit low-cost treatments.

•

Censorship

of information conflicting with selected authorities. For example, individuals and organizations presenting conflicting science were often shadowbanned or completely banned on Twitter and YouTube if the science they were reporting conflicted with the opinions of specific authorities selected by those companies, including when the authorities had not even reviewed the scientific results in question.

•

FDA requires "no adequate, approved, and available alternatives"

in order to grant an EUA for novel high-profit interventions, creating an incentive for authorities to ignore or downplay existing low-cost treatments.

•

Regulatory capture

biases authorities towards high-profit interventions.

•

Authorities ignored most evidence for low-cost treatments

, for example the NIH references only 2% of studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.

•

Media coverage of science is often not very accurate

, e.g., misunderstanding confounding issues. For example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but very late treatment of late-stage patients (mostly on oxygen already) with an excessive toxic dose (shown dangerous in a dose comparison RCT) provides no information on the recommended early/prophylactic treatment. With difficulting in understanding basic confounders like treatment delay and dose, the media may favor deferring to authorities. Many studies for low-cost treatments require greater expertise to analyze.

•

Substantial funding from pharmaceutical advertising

biases editorial decisions towards high-profit interventions.

•

PR power

- companies/teams with strong PR presence are favored in the media, which correlates with high-profit and high conflict of interest studies.

•

The media was very negative in general

, inflating risk, and elevating fear and anxiety. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage.

Authority Delay

26 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?

The harms of smoking here hidden for 25 yearsC. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.

Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.

Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.

A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.

c19early.org

Delayed public health acknowledgments

Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.

	Evidence	Official Acknowledgment	Approx. Delay
Citrus Fruit (vitamin C) for Scurvy (effectiveness)	1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors.	1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease.	48 years
Handwashing (lower mortality)	1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%.	~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death).	~20+ years
Helicobacter pylori (bacteria causes ulcers)	1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food.	1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy.	~12 years
Asbestos (causes asbestosis & cancer)	1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers.	1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it.	~47 years
Leaded Gasoline (neurotoxicity)	~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning.	1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996.	~49 years
Harms of Smoking (causes lung cancer)	1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s.	1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease.	25 years

Heterogeneity

Heterogeneity in COVID-19 studies arises from many factors including:

Treatment delay

The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours75,76. 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.

Early treatment is more effective: baloxavir marboxil

Pooled effects are no longer required to show efficacy as of June 2022

November 2026

Treatment delayResult

Post-exposure prophylaxis86% fewer cases77

<24 hours-33 hours symptoms78

24-48 hours-13 hours symptoms78

Inpatients-2.5 hours to improvement79

Table 2. Studies of baloxavir marboxil for influenza show that early treatment is more effective.

Fig. 29 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Fig. 29. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.

Patient demographics

Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.

SARS-CoV-2 variants

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants81, for example the Gamma variant shows significantly different characteristics82-85. 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 variants86,87.

Treatment regimen

Effectiveness may depend strongly on the dosage and treatment regimen.

Medication quality

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.

Other treatments

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic90-114, therefore efficacy may depend strongly on combined treatments.

Effect measured

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.

Meta-analysis

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.

Pooled Effects

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 nitric oxide as of June 2022. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes in RCTs was delayed by 31.3 months compared to using pooled outcomes in RCTs.

Combining studies is required

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.

Specific outcome and pooled analyses

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.

Ethical and practical issues limit high-risk trials

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.

Validating pooled outcome analysis for COVID-19

For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, 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 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 30 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 31 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). 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. Fig. 32 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 = 0.0000000074 to p = 0.00000000015.

Fig. 30. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 31. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 30. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.

Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)

Currently, 59 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 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 33 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 33. 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.

Limitations

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 differences in treatment delay are more likely to hide efficacy.

Summary

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.

Humming increases nasal nitric oxide

Humming increases nasal nitric oxide - Weitzberg et al. show a 15-fold increase. One case report showed strong humming at a low pitch (approximately 130 Hz, aiming to maximally increase intranasal vibrations) for one hour daily eliminated chronic rhinosinusitis in four days, hypothesized to work through increased endogenous nasal NO production with antifungal, antiviral, and antibacterial effects117.

The paranasal sinuses act as a continuous reservoir maintaining localized NO concentrations of 5-20 ppm, which is thought to contribute to local host defenses and an antimicrobial sinus microenvironment118. Normal tidal breathing only draws a fraction of this into the airway. Humming greatly accelerates the gas exchange between the sinuses and the nasal cavity, causing exhaled NO to spike up to 15-fold116. This significant endogenous boost could theoretically be beneficial for COVID-19 prevention/treatment.

