Fluvoxamine reduces COVID-19 risk: real-time meta analysis of 21 studies
Abstract
Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 14 studies from 14 independent teams in 8 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
39% [21‑52%] lower risk. Results are similar for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 12 of
21 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
All data and sources to reproduce this analysis are in the appendix.
Fluvoxamine for COVID-19 — Highlights
Fluvoxamine reduces
risk with very high confidence for mortality and in pooled analysis, high confidence for hospitalization, progression, and recovery, and low confidence for cases, however increased risk is seen with very low confidence for viral clearance.
27th treatment shown effective in
November 2021, now with p = 0.00014 from 21 studies, recognized in 3 countries.
Real-time updates
and corrections with a consistent protocol for 112 treatments. Outcome specific analysis and combined evidence from all
studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in fluvoxamine 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 22.4 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 12.2 months, compared to using pooled outcomes.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury9-20 and
cognitive deficits12,17, cardiovascular
complications21-23, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,24-30, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk31, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
fluvoxamine
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 shows potential mechanisms of action for
the treatment of COVID-19 using fluvoxamine.
| FIASMA | Fluvoxamine is a functional inhibitor of acid sphingomyelinase (FIASMA). SARS-CoV-2 activates the ASM/ceramide system which may facilitate viral entry. ASM inhibition may reduce the concentration of ceramides and inhibit viral entry34-37. |
| Sigma-1 activation | Fluvoxamine may reduce clinical deterioration via σ-1 (S1R) receptor activation, which regulates cytokine production37-39. |
| Platelet activation | Platelet activation may contribute to COVID-19 severity. Fluvoxamine inhibits platelet activation38,40. |
| Lysosomal trafficking | SARS-CoV-2 uses lysosomal trafficking to escape from infected cells. Fluvoxamine is lysosomotropic and interferes with endolysosomal viral trafficking37,38,41. |
| Heme oxygenase | COVID-19 risk may be related to low intracellular heme oxygenase (HO-1). Fluvoxamine increases HO-1 and HO-1 has cytoprotective and anti-inflammatory properties42-44. |
| Mast cell degranulation | Fluvoxamine may reduce cytokine storm due to decreased mast cell degranulation38. |
| Melatonin | Melatonin may be beneficial for COVID-19, and fluvoxamine may elevate melatonin levels via CYP1A2 and CYP2C19 inhibition37,38,45-47. |
Fluvoxamine may inhibit SARS-CoV-2 cell entry by preventing the formation of ceramide platforms that facilitates viral uptake32 and may help restore autophagic processes disrupted by NSP6, thereby reducing SARS-CoV-2 replication and improving host cellular defenses33.
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 2 summarizes the results for all stages combined, for Randomized Controlled Trials, and for specific outcomes.
Table 3 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, and 12
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, and viral clearance.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 39% [21‑52%] *** | 21 | 38,283 | 307 |
| Randomized Controlled TrialsRCTs | 33% [2‑53%] * | 10 | 6,462 | 221 |
| Mortality | 44% [15‑63%] ** | 10 | 5,101 | 136 |
| VentilationVent. | 42% [-151‑86%] | 3 | 2,747 | 60 |
| ICU admissionICU | -10% [-326‑72%] | 3 | 855 | 17 |
| HospitalizationHosp. | 51% [8‑73%] * | 13 | 7,210 | 245 |
| Recovery | 49% [7‑72%] * | 4 | 2,368 | 57 |
| Cases | 27% [18‑35%] **** | 2 | 9,116 | 10 |
| Viral | -25% [-78‑13%] | 2 | 1,003 | 31 |
| RCT mortality | 27% [-33‑60%] | 2 | 2,973 | 62 |
| RCT hospitalizationRCT hosp. | 49% [-27‑79%] | 8 | 6,325 | 204 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 48% [1‑73%] * | 48% [23‑65%] ** | 27% [18‑35%] **** |
| Randomized Controlled TrialsRCTs | 33% [-37‑67%] | 31% [-9‑56%] | |
| Mortality | 71% [-52‑95%] | 51% [22‑69%] ** | 5% [-76‑49%] |
| VentilationVent. | 77% [-1354‑100%] | 22% [-28‑54%] | |
| ICU admissionICU | 37% [-160‑85%] | -395% [-7042‑66%] | |
| HospitalizationHosp. | 62% [5‑85%] * | 22% [-3‑41%] | 1% [-118‑55%] |
| Recovery | 88% [-446‑100%] | 45% [6‑68%] * | |
| Cases | 27% [18‑35%] **** |
||
| Viral | -4% [-19‑9%] | -49% [-138‑6%] | |
| RCT mortality | -200% [-7253‑88%] | 30% [-26‑63%] | |
| RCT hospitalizationRCT hosp. | 53% [-69‑87%] | 22% [-3‑41%] |
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Figure 3. 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.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
Figure 13 shows a comparison of results for RCTs and non-RCT studies.
Figure 14, 15, and 16
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 2 and Table 3.
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Figure 13. Results for RCTs and non-RCT studies.
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Figure 14. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 15. Random effects meta-analysis for RCT mortality results.
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Figure 16. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases49, and
analysis of double-blind RCTs has identified extreme levels of bias50.
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, reporting, 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.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
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 112 treatments we have analyzed,
65% 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 fluvoxamine 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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 112 treatments52.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 112 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.92‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.82‑1.03].
Details can be found in the
supplementary data.
Lee (B) et al. showed 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 remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see56,57.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
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.
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 hours58,59. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
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 et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases60 |
| <24 hours | -33 hours symptoms61 |
| 24-48 hours | -13 hours symptoms61 |
| Inpatients | -2.5 hours to improvement62 |
Figure 17 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 112 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 112 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, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants64, for example the Gamma variant shows significantly
different characteristics65-68. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants69,70.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
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.
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.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for fluvoxamine as of November 2022. Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 12.2 months compared to using pooled outcomes.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 112
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 18 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 19 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 20 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000032 to p = 0.000000011.
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Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 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 these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 21 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 21. 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.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present 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 results86-89.
For fluvoxamine, 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 22 shows a scatter plot of
results for prospective and retrospective studies.
71% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
64% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 27% improvement,
compared to 36% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 22. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 23 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 < 0.0590-97.
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 23. 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. Fluvoxamine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 fluvoxamine 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 fluvoxamine 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 for 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 with conflicts of interest 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 alone73-84.
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 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 21
studies compare against other treatments, which may reduce the effect
seen.
1 of 21 studies
combine treatments. The results of
fluvoxamine
alone may differ.
1 of 10 RCTs use combined treatment.
Currently all studies are peer-reviewed.
