Bebtelovimab for COVID-19: real-time meta analysis of 6 studies
Abstract
Meta analysis using the most serious outcome reported shows
34% [-24‑65%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies and worse for Randomized Controlled Trials.
2 studies from 2 independent teams (both from the same country) show significant
improvements.
Currently there is limited data, with only 34 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BQ.1.11, BA.5, BA.2.75, XBB, XBB.1.5, XMM.1.9.12,3. mAb use may create new variants that spread globally4,5, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5-8.
Prescription treatments have been preferentially used by patients at lower risk9. 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
may be more effective.
All data to reproduce this paper and
sources are in the appendix.
Bebtelovimab for COVID-19 — Highlights
Bebtelovimab reduces
risk with low confidence for mortality, ICU admission, and hospitalization, and very low confidence for pooled analysis, however increased risk is seen with low confidence for progression.
Efficacy is variant dependent.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 109
treatments, outcome specific analyses and combined evidence from all studies.
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 bebtelovimab 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 injury10-20 and
cognitive deficits12,17, cardiovascular
complications21-23, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,24-29, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk30, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
bebtelovimab
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Currently all bebtelovimab studies use early treatment.
Figure 2. Treatment stages.
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing
antibodies from monoclonal antibody treatments31,32, resulting in efficacy
that is highly variant dependent.
For example, in vitro research suggests
that bebtelovimab is not effective for omicron BQ.1.11.
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 studies, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes.
Figure 3, 4, 5, 6, 7, 8, and 9
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ICU admission, hospitalization, progression, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 34% [-24‑65%] | 6 | 13,329 | 70 |
| Peer-reviewed studiesPeer-reviewed | 46% [-10‑73%] | 4 | 12,569 | 47 |
| Randomized Controlled TrialsRCTs | -46% [-522‑66%] | 2 | 760 | 23 |
| Mortality | 60% [-11‑86%] | 4 | 12,478 | 32 |
| HospitalizationHosp. | 33% [-3‑56%] | 5 | 12,858 | 54 |
| Viral | 15% [-22‑41%] | 2 | 633 | 23 |
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Figure 3. 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 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for progression.
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Figure 8. Random effects meta-analysis for viral clearance.
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Figure 9. 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 10 shows a comparison of results for RCTs and non-RCT studies.
Figure 11 and 12
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 2.
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Figure 10. Results for RCTs and non-RCT studies.
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Figure 11. 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 12. 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 biases36, and
analysis of double-blind RCTs has identified extreme levels of bias37.
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 treatments39.
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 see43,44.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to
evaluate each trial on its own merits. RCTs for a given medication and disease
may be more reliable, however they may also be less reliable. For off-patent
medications, very high conflict of interest trials may be more likely to be
RCTs, and more likely to be large trials that dominate meta analyses.
Heterogeneity in COVID-19 studies arises from many factors including:
The time
between infection or the onset of symptoms and treatment may critically affect
how well a treatment works. For example an antiviral may be very effective
when used early but may not be effective in late stage disease, and may even
be harmful. Oseltamivir, for example, is generally only considered effective
for influenza when used within 0-36 or 0-48 hours45,46.
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 cases47 |
| <24 hours | -33 hours symptoms48 |
| 24-48 hours | -13 hours symptoms48 |
| Inpatients | -2.5 hours to improvement49 |
Figure 13 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 13. 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 variants51, for
example the Gamma variant shows significantly different characteristics52-55. 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 variants56,57.
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 synergistic58-69, 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.
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 14 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 15 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 16 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000042 to p = 0.00000002.
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Figure 14. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 15. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 14. 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 17 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 17. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
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 bebtelovimab, there is currently not
enough data to evaluate publication bias with high confidence.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 18 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.0573-80.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 18. 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 alone58-69.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors24-29,
providing many therapeutic targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk30, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 19 shows an overview of the results for bebtelovimab
in the context of multiple COVID-19 treatments, and Figure 20 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 19.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 8,000+ proposed treatments show efficacy84.
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Figure 20. Efficacy vs. cost for COVID-19 treatments.
