Several in vitro studies show that PVP-I is effective for SARS-CoV-2 at clinically relevant concentrations [Anderson, Bidra, Frank, Hassandarvish, Pelletier, Tucker, Xu].

Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.

Table 1 summarizes the results for all stages combined, 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, hospitalization, recovery, cases, viral clearance, and peer reviewed studies.

To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.

The studies excluded are as below. Figure 14 shows a forest plot for random effects meta-analysis of all studies after exclusions.

[Pablo-Marcos], unadjusted results with no group details.

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.

Figure 15 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

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 (B)] 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 16. 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, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 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.

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.

Safety analysis can be found in [Frank (B), Frank (C), Khan]. [Frank (B)] conclude that PVP-I can safely be used in the nose at concentrations up to 1.25% and in the mouth at concentrations up to 2.5% for up to 5 months.

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 povidone-iodine, 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.

Figure 17 shows a scatter plot of results for prospective and retrospective studies. Prospective studies show 49% [33‑62%] improvement in meta analysis, compared to 57% [31‑73%] for retrospective studies, showing no significant difference. However, there has only been one retrospective study to date.

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 18 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.

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. PVP-I for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 povidone-iodine 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 povidone-iodine 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.

6 of the 20 studies compare against other treatments, which may reduce the effect seen. 1 of 20 studies combine treatments. The results of povidone-iodine alone may differ. 1 of 17 RCTs use combined treatment. Other meta analyses for povidone-iodine can be found in [Hasan, Idrees], showing significant improvements for one or more of viral load and viral clearance.

PVP-I is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, cases, and viral clearance. 11 studies from 11 independent teams in 9 different countries show statistically significant improvements in isolation (7 for the most serious outcome). Meta analysis using the most serious outcome reported shows 50% [37‑61%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment. Results are robust — in exclusion sensitivity analysis 15 of 20 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.

Some studies only test short term viral load after a single application. Excessive use of PVP-I could affect thyroid function.

[Arefin] RCT with 189 patients showing significantly greater viral clearance with a single application of PVP-I. Authors recommend using PVP-I prophylactically in the nasopharynx and oropharynx. NCT04549376 [trialsjournal.biomedcentral.com].

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[Baxter] Small RCT 79 PCR+ patients 55+ comparing pressure-based nasal irrigation with povidone-iodine and sodium bicarbonate, showing improved recovery with povidone-iodine. Not all results comparing povidone-iodine and sodium bicarbonate are in the journal version, as authors focus on the comparison with CDC data. Earlier versions can be found at [medrxiv.org]. The reported hospitalization switched groups between the preprint and the journal version.

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[Choudhury] RCT 606 patients in Bangladesh for povidone iodine mouthwash/gargle, nasal drops and eye drops showing significantly lower death, hospitalization, and PCR+ at day 7.

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[Elsersy] RCT with 200 patients and 421 contacts in Egypt, with 100 patients and their contacts treated with nasal and oropharyngeal sprays containing povidone-iodine and glycyrrhizic acid, showing significantly faster viral clearance and recovery, and significantly lower transmission.

SOC included vitamin C and zinc. The spray active ingredients included a compound of glycyrrhizic acid in the form of ammonium glycyrrhizate 2.5 mg/ml plus PVI 0.5% for oropharyngeal and dipotassium glycyrrhizinate 2.5 mg/ml plus PVI 0.5% for nasal spray. Patients were advised to concomitantly use oropharyngeal and nasal sprays 6 times per day. They were instructed to abstain from food, drink, and smoke for 20min, particularly after oropharyngeal spray. The oropharyngeal spray bottle contains an atomizer that ends with a long arm applicator to insert inside the mouth cavity and can be directed up, down, right, or left to cover the entire pharyngeal area.

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[Elzein] Small RCT comparing mouthwashing with PVP-I, Chlorhexidine, and water, showing significant efficacy for both PVP-I and Chlorhexidine, with PVP-I increasing Ct by a mean of 4.45 (p < 0.0001) and Chlorhexidine by a mean of 5.69 (p < 0.0001), compared to no significant difference for water.

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[Fantozzi] Mouthrinse RCT in Italy comparing short-term viral load after a single 60 second treatment with povidone-iodine, hydrogen peroxide, chlorhexidine, and saline. The greatest efficacy was seen with povidone-iodine, especially for patients with low viral load at baseline.

