Phthalocyanine for COVID-19: real-time meta analysis of 4 studies
@CovidAnalysis, December 2023
https://c19early.org/ptmeta.html
•Statistically significant lower risk is seen for ICU admission, hospitalization, recovery, and viral clearance. 3 studies from 3 independent teams (all from the same country) show statistically significant
improvements.
•Meta analysis using the most serious outcome reported shows
38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials.
•No treatment or intervention
is 100% effective. 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.
•All data to reproduce this paper and
sources are in the appendix.
Highlights
Phthalocyanine reduces
risk for COVID-19 with very high confidence for pooled analysis, low confidence for ICU admission, hospitalization, recovery, cases, and viral clearance, and 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 62
treatments.
C
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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.
0.7% of 5,900 proposed treatments show efficacy
c19early.org.
D. Timeline of results in phthalocyanine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 18.5 months, compared to using all studies.
We analyze all significant studies
concerning the use of
phthalocyanine
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, 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 and after exclusions.
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, ICU admission, hospitalization, recovery, cases, and viral clearance.
Improvement | Studies | Patients | Authors | |
---|---|---|---|---|
All studies | 38% [20‑51%] *** | 4 | 5,245 | 47 |
Randomized Controlled TrialsRCTs | 38% [14‑55%] ** | 3 | 205 | 40 |
Early treatment | Late treatment | Prophylaxis | |
---|---|---|---|
All studies | 29% [4‑47%] * | 52% [21‑71%] ** | 54% [-8‑80%] |
Randomized Controlled TrialsRCTs | 29% [4‑47%] * | 52% [21‑71%] ** |
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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 ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for recovery.
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Figure 8. Random effects meta-analysis for cases.
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Figure 9. Random effects meta-analysis for viral clearance.
Figure 10 shows a comparison of results for RCTs and non-RCT studies.
Figure 11 and 12
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.
Bias in clinical research may be defined as something that tends to make
conclusions differ systematically from the truth. RCTs help to make study
groups more similar and can provide a higher level of evidence, however they
are subject to many biases Jadad, and analysis of double-blind RCTs
has identified extreme levels of bias Gøtzsche.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, 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 62 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 phthalocyanine 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, 41 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 41 treatments with statistically significant efficacy/harm, 25 have been confirmed in RCTs, with a mean delay of 5.5 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 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 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 delay | Result |
Post exposure prophylaxis | 86% fewer cases Ikematsu |
<24 hours | -33 hours symptoms Hayden |
24-48 hours | -13 hours symptoms Hayden |
Inpatients | -2.5 hours to improvement Kumar |
Figure 13 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 62 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 13. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 62 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.
Currently, 41 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 52% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months.
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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 phthalocyanine, 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 15 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 15. 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. Phthalocyanine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 phthalocyanine 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 phthalocyanine 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.
Studies to date show that phthalocyanine is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for ICU admission, hospitalization, recovery, and viral clearance. 3 studies from 3 independent teams (all from the same country) show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials.
Brito-Reia:
Comparison of two similar communities in Brazil, with one using a phthalocyanine derivative mouthwash, suggesting efficacy of the treatment in lowering COVID-19 cases. There was 54% lower risk of confirmed cases during the intervention in the treatment community, compared with 15% higher and 8% lower risk before and after the intervention. Gargle/rinse with 5mL of mouthwash containing phthalocyanine derivative for 1 minute, 3 to 5 times per day.
Colado Simão:
RCT 75 patients in Brazil, showing significantly lower viral load with phthalocyanine mouthwash and nasal spray. The combination was more effective than mouthwash alone.
da Silva Santos:
RCT 41 patients in Brazil, 20 treated with a phthalocyanine derivative mouthwash, showing shorter hosptalization and lower ICU admission with treatment. One minute gargling/rinsing 5 times per day.
Poleti:
RCT 500 patients in Brazil, showing improved recovery with a phthalocyanine derivative mouthwash and toothpaste. Toothbrushing for 2 minutes, three times per day, and gargling/rising (5ml) for one minute, three times a day, for 7 days.
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 phthalocyanine, 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 phthalocyanine 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
(3.11.6) with
scipy (1.11.3), pythonmeta (1.26), numpy (1.26.1), statsmodels (0.14.0), and plotly (5.17.0).
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.
Grobid 0.8.0 is used to parse PDF documents.
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 https://c19early.org/ptmeta.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.
Poleti, 12/8/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 10 authors, study period 6 November, 2020 - 19 November, 2020, trial RBR-8x8g36. | risk of no recovery, 29.1% lower, RR 0.71, p = 0.02, treatment 29 of 59 (49.2%), control 52 of 75 (69.3%), NNT 5.0, day 7. |
risk of no recovery, 22.1% lower, RR 0.78, p = 0.02, treatment 38 of 59 (64.4%), control 62 of 75 (82.7%), NNT 5.5, day 3. | |
risk of no recovery, 45.5% lower, RR 0.54, p = 0.04, treatment 12 of 59 (20.3%), control 28 of 75 (37.3%), NNT 5.9, day 7, dyspnea. | |
risk of no recovery, 32.5% lower, RR 0.68, p = 0.11, treatment 17 of 59 (28.8%), control 32 of 75 (42.7%), NNT 7.2, day 3, dyspnea. |
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.
Colado Simão, 6/23/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 13 authors, study period 1 November, 2020 - 1 February, 2021, average treatment delay 5.4 days. | relative Ct improvement, 50.7% better, RR 0.49, p = 0.008, treatment mean 11.21 (±4.35) n=15, control mean 5.53 (±6.28) n=15, mouthwash and nasal spray, day 3. |
relative Ct improvement, 38.3% better, RR 0.62, p = 0.08, treatment mean 8.96 (±4.01) n=16, control mean 5.53 (±6.28) n=15, mouthwash only, day 3. | |
relative Ct improvement, 71.7% better, RR 0.28, p = 0.06, treatment mean 5.48 (±5.33) n=15, control mean 1.55 (±5.54) n=15, mouthwash and nasal spray, day 1. | |
relative Ct improvement, 68.4% better, RR 0.32, p = 0.08, treatment mean 4.91 (±4.89) n=16, control mean 1.55 (±5.54) n=15, mouthwash only, day 1. | |
da Silva Santos, 10/7/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 17 authors, study period 10 August, 2020 - 4 November, 2020. | risk of death, 85.4% lower, RR 0.15, p = 0.23, treatment 0 of 20 (0.0%), control 3 of 21 (14.3%), NNT 7.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
risk of ICU admission, 92.1% lower, RR 0.08, p = 0.02, treatment 0 of 20 (0.0%), control 6 of 21 (28.6%), NNT 3.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
discharge, 53.7% lower, HR 0.46, p = 0.03, treatment 20, control 21, inverted to make HR<1 favor treatment, Cox proportional hazards. |
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.
Brito-Reia, 11/15/2021, prospective, Brazil, peer-reviewed, 7 authors, trial RBR-6c9xnw3. | risk of case, 54.0% lower, RR 0.46, p = 0.08, treatment 6 of 1,153 (0.5%), control 44 of 3,887 (1.1%), NNT 164. |
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:/10.1016/j.micpath.2020.104228.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
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