8 other meta analyses show significant improvements with fluvoxamine for mortality1,2, hospitalization1,3-7, progression2,7, and severity8.
SARS-CoV-2 infection and replication involves a complex
interplay of 50+ host and viral proteins and other
factors24-30, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk31, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 24 shows an overview of the results for fluvoxamine
in the context of multiple COVID-19 treatments, and Figure 25 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 24.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.5% of 8,000+ proposed treatments show efficacy102.
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Figure 25. Efficacy vs. cost for COVID-19 treatments.
Fluvoxamine is
an effective treatment for COVID-19.
Significantly lower risk is seen for mortality, hospitalization, progression, recovery, and cases. 14 studies from 14 independent teams in 8 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
39% [21‑52%] lower risk. Results are similar for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 12 of
21 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
COVID-OUT remotely operated RCT,
showing no significant difference in outcomes. Results for other treatments
are listed separately -
metformin,
ivermectin.
The "control" group includes patients receiving metformin,
which is known to be beneficial for COVID-19103.
Authors note that the dosage used in the trial is lower than
that of other trials104.
Control arm results are very different between treatments, for
example considering hospitalization/death, this was 1.0% for ivermectin vs.
2.7% for overall control, however it was 1.3% for the ivermectin-specific
control. 394 control patients are shared. The rate for the non-shared 261
metformin control patients is 5%, compared to 1.3% for ivermectin control
patients. The metformin arm started earlier, however it is unclear why the
difference in outcomes is so large.
Results were delayed for 6 months with no explanation, with
followup ending Feb 14, 2022.
Multiple outcomes are missing, for example time to recovery
(where ACTIV-6 showed superiority of ivermectin).
Adherence was very low, with 77% overall reporting 70+%
adherence. Numbers for 100% adherence are not provided.
Treatment was 14 days for metformin and fluvoxamine, but only 3
days for ivermectin.
Medication delivery varied significantly over the trial. In
this presentation107, author indicates that
delivery was initially local, later via FedEx, was much slower in August,
there were delays due to team bandwidth issues, and they only realized they
could use FedEx same day delivery in September.
Prospective PSM study of 51 COVID-19 ICU patients in Croatia and 51 matched controls, showing significantly lower mortality with treatment.
TriNetX PSM retrospective 82,069 OCD patients, showing lower risk of COVID-19 with fluvoxamine use.
RCT 100 mild/moderate COVID-19 outpatients in Iran, showing lower post COVID symptoms 12 weeks after infection, statistically significant only for fatigue with the small sample size. All symptoms may occur for non-COVID-19 reasons, smell/taste disorder may be the most likely to be related to COVID-19 infection. Fluvoxamine 100mg daily for 10 days.
Retrospective 25,034 COVID+ outpatients showing significantly lower ER/hospitalization with antidepressants and FIASMA antidepressants, and a dose-dependent response.
Prospective study of 316 hospitalized patients in Uganda, 94 receiving fluvoxamine, showing significantly lower mortality and improved recovery with treatment.
RCT 152 outpatients, 80 treated with fluvoxamine showing lower progression with treatment (0 of 80 versus 6 of 72 control).
Retrospective database analysis of 83,584 patients in the USA, showing lower mortality with existing fluoxetine use in PSM analysis. There were 11 fluvoxamine patients, showing non-statistically significant higher mortality.
Prospective study of 657 COVID+ outpatients in Honduras, 594 accepting fluvoxamine treatment, showing significantly lower mortality and hospitalization with treatment.
Remote RCT 547 outpatients a median of 5 days from onset, showing no significant differences with fluvoxamine. The trial was stopped early and underpowered due to low event rates. The trial does not report outcomes that may not be underpowered like time to recovery. Authors note that treatment may have been too late.
Low-risk (1% hospitalization) outpatient RCT with 738 fluvoxamine + budesonide patients and 738 placebo patients, showing significantly lower hospitalization/ER visits with treatment.
The TOGETHER trial has extreme COI, impossible data,
blinding failure, randomization failure, uncorrected errors, and many
protocol violations. Authors do not respond to these issues and they
have refused to release the data as promised. Some issues may apply only
to specific arms. For more details see108-112.
Together Trial showing significantly lower hospitalization/extended ER visits with fluvoxamine treatment. Adherence was only 73.2%. Symptom onset was unspecified or >= 4 days for 57% of patients. The schedule of study activities specifies treatment administration only one day after randomization, adding an additional day delay. Overall mortality is high for the patient population. Results may be impacted by late treatment, poor SOC, and may be specific to local variants113114. Per-protocol analysis shows significantly improved results in this trial, however this may be subject to bias - the probability of adherence may be related to the probability of the outcome.
Regarding the combined hospitalization/extended ER observation outcome, authors have noted that at the study sites, extended medical observation was essentially equivalent to being hospitalized. “These were not standard emergency rooms but instead were COVID-19 emergency centers that were set up due to hospitals being overloaded,” Reiersen noted in an email to The Scientist. “A stay in these centers >6 hours was an indication that the patient was receiving care equivalent to hospitalization.”
Authors state "this study is only the second study to show an important treatment benefit for a repurposed drug in the early treatment population", however the actual number is at least 66 based on our database at the time of publication, using a conservative definition of at least 10% benefit (with statistical significance).
The total dose used is less than half of that in Lenze et al. There is an unusual amount of missing data - age is unknown for 6.5% of patients according to the sub-group analysis. Both age <=50 and >50 show better results on the primary outcome than the overall result. The number of placebo patients changed significantly between the preprint and journal version. The number of treatment patients with viral clearance results reduced significantly between the preprint and journal version. Also see115. NCT04727424.
Authors do not specify if the placebo looks identical to the film-coated Luvox tablets. Reportedly there is no registration of manufacturing for matching tablets by Abbott in Brazil, and no import license for identical placebo tablets abroad. This would be an additional reason for blinding failure if the placebo tablets are not identical in appearance.
For other issues with this trial see:116117118.
Regarding the combined hospitalization/extended ER observation outcome, authors have noted that at the study sites, extended medical observation was essentially equivalent to being hospitalized. “These were not standard emergency rooms but instead were COVID-19 emergency centers that were set up due to hospitals being overloaded,” Reiersen noted in an email to The Scientist. “A stay in these centers >6 hours was an indication that the patient was receiving care equivalent to hospitalization.”
Authors state "this study is only the second study to show an important treatment benefit for a repurposed drug in the early treatment population", however the actual number is at least 66 based on our database at the time of publication, using a conservative definition of at least 10% benefit (with statistical significance).
