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Nitric Oxide for COVID-19: real-time meta analysis of 6 studies
Covid Analysis, August 12, 2022, DRAFT
https://c19early.com/nometa.html
0 0.5 1 1.5+ All studies 46% 6 1,166 Improvement, Studies, Patients Relative Risk Mortality 36% 3 368 Ventilation 48% 3 368 ICU admission 39% 1 71 Cases 75% 1 625 Viral clearance 43% 3 238 RCTs 44% 3 198 Peer-reviewed 28% 5 541 Prophylaxis 75% 1 625 Early 42% 2 173 Late 36% 3 368 Nitric Oxide for COVID-19 c19early.com/no Aug 2022 Favorsnitric oxide Favorscontrol
Statistically significant improvements are seen for cases and viral clearance. 4 studies from 3 independent teams in 3 different countries show statistically significant improvements in isolation (2 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 46% [-4‑72%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for peer-reviewed studies.
0 0.5 1 1.5+ All studies 46% 6 1,166 Improvement, Studies, Patients Relative Risk Mortality 36% 3 368 Ventilation 48% 3 368 ICU admission 39% 1 71 Cases 75% 1 625 Viral clearance 43% 3 238 RCTs 44% 3 198 Peer-reviewed 28% 5 541 Prophylaxis 75% 1 625 Early 42% 2 173 Late 36% 3 368 Nitric Oxide for COVID-19 c19early.com/no Aug 2022 Favorsnitric oxide Favorscontrol
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 33% of nitric oxide studies show zero events in the treatment arm. Multiple treatments are typically used in combination, and other treatments may be more effective.
No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Highlights
Nitric Oxide reduces risk for COVID-19 with very high confidence for cases, high confidence for viral clearance, low confidence for pooled analysis, and very low confidence for ICU admission.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 43 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% 0.58 [0.36-0.94] no improv. 8/15 23/25 Improvement, RR [CI] Treatment Control Tandon (DB RCT) 42% 0.58 [0.33-1.01] no improv. 14/64 26/69 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 0.58 [0.40-0.84] 22/79 49/94 42% improvement Chandel -54% 1.54 [0.72-2.78] death 12/66 36/206 Improvement, RR [CI] Treatment Control Moni (RCT) 90% 0.10 [0.01-1.67] death 0/14 4/11 ICU patients Valsecchi 58% 0.42 [0.02-9.86] death 0/20 1/51 Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 0.64 [0.12-3.49] 12/100 41/268 36% improvement SaNOtize 75% 0.25 [0.14-0.43] cases 13/203 108/422 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 0.25 [0.14-0.43] 13/203 108/422 75% improvement All studies 46% 0.54 [0.28-1.04] 47/382 198/784 46% improvement 6 nitric oxide COVID-19 studies c19early.com/no Aug 2022 Tau​2 = 0.40, I​2 = 76.2%, p = 0.065 Effect extraction pre-specified(most serious outcome, see appendix) Favors nitric oxide Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% improvement Improvement Relative Risk [CI] Tandon (DB RCT) 42% improvement Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 42% improvement Chandel -54% death Moni (RCT) 90% death ICU patients Valsecchi 58% death Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 36% improvement SaNOtize 75% case Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 75% improvement All studies 46% 46% improvement 6 nitric oxide COVID-19 studies c19early.com/no Aug 2022 Tau​2 = 0.40, I​2 = 76.2%, p = 0.065 Effect extraction pre-specifiedRotate device for details Favors nitric oxide Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, and 10 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, cases, viral clearance, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS PEER-REVIEWED All Prophylaxis Early Late Nitric Oxide for COVID-19 C19EARLY.COM/NO AUG 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 2 2 100% 42% improvement
RR 0.58 [0.40‑0.84]
p = 0.0036
Late treatment 2 3 66.7% 36% improvement
RR 0.64 [0.12‑3.49]
p = 0.61
Prophylaxis 1 1 100% 75% improvement
RR 0.25 [0.14‑0.43]
p < 0.0001
All studies 5 6 83.3% 46% improvement
RR 0.54 [0.28‑1.04]
p = 0.065
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 642% [16‑60%]36% [-249‑88%]75% [57‑86%] 1,166 65
Peer-reviewed 542% [16‑60%]36% [-249‑88%] 541 64
