Dexamethasone for COVID-19: real-time meta analysis of 11 studies
ControlAbstract
Meta analysis using the most serious outcome reported shows
12% [-7‑34%] higher risk, without reaching statistical significance.
While overall meta-analysis shows poor results, dexamethasone may reduce risk for a subgroup of later stage patients1,2.
All data and sources to reproduce this analysis are in the appendix.
Dexamethasone for COVID-19 — Highlights
Meta analysis of studies to date shows no significant improvements with dexamethasone.
Real-time updates and corrections with a consistent protocol for 172 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 dexamethasone 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 injury4-16 and
cognitive deficits7,12, cardiovascular
complications17-21, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits22—the spike protein binds to fibrin leading to
fibrinolysis-resistant blood clots, thromboinflammation, and
neuropathology.
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,23-30, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 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
dexamethasone
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, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 3 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 3. Treatment stages.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, after exclusions, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 4 plots individual results by treatment stage.
Figure 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, and recovery.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | -12% [-34‑7%] | 11 | 11,798 | 139 |
| After exclusions | 1% [-16‑15%] | 10 | 11,126 | 130 |
| Randomized Controlled TrialsRCTs | 10% [-6‑24%] | 4 | 6,856 | 83 |
| Mortality | -10% [-32‑9%] | 9 | 11,666 | 116 |
| VentilationVent. | 2% [-131‑58%] | 2 | 5,544 | 40 |
| ICU admissionICU | -351% [-2454‑20%] | 2 | 156 | 20 |
| HospitalizationHosp. | -51% [-244‑34%] | 3 | 162 | 29 |
| Recovery | -67% [-939‑73%] | 2 | 6,431 | 35 |
| RCT mortality | 12% [-1‑24%] | 2 | 6,724 | 60 |
| Early treatment | Late treatment | |
|---|---|---|
| All studies | -132% [-238‑-60%] **** | 1% [-15‑15%] |
| After exclusions | -300% [-5722‑73%] | 1% [-15‑15%] |
| Randomized Controlled TrialsRCTs | -300% [-5722‑73%] | 11% [-4‑24%] |
| Mortality | -130% [-240‑-60%] **** | 2% [-14‑16%] |
| VentilationVent. | 2% [-131‑58%] | |
| ICU admissionICU | -351% [-2454‑20%] | |
| HospitalizationHosp. | -300% [-5722‑73%] | -40% [-252‑44%] |
| Recovery | -600% [-8840‑45%] | 9% [3‑15%] ** |
| RCT mortality | 12% [-1‑24%] |
Figure 4. 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.
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 mortality results.
Figure 7. Random effects meta-analysis for ventilation.
Figure 8. Random effects meta-analysis for ICU admission.
Figure 9. Random effects meta-analysis for hospitalization.
Figure 10. Random effects meta-analysis for recovery.
Figure 11 shows a comparison of results for RCTs and non-RCT studies.
Figure 12 and 13
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 1 and Table 2.
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.
Figure 13. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases32, and
analysis of double-blind RCTs has identified extreme levels of bias33.
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 172 treatments we have analyzed,
67% 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.
Currently, 54 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, 59% have been confirmed in RCTs, with a mean delay of 7.7 months (66% with 8.9 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.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 14 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Madamombe, substantial unadjusted confounding by indication possible.
Figure 14. Random effects meta-analysis for all studies after exclusions.
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.
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 hours36,37. 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 cases38 |
| <24 hours | -33 hours symptoms39 |
| 24-48 hours | -13 hours symptoms39 |
| Inpatients | -2.5 hours to improvement40 |
Figure 15 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 172 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 15. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 172 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
variants42, for example the Gamma variant shows significantly
different characteristics43-46. 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 variants47,48.
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 172
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 16 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 17 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 18 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 = 0.00000009 to p = 0.0000000039.
Figure 16. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 17. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 16. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 54 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 90% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.9 months. When restricting to RCTs only, 57% 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 19 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 19. 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 results69-72.
For dexamethasone, there is currently not
enough data to evaluate publication bias with high confidence.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 20 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 < 0.0573-80.
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 20. 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. Dexamethasone for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 dexamethasone 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 dexamethasone 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 alone51-67.
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.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors23-30, providing many therapeutic
targets.
Over 9,000 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 21 shows an overview of the results for dexamethasone
in the context of multiple COVID-19 treatments, and Figure 22 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 21.
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 efficacy125.
Figure 22. Efficacy vs. cost for COVID-19 treatments.
Meta analysis using the most serious outcome reported shows
12% [-7‑34%] higher risk, without reaching statistical significance.
Retrospective 410 hospitalized COVID-19 patients in the Democratic Republic of Congo showing significantly lower mortality with vitamin C treatment.
