Sotrovimab for COVID-19: real-time meta analysis of 25 studies
Abstract
Statistically significant lower risk is seen for mortality, ICU admission, and hospitalization. 14 studies from 13 independent teams in 6 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
27% [11‑40%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
Results are robust — in exclusion sensitivity analysis 9 of 25
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.11-3, BA.4, BA.54, XBB.1.9.3, XBB.1.5.24, XBB.2.9, CH.1.15, and no efficacy for BA.26, XBB, XBB.1.5, Đ¥Đ’Đ’.1.9.17, XBB.1.16, BQ.1.1.45, and CL.15. US EUA has been revoked. mAb use may create new variants that spread globally8,9, and may be associated with prolonged viral loads, clinical deterioration, and immune escape9-12.
Prescription treatments have been preferentially used by patients at lower risk13. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
No treatment or intervention
is 100% effective. All practical, effective, and safe means should be used
based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
are more effective.
All data to reproduce this paper and
sources are in the appendix.
Sotrovimab for COVID-19 — Highlights
Sotrovimab reduces
risk with very high confidence for pooled analysis, high confidence for mortality and hospitalization, and low confidence for ventilation, ICU admission, and progression.
Efficacy is variant dependent.
41st treatment shown effective with ≥3 clinical studies in
May 2023, now with p = 0.002 from 25 studies, and recognized in 38 countries.
Outcome specific analyses and combined evidence from all
studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments.
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 sotrovimab studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome. Efficacy based on specific outcomes was delayed by 8.7 months, compared to using pooled outcomes.
mAb treatments
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Bebtelovimab
Casirivimab/i..
Pemivibart
Ravulizumab
Regdanvimab
Siltuximab
Sotrovimab
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Vilobelimab
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 injury14-24 and
cognitive deficits16,21, cardiovascular
complications25-27, 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,28-33, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk34, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
sotrovimab
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, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
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.
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing
antibodies from monoclonal antibody treatments35,36, resulting in efficacy
that is highly variant dependent.
For example, in vitro research suggests
that sotrovimab is not effective for omicron BA.26.
While the FDA has suspended the EUA for sotrovimab due to a predicted
lack of efficacy, it may retain efficacy for certain post-suspension
variants37,38.
Table 1 shows efficacy by variant for several monoclonal
antibodies. This table covers earlier SARS-CoV-2 variants and has not been
updated for more recent variants.
| Bamlanivimab/ etesevimab |
Casirivimab/ imdevimab |
Sotrovimab | Bebtelovimab | Tixagevimab/ cilgavimab |
|
|---|---|---|---|---|---|
| Alpha B.1.1.7 | |||||
| Beta/ Gamma BA1.351/ P.1 | |||||
| Delta B.1.617.2 | |||||
| Omicron BA.1/ BA.1.1 | |||||
| Omicron BA.2 | |||||
| Omicron BA.5 | |||||
| Omicron BA.4.6 | |||||
| Omicron BQ.1.1 |
Table 2 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, 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, and 11
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 27% [11‑40%] ** | 25 | 54,533 | 478 |
| After exclusions | 30% [14‑43%] *** | 23 | 46,442 | 452 |
| Peer-reviewed studiesPeer-reviewed | 33% [11‑49%] ** | 22 | 44,921 | 385 |
| Randomized Controlled TrialsRCTs | 10% [-109‑61%] | 2 | 1,417 | 135 |
| Mortality | 51% [7‑74%] * | 12 | 19,026 | 241 |
| VentilationVent. | 71% [-23‑93%] | 2 | 2,745 | 75 |
| ICU admissionICU | 69% [28‑86%] ** | 2 | 2,656 | 23 |
| HospitalizationHosp. | 26% [2‑44%] * | 8 | 16,733 | 90 |
| RCT mortality | 10% [-109‑61%] | 2 | 1,417 | 135 |
| Early treatment | Late treatment | |
|---|---|---|
| All studies | 30% [14‑43%] *** | -40% [-214‑38%] |
| After exclusions | 33% [18‑46%] *** | -40% [-214‑38%] |
| Peer-reviewed studiesPeer-reviewed | 38% [17‑54%] ** | -40% [-214‑38%] |
| Randomized Controlled TrialsRCTs | 80% [-316‑99%] | -2% [-117‑52%] |
| Mortality | 74% [61‑82%] **** | -40% [-214‑38%] |
| VentilationVent. | 71% [-23‑93%] | |
| ICU admissionICU | 69% [28‑86%] ** | |
| HospitalizationHosp. | 26% [2‑44%] * | |
| RCT mortality | 80% [-316‑99%] | -2% [-117‑52%] |
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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 peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Figure 12 shows a comparison of results for RCTs and non-RCT studies.