The total dose used is less than half of that in Lenze et al. There is an unusual amount of missing data - age is unknown for 6.5% of patients according to the sub-group analysis. Both age <=50 and >50 show better results on the primary outcome than the overall result. The number of placebo patients changed significantly between the preprint and journal version. The number of treatment patients with viral clearance results reduced significantly between the preprint and journal version. Also see115. NCT04727424.
Authors do not specify if the placebo looks identical to the film-coated Luvox tablets. Reportedly there is no registration of manufacturing for matching tablets by Abbott in Brazil, and no import license for identical placebo tablets abroad. This would be an additional reason for blinding failure if the placebo tablets are not identical in appearance.
For other issues with this trial see:116117118.
The TOGETHER trial has extreme COI, impossible data,
blinding failure, randomization failure, uncorrected errors, and many
protocol violations. Authors do not respond to these issues and they
have refused to release the data as promised. Some issues may apply only
to specific arms. For more details see108,109,111,112,119.
Prospective quasi-randomized (patient choice) study with 125 outpatients, 77 treated with fluvoxamine, showing lower death/ICU admission (0 of 77 vs. 2 of 48), lower hospitalization (0 of 77 vs. 6 of 48), and faster recovery with treatment. Note that 12 treatment patients were added but are not reflected in the table in the paper (because the numbers had been previously published and the IRB did not allow updating the table).
Early terminated RCT with 52 COVID+ patients in South Korea, showing no significant difference in progression with fluvoxamine treatment. There were only 2 events in each arm, and only one event for fluvoxamine in PP analysis. The trial was terminated early because the treatment center closed. 100mg fluvoxamine bid for 10 days.
Retrospective 752 patients in Thailand showing mixed results with 50mg fluvoxamine bid. Authors note that trials showing benefit mostly used 100mg bid.
RCT 327 outpatients in Thailand, showing no significant difference with 50mg fluvoxamine bid added to favipiravir. Authors note that trials showing benefit mostly used 100mg bid.
Late treatment low risk population RCT showing lower progression to hospitalization or urgent care/ER visits with fluvoxamine, without statistical significance.
There was no mortality and only three hospitalizations. Authors provide no details on the cause of hospitalization, but they appear to be unrelated to COVID-19. eFigure 5 shows no COVID-19 clinical progression to hospitalization (note that a hospitalization can be seen in the equivalent plot for the low dose arm), and the text indicates that the "COVID clinical progression scale simplified into a self-reported evaluation of home levels (limited vs not)".
Note that the urgent care/ER visit outcome is also likely diluted due to inclusion of all-cause events, and could be statistically significant for only COVID-19 events.
The sustained recovery outcome, which shows no difference, was a post-hoc creation used to hide efficacy for ivermectin, and is not logical for evaluating efficacy in this trial. The definition includes any minor symptom within a three day period - e.g., any minor cough, headache, body ache, or fatigue that occurs in a three day period, regardless of cause, results in the treatment being considered a failure. For example, late treatment that is effective at minimizing progression, but has no improvement in resolution of cough, would not be detected. (Authors even use the end of the three day period to further minimize efficacy).
Late treatment - median 5 days, 75% 4+ days, 25% 7+ days, up to 12 days.
Also see Naggie for many issues with this trial, and McCarthy for the lower dose arm.
There was no mortality and only three hospitalizations. Authors provide no details on the cause of hospitalization, but they appear to be unrelated to COVID-19. eFigure 5 shows no COVID-19 clinical progression to hospitalization (note that a hospitalization can be seen in the equivalent plot for the low dose arm), and the text indicates that the "COVID clinical progression scale simplified into a self-reported evaluation of home levels (limited vs not)".
Note that the urgent care/ER visit outcome is also likely diluted due to inclusion of all-cause events, and could be statistically significant for only COVID-19 events.
The sustained recovery outcome, which shows no difference, was a post-hoc creation used to hide efficacy for ivermectin, and is not logical for evaluating efficacy in this trial. The definition includes any minor symptom within a three day period - e.g., any minor cough, headache, body ache, or fatigue that occurs in a three day period, regardless of cause, results in the treatment being considered a failure. For example, late treatment that is effective at minimizing progression, but has no improvement in resolution of cough, would not be detected. (Authors even use the end of the three day period to further minimize efficacy).
Late treatment - median 5 days, 75% 4+ days, 25% 7+ days, up to 12 days.
Also see Naggie for many issues with this trial, and McCarthy for the lower dose arm.
Retrospective COVID+ patients in Croatia, showing no significant difference in outcomes with fluvoxamine prophylaxis.
Retrospective 103 outpatients in Greece, showing lower risk of progression with fluvoxamine 100mg bid for 10 days. 2 patients (4%) in the fluvoxamine group had clinical deterioration compared to 8 patients (16%) in the standard care group (p<0.05). After adjusting for confounders, fluvoxamine was associated with a lower risk of clinical deterioration (adjusted OR 0.12, p=0.02). Fluvoxamine was also associated with improved lymphocyte count. Control patients were during Sep-Nov 2021, and treatment patients Nov-Dec 2021, introducing potential confounding by time due to changes in variants, although the change in risk during this period is expected to be relatively low.
Retrospective 86,602 patients in Spain, showing lower COVID-19 risk SSRIs citalopram and paroxetine. There were no significant difference for fluvoxamine, which few patients were taking.
RCT 995 outpatients showing significantly lower progression with early treatment within 48 hours using fluvoxamine, fluvoxamine+bromhexine, fluvoxamine+cyproheptadine, and niclosamide+bromhexine.
70% of patients received treatment within 12 hours of symptom onset.
Treatments groups showed significantly lower long COVID (PASC). The combined treatment groups showed significantly lower viral load as early as day 3. The 3 combination arms were superior to fluvoxamine alone.
The study was open-label. 593 out of 1,900 randomized participants did not receive the treatment, mostly due to inability to confirm eligibility, however baseline characteristics were similar for these patients.
There was a very high hospitalization rate in the control arm. Authors note that the majority of cases were mild - the threshold for hospitalization may have been very low (in some places/times all cases were hospitalized). Authors also note that the patients requiring high flow oxygen all had the delta/alpha variants, and that the population has many health disparities.
Publication was over 500 days after the 90 day followup.
70% of patients received treatment within 12 hours of symptom onset.
Treatments groups showed significantly lower long COVID (PASC). The combined treatment groups showed significantly lower viral load as early as day 3. The 3 combination arms were superior to fluvoxamine alone.
The study was open-label. 593 out of 1,900 randomized participants did not receive the treatment, mostly due to inability to confirm eligibility, however baseline characteristics were similar for these patients.
There was a very high hospitalization rate in the control arm. Authors note that the majority of cases were mild - the threshold for hospitalization may have been very low (in some places/times all cases were hospitalized). Authors also note that the patients requiring high flow oxygen all had the delta/alpha variants, and that the population has many health disparities.