Meta analysis using the most serious outcome reported shows
34% [-24‑65%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies and worse for Randomized Controlled Trials.
2 studies from 2 independent teams (both from the same country) show significant
improvements.
Currently there is limited data, with only 34 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BQ.1.11, BA.5, BA.2.75, XBB, XBB.1.5, XMM.1.9.12,3. mAb use may create new variants that spread globally4,5, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5-8.
Prescription treatments have been preferentially used by patients at lower risk9. 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.
Dougan:
RCT showing improved viral clearance with bebtelovimab. Results refer to the placebo controlled portion of the trial.
Dryden-Peterson:
Retrospective 377 outpatients in the USA and matched controls, showing lower hospitalization/mortality with bebtelovimab treatment, without statistical significance. Notably, none of the patients that died in the control group were hospitalized within 14 days (later hospitalization is not reported). There may be a difference in the populations in terms of propensity to receive SOC.
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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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.
Lilly:
RCT with 127 bamlanivimab, etesevimab, and bebtelovimab patients, 125 bebtelovimab patients, and 128 control patients, showing no significant differences in hospitalization and mortality. Viral clearance was improved although not statistically significant. NCT04634409.
Molina:
Retrospective 3,739 patients treated with bebteloviman in the USA and matched controls, showing lower mortality and hospitalization with treatment, but higher emergency department visits.
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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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.
Sridhara:
PSM retrospective 19,778 high-risk outpatients in the USA, showing no significant difference in outcomes with bebtelovimab 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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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 hygiene88,89, vitamin D90, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab 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.
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 bebtelovimab 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 bebtelovimab for COVID-19 that report
a comparison with a control group are included in the main analysis.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral test status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to94.
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 197.
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 PythonMeta98
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 effective45,46.
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/btmeta.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.
| Dougan, 3/12/2022, Randomized Controlled Trial, USA, preprint, 22 authors, study period 19 April, 2021 - 19 July, 2021, average treatment delay 3.0 days, trial NCT04634409 (history) (BLAZE-4). | risk of hospitalization, 27.0% higher, RR 1.27, p = 1.00, treatment 5 of 252 (2.0%), control 2 of 128 (1.6%), combined bebtelovimab arms. |
| risk of hospitalization, 2.4% higher, RR 1.02, p = 1.00, treatment 2 of 125 (1.6%), control 2 of 128 (1.6%), bebtelovimab only. | |
| relative viral load reduction, 4.0% better, RR 0.96, p < 0.001, treatment mean 3.77 (±0.21) n=125, control mean 3.62 (±0.2) n=128, day 7. | |
| relative viral load reduction, 29.7% better, RR 0.70, p < 0.001, treatment mean 3.03 (±0.19) n=125, control mean 2.13 (±0.19) n=128, day 5. | |
| relative viral load reduction, 15.4% better, RR 0.85, p < 0.001, treatment mean 1.43 (±0.2) n=125, control mean 1.21 (±0.2) n=128, day 3. | |
| risk of no viral clearance, 38.6% lower, RR 0.61, p = 0.12, treatment 15 of 125 (12.0%), control 25 of 128 (19.5%), NNT 13, persistently high viral load, day 7, primary outcome. | |
