Indomethacin for COVID-19: real-time meta analysis of 4 studies
Abstract
Significantly lower risk is seen for recovery. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
74% [-20‑94%] lower risk, without reaching statistical significance. Results are worse for Randomized Controlled Trials.
Currently there is limited data, with only 605 patients in trials to date. Studies to date are from only 3 different groups.
Concerns have been raised over potential harm with the use of NSAIDs for COVID-19 due to the suppression of beneficial immune and inflammatory responses during early infection, and delaying further care. There are currently no early treatment studies for indomethacin. Early treatment results for NSAID ibuprofen suggest higher risk. Indomethacin may be beneficial for cough1, which may not respond to other treatments.
Indomethacin for COVID-19 — Highlights
Indomethacin reduces risk with low confidence for recovery and in pooled analysis, and very low confidence for progression and viral clearance.
Real-time updates and corrections with a consistent protocol for 131 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 indomethacin studies.
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 injury2-14 and
cognitive deficits5,10, cardiovascular
complications15-19, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,20-27, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk28, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
indomethacin
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, 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.
Currently all indomethacin studies use late treatment.
Figure 2. Treatment stages.
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.
Stuart et al. performed a retrospective study of 142,925
outpatients in the UK showing significantly higher risk of hospitalization or
death with prescription of NSAIDs for respiratory tract infections, OR 3.19
[2.42-4.23]. Practice-level analysis also found a 0.32 percentage point
increase in hospitalizations/deaths for every 1 percentage point increase in
NSAID prescribing, which increases confidence in an assocation rather than
confounding by indication.
NSAIDs may be harmful due to suppression of inflammatory and
immune responses needed to clear infections. They inhibit cyclooxygenase
enzymes and production of prostaglandins involved in inflammation. This
anti-inflammatory effect could hamper the body's ability to fight the
infection. NSAIDs may mask symptoms of worsening infection. By reducing pain,
fever, and inflammation, they could provide symptomatic relief while the
infection progresses unchecked, delaying further medical care. NSAIDs may
increase risks of certain complications, for example some evidence links
NSAIDs to a higher risk of cardiovascular events.
For COVID-19, the potential harm or benefit may depend strongly
on the timing of use, and any direct antiviral effects of the specific NSAID.
For example, anti-inflammatory effects may be detrimental at the early stage
of COVID-19 infection, but may be helpful in later stages depending on
severity.
For indomethacin, there are currently no early treatment results,
and late treatment results suggest benefit, without statistical significance.
Fever is an important component of the acute response to coronavirus
infection36.
The evolutionary conservation of fever for over 600 million years supports
a survival benefit37.
Viral particle sensing occurs via pattern recognition receptors, such as toll-like
receptors, triggering release of endogenous pyrogens such as interleukin-1.
These cytokines induce thermoregulatory centers in the hypothalamus to elevate
core temperature setpoints above normal homeostasis. The resulting fever
enhances multiple aspects of the innate and adaptive immune systems37,
and creates a suboptimal internal
environment that impairs SARS-CoV-2 enzyme function and replication.
In Vitro studies demonstrate reduced viral output at sustained febrile
temperatures of 38-39°C compared to basal 37°C conditions. Fever also
correlates clinically with heightened interferon-γ, interleukin-6, lymphocyte
activation, and antibody production critical for viral clearance.
Los et al. showed that higher temperature enhanced the expression of
antiviral genes and reduced SARS-CoV-2 replication in Calu-3 and Caco-2 cells.
An in vivo hamster model showed that higher body temperature at the time of
infection correlated with lower viral loads.
Zhou et al. showed that SARS-CoV-2 patients with higher fever had lower viral load.
Molecular dynamics simulations, surface plasmon resonance experiments, and pseudovirus cell entry assays showed decreased SARS-CoV-2 binding affinity to the human ACE2 receptor at higher temperature (40°C vs. 37°C).
Downing et al. induced hyperthermia (fever-like
temperatures) in human volunteers by immersing them in warm water baths. They
found that lymphocytes isolated from individuals with core body temperatures
elevated to 39°C produced up to 10 times more interferon-γ, as shown in
Figure 3. They also found an increase in suppressor/cytotoxic T
cells and natural killer cells. The threshold of 39°C suggests relevance to
fever, and the results suggest fever may play a role in boosting antiviral and
immunoregulatory activities.