Publication was over 500 days after the 90 day followup.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are fluvoxamine and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of fluvoxamine 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 have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. 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 outcomes are considered more important than viral test 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 little or
no room for an effective treatment to do better, however faster recovery is
valuable.
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 compute the relative risk when
possible, or convert to a relative risk according to122.
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted 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 1125.
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
(3.13.1) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta126
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 is used to parse PDF documents.
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 effective58,59.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/fmeta.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.
| Bramante, 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 37 authors, average treatment delay 5.0 days, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04510194 (history) (COVID-OUT). | risk of death/hospitalization, 10.8% higher, RR 1.11, p = 0.88, treatment 6 of 329 (1.8%), control 5 of 324 (1.5%), odds ratio converted to relative risk. |
| risk of progression, 16.1% higher, RR 1.16, p = 0.68, treatment 18 of 329 (5.5%), control 15 of 324 (4.6%), odds ratio converted to relative risk, combined ER, hospitalization, death. | |
| risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S8, day 28. | |
| risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S7, day 14. | |
| risk of progression, 4.6% lower, RR 0.95, p = 0.75, treatment 79 of 329 (24.0%), control 80 of 321 (24.9%), NNT 110, odds ratio converted to relative risk, combined hypoxemia, ER, hospitalization, death, primary outcome. | |
| Farahani, 3/31/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 38.5, 3 authors, study period March 2022 - June 2022. | risk of PASC, 50.8% lower, RR 0.49, p = 0.06, treatment 42, control 43, smell and taste disturbance combined. |
| risk of PASC, 44.2% lower, RR 0.56, p = 0.28, treatment 6 of 42 (14.3%), control 11 of 43 (25.6%), NNT 8.9, smell. | |
| risk of PASC, 61.6% lower, RR 0.38, p = 0.20, treatment 3 of 42 (7.1%), control 8 of 43 (18.6%), NNT 8.7, taste. | |
| risk of PASC, 51.5% lower, RR 0.48, p = 0.04, treatment 9 of 42 (21.4%), control 19 of 43 (44.2%), NNT 4.4, fatigue, primary outcome. | |
| risk of PASC, 48.8% lower, RR 0.51, p = 0.48, treatment 3 of 42 (7.1%), control 6 of 43 (14.0%), NNT 15, headache. | |
| risk of PASC, 59.0% lower, RR 0.41, p = 0.43, treatment 2 of 42 (4.8%), control 5 of 43 (11.6%), NNT 15, memory impairment. | |
| risk of PASC, 17.0% higher, RR 1.17, p = 0.78, treatment 8 of 42 (19.0%), control 7 of 43 (16.3%), poor concentration. | |
| risk of PASC, 7.9% lower, RR 0.92, p = 1.00, treatment 9 of 42 (21.4%), control 10 of 43 (23.3%), NNT 55, insomnia. | |
| risk of PASC, 74.4% lower, RR 0.26, p = 0.36, treatment 1 of 42 (2.4%), control 4 of 43 (9.3%), NNT 14, aggression. | |
| risk of PASC, 85.4% lower, RR 0.15, p = 0.06, treatment 1 of 42 (2.4%), control 7 of 43 (16.3%), NNT 7.2, depression. | |
| risk of PASC, 37.0% lower, RR 0.63, p = 0.32, treatment 8 of 42 (19.0%), control 13 of 43 (30.2%), NNT 8.9, anxiety. | |
| risk of PASC, 26.9% lower, RR 0.73, p = 0.76, treatment 5 of 42 (11.9%), control 7 of 43 (16.3%), NNT 23, myalgia. | |
| risk of PASC, 6.4% lower, RR 0.94, p = 0.60, treatment 32 of 42 (76.2%), control 35 of 43 (81.4%), NNT 19, PCS. | |
| Lenze, 11/12/2020, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 11 authors, study period 10 April, 2020 - 5 August, 2020, average treatment delay 4.0 days, trial NCT04342663 (history) (STOP COVID). | risk of progression, 92.7% lower, RR 0.07, p = 0.009, treatment 0 of 80 (0.0%), control 6 of 72 (8.3%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), clinical deterioration over 15 days. |
| risk of hospitalization, 82.0% lower, RR 0.18, p = 0.009, treatment 1 of 80 (1.2%), control 5 of 72 (6.9%), NNT 18, COVID-19 hospitalization within 15 days, see supplemental appendix for details. | |
| Pineda, 10/4/2022, prospective, Honduras, peer-reviewed, mean age 48.1, 24 authors, study period November 2020 - January 2022. | risk of death, 94.0% lower, RR 0.06, p = 0.01, treatment 1 of 594 (0.2%), control 4 of 63 (6.3%), NNT 16, adjusted per study. |
| risk of oxygen therapy, 73.0% lower, RR 0.27, p < 0.001, treatment 15 of 594 (2.5%), control 13 of 63 (20.6%), NNT 5.5, adjusted per study. | |
| risk of hospitalization, 51.0% lower, RR 0.49, p = 0.04, treatment 23 of 594 (3.9%), control 11 of 63 (17.5%), NNT 7.4, adjusted per study. | |
| hospitalization time, 71.4% higher, relative time 1.71, p = 0.08, treatment 23, control 11. | |
| Reiersen, 8/20/2021, Double Blind Randomized Controlled Trial, USA, peer-reviewed, median age 47.0 (treatment) 48.0 (control), 24 authors, study period 22 December, 2020 - 21 May, 2021, average treatment delay 5.0 days, trial NCT04668950 (history) (STOP COVID 2). | risk of oxygen therapy, 201.1% higher, RR 3.01, p = 0.50, treatment 1 of 272 (0.4%), control 0 of 275 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), non-invasive ventilation. |
| risk of oxygen therapy, 32.6% lower, RR 0.67, p = 0.60, treatment 6 of 272 (2.2%), control 9 of 275 (3.3%), NNT 94. | |
| risk of oxygen therapy, 37.5% lower, RR 0.62, p = 0.74, treatment 3 of 164 (1.8%), control 6 of 205 (2.9%), NNT 91, per-protocol. | |
| risk of hospitalization, 9.0% lower, RR 0.91, p = 1.00, treatment 9 of 272 (3.3%), control 10 of 275 (3.6%), NNT 305. | |
| risk of progression, 12.4% lower, RR 0.88, p = 0.85, treatment 13 of 272 (4.8%), control 15 of 275 (5.5%), NNT 148, primary outcome. | |