Figure 13 shows a forest plot for random
effects meta-analysis of all Randomized Controlled Trials.
RCT results are included in Table 2 and Table 3.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. 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 biases42, and
analysis of double-blind RCTs has identified extreme levels of bias43.
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 109 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.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 109 treatments45.
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 109 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 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 see49,50.
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.
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.
Brown, unadjusted results with no group details; significant unadjusted confounding possible.
Zheng, study compares against another treatment showing significant efficacy.
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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 hours53,54.
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 cases55 |
| <24 hours | -33 hours symptoms56 |
| 24-48 hours | -13 hours symptoms56 |
| Inpatients | -2.5 hours to improvement57 |
Figure 15 shows a mixed-effects meta-regression of efficacy as
a function of treatment delay in COVID-19 sotrovimab studies, with group estimates for different stages when a specific value is not provided. For comparison, Figure 16 shows a meta-regression for
all studies providing specific values across 109 treatments.
Efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 sotrovimab studies.
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Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 109 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
LĂ³pez-Medina et al.
Efficacy may
depend critically on the distribution of SARS-CoV-2 variants encountered by
patients. Risk varies significantly across variants59, for
example the Gamma variant shows significantly different characteristics60-63. 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 variants64,65.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use
of other treatments may significantly affect outcomes, including supplements,
other medications, or other interventions such as prone positioning.
Treatments may be synergistic66-77, therefore
efficacy may depend strongly on combined treatments.
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 note that pooled effects are no longer
required for sotrovimab as of February 2024.
Efficacy is now known based on specific outcomes. Efficacy based on specific outcomes was delayed by 8.7 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.
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.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
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 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 109
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 17 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 18 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 19 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 = 0.00000042 to p = 0.00000002.
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Figure 17. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 17. 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 5.1 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 20 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 20. 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.
Wilcock et al. show that COVID-19 prescription treatments have been preferentially used by patients at lower risk. Retrospective studies may overestimate efficacy, and data for accurate adjustment may not be available. For example, patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
Publishing is often biased
towards positive results. Trials with patented drugs may have a financial conflict of interest that
results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to
date (CTRI/2021/05/033864 and CTRI/2021/08/0354242).
For sotrovimab, 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 21 shows a scatter plot of
results for prospective and retrospective studies.
57% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
50% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 30% improvement,
compared to 39% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 21. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Studies for sotrovimab were primarily for early treatment, in contrast
with typical low cost treatments that were mostly tested with late treatment.
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Figure 22. Patented treatments received mostly early treatment studies, while low cost treatments were typically tested for late treatment.
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 < 0.0581-88.
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.
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 alone66-77.
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.
2 of the 25
studies compare against other treatments, which may reduce the effect
seen.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors28-33,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk34, 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 sotrovimab
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.
0.6% of 8,000+ proposed treatments show efficacy92.
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Figure 25. Efficacy vs. cost for COVID-19 treatments.
