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Alkalinization for COVID-19: real-time meta analysis of 7 studies
Covid Analysis, March 2023
https://c19early.org/phmeta.html
 
0 0.5 1 1.5+ All studies 43% 7 1,092 Improvement, Studies, Patients Relative Risk Mortality 42% 4 898 Hospitalization 39% 2 134 Recovery 25% 2 728 RCTs 35% 5 834 RCT mortality 22% 2 640 Peer-reviewed 48% 6 546 Early 65% 1 79 Late 43% 6 1,013 Alkalinization for COVID-19 c19early.org/ph Mar 2023 Favorsalkalinization Favorscontrol
Statistically significant improvements are seen for mortality, hospitalization, and recovery. 6 studies from 5 independent teams in 5 different countries show statistically significant improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 43% [23‑58%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.5 1 1.5+ All studies 43% 7 1,092 Improvement, Studies, Patients Relative Risk Mortality 42% 4 898 Hospitalization 39% 2 134 Recovery 25% 2 728 RCTs 35% 5 834 RCT mortality 22% 2 640 Peer-reviewed 48% 6 546 Early 65% 1 79 Late 43% 6 1,013 Alkalinization for COVID-19 c19early.org/ph Mar 2023 Favorsalkalinization Favorscontrol
SARS-CoV-2 requires acidic pH for fusion [Kreutzberger]. Alkalinization of the respiratory mucosa may reduce risk. Treatments investigated to date typically use sodium bicarbonate.
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 may be more effective. Only 14% of alkalinization studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Percentage improvement with alkalinization (more)
Early treatment Late treatment All studies Studies Patients Authors
All studies65% [-727‑99%]43% [22‑59%]
***
43% [23‑58%]
***
7 1,092 69
Randomized Controlled TrialsRCTs65% [-727‑99%]35% [16‑50%]
**
35% [18‑49%]
***
5 834 45
Mortality-42% [4‑65%]
*
42% [4‑65%]
*
4 898 43
HospitalizationHosp.65% [-727‑99%]39% [18‑54%]
***
39% [19‑54%]
***
2 134 25
RCT mortality-22% [-5‑41%]22% [-5‑41%] 2 640 19
Highlights
Alkalinization reduces risk for COVID-19 with very high confidence for pooled analysis, high confidence for mortality, and low confidence for hospitalization and recovery.
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 50 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Baxter (RCT) 65% 0.35 [0.01-8.27] hosp. 0/37 1/42 OT​1 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.52 Early treatment 65% 0.35 [0.01-8.27] 0/37 1/42 65% improvement Mody (RCT) 64% 0.36 [0.19-0.68] no improv. 8/30 22/30 Improvement, RR [CI] Treatment Control Soares (ICU) 76% 0.24 [0.11-0.54] death 6/44 18/32 ICU patients Delić (RCT) 20% 0.80 [0.54-1.18] death 20/42 31/52 Intubated patients El-Badrawy 57% 0.43 [0.09-2.08] death 3/127 3/55 El-Badrawy (RCT) 23% 0.77 [0.50-1.18] death 32/272 42/274 Wang (RCT) 39% 0.61 [0.46-0.82] hosp. time 23 (n) 32 (n) Tau​2 = 0.07, I​2 = 53.6%, p = 0.00042 Late treatment 43% 0.57 [0.41-0.78] 69/538 116/475 43% improvement All studies 43% 0.57 [0.42-0.77] 69/575 117/517 43% improvement 7 alkalinization COVID-19 studies c19early.org/ph Mar 2023 Tau​2 = 0.06, I​2 = 45.0%, p = 0.00023 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors alkalinization Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Baxter (RCT) 65% hospitalization OT​1 Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.52 Early treatment 65% 65% improvement Mody (RCT) 64% improvement Soares (ICU) 76% death ICU patients Delić (RCT) 20% death Intubated patients El-Badrawy 57% death El-Badrawy (RCT) 23% death Wang (RCT) 39% hospitalization Tau​2 = 0.07, I​2 = 53.6%, p = 0.00042 Late treatment 43% 43% improvement All studies 43% 43% improvement 7 alkalinization COVID-19 studies c19early.org/ph Mar 2023 Tau​2 = 0.06, I​2 = 45.0%, p = 0.00023 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors alkalinization Favors control
B
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C
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D
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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. 1.3% of 2,741 proposed treatments show efficacy [c19early.org]. D. Timeline of results in alkalinization 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, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 10.6 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 10.6 months, compared to using pooled outcomes.