| Reis (F), 4/17/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 35 authors, study period 15 January, 2022 - 6 July, 2022, average treatment delay 3.0 days, this trial uses multiple treatments in the treatment arm (combined with budesonide) - results of individual treatments may vary, trial NCT04727424 (history) (TOGETHER). | risk of death, 200.0% higher, RR 3.00, p = 1.00, treatment 1 of 738 (0.1%), control 0 of 738 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of hospitalization, 12.5% lower, RR 0.88, p = 1.00, treatment 7 of 738 (0.9%), control 8 of 738 (1.1%), NNT 738. | |
| hospitalization or ER >6hrs, 50.0% lower, RR 0.50, p = 0.04, treatment 13 of 738 (1.8%), control 27 of 738 (3.7%), NNT 53, adjusted per study, day 28, primary outcome. | |
| Seftel, 2/1/2021, prospective quasi-randomized (patient choice), USA, peer-reviewed, 2 authors. | risk of death, 72.3% lower, RR 0.28, p = 0.38, treatment 0 of 77 (0.0%), control 1 of 48 (2.1%), NNT 48, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of death/ICU, 83.9% lower, RR 0.16, p = 0.15, treatment 0 of 77 (0.0%), control 2 of 48 (4.2%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of hospitalization, 94.0% lower, RR 0.06, p = 0.003, treatment 0 of 77 (0.0%), control 6 of 48 (12.5%), NNT 8.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of no recovery, 98.7% lower, RR 0.01, p < 0.001, treatment 0 of 77 (0.0%), control 29 of 48 (60.4%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| Seo, 3/3/2022, Single Blind Randomized Controlled Trial, placebo-controlled, South Korea, peer-reviewed, median age 53.5, 14 authors, study period 15 January, 2021 - 19 February, 2021. | risk of progression, no change, RR 1.00, p = 1.00, treatment 2 of 26 (7.7%), control 2 of 26 (7.7%). |
| risk of progression, 34.2% lower, RR 0.66, p = 1.00, treatment 1 of 19 (5.3%), control 2 of 25 (8.0%), NNT 37, PP. | |
| time to progression, 13.3% lower, relative time 0.87, p = 0.16, treatment mean 6.5 (±0.7) n=26, control mean 7.5 (±3.5) n=26. | |
| Siripongboonsitti, 10/6/2023, retrospective, Thailand, peer-reviewed, 4 authors, study period 16 April, 2021 - 24 July, 2021, trial TCTR20230401001 (Fluvoxa). | risk of death, 59.2% lower, RR 0.41, p = 1.00, treatment 0 of 234 (0.0%), control 1 of 518 (0.2%), NNT 518, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28. |
| risk of mechanical ventilation, 47.6% higher, RR 1.48, p = 0.65, treatment 2 of 234 (0.9%), control 3 of 518 (0.6%). | |
| risk of ICU admission, 26.2% lower, RR 0.74, p = 1.00, treatment 2 of 234 (0.9%), control 6 of 518 (1.2%), NNT 329. | |
| risk of oxygen therapy, 67.3% higher, RR 1.67, p = 0.02, treatment 34 of 234 (14.5%), control 45 of 518 (8.7%). | |
| risk of deterioration, 41.9% lower, RR 0.58, p = 0.08, treatment 13 of 217 (6.0%), control 49 of 475 (10.3%), NNT 23, day 14. | |
| risk of deterioration, 35.0% lower, RR 0.65, p = 0.047, treatment 23 of 166 (13.9%), control 88 of 413 (21.3%), NNT 13, day 5. | |
| risk of deterioration, 32.9% lower, RR 0.67, p = 0.004, treatment 51 of 217 (23.5%), control 132 of 377 (35.0%), NNT 8.7, day 2. | |
| risk of progression, 47.6% higher, RR 1.48, p = 0.65, treatment 2 of 234 (0.9%), control 3 of 518 (0.6%), ARDS. | |
| WHO-CPS, 33.6% lower, RR 0.66, p < 0.001, treatment mean 0.73 (±0.67) n=234, control mean 1.1 (±0.75) n=518, WHO-CPS score, day 14. | |
| WHO-CPS, 7.9% higher, RR 1.08, p = 0.06, treatment mean 2.06 (±1.07) n=234, control mean 1.91 (±0.98) n=518, WHO-CPS score, day 5. | |
| WHO-CPS, 3.3% higher, RR 1.03, p = 0.43, treatment mean 2.21 (±1.25) n=234, control mean 2.14 (±1.06) n=518, WHO-CPS score, day 2. | |
| risk of no viral clearance, 3.6% higher, RR 1.04, p = 0.66, treatment 130 of 210 (61.9%), control 218 of 365 (59.7%), day 14. | |
| Siripongboonsitti (B), 6/29/2023, Randomized Controlled Trial, Thailand, peer-reviewed, 9 authors, study period 26 June, 2021 - 22 February, 2022, trial TCTR20210615002 (EFFaCo). | risk of oxygen therapy, 1.9% higher, RR 1.02, p = 1.00, treatment 4 of 162 (2.5%), control 4 of 165 (2.4%). |
| risk of hospitalization, 22.2% higher, RR 1.22, p = 0.77, treatment 6 of 162 (3.7%), control 5 of 165 (3.0%), day 28. | |
| Tsiakalos, 8/12/2023, retrospective, Greece, peer-reviewed, 5 authors, study period 1 September, 2021 - 31 December, 2021. | risk of ICU admission, 67.3% lower, RR 0.33, p = 0.49, treatment 0 of 53 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 84.3% lower, RR 0.16, p = 0.06, treatment 1 of 53 (1.9%), control 6 of 50 (12.0%), NNT 9.9. | |
| risk of progression, 86.0% lower, RR 0.14, p = 0.02, treatment 2 of 53 (3.8%), control 8 of 50 (16.0%), NNT 8.2, adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Wannigama, 3/14/2024, Randomized Controlled Trial, Thailand, peer-reviewed, 29 authors, study period 1 October, 2021 - 21 June, 2022, average treatment delay 0.5 days, trial NCT05087381 (history). | risk of mechanical ventilation, 97.9% lower, RR 0.02, p < 0.001, treatment 0 of 162 (0.0%), control 32 of 336 (9.5%), NNT 10, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28. |
| risk of mechanical ventilation, 97.6% lower, RR 0.02, p < 0.001, treatment 0 of 162 (0.0%), control 27 of 336 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14. | |
| risk of mechanical ventilation, 96.6% lower, RR 0.03, p < 0.001, treatment 0 of 162 (0.0%), control 19 of 336 (5.7%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 9. | |
| risk of oxygen therapy, 89.1% lower, RR 0.11, p < 0.001, treatment 9 of 162 (5.6%), control 171 of 336 (50.9%), NNT 2.2, day 28. | |
| risk of oxygen therapy, 99.6% lower, RR 0.004, p < 0.001, treatment 0 of 162 (0.0%), control 150 of 336 (44.6%), NNT 2.2, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14. | |
| risk of oxygen therapy, 99.4% lower, RR 0.006, p < 0.001, treatment 0 of 162 (0.0%), control 117 of 336 (34.8%), NNT 2.9, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 9. | |
| risk of hospitalization, 94.2% lower, RR 0.06, p < 0.001, treatment 9 of 162 (5.6%), control 321 of 336 (95.5%), NNT 1.1, day 28. | |
| risk of hospitalization, 97.6% lower, RR 0.02, p < 0.001, treatment 0 of 162 (0.0%), control 27 of 336 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14. | |
| risk of hospitalization, 96.6% lower, RR 0.03, p < 0.001, treatment 0 of 162 (0.0%), control 19 of 336 (5.7%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 9. | |
| risk of PASC, 40.1% lower, RR 0.60, p < 0.001, treatment 97 of 162 (59.9%), control 336 of 336 (100.0%), NNT 2.5, day 90. |
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.
