Tixagevimab/cilgavimab for COVID-19: real-time meta analysis of 17 studies
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, and cases. 9 studies from 9 independent teams in 3 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
43% [26‑56%] lower risk. Results are similar for higher quality and peer-reviewed studies and slightly worse for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 10 of
17 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.11, BA.5, BA.2.75, XBB2,3, XBB.1.53, ХВВ.1.9.13, XBB.1.9.3, XBB.1.5.24, XBB.1.16, XBB.2.9, BQ.1.1.45, CL.1, and CH.1.14. US EUA has been revoked. mAb use may create new variants that spread globally5,6, and may be associated with prolonged viral loads, clinical deterioration, and immune escape6-9.
No treatment or intervention
is 100% effective. All practical, effective, and safe means should be used
based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
All data to reproduce this paper and
sources are in the appendix.
Soeroto et al. present another meta analysis for tixagevimab/cilgavimab, showing significant improvements for mortality, hospitalization, severity, and cases.
Tixagevimab/cilgavimab for COVID-19 — Highlights
Tixagevimab/cilgavimab reduces
risk with very high confidence for mortality, hospitalization, and in pooled analysis, and high confidence for cases, however increased risk is seen with very low confidence for ventilation and viral clearance.
Efficacy is variant dependent.
38th treatment shown effective with ≥3 clinical studies in
May 2022, now with p = 0.000029 from 17 studies, and recognized in 31 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 tixagevimab/cilgavimab studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 1.3 months, compared to using all studies.
mAb treatments
AdintrevimabAmubarv../r..
Bamlaniv../e..
Bebtelovimab
Casirivimab/i..
Pemivibart
Ravulizumab
Regdanvimab
Siltuximab
Sotrovimab
Tixagev../c..
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 injury11-21 and
cognitive deficits13,18, cardiovascular
complications22-24, 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,25-30, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk31, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
tixagevimab/cilgavimab
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 treatments32,33, resulting in efficacy
that is highly variant dependent.
For example, in vitro research suggests
that tixagevimab/cilgavimab is not effective for omicron BA.4.6 and BQ.1.11.
While the FDA has suspended the EUA for tixagevimab/cilgavimab due to a predicted
lack of efficacy, it may retain efficacy for certain post-suspension
variants34.
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, 11, 12, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 43% [26‑56%] **** | 17 | 29,530 | 397 |
| After exclusions | 45% [29‑57%] **** | 15 | 29,358 | 352 |
| Peer-reviewed studiesPeer-reviewed | 41% [24‑55%] **** | 15 | 21,335 | 366 |
| Randomized Controlled TrialsRCTs | 33% [15‑48%] ** | 5 | 8,839 | 254 |
| Mortality | 42% [16‑60%] ** | 10 | 16,858 | 311 |
| ICU admissionICU | 65% [-2078‑99%] | 2 | 430 | 28 |
| HospitalizationHosp. | 63% [39‑77%] **** | 9 | 14,264 | 118 |
| Recovery | 6% [-4‑15%] | 2 | 1,643 | 189 |
| Cases | 69% [11‑89%] * | 10 | 25,258 | 121 |
| RCT mortality | 31% [9‑48%] ** | 4 | 7,718 | 233 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | -29% [-295‑58%] | 32% [9‑49%] ** | 49% [31‑62%] **** |
| After exclusions | 0% [-207‑68%] | 32% [9‑49%] ** | 51% [33‑64%] **** |
| Peer-reviewed studiesPeer-reviewed | 0% [-207‑68%] | 32% [9‑49%] ** | 46% [24‑62%] *** |
| Randomized Controlled TrialsRCTs | 0% [-207‑68%] | 32% [9‑49%] ** | 44% [5‑67%] * |
| Mortality | -29% [-295‑58%] | 32% [9‑49%] ** | 67% [39‑82%] *** |
| ICU admissionICU | 65% [-2078‑99%] | ||
| HospitalizationHosp. | 55% [24‑74%] ** | 67% [35‑83%] ** |
|
| Recovery | 6% [-4‑15%] | ||
| Cases | 69% [11‑89%] * |
||
| RCT mortality | 0% [-207‑68%] | 32% [9‑49%] ** | 86% [-198‑99%] |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. 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 14 shows a comparison of results for RCTs and non-RCT studies.