Sotrovimab is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, ICU admission, and hospitalization. 14 studies from 13 independent teams in 6 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
27% [11‑40%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
Results are robust — in exclusion sensitivity analysis 9 of 25
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.11-3, BA.4, BA.54, XBB.1.9.3, XBB.1.5.24, XBB.2.9, CH.1.15, and no efficacy for BA.26, XBB, XBB.1.5, Đ¥Đ’Đ’.1.9.17, XBB.1.16, BQ.1.1.45, and CL.15. US EUA has been revoked. mAb use may create new variants that spread globally8,9, and may be associated with prolonged viral loads, clinical deterioration, and immune escape9-12.
Prescription treatments have been preferentially used by patients at lower risk13. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
Aggarwal:
Retrospective 30,247 outpatients in the USA, showing no significant differences with sotrovimab with omicron BA.1.
Aggarwal (B):
PSM retrospective 10,036 outpatients, 522 treated with sotrovimab, showing lower mortality and hospitalization with treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Behzad:
Analysis of 1,180 high-risk COVID-19 outpatients infected with Omicron BA.2 showing lower risk of death or ICU admission with sotrovimab treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Bell:
Retrospective 35,485 high-risk COVID-19 outpatients showing lower ICU admission and respiratory support with sotrovimab. There was no significant difference for hospitalization.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Bell (B):
N3C retrospective 4,992 high-risk outpatients with mild-to-moderate COVID-19 showing reduced risk of hospitalization or death with sotrovimab treatment compared to 541,325 untreated controls during periods of Delta and Omicron BA.2 variant predominance in the US (September 2021-April 2022).
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Brown:
Retrospective 186 patients in the UK treated with sotrovimab, and 222 eligible but declining treatment, showing no significant difference in hospitalization. No group details are provided and the results are subject to confounding by indication.
De Vito:
Retrospective 689 COVID-19 patients in Italy, showing lower mortality with sotrovimab treatment.
Drysdale:
Retrospective 599 high-risk sotrovimab patients and 5,191 untreated controls, showing lower hospitalization/mortality with treatment, without statistical significance in the overall cohort. Efficacy was better for those ≥65, and efficacy was lower in later time periods.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Evans:
Retrospective high risk outpatients in the UK, showing lower hospitalization/death with sotrovimab treatment. Residual confounding is likely with adjustments having no detail on specific comorbidities.
Farmer:
Retrospective propensity-matched study of 22,289 high-risk COVID-19 outpatients in Canada, showing no significant difference in combined hospitalization/mortality with sotrovimab treatment. In a subgroup analysis of patients with no comorbidities, sotrovimab was associated with lower odds of severe outcomes. The study period included Omicron BA.1 and BA.2 variants, which may have contributed to the reduced efficacy compared to earlier studies.
Goodwin:
Retrospective 604 outpatients in the UK, showing lower risk of hospitalization with sotrovimab treatment, without statistical significance due to the small number of hospitalizations.
Gupta:
RCT 1,057 outpatients, 529 treated with sotrovimab, showing significantly lower hospitalization >24h or mortality with treatment.
Kip:
Retrospective 2,571 patients treated with mAbs in the USA, and 5,135 control patients, showing lower combined mortality/hospitalization for bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab, and bebtelovimab, with statistical significance only for casirivimab/imdevimab.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Kneidinger:
Retrospective 218 COVID+ lung transplant patients in Germany, showing no significant difference in severe cases with early sotrovimab use.
Maria:
Retrospective 81 severely immunocompromised COVID-19 outpatients in Italy showing improved composite outcome of death, hospitalization, and emergency department encounters with early combination therapy of an antiviral plus sotrovimab compared to antiviral monotherapy.
Miyashita:
Retrospective 844 patients treated with sotrovimab and matched controls in Japan, showing lower risk of oxygen therapy with treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene95,96, vitamin D97, etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Ong:
Retrospective 19 sotrovimab patients and 75 controls is Singapore, showing lower progression with treatment.
Piccicacco:
Retrospective high-risk outpatients in the USA, 82 treated with remdesivir, 88 with sotrovimab, and 90 control patients, showing significantly lower combined hospitalization/ER visits with both treatments in unadjusted results. The dominant variant was omicron B.1.1.529.
