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Nitric Oxide for COVID-19: real-time meta analysis of 8 studies
Covid Analysis, December 2022
https://c19early.org/nometa.html
 
0 0.5 1 1.5+ All studies 28% 8 1,450 Improvement, Studies, Patients Relative Risk Mortality -11% 5 652 Ventilation 48% 3 368 ICU admission 39% 1 71 Cases 75% 1 625 Viral clearance 43% 3 238 RCTs 44% 3 198 Peer-reviewed 11% 7 825 Prophylaxis 75% 1 625 Early 42% 2 173 Late -11% 5 652 Nitric Oxide for COVID-19 c19early.org/no Dec 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 28% [-10‑53%] improvement, without reaching statistical significance. Results are better for Randomized Controlled Trials and worse for peer-reviewed studies. Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
Mortality results are negative, however all results to date are from late treatment trials.
0 0.5 1 1.5+ All studies 28% 8 1,450 Improvement, Studies, Patients Relative Risk Mortality -11% 5 652 Ventilation 48% 3 368 ICU admission 39% 1 71 Cases 75% 1 625 Viral clearance 43% 3 238 RCTs 44% 3 198 Peer-reviewed 11% 7 825 Prophylaxis 75% 1 625 Early 42% 2 173 Late -11% 5 652 Nitric Oxide for COVID-19 c19early.org/no Dec 2022 Favorsnitric oxide Favorscontrol
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 25% of nitric oxide studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Nitric Oxide reduces risk for COVID-19 with high confidence for viral clearance, low confidence for cases and in pooled analysis, and very low confidence for ICU admission, however increased risk is seen with very low confidence for mortality.
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 47 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 Poonam 14% 0.86 [0.72-1.04] death 32/41 56/62 Ventilated patients OT​1 Valsecchi 58% 0.42 [0.02-9.86] death 0/20 1/51 Al Sulaiman (ICU) -40% 1.40 [0.94-2.11] death 44/56 52/125 ICU patients Tau​2 = 0.10, I​2 = 71.8%, p = 0.64 Late treatment -11% 1.11 [0.74-1.66] 88/197 149/455 -11% 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 28% 0.72 [0.47-1.10] 123/479 306/971 28% improvement 8 nitric oxide COVID-19 studies c19early.org/no Dec 2022 Tau​2 = 0.24, I​2 = 84.2%, p = 0.12 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors nitric oxide Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Winches.. (DB RCT) 42% 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 Poonam 14% death Ventilated patients OT​1 Valsecchi 58% death Al Sulaiman (ICU) -40% death ICU patients Tau​2 = 0.10, I​2 = 71.8%, p = 0.64 Late treatment -11% -11% improvement SaNOtize 75% case Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 75% 75% improvement All studies 28% 28% improvement 8 nitric oxide COVID-19 studies c19early.org/no Dec 2022 Tau​2 = 0.24, I​2 = 84.2%, p = 0.12 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors nitric oxide Favors control
B
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in nitric oxide studies.
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.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, and 9 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, cases, viral clearance, and peer reviewed studies.
Improvement Studies Patients Authors
All studies28% [-10‑53%]8 1,450 99
Peer-reviewed studiesPeer-reviewed11% [-26‑38%]7 825 98
Randomized Controlled TrialsRCTs44% [19‑61%]3 198 30
Mortality-11% [-66‑26%]5 652 84
VentilationVent.48% [-109‑87%]3 368 50
Viral43% [8‑65%]3 238 30
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies42% [16‑60%] 2-11% [-66‑26%] 575% [57‑86%] 1
Peer-reviewed studiesPeer-reviewed42% [16‑60%] 2-11% [-66‑26%] 5-
Randomized Controlled TrialsRCTs42% [16‑60%] 290% [-67‑99%] 1-
Mortality--11% [-66‑26%] 5-
VentilationVent.-48% [-109‑87%] 3-
Viral35% [-6‑60%] 264% [26‑83%] 1-
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for cases.
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Figure 8. Random effects meta-analysis for viral clearance.
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Figure 9. 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 the 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.
Figure 10 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 42% improvement, compared to 14% for other studies. Figure 11 and 12 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
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Figure 10. Results for RCTs and non-RCT studies.
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Figure 11. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 12. Random effects meta-analysis for RCT mortality results.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 13 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 13. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
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.
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].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The 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.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 14. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 14. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
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.
60% 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 14% improvement, compared to 42% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 15 shows a scatter plot of results for prospective and retrospective studies.
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Figure 15. Prospective vs. retrospective studies.
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.
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.
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.
1 of the 8 studies compare against other treatments, which may reduce the effect seen.
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 by 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.
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 affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
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 28% [-10‑53%] improvement, without reaching statistical significance. Results are better for Randomized Controlled Trials and worse for peer-reviewed studies. Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
Mortality results are negative, however all results to date are from late treatment trials.
0 0.5 1 1.5 2+ Mortality -40% Improvement Relative Risk Mortality, day 30 -18% c19early.org/no Al Sulaiman et al. Nitric Oxide for COVID-19 ICU Favors nitric oxide Favors control
[Al Sulaiman] Retrospective 815 COVID-19 ICU patients in Saudi Arabia, showing significant improvement in oxygenation. There was no significant difference in mortality, and ICU and hospitalization time was longer.
0 0.5 1 1.5 2+ Mortality -54% Improvement Relative Risk Ventilation -27% c19early.org/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.org/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+ Mortality 14% Improvement Relative Risk c19early.org/no Poonam et al. Nitric Oxide for COVID-19 ICU Favors nitric oxide Favors epoprostenol
[Poonam] Retrospective 103 mechanically ventilated patients, 41 treated with inhaled nitric oxide, and 62 with inhaled epoprostenol, showing no significant difference in outcomes.
0 0.5 1 1.5 2+ Case 75% Improvement Relative Risk c19early.org/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.org/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.org/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.org/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.org. 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
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.org/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, inverted to make RR<1 favor treatment, mITT high risk, Kaplan–Meier.
time to viral-, 6.5% lower, relative time 0.94, p = 0.66, treatment 105, control 102, inverted to make RR<1 favor treatment, 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.
[Al Sulaiman], 10/3/2022, retrospective, Saudi Arabia, peer-reviewed, mean age 62.5, 29 authors, study period 1 March, 2020 - 31 July, 2021. risk of death, 40.0% higher, HR 1.40, p = 0.10, treatment 44 of 56 (78.6%), control 52 of 125 (41.6%), adjusted per study, in-hospital mortality, multivariable, Cox proportional hazards.
risk of death, 18.0% higher, HR 1.18, p = 0.45, treatment 41 of 56 (73.2%), control 44 of 122 (36.1%), adjusted per study, multivariable, Cox proportional hazards, day 30.
[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, inverted to make RR<1 favor treatment, 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, inverted to make RR<1 favor treatment, N gene.
time to viral load reduction, 63.4% lower, RR 0.37, p = 0.005, treatment 14, control 11, adjusted per study, inverted to make RR<1 favor treatment, Orf1ab gene.
[Poonam], 6/27/2022, retrospective, USA, peer-reviewed, 5 authors, study period 1 March, 2020 - 30 June, 2020, this trial compares with another treatment - results may be better when compared to placebo. risk of death, 13.6% lower, RR 0.86, p = 0.10, treatment 32 of 41 (78.0%), control 56 of 62 (90.3%), NNT 8.1.
[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.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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