The concentration of NO achieved by humming may be significantly below what can be achieved with pharmaceutical sprays, although the levels achieved by current sprays are not well characterized. On the other hand, humming is more amenable to frequent use, and could also be combined with nitric-oxide generating nasal sprays or lozenges. Humming can also be started immediately on the first hint of symptoms, whereas other treatments not being permanently carried around involve a delay.

Nitric Oxide is broadly antifungal, antiviral, and antibacterial, suggesting potential benefit beyond COVID-19. No studies have been done to directly test the efficacy of humming for COVID-19 prevention/treatment, or for any other viral respiratory infections. We believe this is worth investigating - the excellent safety profile, zero cost, and immediate availability mean a trial could be implemented at very low cost, and even a small effect would be valuable.

A very large trial could be conducted at very low cost, allowing the detection of even a small effect, however the design would be critical to avoid confounding factors such as healthy-user bias. The control arm could perform a sham vocalization chosen to match time, attention, and exhalation duration while minimizing nasal vibration and NO generation, for example vocalizing a sustained vowel such as "ahhhh" with the mouth open.

One related trial tested Pranayama where one component was Bhramari Pranayama, which is a calming yoga breathing technique where a steady humming sound is produced during slow, controlled exhalations119. This trial showed a significant benefit, however the number of events was very small, the intervention included several other components, and the humming pitch may be suboptimal for NO generation.

Discussion

PCR viral load

Analysis of short-term changes in viral load using PCR may not detect effective treatments because PCR is unable to differentiate between intact infectious virus and non-infectious or destroyed virus particles. For example Tarragó-Gil, Alemany perform RCTs with cetylpyridinium chloride (CPC) mouthwash that show no difference in PCR viral load, however there was significantly increased detection of SARS-CoV-2 nucleocapsid protein, indicating viral lysis. CPC inactivates SARS-CoV-2 by degrading its membrane, exposing the nucleocapsid of the virus. To better estimate changes in viral load and infectivity, methods like viral culture that can differentiate intact vs. degraded virus are preferred.

Nasopharyngeal/oropharyngeal administration

Studies to date use a variety of administration methods to the respiratory tract, including nasal and oral sprays, nasal irrigation, oral rinses, and inhalation. Table 3 shows the relative efficacy for nasal, oral, and combined administration. Combined administration shows the best results, and nasal administration is more effective than oral. Precise efficacy depends on the details of administration, e.g., mucoadhesion and sprayability for sprays.

c19early.org

Combined nasal and oral administration is most effective

Across all nasopharyngeal/oropharyngeal treatments we cover, combined nasal and oral administration shows the highest efficacy, followed by nasal administration, with oral administration alone showing the lowest efficacy.

Administration	Improvement	Studies
Nasal & oral	88% [72‑95%]	10
Nasal spray/rinse	59% [50‑66%]	21
Oral spray/rinse	38% [25‑49%]	11

Table 3. Respiratory tract administration efficacy. Relative efficacy of nasal, oral, and combined nasal/oral administration for treatments administered directly to the respiratory tract. Results show random-effects meta-analysis for the most serious outcome reported for all prophylaxis and early treatment studies.

Nasopharyngeal/oropharyngeal treatment mechanisms of action

Nasopharyngeal/oropharyngeal treatments work via different methods. Some are drugs with other primary uses and have a greater potential for side effects and drug interactions, for example azelastine and chlorpheniramine are antihistamines. Table 4 summarizes the primary classes of mechanisms of action, and Table 5 shows mechanisms of action for specific treatments.

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Nasal/oral sprays and rinses—primary mechanisms

Primary mechanisms of action for nasopharyngeal/oropharyngeal sprays and rinses. Note: sequenced application is possible to maximize efficacy—for example, using a virucidal spray/wash first (to clean), followed by a barrier spray (to protect), with a 5-10 minute drying window in between.

Virucidal action	Chemically inactivating or destroying the structure of viral particles
Blocking attachment	Binding to the virus or host cells to prevent viral attachment to host cells
Physical barrier	Forming a physical layer over the nasal mucosa preventing viral access to host cells
Physical removal	Mechanical washout/flushing of viral particles and mucus (e.g., large volume irrigation)
Mucociliary clearance	Stimulating the natural beating of nasal cilia to accelerate the clearing of trapped pathogens

Table 4. Primary classes for mechanisms of action for nasopharyngeal/oropharyngeal treatments.