Randomized Controlled TrialsRCTs 342% [16‑60%]90% [-67‑99%] 198 30
Table 2. Results by treatment stage for all studies and with different exclusions.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% 0.58 [0.36-0.94] no improv. 8/15 23/25 Improvement, RR [CI] Treatment Control Tandon (DB RCT) 42% 0.58 [0.33-1.01] no improv. 14/64 26/69 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 0.58 [0.40-0.84] 22/79 49/94 42% improvement Chandel -54% 1.54 [0.72-2.78] death 12/66 36/206 Improvement, RR [CI] Treatment Control Moni (RCT) 90% 0.10 [0.01-1.67] death 0/14 4/11 ICU patients Valsecchi 58% 0.42 [0.02-9.86] death 0/20 1/51 Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 0.64 [0.12-3.49] 12/100 41/268 36% improvement SaNOtize 75% 0.25 [0.14-0.43] cases 13/203 108/422 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 0.25 [0.14-0.43] 13/203 108/422 75% improvement All studies 46% 0.54 [0.28-1.04] 47/382 198/784 46% improvement 6 nitric oxide COVID-19 studies c19early.com/no Aug 2022 Tau​2 = 0.40, I​2 = 76.2%, p = 0.065 Effect extraction pre-specified(most serious outcome, see appendix) Favors nitric oxide Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% improvement Improvement Relative Risk [CI] Tandon (DB RCT) 42% improvement Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 42% improvement Chandel -54% death Moni (RCT) 90% death ICU patients Valsecchi 58% death Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 36% improvement SaNOtize 75% case Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 75% improvement All studies 46% 46% improvement 6 nitric oxide COVID-19 studies c19early.com/no Aug 2022 Tau​2 = 0.40, I​2 = 76.2%, p = 0.065 Effect extraction pre-specifiedRotate device for details Favors nitric oxide Favors control
Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chandel -54% 1.54 [0.72-2.78] 12/66 36/206 Improvement, RR [CI] Treatment Control Moni (RCT) 90% 0.10 [0.01-1.67] 0/14 4/11 ICU patients Valsecchi 58% 0.42 [0.02-9.86] 0/20 1/51 Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 0.64 [0.12-3.49] 12/100 41/268 36% improvement All studies 36% 0.64 [0.12-3.49] 12/100 41/268 36% improvement 3 nitric oxide COVID-19 mortality results c19early.com/no Aug 2022 Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Favors nitric oxide Favors control
Figure 5. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chandel -27% 1.27 [0.82-1.73] 29/66 79/206 Improvement, RR [CI] Treatment Control Moni (RCT) 90% 0.10 [0.01-1.67] 0/14 4/11 ICU patients Valsecchi 68% 0.32 [0.08-1.26] 2/20 16/51 Tau​2 = 0.97, I​2 = 69.8%, p = 0.37 Late treatment 48% 0.52 [0.13-2.09] 31/100 99/268 48% improvement All studies 48% 0.52 [0.13-2.09] 31/100 99/268 48% improvement 3 nitric oxide COVID-19 mechanical ventilation results c19early.com/no Aug 2022 Tau​2 = 0.97, I​2 = 69.8%, p = 0.37 Favors nitric oxide Favors control
Figure 6. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Valsecchi 39% 0.61 [0.27-1.39] 5/20 21/51 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.24 Late treatment 39% 0.61 [0.27-1.39] 5/20 21/51 39% improvement All studies 39% 0.61 [0.27-1.39] 5/20 21/51 39% improvement 1 nitric oxide COVID-19 ICU result c19early.com/no Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.24 Favors nitric oxide Favors control
Figure 7. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ SaNOtize 75% 0.25 [0.14-0.43] cases 13/203 108/422 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 0.25 [0.14-0.43] 13/203 108/422 75% improvement All studies 75% 0.25 [0.14-0.43] 13/203 108/422 75% improvement 1 nitric oxide COVID-19 case result c19early.com/no Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Favors nitric oxide Favors control
Figure 8. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 51% 0.49 [0.32-0.75] viral load 40 (n) 40 (n) Improvement, RR [CI] Treatment Control Tandon (DB RCT) 20% 0.80 [0.75-0.86] viral load 64 (n) 69 (n) Tau​2 = 0.10, I​2 = 80.5%, p = 0.083 Early treatment 35% 0.65 [0.40-1.06] 0/104 0/109 35% improvement Moni (RCT) 64% 0.36 [0.17-0.74] viral load 14 (n) 11 (n) ICU patients Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0053 Late treatment 64% 0.36 [0.17-0.74] 0/14 0/11 64% improvement All studies 43% 0.57 [0.35-0.92] 0/118 0/120 43% improvement 3 nitric oxide COVID-19 viral clearance results c19early.com/no Aug 2022 Tau​2 = 0.14, I​2 = 79.5%, p = 0.022 Favors nitric oxide Favors control