Figure 15 and 16
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 2 and Table 3.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 16. 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 biases38, and
analysis of double-blind RCTs has identified extreme levels of bias39.
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 treatments41.
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 see45,46.
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 17 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Din, unadjusted results with no group details.
Lombardi, study compares against another treatment showing significant efficacy.
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Figure 17. 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 hours49,50.
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 cases51 |
| <24 hours | -33 hours symptoms52 |
| 24-48 hours | -13 hours symptoms52 |
| Inpatients | -2.5 hours to improvement53 |
Figure 18 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 109 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 18. 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
López-Medina et al.
Efficacy may
depend critically on the distribution of SARS-CoV-2 variants encountered by
patients. Risk varies significantly across variants55, for
example the Gamma variant shows significantly different characteristics56-59. 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 variants60,61.
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 synergistic62-73, 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 tixagevimab/cilgavimab as of May 2022.
Efficacy is now known for tixagevimab/cilgavimab based on specific outcomes for all studies and when restricted to RCTs.
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 19 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 20 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 21 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 19. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. 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 22 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 22. 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. 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 tixagevimab/cilgavimab, 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 23 shows a scatter plot of
results for prospective and retrospective studies.
50% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
60% of prospective studies, consistent with a bias toward publishing negative results.
The median effect size for
retrospective studies is 42% improvement,
compared to 40% for prospective
studies, showing similar results.
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Figure 23. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 24 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 < 0.0577-84.
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 24. 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 alone62-73.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
1 of the 17
studies compare against other treatments, which may reduce the effect
seen.
Soeroto et al. present another meta analysis for tixagevimab/cilgavimab, showing significant improvements for mortality, hospitalization, severity, and cases.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors25-30,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk31, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 25 shows an overview of the results for tixagevimab/cilgavimab
in the context of multiple COVID-19 treatments, and Figure 26 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 25.
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 efficacy89.
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Figure 26. Efficacy vs. cost for COVID-19 treatments.
Tixagevimab/cilgavimab is
an effective treatment for COVID-19.
Statistically significant lower risk is seen for mortality, hospitalization, and cases. 9 studies from 9 independent teams in 3 countries show significant
improvements.
Meta analysis using the most serious outcome reported shows
43% [26‑56%] lower risk. Results are similar for higher quality and peer-reviewed studies and slightly worse for Randomized Controlled Trials.
Results are very robust — in exclusion sensitivity analysis 10 of
17 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.11, BA.5, BA.2.75, XBB2,3, XBB.1.53, ХВВ.1.9.13, XBB.1.9.3, XBB.1.5.24, XBB.1.16, XBB.2.9, BQ.1.1.45, CL.1, and CH.1.14. US EUA has been revoked. mAb use may create new variants that spread globally5,6, and may be associated with prolonged viral loads, clinical deterioration, and immune escape6-9.
Soeroto et al. present another meta analysis for tixagevimab/cilgavimab, showing significant improvements for mortality, hospitalization, severity, and cases.
Al Jurdi:
Retrospective cohort study of 444 solid organ transplant recipients showing significantly lower risk of SARS-CoV-2 breakthrough infections with tixagevimab/cilgavimab pre-exposure prophylaxis compared to controls during the omicron period.
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 hygiene91,92, vitamin D93, etc.) — either because the physician recommending tixagevimab/cilgavimab also recommended them, or because the patient seeking out tixagevimab/cilgavimab 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 hygiene91,92, vitamin D93, etc.) — either because the physician recommending tixagevimab/cilgavimab also recommended them, or because the patient seeking out tixagevimab/cilgavimab 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.
Bes-Berlandier:
Retrospective 91 lung transplant recipients with COVID-19 showing no significant difference in poor outcomes with casirivimab/imdevimab or tixagevimab/cilgavimab prophylaxis in univariate analysis.