Retrospective propensity score matched study of 529 hospitalized diabetic COVID-19 patients showing no significant difference in mortality or clinical improvement with dexamethasone treatment.
RCT 126 hospitalized COVID-19 pneumonia patients not requiring oxygen at admission, showing no significant difference in outcomes with dexamethasone treatment.
Retrospective 30 hospitalized patients with sickle cell disease (SCD) showing increased risk of venous thromboembolism (VTE) with dexamethasone treatment for COVID-19. There were also trends towards increased ICU admission and longer hospital stays with dexamethasone, without statistical significance.
RCT 6,425 hospitalized COVID-19 patients showing lower 28-day mortality with dexamethasone treatment. The benefit was most pronounced in patients who had symptoms for more than 7 days at randomization, suggesting dexamethasone is most effective when the disease is dominated by inflammatory processes rather than viral replication.
Pilot RCT of 7 outpatients with non-severe COVID-19 suggesting potential harmful effects of dexamethasone treatment. Time to recovery was significantly longer in the dexamethasone group compared to controls (p=0.03). Authors note that systemic corticosteroids, while beneficial for hospitalized COVID-19 patients requiring oxygen, may be harmful in non-severe cases by potentially inhibiting normal immune response when administered too early.
Retrospective 672 COVID-19 patients in Zimbabwe, showing higher mortality with dexamethasone treatment.
PSM retrospective 80,699 hospitalized COVID-19 patients showing reduced mortality or discharge to hospice with dexamethasone in patients requiring supplemental oxygen or mechanical ventilation/ECMO, but no significant difference in patients not requiring supplemental oxygen or on NIPPV.
RCT 299 patients with moderate or severe COVID-19-related ARDS showing increased ventilator-free days with dexamethasone treatment. There was no significant difference in 28-day mortality (56.3% vs 61.5%), ICU-free days, or mechanical ventilation duration.
Retrospective 2,196 COVID-19 patients in Taiwan (49% mild cases, 44% moderate, 7% severe) showing significantly higher mortality with dexamethasone.
Retrospective 576 hospitalized COVID-19 patients showing lower mortality with dexamethasone treatment.
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 dexamethasone 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 dexamethasone for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
Studies with major unexplained data issues, for example major outcome data that
is impossible to be correct with no response from the authors, are excluded.
This is a living analysis and is updated regularly.
Figure 23.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
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
reported then they are both used in specific outcome analyses, while mortality
is used for pooled analysis.
If symptomatic
results are reported at multiple times, we use 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 outcomes.
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.
An IPD meta-analysis confirms that intermediate viral load reduction
is more closely associated with hospitalization/death than later
viral load reduction126.
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 to Zhang et al.
Reported confidence intervals and p-values are used when available,
and adjusted values are used 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 1130.
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
(3.13.4) with
scipy (1.15.3), pythonmeta (1.26), numpy (2.3.0), statsmodels (0.14.4), and plotly (6.1.2).
Forest plots are computed using PythonMeta131
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 0.8.2 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 effective36,37.
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/dexmeta.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.
| Kocks, 4/30/2022, Double Blind Randomized Controlled Trial, Netherlands, peer-reviewed, 9 authors, trial NCT04746430 (history) (COPPER). | risk of hospitalization, 300.0% higher, RR 4.00, p = 0.47, treatment 2 of 4 (50.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of severe case, 450.0% higher, RR 5.50, p = 0.40, treatment 3 of 4 (75.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). | |
| risk of no recovery, 600.0% higher, RR 7.00, p = 0.07, treatment 4 of 4 (100.0%), control 0 of 2 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). | |
| Madamombe, 3/21/2023, retrospective, Zimbabwe, peer-reviewed, 9 authors, study period April 2020 - April 2022, excluded in exclusion analyses: substantial unadjusted confounding by indication possible. | risk of death, 130.0% higher, OR 2.30, p < 0.001, treatment 245, control 427, adjusted per study, multivariable, RR approximated with OR. |
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.