| Calusic, 11/1/2021, prospective, propensity score matching, Croatia, peer-reviewed, 7 authors, study period 1 April, 2021 - 31 May, 2021. | risk of death, 42.0% lower, HR 0.58, p = 0.03, treatment 30 of 51 (58.8%), control 39 of 51 (76.5%), NNT 5.7, adjusted per study, propensity score matching. |
| Kirenga, 3/3/2023, prospective, Uganda, peer-reviewed, 19 authors, study period December 2021 - February 2022. | risk of death, 68.0% lower, HR 0.32, p < 0.001, treatment 29 of 94 (30.9%), control 126 of 222 (56.8%), NNT 3.9, adjusted for unbalanced covariates, propensity score weighting, Cox proportional hazards. |
| symptom resolution, 53.1% lower, HR 0.47, p = 0.04, treatment 94, control 222, inverted to make HR<1 favor treatment, propensity score weighting, Cox proportional hazards, RR approximated with OR. | |
| Reis (C), 8/23/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 27 authors, study period 20 January, 2021 - 5 August, 2021, trial NCT04727424 (history) (TOGETHER). | risk of death, 30.3% lower, RR 0.70, p = 0.24, treatment 17 of 741 (2.3%), control 25 of 756 (3.3%), NNT 99, odds ratio converted to relative risk, ITT. |
| risk of death, 90.8% lower, RR 0.09, p = 0.02, treatment 1 of 548 (0.2%), control 12 of 618 (1.9%), NNT 57, odds ratio converted to relative risk, per protocol. | |
| risk of mechanical ventilation, 22.2% lower, RR 0.78, p = 0.33, treatment 26 of 741 (3.5%), control 34 of 756 (4.5%), NNT 101, odds ratio converted to relative risk, ITT. | |
| risk of hospitalization, 21.6% lower, RR 0.78, p = 0.10, treatment 75 of 741 (10.1%), control 97 of 756 (12.8%), NNT 37, odds ratio converted to relative risk, ITT. | |
| extended ER observation or hospitalization, 32.0% lower, RR 0.68, p = 0.004, treatment 79 of 741 (10.7%), control 119 of 756 (15.7%), NNT 20, ITT, primary outcome. | |
| extended ER observation or hospitalization, 31.0% lower, RR 0.69, p = 0.006, treatment 78 of 740 (10.5%), control 115 of 752 (15.3%), NNT 21, mITT. | |
| extended ER observation or hospitalization, 66.0% lower, RR 0.34, p < 0.001, treatment 541, control 609, per protocol. | |
| risk of no viral clearance, 49.3% higher, RR 1.49, p = 0.09, treatment 167 of 207 (80.7%), control 163 of 221 (73.8%), adjusted per study, inverted to make RR<1 favor treatment. | |
| Stewart, 9/13/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 32 authors, study period 5 August, 2022 - 20 January, 2023, average treatment delay 5.0 days, trial NCT04885530 (history) (ACTIV-6). | risk of progression, 31.0% lower, RR 0.69, p = 0.34, treatment 14 of 589 (2.4%), control 21 of 586 (3.6%), NNT 83, adjusted per study, urgent or emergency care visits, hospitalizations, or death. |
| clinical progression, 34.0% lower, OR 0.66, p = 0.32, treatment 589, control 586, mid-recovery, day 14, RR approximated with OR. | |
| clinical progression, 15.0% higher, OR 1.15, p = 0.68, treatment 589, control 586, day 7, RR approximated with OR. | |
| clinical progression, 6.0% lower, OR 0.94, p = 0.90, treatment 589, control 586, day 28, RR approximated with OR. | |
| risk of no recovery, 1.0% higher, HR 1.01, p = 0.86, treatment 589, control 586, inverted to make HR<1 favor treatment, post-hoc primary outcome. | |
| risk of hospitalization, 49.0% lower, RR 0.51, p = 0.59, treatment 1 of 589 (0.2%), control 2 of 586 (0.3%), NNT 583, non-COVID-19 hospitalization, day 28. |
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.
| Diaz, 10/6/2022, retrospective, USA, peer-reviewed, 2 authors. | risk of case, 28.0% lower, OR 0.72, p < 0.001, treatment 4,558, control 4,558, propensity score matching, RR approximated with OR. |
| Fritz, 8/22/2022, retrospective, USA, peer-reviewed, 5 authors, study period 1 March, 2020 - 16 May, 2021. | risk of hospitalization/ER, 19.4% lower, RR 0.81, p = 0.69, treatment 4 of 17 (23.5%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, fluvoxamine, multivariable. |
| risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.03, treatment 707 of 3,414 (20.7%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, FIASMA, multivariable. | |
| risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.04, treatment 559 of 2,744 (20.4%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, SSRI, multivariable. | |
| risk of hospitalization/ER, 10.1% lower, RR 0.90, p = 0.04, treatment 971 of 4,577 (21.2%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, all antidepressants, multivariable. | |
| Oskotsky, 11/15/2021, retrospective, propensity score matching, USA, peer-reviewed, 8 authors. | risk of death, 57.9% higher, RR 1.58, p = 0.62, treatment 2 of 11 (18.2%), control 19 of 165 (11.5%), fluvoxamine. |
| risk of death, 26.0% lower, RR 0.74, p = 0.04, treatment 48 of 481 (10.0%), control 956 of 7,215 (13.3%), NNT 31, fluoxetine. | |
| Trkulja, 11/7/2022, retrospective, Croatia, peer-reviewed, 2 authors. | risk of death, 27.0% lower, RR 0.73, p = 0.41, treatment 749, control 31,336, cohort A vs. B, propensity score matching. |
| risk of hospitalization, 37.0% higher, RR 1.37, p = 0.50, treatment 749, control 31,336, cohort A vs. B, COVID-related, propensity score matching. | |
| Visos-Varela, 4/23/2023, retrospective, Spain, peer-reviewed, 8 authors. | risk of death, 103.0% higher, OR 2.03, p = 0.52, treatment 1 of 413 (0.2%) cases, 7 of 7,408 (0.1%) controls, adjusted per study, case control OR. |
| risk of ICU admission, 395.0% higher, OR 4.95, p = 0.24, treatment 1 of 228 (0.4%) cases, 2 of 4,398 (0.0%) controls, adjusted per study, case control OR. | |
| risk of hospitalization, 40.0% lower, OR 0.60, p = 0.39, treatment 3 of 3,060 (0.1%) cases, 69 of 56,785 (0.1%) controls, adjusted per study, case control OR. | |
| risk of progression, 32.0% lower, OR 0.68, p = 0.56, treatment 3 of 3,060 (0.1%) cases, 25 of 26,757 (0.1%) controls, adjusted per study, case control OR. | |
| risk of case, 12.0% lower, OR 0.88, p = 0.60, treatment 28 of 29,817 (0.1%) cases, 69 of 56,785 (0.1%) controls, adjusted per study, case control OR. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
Deng et al., Efficacy and safety of selective serotonin reuptake inhibitors in COVID-19 management: A systematic review and meta-analysis, Clinical Microbiology and Infection, doi:10.1016/j.cmi.2023.01.010.