Desai:
TriNetX PSM retrospective 408 IBD patients receiving tixagevimab/cilgavimab and matched controls, showing no significant difference in COVID-19 cases or hospitalization.
Din:
Retrospective 64 COVID+ CAR-T cell therapy recipients, showing lower hospitalization with tixagevimab/cilgavimab prophylaxis in unadjusted results, without statistical significance.
Hites:
RCT 173 hospitalized COVID-19 patients showing no significant difference in clinical status, time to recovery, viral clearance, or mortality with tixagevimab/cilgavimab. Mortality was lower, without statistical significance. The trial was terminated early due to concerns about reduced efficacy against circulating variants.
Holland:
RCT with 710 hospitalized patients treated with tixagevimab/cilgavimab, and 707 placebo patients, showing lower mortality with treatment.
Kaminski:
Retrospective 430 kidney transplant recipients showing significantly lower symptomatic COVID-19 and hospitalization with tixagevimab/cilgavimab preexposure prophylaxis compared to 97 patients who did not receive it, during an omicron wave.
Kertes:
Retrospective 825 immunocompromised individuals treated with tixagevimab-cilgavimab and 4229 untreated in Israel, showing significantly lower infection and hospitalization/death with treatment. Omicron was the dominant variant.
Levin:
PrEP RCT with 3,441 tixagevimab/cilgavimab patients and 1,731 control patients, showing lower risk of symptomatic cases with treatment.
Levin (B):
1,121 patient PEP RCT showing lower symptomatic cases with tixagevimab/cilgavimab, without statistical significance.
Lombardi:
Retrospective immunocompromised patients, showing no significant difference between tixagevimab/cilgavimab and other mAbs.
Montgomery:
RCT 910 outpatients in the USA, 456 treated with tixagevimab/cilgavimab, showing significantly lower severe cases and hospitalization with treatment, but no difference in mortality.
Najjar-Debbiny:
Retrospective 732 immunocompromised patients in Israel treated with tixagevimab/cilgavimab, and 2,812 matched controls, showing significantly lower cases and hospitalization with treatment.
Ollila:
Retrospective 378 patients with hematologic malignancies analyzing seroconversion and outcomes post-vaccination. Among 25 seronegative patients after booster vaccination who received tixagevimab/cilgavimab prophylaxis, no COVID-19 infections occurred, whereas 3 infections and 1 death occurred among 12 comparable patients not receiving prophylaxis.
Sindu:
Retrospective 546 lung transplant recipients, 203 receiving tixagevimab/cilgavimab, and 343 out of state or declining treatment, showing a trend towards lower incidence of cases, but no significant difference in clinical outcomes.
Solera:
Retrospective 1,975 solid organ transplant recipients with COVID-19 showing lower risk of severe cases with tixagevimab/cilgavimab prophylaxis, without statistical significance.
Young-Xu:
PSM retrospective 1,848 immunocompromised patients given tixagevimab/cilgavimab prophylaxis, showing lower mortality, hospitalization, and cases.
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 tixagevimab, cilgavimab, Evusheld 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 tixagevimab/cilgavimab 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 to108.
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 1111.
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 PythonMeta112
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 effective49,50.
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/tcmeta.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.
| Lombardi, 1/19/2023, retrospective, Italy, preprint, 21 authors, study period 28 August, 2022 - 15 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, 368.4% higher, RR 4.68, p = 0.32, treatment 1 of 19 (5.3%), control 1 of 89 (1.1%), day 14. |
| risk of hospitalization, 33.1% lower, RR 0.67, p = 1.00, treatment 1 of 19 (5.3%), control 7 of 89 (7.9%), NNT 38, day 14. | |
| risk of no viral clearance, 23.7% higher, RR 1.24, p = 0.30, treatment 14 of 19 (73.7%), control 53 of 89 (59.6%), day 14. | |
| Montgomery, 6/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 20 authors, study period 28 January, 2021 - 22 July, 2021, trial NCT04723394 (history) (TACKLE). | risk of death, 0.2% lower, RR 1.00, p = 1.00, treatment 6 of 452 (1.3%), control 6 of 451 (1.3%), NNT 33975, all cause mortality. |
| risk of death, 50.1% lower, RR 0.50, p = 0.34, treatment 3 of 452 (0.7%), control 6 of 451 (1.3%), NNT 150, COVID-19 mortality. | |
| risk of severe case, 50.4% lower, RR 0.50, p = 0.010, treatment 18 of 407 (4.4%), control 37 of 415 (8.9%), NNT 22, primary outcome. | |
| risk of hospitalization, 56.7% lower, RR 0.43, p = 0.002, treatment 17 of 413 (4.1%), control 40 of 421 (9.5%), NNT 19. |
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.