Figure 3.
A 10 fold increase in interferon-γ production was seen
when core body temperature reached 39°C, from Downing et al.
Herder et al. perform in vitro analysis with a 3D
respiratory epithelial model using cells from human donors. Authors showed
that elevated temperature (39-40°C) restricts SARS-CoV-2 infection and
replication independently of interferon-mediated antiviral defenses. Authors
found SARS-CoV-2 can still enter respiratory cells at 40°C but viral
transcription and replication are inhibited, limiting the production of
infectious virus. This temperature-dependent restriction correlates with
altered host gene expression related to antiviral immunity and epigenetic
regulation. The results suggest that febrile temperature ranges may confer
protection to respiratory tissues by restricting SARS-CoV-2 propagation.
Dominguez-Nicolas et al. induced localized hyperthermia
using LF-ThMS applied to the dorsal thorax (up to 44°C externally), resulting
in significantly increased peripheral oxygen saturation (SpO2)
levels in COVID-19 patients, as shown in Figure 4.
Figure 4.
Rapidly increasing SpO2 in COVID-19 patients with localized thoracic hyperthermia, from Dominguez-Nicolas et al.
Ramirez et al. compared COVID-19 mortality in Finland and
Estonia, where sauna use is part of the culture and is typically practiced at
least once a week, with the rest of Europe. Authors found significantly lower
mortality with sauna culture, and suggest this may be due to the beneficial
effects of hydrothermotherapy.
Ruble et al. compared army hospital vs. sanitarium
treatment for the 1918 Spanish influenza, showing lower progression to
pneumonia and lower mortality with sanitarium treatment, which involves
hydrothermotherapy, sunlight, and fresh air.
Stewart reports on the use of diathermy in the treatment of
pneumonia in 1926, with case reports from several physicians covering over 300
patients. Author reports that diathermy had consistent positive effects
without significant adverse events, resulted in about half the mortality of
the control group, significantly alleviated symptoms such as dyspnea, pain,
and cardiac strain, and improved sleep and reduced respiratory rates.
Recent atom-level work strengthens the mechanistic case for fever-mediated viral
attenuation. Xie et al. performed 200-ns equilibration followed by replicate 100-ns
all-atom MD simulations of the spike RBD–ACE2 peptidase complex across physiologic-to-febrile
temperatures. At 315 K the interface lost ~1 hydrogen bond, solvent exposure grew by ~4 Ų,
dissociation probability tripled, and MM-PBSA binding free energy became ≈59 kcal mol-¹ less
favorable, driven by heat-induced straightening of the ACE2 α1-helix and withdrawal of
the β3β4 hairpin that jointly destabilise the two anchor regions. Mild-cool conditions (305 K)
had the opposite effect, α1-helix curvature tightened the interface, dissociation dropped eight-fold, and binding free energy became ~21 kcal mol-¹ more favorable. These thermodynamic
shifts directly support febrile-range hyperthermia as a barrier to initial viral attachment.
In summary, fever is a key component of the response to
infection. Fever enhances immune cell performance, induces cellular stress on
pathogens, and may act synergistically with other stressors like iron
deprivation. While results show beneficial effects of fever, it is not
universally beneficial. Extreme or prolonged cases may be harmful. Fever may
be more detrimental for individuals with lower tolerance for the increased
metabolic demands.
Fever may also reduce transmissibility. Fever helps clear
infection faster by enhancing immune responses and applying cellular stress to
pathogens. Faster clearance gives the pathogen less time to amplify within the
host to reach contagious levels. Fever may also apply evolutionary pressure
resulting in sacrificing replicative fitness at normal temperatures,
minimizing infection in other hosts. Further, fever promotes reduced activity,
minimizing the opportunity for transmission.
The beneficial effects of fever suggest potential harm from
fever-reducing medications in terms of an increased risk of poor outcomes and
increased transmission. However, these may be offset by other effects of specific
medications, including anticoagulant, anti-inflammatory, or antiviral effects.