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Nasal/oral sprays and rinses—mechanisms of action

Nasopharyngeal/oropharyngeal treatments have many different mechanisms of action. Specific treatments may have significant systemic effects or significantly alter the microbiome.

Treatment	Mechanisms	Notes
Azelastine122	Antiviral: inhibits interaction between spike protein and ACE2 Antiviral: potential inhibition of viral protease (Mpro) Other: H1-receptor antagonist (antihistamine) Other: mast cell stabilizer	Antihistamine. Designed to affect human receptors. Risks include dysgeusia, drowsiness, and nasal burning.
Cetylpyridinium Chloride123	Virucidal: disrupts viral lipid envelope Antiseptic: quaternary ammonium compound	Chemical virucide. Common in mouthwashes. Can cause temporary staining of teeth or tongue irritation if used frequently.
Chlorhexidine124	Virucidal: disrupts viral lipid membranes Antiseptic: cationic polybiguanide	Chemical virucide. May cause tooth staining and altered taste.
Chlorpheniramine125	Antiviral: binds to viral spike protein to block entry Antiviral: high affinity for viral transport proteins Other: H1-receptor antagonist (1st generation antihistamine) Other: anticholinergic activity	Antihistamine. Stronger systemic risks than azelastine. Known to cause significant sedation/drowsiness and cognitive impairment.
Hydrogen Peroxide126	Virucidal: oxidizing agent that destroys viral parts Other: tissue debridement	Chemical virucide. Can be toxic to healthy tissue if concentration is too high (>1%). Long-term safety on nasal mucosa is debated.
Hypochlorous Acid127	Virucidal: oxidizes viral proteins, causing aggregation and loss of function. Impairs spike-ACE2 binding Antiseptic: broad-spectrum antimicrobial activity via oxidative damage to microbial proteins and membranes	Chemical virucide. Activity depends on concentration, pH, and contact time. Saliva may reduce activity.
Inhaled Heparin128	Antiviral: acts as a decoy receptor (mimics heparan sulfate) Antiviral: anti-inflammatory effects on lung tissue Other: Anticoagulant	Anticoagulant. Use requires caution regarding bleeding risks.
Iota-carrageenan129	Barrier: forms a viscous physical layer on mucosa Trap: electrostatistically traps virus particles (mimics cell surface)	Physical barrier. High safety profile for daily use.
NaCl130	Cleaning: physically washes away viral particles Support: moisturizes mucosa to support natural immune barrier	Physical wash. High degree of safety, reduces viral load via physical removal.
Nitric Oxide	Virucidal: physically damages viral structure via nitrosylation Other: vasodilator (relaxes blood vessels) in systemic use	Virucide/drug hybrid. In nasal spray form, it acts primarily as a topical disinfectant. Rapidly cleared, so systemic vasodilation risks are low but present.
Phthalocyanine131	Virucidal: generates reactive oxygen species (ROS) when exposed to light to kill virus Other: photosensitizer	Chemical virucide. A synthetic compound often used in photodynamic therapy. Works by creating an oxidative environment hostile to the virus.
Povidone-Iodine132	Virucidal: oxidizes viral proteins and destabilizes membrane structures Antiseptic: broad-spectrum bacterial/fungal killer	Chemical virucide. Highly effective but risk of thyroid absorption with chronic use. Can be irritating to mucous membranes.
Sodium Bicarbonate133	Environment: raises pH to inhibit viral fusion Cleaning: improves mucociliary clearance (washing)	Physical/chemical environment. Changes the environment rather than attacking the virus directly. High degree of safety.

Table 5. Mechanisms of action for nasopharyngeal/oropharyngeal treatments.

Impact on the microbiome

Nasopharyngeal/oropharyngeal treatments may not be highly selective. In addition to inhibiting or disabling SARS-CoV-2, they may also be harmful to beneficial microbes, disrupting the natural microbiome in the oral cavity and nasal passages that have important protective and metabolic roles134,135. This may be especially important for prolonged use or overuse. Table 6 summarizes the potential for common nasopharyngeal/oropharyngeal treatments to affect the natural microbiome.

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Nasal/oral sprays and rinses may affect the microbiome

Nasopharyngeal/oropharyngeal treatments may significantly alter the microbiome. These effects may be more important with longer-term prophylaxis.