| Hites, 2/16/2024, Double Blind Randomized Controlled Trial, placebo-controlled, France, peer-reviewed, 86 authors, trial NCT04315948 (history) (DisCoVeRy). | risk of death, 39.9% lower, RR 0.60, p = 0.17, treatment 12 of 123 (9.8%), control 16 of 103 (15.5%), NNT 17, odds ratio converted to relative risk, day 90. |
| risk of 7-point scale, 17.6% higher, OR 1.18, p = 0.52, treatment 123, control 103, inverted to make OR<1 favor treatment, day 15, primary outcome, RR approximated with OR. | |
| recovery time, 1.0% lower, relative time 0.99, p = 0.93, treatment 123, control 103, inverted to make RR<1 favor treatment. | |
| time to discharge, 9.1% lower, relative time 0.91, p = 0.49, treatment 123, control 103, inverted to make RR<1 favor treatment. | |
| Holland, 7/8/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 103 authors, study period 10 February, 2021 - 30 September, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (ACTIV-3-TICO). | risk of death, 30.0% lower, RR 0.70, p = 0.03, treatment 61 of 710 (8.6%), control 86 of 707 (12.2%), NNT 28, day 90. |
| risk of no recovery, 7.4% lower, RR 0.93, p = 0.21, treatment 710, control 707, inverted to make RR<1 favor treatment, sustained recovery, day 90, primary outcome. |
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.
| Al Jurdi, 12/31/2022, retrospective, USA, peer-reviewed, 6 authors. | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 222 (0.0%), control 3 of 222 (1.4%), NNT 74, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 83.3% lower, RR 0.17, p = 0.12, treatment 1 of 222 (0.5%), control 6 of 222 (2.7%), NNT 44. | |
| risk of case, 65.6% lower, RR 0.34, p = 0.001, treatment 11 of 222 (5.0%), control 32 of 222 (14.4%), NNT 11. | |
| Bes-Berlandier, 5/28/2024, retrospective, France, peer-reviewed, median age 51.0, 10 authors, study period March 2020 - April 2022. | oxygen increase, ICU, or mortality, 10.0% higher, RR 1.10, p = 1.00, treatment 3 of 14 (21.4%), control 15 of 77 (19.5%). |
| Desai, 9/6/2023, retrospective, USA, peer-reviewed, 4 authors, study period 1 January, 2022 - 28 October, 2022. | risk of hospitalization, 12.0% lower, OR 0.88, p = 0.81, treatment 391, control 391, RR approximated with OR. |
| risk of case, 35.0% higher, OR 1.35, p = 0.18, treatment 391, control 391, RR approximated with OR. | |
| Din, 8/8/2023, retrospective, USA, peer-reviewed, 24 authors, study period June 2020 - February 2022, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 19.0% lower, RR 0.81, p = 0.77, treatment 5 of 23 (21.7%), control 11 of 41 (26.8%), NNT 20. |
| Kaminski, 10/31/2022, retrospective, France, peer-reviewed, 21 authors, study period 28 December, 2021 - 28 February, 2022. | risk of death, 92.4% lower, HR 0.08, p = 0.07, treatment 1 of 333 (0.3%), control 2 of 97 (2.1%), NNT 57, Cox proportional hazards. |
| risk of ICU admission, 95.5% lower, HR 0.04, p = 0.001, treatment 2 of 333 (0.6%), control 6 of 97 (6.2%), NNT 18, Cox proportional hazards. | |
| risk of hospitalization, 95.4% lower, HR 0.05, p = 0.001, treatment 4 of 333 (1.2%), control 11 of 97 (11.3%), NNT 9.9, Cox proportional hazards. | |
| risk of symptomatic case, 98.9% lower, HR 0.01, p = 0.001, treatment 41 of 333 (12.3%), control 42 of 97 (43.3%), NNT 3.2, Cox proportional hazards. | |