Self:
RCT with 182 sotrovimab patients, 176 BRII-196+BRII-198 patients, and 178 control patients, median 8 days from symptom onset, showing no significant differences and terminated early due to futility.
Suzuki:
Retrospective 1,921 patients in Japan, showing no significant difference in progression with sotrovimab use.
Tazare:
OpenSAFELY retrospective 75,048 outpatients in the UK, using the clone-censor-weight approach to address immortal time bias, showing lower combined mortality/hospitalization with sotrovimab treatment.
Woo:
PSM retrospective 1,254 hospitalized patients in Germany, 147 treated with sotrovimab, showing higher mortality with sotrovimab, without statistical significance.
Zaqout:
Retrospective 345 sotrovimab treated patients in Qatar matched with 583 patients that opted not to receive treatment, showing higher progression with treatment, without statistical significance.
Zheng:
OpenSAFELY retrospective 7,683 outpatients in the UK, showing no significant difference in hospitalization/death between paxlovid and sotrovimab.
Zheng (B):
Retrospective 3,331 sotrovimab and 2,689 molnupiravir patients in the UK, showing lower risk of combined hospitalization/death with sotrovimab.
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 sotrovimab 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 sotrovimab 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.
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 to119.
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 1122.
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
(3.13.0) 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 PythonMeta123
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 effective53,54.
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/vmeta.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.
| Aggarwal, 6/18/2022, retrospective, USA, peer-reviewed, 10 authors, study period 26 December, 2021 - 10 March, 2022. | risk of death, 38.0% lower, RR 0.62, p = 0.62, treatment 1 of 1,542 (0.1%), control 7 of 3,663 (0.2%), odds ratio converted to relative risk. |
| risk of hospitalization, 17.5% lower, RR 0.82, p = 0.32, treatment 39 of 1,542 (2.5%), control 116 of 3,663 (3.2%), NNT 157, odds ratio converted to relative risk, primary outcome. | |
| risk of progression, 2.8% higher, RR 1.03, p = 0.83, treatment 93 of 1,542 (6.0%), control 224 of 3,663 (6.1%), NNT 1189, odds ratio converted to relative risk, ED visit. | |
| Aggarwal (B), 4/5/2022, retrospective, USA, peer-reviewed, 14 authors, study period 1 October, 2021 - 11 December, 2021. | risk of death, 88.9% lower, RR 0.11, p = 0.048, treatment 0 of 522 (0.0%), control 15 of 1,563 (1.0%), NNT 104, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28. |
| risk of hospitalization, 61.6% lower, RR 0.38, p = 0.002, treatment 11 of 522 (2.1%), control 89 of 1,563 (5.7%), NNT 28, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28, primary outcome. | |
| ED visit, 11.0% higher, RR 1.11, p = 0.55, treatment 44 of 522 (8.4%), control 119 of 1,563 (7.6%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28. | |
| Behzad, 12/4/2023, retrospective, Bahrain, peer-reviewed, 6 authors, study period 1 January, 2022 - 31 March, 2022. | risk of death/ICU, 74.4% lower, HR 0.26, p = 0.001, treatment 569, control 611. |
| Bell, 7/16/2024, retrospective, USA, peer-reviewed, 13 authors, study period 26 May, 2021 - 23 April, 2022. | risk of death, 50.0% lower, RR 0.50, p = 0.20, treatment 5 of 854 (0.6%), control 20 of 1,708 (1.2%), NNT 171, propensity score matching. |