| Dryden-Peterson, 10/27/2022, retrospective, USA, peer-reviewed, 7 authors, study period 16 March, 2022 - 31 May, 2022, average treatment delay 3.0 days. | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 377 (0.0%), control 3 of 377 (0.8%), NNT 126, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of death/hospitalization, 43.0% lower, RR 0.57, p = 0.14, treatment 10 of 377 (2.7%), control 17 of 377 (4.5%), NNT 54. | |
| risk of hospitalization, 28.6% lower, RR 0.71, p = 0.53, treatment 10 of 377 (2.7%), control 14 of 377 (3.7%), NNT 94. | |
| Kip, 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. | risk of death/hospitalization, 20.0% lower, RR 0.80, p = 0.65, treatment 6 of 157 (3.8%), control 15 of 314 (4.8%), NNT 105, omicron variant, day 28. |
| Lilly, 2/12/2022, Randomized Controlled Trial, USA, preprint, 1 author, study period 29 October, 2020 - 18 October, 2021, average treatment delay 3.6 days, trial NCT04634409 (history). | risk of death, 150.8% higher, RR 2.51, p = 1.00, treatment 1 of 252 (0.4%), control 0 of 128 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), combined bebtelovimab arms. |
| risk of death, 200.8% higher, RR 3.01, p = 0.50, treatment 1 of 127 (0.8%), control 0 of 128 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), bamlanivimab, etesevimab, and bebtelovimab. | |
| risk of hospitalization, 27.0% higher, RR 1.27, p = 1.00, treatment 5 of 252 (2.0%), control 2 of 128 (1.6%), combined bebtelovimab arms. | |
| risk of hospitalization, 51.2% higher, RR 1.51, p = 0.68, treatment 3 of 127 (2.4%), control 2 of 128 (1.6%), bamlanivimab, etesevimab, and bebtelovimab. | |
| risk of hospitalization, 2.4% higher, RR 1.02, p = 1.00, treatment 2 of 125 (1.6%), control 2 of 128 (1.6%), bebtelovimab. | |
| risk of no viral clearance, 35.5% lower, RR 0.64, p = 0.07, treatment 33 of 252 (13.1%), control 26 of 128 (20.3%), NNT 14, day 7 persistently high viral load, combined bebtelovimab arms, primary outcome. | |
| risk of no viral clearance, 38.0% lower, RR 0.62, p = 0.13, treatment 16 of 127 (12.6%), control 26 of 128 (20.3%), NNT 13, day 7 persistently high viral load, bamlanivimab, etesevimab, and bebtelovimab, primary outcome. | |
| risk of no viral clearance, 33.0% lower, RR 0.67, p = 0.18, treatment 17 of 125 (13.6%), control 26 of 128 (20.3%), NNT 15, day 7 persistently high viral load, bebtelovimab, primary outcome. | |
| Molina, 4/16/2023, retrospective, USA, peer-reviewed, 11 authors, study period 6 April, 2022 - 11 October, 2022. | risk of death, 57.0% lower, RR 0.43, p = 0.14, treatment 3 of 3,739 (0.1%), control 11 of 5,423 (0.2%), NNT 816, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable. |
| risk of ICU admission, 58.6% lower, RR 0.41, p = 0.05, treatment 6 of 3,739 (0.2%), control 21 of 5,423 (0.4%), NNT 441. | |
| risk of hospitalization, 55.5% lower, RR 0.44, p < 0.001, treatment 38 of 3,739 (1.0%), control 107 of 5,423 (2.0%), NNT 105, adjusted per study, odds ratio converted to relative risk, COVID-19, propensity score matching, multivariable. | |
| risk of hospitalization, 46.5% lower, RR 0.54, p < 0.001, treatment 48 of 3,739 (1.3%), control 116 of 5,423 (2.1%), NNT 117, adjusted per study, odds ratio converted to relative risk, all cause, propensity score matching, multivariable. | |
| risk of progression, 32.6% higher, RR 1.33, p = 0.001, treatment 260 of 3,739 (7.0%), control 275 of 5,423 (5.1%), adjusted per study, odds ratio converted to relative risk, ED visit, propensity score matching, multivariable. | |
| Sridhara, 4/28/2023, retrospective, USA, peer-reviewed, 13 authors, study period 5 April, 2022 - 1 August, 2022. | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 1,091 (0.0%), control 3 of 1,091 (0.3%), NNT 364, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), propensity score matching, day 30. |
| risk of death/hospitalization, 25.0% lower, HR 0.75, p = 0.31, treatment 24 of 1,091 (2.2%), control 28 of 1,091 (2.6%), adjusted per study, propensity score matching, multivariable, Cox proportional hazards. | |
| risk of hospitalization, 11.1% lower, RR 0.89, p = 0.78, treatment 24 of 1,091 (2.2%), control 27 of 1,091 (2.5%), NNT 364, propensity score matching, day 30RETRO. |
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and future variants.
We do not provide medical advice. Before taking any medication,
consult a qualified physician who can provide personalized advice and details
of risks and benefits based on your medical history and situation. FLCCC and WCH
provide treatment protocols.