Treatment	Microbiome disruption potential	Notes
Iota-carrageenan129	Low	Primarily antiviral, however extended use may mildly affect the microbiome
Nitric Oxide	Low to moderate	More selective towards pathogens, however excessive concentrations or prolonged use may disrupt the balance of bacteria
Alkalinization136	Moderate	Increases pH, negatively impacting beneficial microbes that thrive in a slightly acidic environment
Cetylpyridinium Chloride123	Moderate	Quaternary ammonium broad-spectrum antiseptic that can disrupt beneficial and harmful bacteria
Hypochlorous Acid127	Moderate	Broad-spectrum oxidizing antiseptic that may affect beneficial and harmful bacteria
Phthalocyanine131	Moderate to high	Photodynamic compound with antimicrobial activity, likely to affect the microbiome
Chlorhexidine124	High	Potent antiseptic with broad activity, significantly disrupts the microbiome
Hydrogen Peroxide126	High	Strong oxidizer, harming both beneficial and harmful microbes
Povidone-Iodine132	High	Potent broad-spectrum antiseptic harmful to beneficial microbes

Table 6. Potential effect of nasopharyngeal/oropharyngeal treatments on the microbiome.

Publication bias

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results137-140. For nitric oxide, there is currently not enough data to evaluate publication bias with high confidence.

One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.

Fig. 34 shows a scatter plot of results for prospective and retrospective studies. 20% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 67% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 23% improvement, compared to 42% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.

Fig. 34. Prospective vs. retrospective studies. The diamonds show the results of random-effects meta-analysis.

Fig. 35. Early treatment was more common for high-profit drugs.

Late treatment bias

Studies for nitric oxide were mostly late treatment studies, in contrast with typical high-profit drugs that were more likely to be tested with early treatment.

Funnel plot 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. Fig. 36 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 < 0.05141-148. 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.

Fig. 36. Example funnel plot analysis for simulated perfect trials.

Conflicts of interest

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Nitric Oxide for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 nitric oxide 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 nitric oxide trials represent the optimal conditions for efficacy.

Limitations

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 alone90-114. 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.

Notes

1 of the 14 studies compare against other treatments, which may reduce the effect seen.

Reviews

Many reviews cover nitric oxide for COVID-19, presenting additional background on mechanisms and related results, including149-154.

Other studies

Additional preclinical or review papers suggesting potential benefits of nitric oxide for COVID-19 include168-172. We have not reviewed these studies in detail.

Perspective

Results compared with other treatments

SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors35-42, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk43, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 37 shows an overview of the results for nitric oxide in the context of multiple COVID-19 treatments, and Fig. 38 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 37. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy173.

Fig. 38. Efficacy vs. cost for COVID-19 treatments.

Conclusion

SARS-CoV-2 infection typically starts in the upper respiratory tract. Progression may lead to cytokine storm, pneumonia, ARDS, neurological issues, organ failure, and death. Stopping replication in the upper respiratory tract, via early or prophylactic nasopharyngeal/oropharyngeal treatment, can avoid the consequences of progression to other tissues, and avoid the requirement for systemic treatments with greater potential for side effects.

Studies to date show that nitric oxide is an effective treatment for COVID-19. Significantly lower risk is seen for hospitalization, cases, and viral clearance. 7 studies from 6 independent teams in 6 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 35% [9‑54%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Early treatment is more effective than late treatment.

Emergent results for the efficacy gradient across stages (p < 0.0000001) that match the biological mechanisms confirm efficacy.

Nitric Oxide may affect the natural microbiome, especially with prolonged use.

Contact.

Funding.

We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.

Conflicts of interest.

We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

AI.

We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.

Updates.

Our COVID-19 meta-analyses involve the extraction of over 229,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/nometa.html.

Dedication.

This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.

Public domain.

This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.

Study Notes

Berra

Outcome

Improvement

Relative Risk · 95% CI

65%0.35 · 0.01–8.21

Ventilation

-14%1.14 · 0.07 to >9

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Trial underpowered to detect differences

Early terminated RCT 62 hosptialized patients, showing no significant difference with nitric oxide treatment. Submit Corrections or Updates.

Bryan et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Faster recovery with nitric oxide

Not statistically significant · p = 0.3

RCT 524 outpatients in the USA for a nitric oxide generating lozenge, showing no significant difference in combined hospitalization, ICU admission, intubation, dialysis, and death. There were only 3 events in each arm, all occuring in 2020, with zero events in 2021 or 2022. Recovery was 11% faster with treatment, without statistical significance. Authors note that a higher dose may have been more effective. Trials showing greater efficacy have used a nasal spray. Submit Corrections or Updates.