| risk of death/hospitalization, 12.5% lower, RR 0.88, p = 0.70, treatment 21 of 854 (2.5%), control 48 of 1,708 (2.8%), NNT 285, propensity score matching. | |
| risk of ICU admission, 74.2% lower, RR 0.26, p = 0.006, treatment 4 of 854 (0.5%), control 31 of 1,708 (1.8%), NNT 74, propensity score matching. | |
| risk of oxygen therapy, 59.5% lower, RR 0.41, p < 0.001, treatment 30 of 854 (3.5%), control 148 of 1,708 (8.7%), NNT 19, propensity score matching. | |
| risk of hospitalization, 20.0% higher, RR 1.20, p = 0.54, treatment 18 of 854 (2.1%), control 30 of 1,708 (1.8%), propensity score matching. | |
| Bell (B), 2/20/2024, retrospective, USA, peer-reviewed, 12 authors, study period 27 September, 2021 - 30 April, 2022. | risk of death/hospitalization, 24.2% lower, RR 0.76, p < 0.001, NNT 107, odds ratio converted to relative risk, propensity score weighting, day 29. |
| risk of hospitalization, 21.3% lower, RR 0.79, p = 0.001, NNT 121, odds ratio converted to relative risk, propensity score weighting, day 29. | |
| Brown, 10/6/2022, retrospective, United Kingdom, peer-reviewed, 17 authors, excluded in exclusion analyses: unadjusted results with no group details; significant unadjusted confounding possible. | risk of hospitalization, 258.1% higher, RR 3.58, p = 0.15, treatment 6 of 186 (3.2%), control 2 of 222 (0.9%). |
| De Vito, 8/17/2023, retrospective, Italy, peer-reviewed, 12 authors, study period 1 January, 2022 - 31 December, 2022, average treatment delay 1.0 days. | risk of death, 81.1% lower, RR 0.19, p < 0.001, treatment 18 of 341 (5.3%), control 63 of 348 (18.1%), NNT 7.8, odds ratio converted to relative risk. |
| risk of oxygen therapy, 91.8% lower, RR 0.08, p < 0.001, treatment 17 of 341 (5.0%), control 144 of 348 (41.4%), NNT 2.7, odds ratio converted to relative risk. | |
| Drysdale, 7/27/2023, retrospective, United Kingdom, peer-reviewed, 14 authors, study period August 2020 - March 2021. | risk of death, 29.0% lower, HR 0.71, p = 0.65, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. |
| risk of death/hospitalization, 50.0% lower, HR 0.50, p = 0.07, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. | |
| risk of hospitalization, 57.0% lower, HR 0.43, p = 0.05, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. | |
| Evans, 1/25/2023, retrospective, United Kingdom, peer-reviewed, 11 authors, study period 16 December, 2021 - 22 April, 2022. | risk of death/hospitalization, 27.0% lower, HR 0.73, p = 0.03, treatment 1,079, control 4,973, Cox proportional hazards. |
| Farmer, 6/19/2024, retrospective, Canada, peer-reviewed, 14 authors, study period 15 December, 2021 - 30 April, 2022. | risk of death/hospitalization, 20.0% higher, OR 1.20, p = 0.20, treatment 1,603, control 6,299, adjusted per study, propensity score matching, multivariable, RR approximated with OR. |
| Goodwin, 3/15/2023, retrospective, United Kingdom, peer-reviewed, 3 authors, study period 22 December, 2021 - 20 February, 2022. | risk of death, 75.0% lower, RR 0.25, p = 0.55, treatment 0 of 169 (0.0%), control 2 of 336 (0.6%), NNT 168, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 60.2% lower, RR 0.40, p = 0.35, treatment 2 of 169 (1.2%), control 10 of 336 (3.0%), NNT 56, COVID-19 related. | |
| risk of hospitalization, 21.5% higher, RR 1.21, p = 0.69, treatment 11 of 169 (6.5%), control 18 of 336 (5.4%), all cause. | |