| Kertes, 7/29/2022, retrospective, Israel, peer-reviewed, 10 authors. | risk of death/hospitalization, 91.9% lower, RR 0.08, p = 0.01, treatment 1 of 825 (0.1%), control 63 of 4,299 (1.5%), NNT 74, adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of case, 47.1% lower, RR 0.53, p = 0.01, treatment 29 of 825 (3.5%), control 308 of 4,299 (7.2%), NNT 27, adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Levin, 4/20/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 24 authors, study period 21 November, 2020 - 22 March, 2021, trial NCT04625725 (history) (PROVENT). | risk of death, 85.7% lower, RR 0.14, p = 0.11, treatment 0 of 3,441 (0.0%), control 2 of 1,731 (0.1%), NNT 866, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of symptomatic case, 82.1% lower, RR 0.18, p < 0.001, treatment 11 of 3,441 (0.3%), control 31 of 1,731 (1.8%), NNT 68, 6 months. | |
| risk of symptomatic case, 76.3% lower, RR 0.24, p < 0.001, treatment 8 of 3,441 (0.2%), control 17 of 1,731 (1.0%), NNT 133, median 83 days followup. | |
| Levin (B), 12/8/2021, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 21 authors, study period 2 December, 2020 - 19 March, 2021, trial NCT04625972 (history) (STORM CHASER). | risk of symptomatic case, 41.7% lower, RR 0.58, p = 0.06, treatment 27 of 749 (3.6%), control 23 of 372 (6.2%), NNT 39, extended data cutoff. |
| risk of symptomatic case, 32.8% lower, RR 0.67, p = 0.23, treatment 23 of 749 (3.1%), control 17 of 372 (4.6%), NNT 67, primary outcome. | |
| Najjar-Debbiny, 10/31/2022, retrospective, Israel, peer-reviewed, 5 authors. | risk of hospitalization, 59.0% lower, HR 0.41, p = 0.02, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), Cox proportional hazards. |
| risk of case, 25.0% lower, HR 0.75, p = 0.03, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), NNT 32, Cox proportional hazards. | |
| Ollila, 7/11/2022, retrospective, USA, peer-reviewed, 13 authors, study period February 2021 - February 2022. | risk of death, 75.5% lower, RR 0.24, p = 0.32, treatment 0 of 25 (0.0%), control 1 of 12 (8.3%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of case, 90.2% lower, RR 0.10, p = 0.03, treatment 0 of 25 (0.0%), control 3 of 12 (25.0%), NNT 4.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| Sindu, 5/12/2023, retrospective, USA, peer-reviewed, median age 67.4, 7 authors, study period December 2021 - August 2022. | risk of death, 1.5% higher, HR 1.01, p = 0.99, treatment 2 of 17 (11.8%), control 2 of 17 (11.8%), propensity score matching, Cox proportional hazards. |
| risk of mechanical ventilation, 95.8% higher, HR 1.96, p = 0.58, propensity score matching, Cox proportional hazards. | |
| risk of ICU admission, 209.6% higher, HR 3.10, p = 0.33, propensity score matching, Cox proportional hazards. | |
| risk of hospitalization, 53.2% lower, HR 0.47, p = 0.17, propensity score matching, Cox proportional hazards. | |
| risk of symptomatic case, 28.9% lower, RR 0.71, p = 0.14, treatment 24 of 203 (11.8%), control 57 of 343 (16.6%), NNT 21, unadjusted. | |
| Solera, 3/31/2024, retrospective, Canada, peer-reviewed, median age 57.5, 12 authors. | risk of severe case, 25.6% lower, RR 0.74, p = 0.48, treatment 7 of 156 (4.5%), control 283 of 1,819 (15.6%), NNT 9.0, adjusted per study, odds ratio converted to relative risk, multivariable. |
| Young-Xu, 5/29/2022, retrospective, propensity score matching, USA, preprint, 10 authors. | risk of death, 64.0% lower, HR 0.36, p = 0.004, treatment 1,733, control 6,354. |
| risk of death/hospitalization/cases, 69.0% lower, HR 0.31, p < 0.001, treatment 17 of 1,733 (1.0%), control 206 of 6,354 (3.2%), NNT 44. | |
| risk of hospitalization, 87.0% lower, HR 0.13, p = 0.04, treatment 1,733, control 6,354. | |
| risk of case, 66.0% lower, HR 0.34, p = 0.03, treatment 1,733, control 6,354. |
Planas et al., Resistance of Omicron subvariants BA.2.75.2, BA.4.6 and BQ.1.1 to neutralizing antibodies, bioRxiv, doi:10.1101/2022.11.17.516888.