Figure 9. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% 0.58 [0.36-0.94] no improv. 8/15 23/25 Improvement, RR [CI] Treatment Control Tandon (DB RCT) 42% 0.58 [0.33-1.01] no improv. 14/64 26/69 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 0.58 [0.40-0.84] 22/79 49/94 42% improvement Chandel -54% 1.54 [0.72-2.78] death 12/66 36/206 Improvement, RR [CI] Treatment Control Moni (RCT) 90% 0.10 [0.01-1.67] death 0/14 4/11 ICU patients Valsecchi 58% 0.42 [0.02-9.86] death 0/20 1/51 Tau​2 = 1.22, I​2 = 50.1%, p = 0.61 Late treatment 36% 0.64 [0.12-3.49] 12/100 41/268 36% improvement All studies 28% 0.72 [0.40-1.28] 34/179 90/362 28% improvement 5 nitric oxide COVID-19 peer reviewed trials c19early.com/no Aug 2022 Tau​2 = 0.21, I​2 = 58.7%, p = 0.26 Effect extraction pre-specified(most serious outcome, see appendix) Favors nitric oxide Favors control
Figure 10. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during a pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 11 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 12 and 13 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for nitric oxide are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, we need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
Figure 11. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winchester (DB RCT) 42% 0.58 [0.36-0.94] no improv. 8/15 23/25 Improvement, RR [CI] Treatment Control Tandon (DB RCT) 42% 0.58 [0.33-1.01] no improv. 14/64 26/69 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0036 Early treatment 42% 0.58 [0.40-0.84] 22/79 49/94 42% improvement Moni (RCT) 90% 0.10 [0.01-1.67] death 0/14 4/11 ICU patients Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.11 Late treatment 90% 0.10 [0.01-1.67] 0/14 4/11 90% improvement All studies 44% 0.56 [0.39-0.81] 22/93 53/105 44% improvement 3 nitric oxide COVID-19 Randomized Controlled Trials c19early.com/no Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.002 Effect extraction pre-specified(most serious outcome, see appendix) Favors nitric oxide Favors control
Figure 12. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Moni (RCT) 90% 0.10 [0.01-1.67] 0/14 4/11 ICU patients Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.11 Late treatment 90% 0.10 [0.01-1.67] 0/14 4/11 90% improvement All studies 90% 0.10 [0.01-1.67] 0/14 4/11 90% improvement 1 nitric oxide COVID-19 RCT mortality result c19early.com/no Aug 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.11 Favors nitric oxide Favors control
Figure 13. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 3 3 100% 44% improvement
RR 0.56 [0.39‑0.81]
p = 0.002
RCT mortality results 1 1 100% 90% improvement
RR 0.10 [0.01‑1.67]
p = 0.11
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Figure 14 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 43 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 14. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 43 treatments. Early treatment is critical.
Patient demographics.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Other treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
Medication quality.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
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 the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant for specific use cases.
Discussion
Publication bias.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For 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.
67% of retrospective studies report positive effects, compared to 100% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 58% improvement, compared to 42% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 15 shows a scatter plot of results for prospective and retrospective studies.
Figure 15. Prospective vs. retrospective studies.