| Bepouka, 5/14/2025, retrospective, DR Congo, peer-reviewed, 14 authors, study period 20 March, 2020 - 2 January, 2022. | risk of death, 104.1% higher, OR 2.04, p = 0.55, treatment 70, control 340, adjusted per study, inverted to make OR<1 favor treatment, multivariable, RR approximated with OR. |
| Bhat, 10/17/2024, retrospective, USA, peer-reviewed, 7 authors, study period 4 March, 2020 - 25 June, 2022. | risk of death, 35.3% higher, RR 1.35, p = 0.20, treatment 46 of 529 (8.7%), control 34 of 529 (6.4%), propensity score matching, day 28. |
| risk of no improvement, 45.6% higher, RR 1.46, p = 0.02, treatment 83 of 529 (15.7%), control 57 of 529 (10.8%), propensity score matching, day 28. | |
| Franco-Moreno, 7/17/2024, Randomized Controlled Trial, Spain, peer-reviewed, mean age 48.8, 14 authors, study period June 2021 - January 2022, average treatment delay 9.0 days, trial NCT04836780 (history) (EARLY-DEX). | risk of mechanical ventilation, 134.5% higher, RR 2.34, p = 0.41, treatment 4 of 58 (6.9%), control 2 of 68 (2.9%). |
| risk of ICU admission, 217.2% higher, RR 3.17, p = 0.46, treatment 1 of 58 (1.7%), control 0 of 68 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). | |
| risk of ARDS, 17.2% higher, RR 1.17, p = 0.81, treatment 10 of 58 (17.2%), control 10 of 68 (14.7%). | |
| hospitalization time, 3.0% lower, relative time 0.97, p = 0.88, treatment mean 6.4 (±5.0) n=58, control mean 6.6 (±8.7) n=68. | |
| Garneau, 11/26/2024, retrospective, USA, peer-reviewed, median age 33.7, 6 authors, study period 1 June, 2020 - 26 June, 2022. | risk of death, 30.8% higher, RR 1.31, p = 1.00, treatment 1 of 13 (7.7%), control 1 of 17 (5.9%). |
| risk of ICU admission, 423.1% higher, RR 5.23, p = 0.14, treatment 4 of 13 (30.8%), control 1 of 17 (5.9%). | |
| hospitalization time, 154.5% higher, relative time 2.55, p = 0.06, treatment 13, control 17. | |
| Horby, 2/25/2021, Randomized Controlled Trial, United Kingdom, peer-reviewed, mean age 66.1, 26 authors, study period 19 March, 2020 - 8 June, 2020, trial NCT04381936 (history) (RECOVERY). | risk of death, 17.0% lower, RR 0.83, p < 0.001, treatment 482 of 2,104 (22.9%), control 1,110 of 4,321 (25.7%), NNT 36, adjusted per study. |
| risk of mechanical ventilation, 21.0% lower, RR 0.79, p = 0.03, treatment 110 of 1,780 (6.2%), control 298 of 3,638 (8.2%), NNT 50, adjusted per study. | |
| risk of no hospital discharge, 9.1% lower, RR 0.91, p = 0.003, treatment 2,104, control 4,321, adjusted per study, inverted to make RR<1 favor treatment. | |
| Mourad, 4/17/2023, retrospective, USA, peer-reviewed, median age 64.0, 7 authors, study period 1 July, 2020 - 31 October, 2021. | risk of death, 9.6% lower, RR 0.90, p < 0.001, treatment 48,579, control 7,789, adjusted per study, all patients. |
| risk of death, 10.0% lower, OR 0.90, p = 0.14, treatment 7,537, control 5,503, adjusted per study, no oxygen, mortality or discharge to hospice, RR approximated with OR. | |
| risk of death, 8.0% lower, OR 0.92, p = 0.01, treatment 48,579, control 7,789, adjusted per study, supplemental oxygen, mortality or discharge to hospice, RR approximated with OR. | |
| risk of death, 13.0% lower, OR 0.87, p = 0.12, treatment 6,826, control 792, adjusted per study, NIPPV, mortality or discharge to hospice, RR approximated with OR. | |
| risk of death, 18.0% lower, OR 0.82, p = 0.04, treatment 2,660, control 1,013, adjusted per study, MV/ECMO, mortality or discharge to hospice, RR approximated with OR. | |
| Tomazini, 10/6/2020, Randomized Controlled Trial, Brazil, peer-reviewed, 34 authors, study period 17 April, 2020 - 23 June, 2020, trial NCT04327401 (history) (CoDEX). | risk of death, 3.0% lower, HR 0.97, p = 0.85, treatment 85 of 151 (56.3%), control 91 of 148 (61.5%), NNT 19, adjusted per study, day 28. |
| 6-point scale, 34.0% lower, OR 0.66, p = 0.07, treatment 151, control 148, day 15, RR approximated with OR. | |
| Yen, 8/20/2024, retrospective, Taiwan, peer-reviewed, 6 authors, study period 1 January, 2022 - 31 July, 2022. | risk of death, 103.0% higher, HR 2.03, p < 0.001, treatment 572, control 1,624, adjusted per study, multivariable, Cox proportional hazards. |
| Zhao, 11/25/2024, retrospective, USA, peer-reviewed, 7 authors. | risk of death, 34.0% lower, HR 0.66, p = 0.01, treatment 288, control 288, propensity score matching, day 365. |
| risk of death, 33.0% lower, HR 0.67, p = 0.01, treatment 288, control 288, propensity score matching, day 28. |
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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