Haars et al., Prevalence of SARS-CoV-2 Omicron Sublineages and Spike Protein Mutations Conferring Resistance against Monoclonal Antibodies in a Swedish Cohort during 2022–2023, Microorganisms, doi:10.3390/microorganisms11102417.
Uraki et al., Antiviral efficacy against and replicative fitness of an XBB.1.9.1 clinical isolate, iScience, doi:10.1016/j.isci.2023.108147.
Pochtovyi et al., In Vitro Efficacy of Antivirals and Monoclonal Antibodies against SARS-CoV-2 Omicron Lineages XBB.1.9.1, XBB.1.9.3, XBB.1.5, XBB.1.16, XBB.2.4, BQ.1.1.45, CH.1.1, and CL.1, Vaccines, doi:10.3390/vaccines11101533.
Focosi et al., Analysis of SARS-CoV-2 mutations associated with resistance to therapeutic monoclonal antibodies that emerge after treatment, Drug Resistance Updates, doi:10.1016/j.drup.2023.100991.
Leducq et al., Spike protein genetic evolution in patients at high-risk of severe COVID-19 treated by monoclonal antibodies, The Journal of Infectious Diseases, doi:10.1093/infdis/jiad523.
Choudhary et al., Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy, medRxiv, doi:10.1101/2021.09.03.21263105.
Günther et al., Variant-specific humoral immune response to SARS-CoV-2 escape mutants arising in clinically severe, prolonged infection, medRxiv, doi:10.1101/2024.01.06.24300890.
Casadevall et al., Single monoclonal antibodies should not be used for COVID-19 therapy: a call for antiviral stewardship, Clinical Infectious Diseases, doi:10.1093/cid/ciae408.
Soeroto et al., Efficacy and safety of tixagevimab‐cilgavimab as pre‐exposure prophylaxis for COVID‐19: A systematic review and meta‐analysis, Reviews in Medical Virology, doi:10.1002/rmv.2420.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Hattab et al., SARS-CoV-2 journey: from alpha variant to omicron and its sub-variants, Infection, doi:10.1007/s15010-024-02223-y.
Focosi (B), D., Monoclonal Antibody Therapies Against SARS-CoV-2: Promises and Realities, Current Topics in Microbiology and Immunology, doi:10.1007/82_2024_268.
Zhang et al., Virological Traits of the SARS-CoV-2 BA.2.87.1 Lineage, Vaccines, doi:10.3390/vaccines12050487.
Davis et al., The Promise and Peril of Anti-SARS-CoV-2 Monoclonal Antibodies, Clinical Infectious Diseases, doi:10.1093/cid/ciac902.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Din et al., COVID-19 infection among CAR-T cell therapy recipients: A single center experience, Hemasphere, doi:10.1097/01.HS9.0000973036.97124.6c.