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. Figure 16 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 16. 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.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Conclusion
Statistically significant improvements are seen for cases and viral clearance. 4 studies from 3 independent teams in 3 different countries show statistically significant improvements in isolation (2 for the most serious outcome). Meta analysis using the most serious outcome reported shows 46% [-4‑72%] improvement, without reaching statistical significance. Results are similar for Randomized Controlled Trials and slightly worse for peer-reviewed studies.
Study Notes
0 0.5 1 1.5 2+ Mortality -54% Improvement Relative Risk Ventilation -27% c19early.com/no Chandel et al. Nitric Oxide for COVID-19 LATE Favors nitric oxide Favors control
[Chandel] Retrospective 272 acute respitory 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.
0 0.5 1 1.5 2+ Mortality 90% Improvement Relative Risk Ventilation 90% <2 point WOS improvem.. 42% Time to viral load reduc.. 64% Time to viral load r.. (b) 63% c19early.com/no Moni et al. ISRCTN16806663 Nitric Oxide RCT ICU Favors nitric oxide Favors control
[Moni] 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%).
0 0.5 1 1.5 2+ Case 75% Improvement Relative Risk c19early.com/no SaNOtize et al. Nitric Oxide for COVID-19 Prophylaxis Favors nitric oxide Favors control
[SaNOtize] PEP retrospective 625 university students in Thailand offered nitric oxide nasal spray, showing significantly lower cases for students that chose to use the treatment.
0 0.5 1 1.5 2+ Improvement, mITT-HR, d.. 42% Improvement Relative Risk Improvement, mITT-H.. (b) 67% Improvement, mITT-H.. (c) 68% Improvement, mITT, day 18 22% Improvement, mITT, day 16 18% Improvement, mITT, day 8 9% Viral load, mITT-HR 20% Viral load, mITT 14% Time to viral-, mITT-HR 26% Time to viral-, mITT 6% c19early.com/no Tandon et al. CTRI/2021/08 Nitric Oxide RCT EARLY Favors nitric oxide Favors control
[Tandon] RCT with 153 patients treated with a nitric oxide nasal spray, and 153 placebo patients, showing faster viral clearance with treatment. NO generated by a nasal spray (NONS) self-administered six times daily as two sprays per nostril (0.45mL of solution/dose) for seven days.
0 0.5 1 1.5 2+ Mortality 58% Improvement Relative Risk Ventilation 68% ICU admission 39% c19early.com/no Valsecchi et al. Nitric Oxide for COVID-19 LATE Favors nitric oxide Favors control
[Valsecchi] Retrospective 71 hospitalized patients in Israel, 20 treated with inhaled nitric oxide, showing
0 0.5 1 1.5 2+ Improvement 42% Improvement Relative Risk Viral load 51% c19early.com/no Winchester et al. Nitric Oxide for COVID-19 RCT EARLY Favors nitric oxide Favors control
[Winchester] RCT with 40 nitric oxide and 40 placebo patients in the UK, showing faster viral clearance and greater improvement with treatment.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.com. Search terms were nitric oxide, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of nitric oxide for COVID-19 that report a comparison with a control group are included in the main analysis. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.9.13) with scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.com/nometa.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Tandon], 6/29/2022, Double Blind Randomized Controlled Trial, placebo-controlled, India, peer-reviewed, 10 authors, study period 10 August, 2021 - 25 January, 2022, trial CTRI/2021/08. risk of no improvement, 41.9% lower, RR 0.58, p = 0.06, treatment 14 of 64 (21.9%), control 26 of 69 (37.7%), NNT 6.3, mITT high risk, day 18.
risk of no improvement, 66.8% lower, RR 0.33, p = 0.04, treatment 4 of 64 (6.2%), control 13 of 69 (18.8%), NNT 7.9, mITT high risk, day 16.
risk of no improvement, 67.7% lower, RR 0.32, p = 0.08, treatment 3 of 64 (4.7%), control 10 of 69 (14.5%), NNT 10, mITT high risk, day 8.
risk of no improvement, 22.3% lower, RR 0.78, p = 0.63, treatment 8 of 105 (7.6%), control 10 of 102 (9.8%), NNT 46, day 18, modified intention-to-treat.