Notably, studies for COVID-19 show significantly increased risk with
acetaminophen47.
Table 1 summarizes the results for all studies, for Randomized Controlled Trials, and for specific outcomes.
Figure 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, ventilation, hospitalization, progression, recovery, and viral clearance.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 74% [-20‑94%] | 4 | 605 | 222 |
| Randomized Controlled TrialsRCTs | 30% [13‑44%] ** | 2 | 255 | 16 |
| Recovery | 34% [15‑49%] ** | 3 | 399 | 22 |
Figure 5. 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.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for hospitalization.
Figure 8. Random effects meta-analysis for progression.
Figure 9. Random effects meta-analysis for recovery.
Figure 10. Random effects meta-analysis for viral clearance.
Figure 11 shows a comparison of results for RCTs and non-RCT studies.
Figure 12 shows a forest plot for random
effects meta-analysis of all Randomized Controlled Trials.
RCT results are included in Table 1.
Figure 11. Results for RCTs and non-RCT studies.
Figure 12. 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.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases48, and
analysis of double-blind RCTs has identified extreme levels of bias49.
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 131 treatments we have analyzed,
66% 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 indomethacin 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 [0.93‑1.07]
across 131 treatments51.
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 131 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.93‑1.07]. Similar results are found for all low-cost treatments, RR
1.02 [0.93‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.91 [0.81‑1.03].
Details can be found in the
supplementary data.
Lee 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 see55,56.
Currently, 52 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 7.7 months (66% with 8.8 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 hours57,58. 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 cases59 |
| <24 hours | -33 hours symptoms60 |
| 24-48 hours | -13 hours symptoms60 |
| Inpatients | -2.5 hours to improvement61 |
Figure 13 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 131 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 13. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 131 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
variants63, for example the Gamma variant shows significantly
different characteristics64-67. 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 variants68,69.
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.
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 131
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 14 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 15 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 16 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 = 0.00000019 to p = 0.0000000094.
Figure 14. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 15. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 14. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 17 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 17. 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 results90-93.
For indomethacin, 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 18 shows a scatter plot of
results for prospective and retrospective studies.
The median effect size for
retrospective studies is 82% improvement,
compared to 48% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
Figure 18. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Indomethacin for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 indomethacin 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 indomethacin 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 alone72-88.
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.
3 of the 4
studies compare against other treatments, which may reduce the effect
seen.
Currently all studies are peer-reviewed.
Moshawih et al. present a review covering indomethacin for COVID-19.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors20-27, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk28, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 19 shows an overview of the results for indomethacin
in the context of multiple COVID-19 treatments, and Figure 20 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 19.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy115.
Figure 20. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for recovery. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
74% [-20‑94%] lower risk, without reaching statistical significance. Results are worse for Randomized Controlled Trials.
Currently there is limited data, with only 605 patients in trials to date. Studies to date are from only 3 different groups.
Concerns have been raised over potential harm with the use of NSAIDs for COVID-19 due to the suppression of beneficial immune and inflammatory responses during early infection, and delaying further care. There are currently no early treatment studies for indomethacin. Early treatment results for NSAID ibuprofen suggest higher risk. Indomethacin may be beneficial for cough1, which may not respond to other treatments.
Analysis of interactions between viral and human proteins for SARS-CoV-2, SARS-CoV-1, and MERS-CoV and genetic screening to identify host factors that enhance or inhibit viral infection.
Authors predict indomethacin will have antiviral activity for SARS-CoV-2 and perform a retrospective study of patients in the USA that started treatment within 21 days after COVID-19 infection - 103 with indomethacin, and 103 using a celecoxib, a clinically similar drug without predicted antiviral activity. There were fewer hospital visits and hospitalizations with indomethacin, without statistical significance.
Authors predict indomethacin will have antiviral activity for SARS-CoV-2 and perform a retrospective study of patients in the USA that started treatment within 21 days after COVID-19 infection - 103 with indomethacin, and 103 using a celecoxib, a clinically similar drug without predicted antiviral activity. There were fewer hospital visits and hospitalizations with indomethacin, without statistical significance.