Prasanth et al., A systematic review and meta-analysis, investigating dose and time of fluvoxamine treatment efficacy for COVID-19 clinical deterioration, death, and Long-COVID complications, Scientific Reports, doi:10.1038/s41598-024-64260-9.
Lee et al., Fluvoxamine for Outpatient Management of COVID-19 to Prevent Hospitalization: A Systematic Review and Meta-analysis, JAMA Network Open, doi:10.1001/jamanetworkopen.2022.6269.
Lu et al., Effect of fluvoxamine on outcomes of nonhospitalized patients with COVID-19: A systematic review and meta-analysis, Journal of Infection and Public Health, doi:10.1016/j.jiph.2022.10.010.
Marcec et al., A meta-analysis regarding fluvoxamine and hospitalization risk of COVID-19 patients: TOGETHER making a difference, Journal of Infection, doi:10.1016/j.jinf.2022.11.011.
Deng (B) et al., Evaluating fluvoxamine for the outpatient treatment of COVID‐19: A systematic review and meta‐analysis, Reviews in Medical Virology, doi:10.1002/rmv.2501.
Zhou et al., The efficacy and safety of fluvoxamine in patients with COVID-19: A systematic review and meta-analysis from randomized controlled trials, PLOS ONE, doi:10.1371/journal.pone.0300512.
Nakhaee et al., The effect of antidepressants on the severity of COVID-19 in hospitalized patients: A systematic review and meta-analysis, PLOS ONE, doi:10.1371/journal.pone.0267423.
Rong et al., Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19, Cell Host & Microbe, doi:10.1016/j.chom.2024.11.007.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Wu et al., Decoding the genome of SARS-CoV-2: a pathway to drug development through translation inhibition, RNA Biology, doi:10.1080/15476286.2024.2433830.
Alkafaas et al., Molecular docking as a tool for the discovery of novel insight about the role of acid sphingomyelinase inhibitors in SARS- CoV-2 infectivity, BMC Public Health, doi:10.1186/s12889-024-17747-z.
Zhang et al., SARS-CoV-2 NSP6 reduces autophagosome size and affects viral replication via sigma-1 receptor, Journal of Virology, doi:10.1128/jvi.00754-24.
Hoertel et al., Repurposing antidepressants inhibiting the sphingomyelinase acid/ceramide system against COVID-19: current evidence and potential mechanisms, Molecular Psychiatry, doi:10.1038/s41380-021-01254-3.
Carpinteiro et al., Inhibition of acid sphingomyelinase by ambroxol prevents SARS-CoV-2 entry into epithelial cells, Journal of Biological Chemistry, doi:10.1016/j.jbc.2021.100701.
Carpinteiro (B) et al., Pharmacological Inhibition of Acid Sphingomyelinase Prevents Uptake of SARS-CoV-2 by Epithelial Cells, Cell Reports Medicine, doi:10.1016/j.xcrm.2020.100142.
Hashimoto et al., Mechanisms of action of fluvoxamine for COVID-19: a historical review, Molecular Psychiatry, doi:10.1038/s41380-021-01432-3.
Sukhatme et al., Fluvoxamine: A Review of Its Mechanism of Action and Its Role in COVID-19, Front. Pharmacol., doi:10.3389/fphar.2021.652688.
Hashimoto (B) et al., Old drug fluvoxamine, new hope for COVID-19, European Archives of Psychiatry and Clinical Neuroscience, doi:10.1007/s00406-021-01326-z.
Norinder et al., Existing highly accumulating lysosomotropic drugs with potential for repurposing to target COVID-19, Biomedicine & Pharmacotherapy, doi:10.1016/j.biopha.2020.110582.
Hooper, P., COVID-19 and heme oxygenase: novel insight into the disease and potential therapies, Cell Stress and Chaperones, doi:10.1007/s12192-020-01126-9.
Almási et al., Lessons on the Sigma-1 Receptor in TNBS-Induced Rat Colitis: Modulation of the UCHL-1, IL-6 Pathway, International Journal of Molecular Sciences, doi:10.3390/ijms21114046.
Hooper (B), P., Heme oxygenase agonists—fluvoxamine, melatonin—are efficacious therapy for Covid-19, Cell Stress and Chaperones, doi:10.1007/s12192-021-01246-w.
Anderson, G., Fluvoxamine, melatonin and COVID-19, Psychopharmacology, doi:10.1007/s00213-020-05753-z.
Ramos et al., The Coronavirus Disease 2019 (COVID-19): Key Emphasis on Melatonin Safety and Therapeutic Efficacy, Antioxidants, doi:10.3390/antiox10071152.
Camp et al., Melatonin interferes with COVID-19 at several distinct ROS-related steps, Journal of Inorganic Biochemistry, doi:10.1016/j.jinorgbio.2021.111546.
Abatematteo et al., A conformational rearrangement of the SARS-CoV-2 host protein sigma-1 is required for antiviral activity: insights from a combined in-silico/in-vitro approach, Scientific Reports, doi:10.1038/s41598-023-39662-w.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
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.
Lee (B) et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
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.