Chandel et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Higher mortality and ventilation

Not statistically significant · p = 0.25 (mortality) and p = 0.26 (ventilation)

Retrospective 272 acute respiratory failure patients in the USA treated with high-flow nasal cannula, 66 treated with inhaled nitric oxide, showing increased mortality with inhaled nitric oxide. There were significant differences in the usage of several other treatments between the groups. Submit Corrections or Updates.

Di Fenza et al.

Outcome

Improvement

Relative Risk · 95% CI

Mortality, day 90 (b)

13%0.87 · 0.52–1.43

Mortality, day 28 (b)

15%0.85 · 0.50–1.46

VV-ECMO

30%0.70 · 0.14–3.39

Neurological symptoms

83%0.17 · 0.04–0.62

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Lower mortality with nitric oxide

Not statistically significant · p = 0.36

RCT 193 mechanically ventilated COVID-19 patients showing improved oxygenation at 48 hours but no difference in mortality with high-dose (80ppm) inhaled nitric oxide (NO) for 48 hours. The NO group had a higher proportion attaining PaO2/FiO2 > 300 mmHg and reduced rates of neurologic symptoms at 90 days. NO was associated with faster viral clearance. No serious adverse events were reported with NO. Submit Corrections or Updates.

Friedrich et al.

Outcome

Improvement

Relative Risk · 95% CI

-50%1.50 · 0.27–8.28

Oxygen therapy, day 7

50%0.50 · 0.24–1.04

Oxygen therapy, day 5

35%0.65 · 0.41–1.02

Oxygen therapy, day 3

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Shorter hospitalization with nitric oxide

p = 0.0014

RCT 54 late stage hospitalized COVID-19 patients showing benefit with high-dose inhaled nitric oxide (iNO). Patients receiving 160 ppm iNO for 6 hours had significantly more days free of supplemental oxygen (11 vs 8 days, p=0.044) and shorter hospital stays (4.5 vs 7.0 days, p=0.004) compared to standard care. The iNO group also had better clinical scores at days 3 and 5, and a higher proportion of patients were free of ventilatory support at day 3 (33% vs 4%, p=0.005). There was no significant difference for mortality. Authors hypothesize that the benefit results from iNO's antimicrobial effects, pulmonary vasodilation, and anti-inflammatory properties.

Enrollment number inconsistencies: the abstract reports 27+27 randomized with 1 excluded; the results text says 28 were allocated to iNO; the flowchart shows 54 randomized as 27+27.

Percentage calculation errors: vaccination rates (70.8% and 66.7%) correspond to a denominator of 24, not the reported 27 per group, suggesting unreported missing data. Submit Corrections or Updates.

Kurnik et al.

Outcome

Improvement

Relative Risk · 95% CI

Time to viral load reduction

23%0.77 · 0.38–1.59

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Lower mortality with nitric oxide

Not statistically significant · p = 0.33

Retrospective 130 elderly (≥70 years) critically ill COVID-19 patients showing no significant difference in long-term mortality with nitric oxide. Submit Corrections or Updates.

Miller et al.

Outcome

Improvement

Relative Risk · 95% CI

Case

75%0.25 · 0.14–0.43

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Fewer cases with nitric oxide

p < 0.000001

Retrospective 625 high-risk university students in Thailand showing reduced SARS-CoV-2 infection rates with nitric oxide nasal spray prophylaxis. Among students exposed to infected individuals, those voluntarily using treatment (n=203) had a 6.4% infection rate compared to 25.6% in the control group (n=422) (p<0.0001). Adverse events were limited to mild nasal burning or irritation in 11.4% of users, with no severe events reported. Authors note the study is limited by its retrospective, open-label nature and lack of randomization. Students who chose the treatment may have been more health-conscious or compliant with other protective measures. Authors are executives of the manufacturer. Submit Corrections or Updates.

Moni et al.

Outcome

Improvement

Relative Risk · 95% CI

<2 point WOS improvement

42%0.58 · 0.05–1.00

64%0.36 · 0.17–0.74

Time to viral load reduction (b)

63%0.37 · 0.18–0.75

00.511.52+

← Nitric Oxide
reduces riskNitric Oxide
increases risk →

Lower mortality and ventilation

p = 0.026 (mortality) and p = 0.026 (ventilation)

RCT 29 ICU patients in India, showing improved clinical outcomes and faster viral clearance with inhaled nitric oxide treatment. The treatment group was younger (mean 54 vs. 66) and had more patients on NIV at baseline (29% vs. 18%). Submit Corrections or Updates.

Poonam et al.

Outcome

Improvement

Relative Risk · 95% CI