risk of no improvement, 17.8% lower, RR 0.82, p = 0.67, treatment 11 of 105 (10.5%), control 13 of 102 (12.7%), NNT 44, day 16, modified intention-to-treat.
risk of no improvement, 8.9% lower, RR 0.91, p = 0.76, treatment 30 of 105 (28.6%), control 32 of 102 (31.4%), NNT 36, day 8, modified intention-to-treat.
viral load, 19.8% lower, relative load 0.80, p < 0.001, treatment mean 2.62 (±0.145) n=64, control mean 2.1 (±0.141) n=69, mITT high risk, day 8.
viral load, 13.5% lower, relative load 0.86, p < 0.001, treatment mean 2.51 (±0.114) n=105, control mean 2.17 (±0.118) n=102, day 8, modified intention-to-treat.
time to viral-, 26.1% lower, relative time 0.74, p = 0.09, treatment 64, control 69, mITT high risk, Kaplan–Meier.
time to viral-, 6.5% lower, relative time 0.94, p = 0.66, treatment 105, control 102, Kaplan–Meier, modified intention-to-treat.
[Winchester], 5/13/2021, Double Blind Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, 4 authors. risk of no improvement, 42.0% lower, RR 0.58, p = 0.008, treatment 8 of 15 (53.3%), control 23 of 25 (92.0%), NNT 2.6.
viral load, 51.3% lower, relative load 0.49, p = 0.001, treatment 40, control 40, AUC relative mean change.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Chandel], 1/31/2021, retrospective, USA, peer-reviewed, 14 authors, study period 1 March, 2020 - 9 June, 2020. risk of death, 54.1% higher, RR 1.54, p = 0.25, treatment 12 of 66 (18.2%), control 36 of 206 (17.5%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of mechanical ventilation, 27.2% higher, RR 1.27, p = 0.26, treatment 29 of 66 (43.9%), control 79 of 206 (38.3%), adjusted per study, odds ratio converted to relative risk, multivariable.
[Moni], 4/20/2021, Randomized Controlled Trial, India, peer-reviewed, 16 authors, study period September 2020 - December 2020, average treatment delay 6.78 days, trial ISRCTN16806663. risk of death, 90.1% lower, RR 0.10, p = 0.03, treatment 0 of 14 (0.0%), control 4 of 11 (36.4%), NNT 2.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28.
risk of mechanical ventilation, 90.1% lower, RR 0.10, p = 0.03, treatment 0 of 14 (0.0%), control 4 of 11 (36.4%), NNT 2.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28.
risk of <2 point WOS improvement, 42.5% better, RR 0.58, p = 0.47, treatment 3 of 14 (21.4%), control 7 of 11 (63.6%), NNT 2.4, adjusted per study, odds ratio converted to relative risk, day 14.
time to viral load reduction, 64.4% lower, RR 0.36, p = 0.005, treatment 14, control 11, adjusted per study, N gene.
time to viral load reduction, 63.4% lower, RR 0.37, p = 0.005, treatment 14, control 11, adjusted per study, Orf1ab gene.
[Valsecchi], 7/7/2022, retrospective, Israel, peer-reviewed, 20 authors, study period March 2020 - December 2021. risk of death, 58.2% lower, RR 0.42, p = 1.00, treatment 0 of 20 (0.0%), control 1 of 51 (2.0%), NNT 51, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of mechanical ventilation, 68.1% lower, RR 0.32, p = 0.08, treatment 2 of 20 (10.0%), control 16 of 51 (31.4%), NNT 4.7.
risk of ICU admission, 39.3% lower, RR 0.61, p = 0.28, treatment 5 of 20 (25.0%), control 21 of 51 (41.2%), NNT 6.2.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[SaNOtize], 4/30/2022, retrospective, Thailand, preprint, 1 author. risk of case, 75.0% lower, RR 0.25, p < 0.001, treatment 13 of 203 (6.4%), control 108 of 422 (25.6%), NNT 5.2.
Supplementary Data
References
Please send us corrections, updates, or comments. Vaccines and treatments are both valuable and complementary. All practical, effective, and safe means should be used. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases mortality, morbidity, collateral damage, and the risk of endemic status. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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