Lombardi et al., Preliminary Evidence of Good Safety Profile and Outcomes of Early Treatment With Tixagevimab/Cilgavimab Compared to Previously Employed Monoclonal Antibodies for COVID-19 in Immunocompromised Patients, Preprints, doi:10.20944/preprints202301.0359.v1.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Focosi (C), D., Monoclonal Antibody Therapies Against SARS-CoV-2: Promises and Realities, Current Topics in Microbiology and Immunology, doi:10.1007/82_2024_268.
Enyeji et al., Effective Treatment of COVID-19 Infection with Repurposed Drugs: Case Reports, Viral Immunology, doi:10.1089/vim.2024.0034.
Focosi (D) et al., Analysis of SARS-CoV-2 mutations associated with resistance to therapeutic monoclonal antibodies that emerge after treatment, Drug Resistance Updates, doi:10.1016/j.drup.2023.100991.
Al Jurdi et al., Tixagevimab/cilgavimab pre-exposure prophylaxis is associated with lower breakthrough infection risk in vaccinated solid organ transplant recipients during the omicron wave, American Journal of Transplantation, doi:10.1111/ajt.17128.
Bes-Berlandier et al., Management of immunosuppression in lung transplant recipients and COVID-19 outcomes: an observational retrospective cohort-study, BMC Infectious Diseases, doi:10.1186/s12879-024-09269-1.
Desai et al., Tixagevimab and Cilgavimab (Evusheld) as Pre-exposure Prophylaxis for COVID-19 in Patients With Inflammatory Bowel Disease: A Propensity Matched Cohort Study, Crohn's & Colitis 360, doi:10.1093/crocol/otad047.
Hites et al., Tixagevimab-cilgavimab (AZD7442) for the treatment of patients hospitalized with COVID-19 (DisCoVeRy): a phase 3, randomized, double-blind, placebo-controlled trial, Journal of Infection, doi:10.1016/j.jinf.2024.106120.
Holland et al., Tixagevimab–cilgavimab for treatment of patients hospitalised with COVID-19: a randomised, double-blind, phase 3 trial, The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00215-6.
Kaminski et al., COVID-19 morbidity decreases with tixagevimab–cilgavimab preexposure prophylaxis in kidney transplant recipient nonresponders or low-vaccine responders, Kidney International, doi:10.1016/j.kint.2022.07.008.
Kertes et al., Association between AZD7442 (tixagevimab-cilgavimab) administration and SARS-CoV-2 infection, hospitalization and mortality, Clinical Infectious Diseases, doi:10.1093/cid/ciac625.
Levin et al., Intramuscular AZD7442 (Tixagevimab–Cilgavimab) for Prevention of Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2116620.
Levin (B) et al., AZD7442 (Tixagevimab/Cilgavimab) for Post-exposure Prophylaxis of Symptomatic COVID-19, Clinical Infectious Diseases, doi:10.1093/cid/ciac899.
Montgomery et al., Efficacy and safety of intramuscular administration of tixagevimab–cilgavimab for early outpatient treatment of COVID-19 (TACKLE): a phase 3, randomised, double-blind, placebo-controlled trial, The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00180-1.
Najjar-Debbiny et al., Effectiveness of Evusheld in Immunocompromised Patients: Propensity Score-Matched Analysis, Clinical Infectious Diseases, doi:10.1093/cid/ciac855.
Ollila et al., Seroconversion and outcomes after initial and booster COVID‐19 vaccination in adults with hematologic malignancies, Cancer, doi:10.1002/cncr.34354.
Sindu et al., Pre-exposure Prophylaxis with Tixagevimab-cilgavimab did not Reduce Severity of COVID-19 in Lung Transplant Recipients with Breakthrough Infection, Transplantation Direct, doi:10.1097/txd.0000000000001485.
Solera et al., Longitudinal outcomes of COVID-19 in solid organ transplant recipients from 2020 to 2023, American Journal of Transplantation, doi:10.1016/j.ajt.2024.03.011.
Young-Xu et al., Tixagevimab/Cilgavimab for Prevention of COVID-19 during the Omicron Surge: Retrospective Analysis of National VA Electronic Data, medRxiv, doi:10.1101/2022.05.28.22275716.
Zhang (B) et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
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