| Gupta, 12/4/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 68 authors, study period 27 August, 2020 - 2 September, 2021, average treatment delay 2.6 days, trial NCT04545060 (history) (COMET-ICE), conflicts of interest: research funding from the drug patent holder, employee of the drug patent holder. | risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 528 (0.0%), control 2 of 529 (0.4%), NNT 264, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. |
| risk of mechanical ventilation, 88.9% lower, RR 0.11, p = 0.12, treatment 0 of 528 (0.0%), control 4 of 529 (0.8%), NNT 132, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. | |
| risk of progression, 75.0% lower, RR 0.25, p < 0.001, treatment 7 of 528 (1.3%), control 28 of 529 (5.3%), NNT 25, day 29. | |
| risk of hospitalization >24hrs or death, 79.0% lower, RR 0.21, p < 0.001, treatment 6 of 528 (1.1%), control 30 of 529 (5.7%), NNT 22, day 29, ITT, primary outcome. | |
| Kip, 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. | risk of death/hospitalization, 30.0% lower, RR 0.70, p = 0.14, treatment 22 of 500 (4.4%), control 63 of 999 (6.3%), NNT 52, delta and omicron variants, day 28. |
| Kneidinger, 9/9/2022, retrospective, Germany, peer-reviewed, 11 authors, study period 1 January, 2022 - 20 March, 2022, lung transplant patients. | risk of severe case, 20.2% higher, RR 1.20, p = 0.79, treatment 21 of 125 (16.8%), control 13 of 93 (14.0%). |
| Maria, 10/4/2024, retrospective, Italy, peer-reviewed, 10 authors, study period 1 January, 2022 - 31 December, 2023, average treatment delay 2.0 days. | risk of death, 72.0% lower, OR 0.28, p = 0.15, treatment 39, control 42, propensity score weighting, RR approximated with OR. |
| risk of progression, 77.0% lower, OR 0.23, p = 0.03, treatment 39, control 42, propensity score weighting, RR approximated with OR. | |
| Miyashita, 5/31/2023, retrospective, Japan, peer-reviewed, 7 authors, study period December 2021 - July 2022. | risk of mechanical ventilation, 60.0% lower, RR 0.40, p = 0.45, treatment 2 of 844 (0.2%), control 5 of 844 (0.6%), NNT 281, all. |
| risk of mechanical ventilation, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 642 (0.3%), control 3 of 642 (0.5%), NNT 642, BA.1. | |
| risk of mechanical ventilation, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 202 (0.0%), control 2 of 202 (1.0%), NNT 101, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), BA.2. | |
| risk of oxygen therapy, 55.3% lower, RR 0.45, p < 0.001, treatment 34 of 844 (4.0%), control 76 of 844 (9.0%), NNT 20, all. | |
| risk of oxygen therapy, 53.6% lower, RR 0.46, p < 0.001, treatment 26 of 642 (4.0%), control 56 of 642 (8.7%), NNT 21, BA.1. | |
| risk of oxygen therapy, 60.0% lower, RR 0.40, p = 0.03, treatment 8 of 202 (4.0%), control 20 of 202 (9.9%), NNT 17, BA.2. | |
| Ong, 3/5/2022, retrospective, Singapore, peer-reviewed, 10 authors, average treatment delay 2.0 days. | risk of death, 60.5% lower, RR 0.39, p = 0.45, treatment 1 of 19 (5.3%), control 10 of 75 (13.3%), NNT 12. |
| risk of ICU admission, 56.1% lower, RR 0.44, p = 0.35, treatment 2 of 19 (10.5%), control 18 of 75 (24.0%), NNT 7.4. | |
| risk of progression, 59.0% lower, HR 0.41, p = 0.047, treatment 19, control 75, Cox proportional hazards. | |
| Piccicacco, 8/1/2022, retrospective, USA, peer-reviewed, 7 authors, study period 27 December, 2021 - 4 February, 2022, average treatment delay 4.4 days. | risk of death, 66.4% lower, RR 0.34, p = 1.00, treatment 0 of 88 (0.0%), control 1 of 90 (1.1%), NNT 90, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. |
| risk of hospitalization, 34.9% lower, RR 0.65, p = 0.46, treatment 7 of 88 (8.0%), control 11 of 90 (12.2%), NNT 23, day 29. | |