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[Ferrer] Small very late (>50% 7+ days from symptom onset, 9 PVP-I patients) RCT testing mouthwashing with cetylpyridinium chloride, chlorhexidine, povidone-iodine, hydrogen peroxide, and distilled water, showing no significant differences. Over 30% of patients show >90% decrease in viral load @2 hrs with all 5. Authors note that a trend was observed for viral load decrease with PVP-I @2h for patients <6 days from onset (p=0.06, Wilcox test).

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[Guenezan] RCT of PCR+ patients with Ct<=20 with 12 treatment and 12 control patients, concluding that nasopharyngeal decolonization may reduce the carriage of infectious SARS-CoV-2 in adults with mild to moderate COVID-19. All patients but 1 had negative viral titer by day 3 (group not specified). There was no significant difference in viral RNA quantification over time. The mean relative difference in viral titers between baseline and day 1 was 75% [43%-95%] in the intervention group and 32% [10%-65%] in the control group. Thyroid dysfunction occurred in 42% of treated patients, with spontaneous resolution after the end of treatment. Patients in the treatment group were younger.

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[Jamir] Retrospective 266 COVID-19 ICU patients in India, showing significantly lower mortality with PVP-I oral gargling and topical nasal use, and non-statistically significant higher mortality with ivermectin and lower mortality with remdesivir.

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[Karaaltin] RCT 120 outpatients in Turkey, showing improved reduction in viral load with PVP-I nasal irrigation.

PVP-I prepared with hypertonic alkaline solution had better results. [Kreutzberger] show that SARS-CoV-2 requires acidic pH to infect cells, therefore alkalinization may add additional benefits.

All patients received favipiravir. PVP-I 1% 4 times per day.

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[Matsuyama] RCT 430 COVID+ patients in Japan, showing significantly lower viral infectivity from culture, and significantly faster PCR viral clearance with PVP-I.

For days 2-4 the study compares treatment with PVP-I vs. water (on day 5 both groups received PVP-I). Most patients were asymptomatic. 4 times per day mouthwashing and gargling with 20mL of 15-fold diluted PVP–I 7% or water.

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[Mohamed] Tiny RCT with 5 PVP-I patients, gargling 30 seconds, 3x per day, and 5 control patients (essential oils and tap water were also tested), showing improved viral clearance with PVP-I.

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[Natto] 60 patient RCT comparing chlorhexidine, PVP-I, and saline in Saudi Arabia with a single mouth rinse treatment and PCR testing 5 minutes later, showing statistically significant improvement in Ct value for PVP-I. PVP-I showed greater improvement than saline, without statistical significance.

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[Pablo-Marcos] Small prospective study with 31 patients gargling povidone-iodine, 17 hydrogen peroxide, and 40 control patients, showing lower viral load mid-recovery with povidone-iodine, without reaching statistical significance. Oropharyngeal only, and only every 8 hours for two days. Results may be better with the addition of nasopharyngeal use, more frequent use, and without the two day limit.

Authors are not familiar with the literature, having found only one of the 7 previous trials for PVP-I and COVID-19. Non-randomized study with no adjustments or group details. Some results in Figure 1 appear to be switched compared to the text and the labels in the figure. The viral clearance figures do not match the group sizes - for example authors report 62% PCR- for PVP-I at the 3rd test, however there is no number of 31 patients that rounds to 62%.

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[Seet] Prophylaxis RCT in Singapore with 3,037 low risk patients, showing lower serious cases, lower symptomatic cases, and lower confirmed cases of COVID-19 with all treatments (ivermectin, HCQ, PVP-I, and Zinc + vitamin C) compared to vitamin C.

Meta-analysis of vitamin C in 6 previous trials shows a benefit of 16%, so the actual benefit of ivermectin, HCQ, and PVP-I may be higher. Cluster RCT with 40 clusters.

There were no hospitalizations and no deaths. NCT04446104.

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[Seneviratne] Small mouthwash RCT with 4 PVP-I patients and 2 water patients concluding that PVP-I may have a sustained effect on reducing the salivary SARS-CoV-2 level in COVID-19 patients. ISRCTN95933274.

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[Sevinç Gül] RCT with 21 PVP-I and 20 saline patients gargling for 30 seconds and testing PCR Ct after 30 minutes, showing greater improvement with PVP-I, without statistical significance.

Ct values differ across testing platforms, however the reported Ct value difference can represent a large difference in viral load. For example, using the calibration included with the ct2vl converter, the reported difference in mean Ct values corresponds to a reduction in viral load of over 3x for PVP-I.

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