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.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Boulware, D., Comments regarding paper rejection, twitter.com/boulware_dr/status/1311331372884205570.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Scheim et al., Back to the Basics of SARS-CoV-2 Biochemistry: Microvascular Occlusive Glycan Bindings Govern Its Morbidities and Inform Therapeutic Responses, Viruses, doi:10.3390/v16040647.
Hashimoto (C), K., Overview of the potential use of fluvoxamine for COVID-19 and long COVID, Discover Mental Health, doi:10.1007/s44192-023-00036-3.
Hoertel (B) et al., Repurposing antidepressants inhibiting the sphingomyelinase acid/ceramide system against COVID-19: current evidence and potential mechanisms, Molecular Psychiatry, doi:10.1038/s41380-021-01254-3.
Reis et al., Effect of Early Treatment with Ivermectin among Patients with Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2115869.
Reis (B) et al., Effect of early treatment with metformin on risk of emergency care and hospitalization among patients with COVID-19: The TOGETHER randomized platform clinical trial, The Lancet Regional Health - Americas, doi:10.1016/j.lana.2021.100142.
Reis (C) et al., Effect of early treatment with fluvoxamine on risk of emergency care and hospitalisation among patients with COVID-19: the TOGETHER randomised, platform clinical trial, The Lancet Global Health, doi:10.1016/S2214-109X(21)00448-4.
Reis (D) et al., Effect of Early Treatment With Hydroxychloroquine or Lopinavir and Ritonavir on Risk of Hospitalization Among Patients With COVID-19 The TOGETHER Randomized Clinical Trial, JAMA Network Open, doi:10.1001/jamanetworkopen.2021.6468.
Reis (E) et al., Early Treatment with Pegylated Interferon Lambda for Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2209760.
web.archive.org, web.archive.org/web/*/https://twitter.com/Covid19Crusher/status/1430170252575395843.
web.archive.org (B), web.archive.org/web/*/https://twitter.com/Covid19Crusher/status/1453726471499894787.
web.archive.org (C), web.archive.org/web/*/https://twitter.com/Covid19Crusher/status/1453803654608269318.
Reis (F) et al., Oral Fluvoxamine With Inhaled Budesonide for Treatment of Early-Onset COVID-19, Annals of Internal Medicine, doi:10.7326/M22-3305.
Naggie et al., Effect of Ivermectin vs Placebo on Time to Sustained Recovery in Outpatients With Mild to Moderate COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2022.18590.
McCarthy et al., Effect of Fluvoxamine vs Placebo on Time to Sustained Recovery in Outpatients With Mild to Moderate COVID-19, JAMA, doi:10.1001/jama.2022.24100.
Zhang (B) et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Sweeting et al., What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data, Statistics in Medicine, doi:10.1002/sim.1761.
Bramante et al., Randomized Trial of Metformin, Ivermectin, and Fluvoxamine for Covid-19, NEJM, doi:10.1056/NEJMoa2201662.
Farahani et al., Effect of fluvoxamine on preventing neuropsychiatric symptoms of post COVID syndrome in mild to moderate patients, a randomized placebo-controlled double-blind clinical trial, BMC Infectious Diseases, doi:10.1186/s12879-023-08172-5.
Lenze et al., Fluvoxamine vs Placebo and Clinical Deterioration in Outpatients With Symptomatic COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2020.22760.
Pineda et al., Impact of fluvoxamine on outpatient treatment of COVID-19 in Honduras in a prospective observational real-world study, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1054644.
Reiersen et al., The STOP COVID 2 study: Fluvoxamine vs placebo for outpatients with symptomatic COVID-19, a fully-remote randomized controlled trial, Open Forurm Infectious Diseases, doi:10.1093/ofid/ofad419.
Seftel et al., Prospective cohort of fluvoxamine for early treatment of COVID-19, Open Forum Infectious Diseases, doi:10.1093/ofid/ofab050.
Seo et al., Fluvoxamine Treatment of Patients with Symptomatic COVID-19 in a Community Treatment Center: A Preliminary Result of Randomized Controlled Trial, Infection & Chemotherapy, doi:10.3947/ic.2021.0142.
Siripongboonsitti et al., The Real-World Effectiveness of Fluvoxamine Therapy in Mild to Moderate COVID-19 Patients; a Historical Cohort Study (Fluvoxa Trial), Journal of Infection and Public Health, doi:10.1016/j.jiph.2023.10.010.
Siripongboonsitti (B) et al., Efficacy of Combination Therapy of Fluvoxamine and Favipiravir versus Favipiravir Monotherapy to Prevent Severe COVID-19 among Mild to Moderate COVID-19 Patients: Open-label Randomized Controlled Trial (EFFaCo Study), International Journal of Infectious Diseases, doi:10.1016/j.ijid.2023.06.018.
Tsiakalos et al., Early Fluvoxamine Reduces the Risk for Clinical Deterioration in Symptomatic Outpatients with COVID-19: A Real-World, Retrospective, before–after Analysis, Microorganisms, doi:10.3390/microorganisms11082073.
Wannigama et al., Early treatment with fluvoxamine, bromhexine, cyproheptadine, and niclosamide to prevent clinical deterioration in patients with symptomatic COVID-19: a randomized clinical trial, eClinicalMedicine, 10.1016/j.eclinm.2024.102517, www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00096-8/fulltext.
Calusic et al., Safety and efficacy of fluvoxamine in COVID-19 ICU patients: an open label, prospective cohort trial with matched controls, British Journal of Clinical Pharmacology, doi:10.1111/bcp.15126.
Kirenga et al., Association of fluvoxamine with mortality and symptom resolution among inpatients with COVID-19 in Uganda: a prospective interventional open-label cohort study, Molecular Psychiatry, doi:10.1038/s41380-023-02004-3.
Stewart et al., Higher-Dose Fluvoxamine and Time to Sustained Recovery in Outpatients With COVID-19, JAMA, doi:10.1001/jama.2023.23363.
Diaz et al., Protective Effect of Fluvoxamine for COVID-19 in Obsessive-Compulsive Disorder, The Primary Care Companion For CNS Disorders, doi:10.4088/PCC.22br03337.
Fritz et al., Association between antidepressant use and ED or hospital visits in outpatients with SARS-CoV-2, Translational Psychiatry, doi:10.1038/s41398-022-02109-3.
Oskotsky et al., Mortality Risk Among Patients With COVID-19 Prescribed Selective Serotonin Reuptake Inhibitor Antidepressants, JAMA Network Open, doi:10.1001/jamanetworkopen.2021.33090.
Trkulja et al., Outpatients prescribed with fluvoxamine around the time of COVID-19 diagnosis are not at a reduced risk of subsequent hospitalization and death compared to their non-prescribed peers: population-based matched cohort study, European Journal of Clinical Pharmacology, doi:10.1007/s00228-023-03479-3.
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