SARS-CoV-2 requires acidic pH for fusion [Kreutzberger]. The mean pH of the airway-facing surface of the nasal cavity was 6.6 in [Kreutzberger], compatible with fusion. pH is neutral in other parts of the nasopharyngeal cavity and in the lung [Effros], suggesting no viral fusion in those locations prior to endocytic uptake. Treatments formulated to increase the pH of respiratory mucosa may inhibit fusion and reduce risk for COVID-19. Studies to date typically use sodium bicarbonate. We analyze all significant studies concerning the use of alkalinization 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, and Randomized Controlled Trials (RCTs).
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, and 8 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, progression, recovery, and peer reviewed studies.
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. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies43% [23‑58%]
***
7 1,092 69
Peer-reviewed studiesPeer-reviewed48% [26‑64%]
***
6 546 62
Randomized Controlled TrialsRCTs35% [18‑49%]
***
5 834 45
Mortality42% [4‑65%]
*
4 898 43
HospitalizationHosp.39% [19‑54%]
***
2 134 25
Recovery25% [18‑32%]
****
2 728 14
RCT mortality22% [-5‑41%]2 640 19
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment
All studies65% [-727‑99%]43% [22‑59%]
***
Peer-reviewed studiesPeer-reviewed65% [-727‑99%]49% [25‑65%]
***
Randomized Controlled TrialsRCTs65% [-727‑99%]35% [16‑50%]
**
Mortality-42% [4‑65%]
*
HospitalizationHosp.65% [-727‑99%]39% [18‑54%]
***
Recovery-25% [18‑32%]
****
RCT mortality-22% [-5‑41%]
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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 hospitalization.
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Figure 6. Random effects meta-analysis for progression.
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Figure 7. Random effects meta-analysis for recovery.
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Figure 8. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] 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. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 9 shows a comparison of results for RCTs and non-RCT studies. Figure 10 and 11 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1 and Table 2.
RCTs help to make study groups more similar and can provide a higher level of evidence. However they are subject to many biases [Jadad]. For example, 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, 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.
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 50 treatments we have analyzed, 64% 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).
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. RCTs for alkalinization 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, and may be greater when the risk of a serious outcome is overstated. 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].
Currently, 37 of 50 treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 37 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 7 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 9. Results for RCTs and non-RCT studies.
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Figure 10. 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 11. 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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 12 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 50 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 12. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 50 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 13. 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.
Currently, 37 of 50 treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 100% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.0 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 13. 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 alkalinization, there is currently not enough data to evaluate publication bias with high confidence.
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 14 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 14. 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. Alkalinization for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 alkalinization 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 alkalinization trials represent the optimal conditions for efficacy.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses 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.
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 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.
1 of the 7 studies compare against other treatments, which may reduce the effect seen.
Studies to date show that alkalinization is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, hospitalization, and recovery. 6 studies from 5 independent teams in 5 different countries show statistically significant improvements in isolation (3 for the most serious outcome). Meta analysis using the most serious outcome reported shows 43% [23‑58%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
SARS-CoV-2 requires acidic pH for fusion [Kreutzberger]. Alkalinization of the respiratory mucosa may reduce risk. Treatments investigated to date typically use sodium bicarbonate.
0 0.5 1 1.5 2+ Hospitalization 65% Improvement Relative Risk Hospitalization, vs. CDC 94% c19early.org/ph Baxter et al. NCT04559035 Alkalinization RCT EARLY Is early treatment with alkalinization beneficial for COVID-19? RCT 79 patients in the USA (September - December 2020) Trial compares with PVP-I, results vs. placebo may differ Trial underpowered to detect differences Significantly lower hospitalization vs. CDC data Baxter et al., Ear, Nose & Throat J., doi:10.1177/01455613221123737 Favors alkalinization Favors PVP-I
[Baxter] Small RCT 79 PCR+ patients 55+ comparing pressure-based nasal irrigation with povidone-iodine and sodium bicarbonate, showing significantly lower hospitalization when compared with CDC data.