RCT with 103 indomethacin and 107 paracetamol patients, showing lower progression and improved recovery with indomethacin. Notably, improvements include faster resolution of cough.1 previously hypothesised the benefit of indomethacin for reducing cough via bradykinin inhibition.
PSM retrospective 72 indomethacin and 72 paracetamol patients in India, showing lower progression and improved recovery with indomethacin.
Very small RCT with 22 indomethacin and 23 control patients, showing no significant difference in outcomes. All patients were treated with HCQ.
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 indomethacin 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 indomethacin 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 to116.
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 1119.
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
(3.13.3) with
scipy (1.15.2), pythonmeta (1.26), numpy (2.2.5), statsmodels (0.14.4), and plotly (6.0.1).
Forest plots are computed using PythonMeta120
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 effective57,58.
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/inmeta.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.
| Gordon, 12/4/2020, retrospective, USA, peer-reviewed, 200 authors, this trial compares with another treatment - results may be better when compared to placebo. | risk of hospitalization, 66.7% lower, RR 0.33, p = 0.34, treatment 1 of 103 (1.0%), control 3 of 103 (2.9%), NNT 51, RSS and PSM, propensity score matching. |
| risk of progression, 57.1% lower, RR 0.43, p = 0.21, treatment 3 of 103 (2.9%), control 7 of 103 (6.8%), NNT 26, RSS and PSM, propensity score matching. | |
| Ravichandran, 4/19/2022, Randomized Controlled Trial, India, peer-reviewed, 8 authors, this trial compares with another treatment - results may be better when compared to placebo, trial CTRI/2021/05/033544. | risk of no recovery, 29.8% lower, RR 0.70, p = 0.002, treatment 52 of 103 (50.5%), control 77 of 107 (72.0%), NNT 4.7, day 14. |
| risk of progression, 97.5% lower, RR 0.02, p < 0.001, treatment 0 of 103 (0.0%), control 20 of 107 (18.7%), NNT 5.4, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), SpO2 ≤93. | |
| recovery time, 57.1% lower, relative time 0.43, p < 0.001, treatment median 3.0 IQR 1.0 n=103, control median 7.0 IQR 2.75 n=107, fever. | |
| recovery time, 42.9% lower, relative time 0.57, p < 0.001, treatment median 4.0 IQR 2.0 n=103, control median 7.0 IQR 2.0 n=107, myalgia. | |
| recovery time, 42.9% lower, relative time 0.57, p < 0.001, treatment median 4.0 IQR 1.0 n=103, control median 7.0 IQR 3.0 n=107, cough. | |
| risk of no viral clearance, 16.7% lower, RR 0.83, p = 0.19, treatment 37 of 62 (59.7%), control 43 of 60 (71.7%), NNT 8.3, day 7. | |
| Ravichandran (B), 7/31/2021, retrospective, India, peer-reviewed, 6 authors, this trial compares with another treatment - results may be better when compared to placebo, trial ISRCTN11970082. | risk of oxygen therapy, 96.4% lower, RR 0.04, p < 0.001, treatment 1 of 72 (1.4%), control 28 of 72 (38.9%), NNT 2.7, propensity score matching. |
| recovery time, 42.9% lower, relative time 0.57, p < 0.001, treatment median 4.0 IQR 1.0 n=72, control median 7.0 IQR 1.0 n=72, fever. | |
| recovery time, 53.8% lower, relative time 0.46, p < 0.001, treatment median 3.0 IQR 2.0 n=72, control median 6.5 IQR 3.25 n=72, myalgia. | |
| recovery time, 62.5% lower, relative time 0.38, p < 0.001, treatment median 3.0 IQR 2.0 n=72, control median 8.0 IQR 2.0 n=72, cough. | |
| Salmasi, 1/13/2022, Randomized Controlled Trial, Iran, peer-reviewed, 8 authors, trial IRCT20200427047215N1. | risk of mechanical ventilation, 66.2% lower, RR 0.34, p = 1.00, treatment 0 of 22 (0.0%), control 1 of 23 (4.3%), NNT 23, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| recovery time, 40.0% higher, relative time 1.40, p = 0.52, treatment 22, control 23. |
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.
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