| risk of hospitalization/ER, 66.3% lower, RR 0.34, p = 0.01, treatment 7 of 88 (8.0%), control 21 of 90 (23.3%), NNT 6.5, odds ratio converted to relative risk, day 29. | |
| risk of progression, 89.8% lower, RR 0.10, p = 0.009, treatment 1 of 88 (1.1%), control 10 of 90 (11.1%), NNT 10, ER visit, day 29. | |
| Suzuki, 10/5/2022, retrospective, Japan, preprint, 53 authors. | risk of progression, 8.3% higher, OR 1.08, p = 0.73, treatment 672, control 1,257, adjusted per study, multivariable, RR approximated with OR. |
| Tazare, 5/16/2023, retrospective, United Kingdom, preprint, 31 authors, study period 16 December, 2021 - 21 May, 2022. | risk of death/hospitalization, 16.0% lower, HR 0.84, p = 0.002, treatment 6,408, control 65,568. |
| Zaqout, 4/21/2022, retrospective, Qatar, peer-reviewed, median age 40.0, 17 authors, study period 20 October, 2021 - 28 February, 2022. | risk of progression, 164.7% higher, RR 2.65, p = 0.19, treatment 4 of 345 (1.2%), control 3 of 583 (0.5%), adjusted per study, odds ratio converted to relative risk, progression to severe/critical disease or mortality. |
| Zheng, 1/22/2023, retrospective, United Kingdom, preprint, mean age 54.3, 9 authors, study period 11 February, 2022 - 1 October, 2022, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: study compares against another treatment showing significant efficacy. | risk of death/hospitalization, 3.8% lower, HR 0.96, p = 0.91, treatment 2,847, control 4,836, inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 60, model 4. |
| risk of death/hospitalization, 13.6% higher, HR 1.14, p = 0.70, treatment 19 of 2,847 (0.7%), control 33 of 4,836 (0.7%), inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 28, model 4. | |
| Zheng (B), 11/16/2022, retrospective, United Kingdom, peer-reviewed, mean age 52.0, 33 authors, study period 16 December, 2021 - 10 February, 2022, this trial compares with another treatment - results may be better when compared to placebo. | risk of death/hospitalization, 50.0% lower, HR 0.50, p = 0.005, treatment 34 of 3,331 (1.0%), control 61 of 2,689 (2.3%), NNT 80, adjusted per study, multivariable, Cox proportional hazards, day 60, model 4. |
| risk of death/hospitalization, 46.0% lower, HR 0.54, p = 0.01, treatment 32 of 3,331 (1.0%), control 55 of 2,689 (2.0%), NNT 92, adjusted per study, multivariable, Cox proportional hazards, day 28, model 4. |
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.
| Self, 12/23/2021, Double Blind Randomized Controlled Trial, multiple countries, peer-reviewed, 67 authors, study period 16 December, 2020 - 1 March, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (TICO). | risk of death, 2.0% higher, RR 1.02, p = 0.96, treatment 14 of 182 (7.7%), control 13 of 178 (7.3%), adjusted per study, day 90. |
| risk of no recovery, 10.7% lower, RR 0.89, p = 0.29, treatment 22 of 182 (12.1%), control 27 of 178 (15.2%), NNT 32, adjusted per study, inverted to make RR<1 favor treatment, day 90, primary outcome. | |
| risk of no recovery, 7.4% lower, RR 0.93, p = 0.69, treatment 181, control 178, adjusted per study, inverted to make RR<1 favor treatment, pulmonary-plus ordinal outcome @day 5, day 5. | |
| Woo, 12/8/2022, retrospective, Germany, peer-reviewed, 13 authors. | risk of death, 140.0% higher, RR 2.40, p = 0.12, treatment 4 of 60 (6.7%), control 10 of 360 (2.8%), non-ICU, propensity score matching. |
| risk of death, 50.0% higher, RR 1.50, p = 0.08, treatment 36 of 87 (41.4%), control 24 of 87 (27.6%), ICU, propensity score matching. |
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