0 0.5 1 1.5 2+ Mortality 20% Improvement Relative Risk c19early.org/ph Delić et al. NCT04755972 Alkalinization RCT ICU Is very late treatment with alkalinization beneficial for COVID-19? RCT 94 patients in Croatia (October 2020 - June 2021) Lower mortality with alkalinization (not stat. sig., p=0.3) Delić et al., Microorganisms, doi:10.3390/microorganisms10061118 Favors alkalinization Favors control
[Delić] RCT mechanically ventilated patients in Croatia, 42 treated with sodium bicarbonate inhalation, and 52 control patients, showing no significant difference in mortality with treatment. Treated patients showed a lower incidence of gram-positive or MRSA-caused ventilator-associated pneumonia.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk Mortality, exc. critical 55% Mortality, moderate 79% Mortality, severe 53% Mortality, critical -23% Recovery time 28% CT score, day 30 33% CT score, day 60 100% c19early.org/ph El-Badrawy et al. NCT05035524 Alkalinization RCT LATE Is late treatment with alkalinization beneficial for COVID-19? RCT 546 patients in Egypt (September 2021 - April 2022) Faster recovery with alkalinization (p<0.000001) Lower mortality for non-critical patients (p=0.02) El-Badrawy et al., Research Square, doi:10.21203/rs.3.rs-2214180/v1 Favors alkalinization Favors control
[El-Badrawy] RCT 546 patients showing significantly faster recovery and lower mortality with sodium bicarbonate (inhaled and nasal drops). The reduction in mortality is only statistically significant when excluding baseline critical cases.

Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
0 0.5 1 1.5 2+ Mortality 57% Improvement Relative Risk Progression 39% Recovery 19% CT score 73% Recovery time 66% c19early.org/ph El-Badrawy et al. NCT04374591 Alkalinization LATE Is late treatment with alkalinization beneficial for COVID-19? Prospective study of 182 patients in Egypt (April - August 2020) Improved recovery with alkalinization (p=0.034) El-Badrawy et al., Indian J. Respiratory Care, doi:10.4103/ijrc.ijrc_48_21 Favors alkalinization Favors control
[El-Badrawy (B)] Prospective study of 182 COVID-19 pneumonia patients, 127 treated with sodium bicarbonate inhalation and nasal drops, showing significantly faster recovery and improved CT scores with treatment.

Authors note that contacts of index cases also received sodium bicarbonate treatment, with none reporting COVID-19.

Inhalation of nebulized sodium bicarbonate 8.4% (5ml every 4h) 7:00am to 23:00pm every day for 30 days together with 8.4% nasal drops 4 times daily (three drops for each nostril).
0 0.5 1 1.5 2+ Improvement 64% Improvement Relative Risk c19early.org/ph Mody et al. CTRI/2020/07/026535 Alkalinization RCT LATE Is late treatment with alkalinization beneficial for COVID-19? RCT 60 patients in India (July - September 2020) Greater improvement with alkalinization (p=0.00066) Mody, K., Acta Scientific Orthopaedics, 4:4 Favors alkalinization Favors control
[Mody] RCT 60 hospitalized patients in India, showing significantly greater clinical improvement with inhaled sodium bicarbonate.

Nasal and oral inhalation of nebulized 50ml 8.4% sodium bicarbonate for 5 minutes twice daily for 5 days.
0 0.5 1 1.5 2+ Mortality 76% Improvement Relative Risk c19early.org/ph Soares et al. Alkalinization for COVID-19 ICU Is very late treatment with alkalinization beneficial for COVID-19? Prospective study of 76 patients in Brazil (December 2020 - May 2021) Lower mortality with alkalinization (p=0.00013) Soares et al., Brazilian J. Development, doi:10.34117/bjdv7n12-039 Favors alkalinization Favors control
[Soares] Analysis of 76 ICU patients in Brazil, 44 treated with bronchoalveolar lavage using 3% sodium bicarbonate, showing significantly lower mortality with treatment.

Bronchoalveolar lavage with 10ml of sodium bicarbonate solution directly into the tube (closed circuit), 500μl for each lung segment, followed by aspiration of the solution, performed every 6 hours for 7 days.
0 0.5 1 1.5 2+ Hospitalization time 39% Improvement Relative Risk c19early.org/ph Wang et al. Alkalinization for COVID-19 RCT LATE Is late treatment with alkalinization beneficial for COVID-19? RCT 55 patients in China Shorter hospitalization with alkalinization (p=0.0009) Wang et al., Frontiers in Public Health, doi:10.3389/fpubh.2023.1145669 Favors alkalinization Favors control
[Wang] RCT 55 mild/moderate patients in China, showing shorter hospitalization with sodium bicarbonate nasal irrigation and oral rinsing. Oral rinse with 5% sodium bicarbonate solution three times daily. Nasal irrigation two times with the solution entering through one nostril and exiting from the other. 30–40mL of solution was used every time and irrigation was performed for at least 30s. Details of randomization are not provided.
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 alkalinization, 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 alkalinization 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 (3.11.2) with scipy (1.10.1), pythonmeta (1.26), numpy (1.24.2), statsmodels (0.13.5), and plotly (5.13.1).
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/phmeta.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.
[Baxter], 8/17/2021, Randomized Controlled Trial, USA, peer-reviewed, 12 authors, study period 24 September, 2020 - 21 December, 2020, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04559035 (history). risk of hospitalization, 65.3% lower, RR 0.35, p = 1.00, treatment 0 of 37 (0.0%), control 1 of 42 (2.4%), NNT 42, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), vs. PVP-I.
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.
[Delić], 5/28/2022, Randomized Controlled Trial, Croatia, peer-reviewed, 12 authors, study period October 2020 - June 2021, trial NCT04755972 (history). risk of death, 20.1% lower, RR 0.80, p = 0.30, treatment 20 of 42 (47.6%), control 31 of 52 (59.6%), NNT 8.3.
[El-Badrawy], 11/18/2022, Randomized Controlled Trial, Egypt, preprint, 7 authors, study period 1 September, 2021 - 30 April, 2022, trial NCT05035524 (history). risk of death, 23.2% lower, RR 0.77, p = 0.26, treatment 32 of 272 (11.8%), control 42 of 274 (15.3%), NNT 28, all cases.
risk of death, 54.8% lower, RR 0.45, p = 0.02, treatment 12 of 247 (4.9%), control 27 of 251 (10.8%), NNT 17, mild/moderate/severe cases.
risk of death, 79.2% lower, RR 0.21, p = 0.21, treatment 1 of 125 (0.8%), control 5 of 130 (3.8%), NNT 33, moderate cases.
risk of death, 53.2% lower, RR 0.47, p = 0.02, treatment 11 of 63 (17.5%), control 22 of 59 (37.3%), NNT 5.0, severe cases.
risk of death, 22.7% higher, RR 1.23, p = 0.33, treatment 20 of 25 (80.0%), control 15 of 23 (65.2%), critical cases.
recovery time, 27.6% lower, relative time 0.72, p < 0.001, treatment mean 4.2 (±2.5) n=272, control mean 5.8 (±3.1) n=274, time to clinical improvement.
CT score, 33.3% lower, RR 0.67, p = 0.001, treatment 238, control 229, CT score, day 30.
[El-Badrawy (B)], 6/12/2022, prospective, Egypt, peer-reviewed, 7 authors, study period 15 April, 2020 - 31 August, 2020, trial NCT04374591 (history). risk of death, 56.7% lower, RR 0.43, p = 0.37, treatment 3 of 127 (2.4%), control 3 of 55 (5.5%), NNT 32.
risk of progression, 39.4% lower, RR 0.61, p = 0.52, treatment 7 of 127 (5.5%), control 5 of 55 (9.1%), NNT 28, deterioration or death, day 30.
risk of no recovery, 19.2% lower, RR 0.81, p = 0.03, treatment 84 of 127 (66.1%), control 45 of 55 (81.8%), NNT 6.4, day 30.
relative CT score, 72.7% better, RR 0.27, p < 0.001, treatment 127, control 55, day 30.
recovery time, 66.2% lower, relative time 0.34, p < 0.001, treatment mean 3.31 (±0.99) n=127, control mean 9.79 (±6.288) n=55, time to clinical improvement.
[Mody], 3/19/2021, Randomized Controlled Trial, India, peer-reviewed, 1 author, study period July 2020 - September 2020, trial CTRI/2020/07/026535. risk of no improvement, 63.6% lower, RR 0.36, p < 0.001, treatment 8 of 30 (26.7%), control 22 of 30 (73.3%), NNT 2.1.
[Soares], 12/29/2021, prospective, Brazil, peer-reviewed, 17 authors, study period December 2020 - May 2021. risk of death, 75.8% lower, RR 0.24, p < 0.001, treatment 6 of 44 (13.6%), control 18 of 32 (56.2%), NNT 2.3.
[Wang], 3/15/2023, Randomized Controlled Trial, China, peer-reviewed, 13 authors. hospitalization time, 38.5% lower, relative time 0.61, p < 0.001, treatment mean 7.7 (±4.15) n=23, control mean 12.53 (±5.56) n=32.
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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