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Bebtelovimab for COVID-19: real-time meta analysis of 6 studies

@CovidAnalysis, June 2024, Version 16V16
 
0 0.5 1 1.5+ All studies 34% 6 13,329 Improvement, Studies, Patients Relative Risk Mortality 60% 4 12,478 ICU admission 59% 1 9,162 Hospitalization 33% 5 12,858 Viral clearance 15% 2 633 RCTs -46% 2 760 Peer-reviewed 46% 4 12,569 Early 34% 6 13,329 Bebtelovimab for COVID-19 c19early.org June 2024 Favorsbebtelovimab Favorscontrol
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, XBB2. mAb use may create new variants that spread globally3,4, and may be associated with prolonged viral loads, clinical deterioration, and immune escape4-6.
Prescription treatments have been preferentially used by patients at lower risk7. 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.
Evolution of COVID-19 clinical evidence Bebtelovimab p=0.2 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org June 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
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 75 treatments, outcome specific analyses and combined evidence from all studies.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lilly (RCT) -151% 2.51 [0.10-61.1] death 1/252 0/128 Improvement, RR [CI] Treatment Control BLAZE-4 Dougan (RCT) -27% 1.27 [0.25-6.46] hosp. 5/252 2/128 Dryden-Peterson 86% 0.14 [0.01-2.76] death 0/377 3/377 Kip 20% 0.80 [0.32-2.02] death/hosp. 6/157 15/314 Molina (PSM) 57% 0.43 [0.11-1.30] death 3/3,739 11/5,423 Sridhara (PSM) 86% 0.14 [0.01-2.76] death 0/1,091 3/1,091 Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Early treatment 34% 0.66 [0.35-1.24] 15/5,868 34/7,461 34% lower risk All studies 34% 0.66 [0.35-1.24] 15/5,868 34/7,461 34% lower risk 6 bebtelovimab COVID-19 studies c19early.org June 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Effect extraction pre-specified(most serious outcome, see appendix) Favors bebtelovimab Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lilly (RCT) -151% death Improvement Relative Risk [CI] BLAZE-4 Dougan (RCT) -27% hospitalization Dryden-Peterson 86% death Kip 20% death/hosp. Molina (PSM) 57% death Sridhara (PSM) 86% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Early treatment 34% 34% lower risk All studies 34% 34% lower risk 6 bebtelovimab C19 studies c19early.org June 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Effect extraction pre-specifiedRotate device for details Favors bebtelovimab Favors control
B
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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.
Introduction
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 injury8-15 and cognitive deficits10,15, cardiovascular complications16, 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,17-21, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk22, 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.
Variant Dependence
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing antibodies from monoclonal antibody treatments23, 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.
Table 1. Predicted efficacy by variant from Davis et al. (not updated for more recent variants).    : likely effective    : likely ineffective    : unknown. Submit updates.
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
Results
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.
Table 2. Random effects meta-analysis for all studies, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Improvement Studies Patients Authors
All studies34% [-24‑65%]6 13,329 70
Peer-reviewed studiesPeer-reviewed46% [-10‑73%]4 12,569 47
Randomized Controlled TrialsRCTs-46% [-522‑66%]2 760 23
Mortality60% [-11‑86%]4 12,478 32
HospitalizationHosp.33% [-3‑56%]5 12,858 54
Viral15% [-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.
Randomized Controlled Trials (RCTs)
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 biases27, and analysis of double-blind RCTs has identified extreme levels of bias28. 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 75 treatments we have analyzed, 64% 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.
Evidence shows that non-RCT 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. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. 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 Internet 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 see33,34.
Currently, 46 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, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.4 months. For the 16 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 13 are all consistent with the overall results (benefit or harm), with 10 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
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
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 hours35,36. Baloxavir 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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases37
<24 hours-33 hours symptoms38
24-48 hours-13 hours symptoms38
Inpatients-2.5 hours to improvement39
Figure 13 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 75 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 75 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 variants41, for example the Gamma variant shows significantly different characteristics42-45. 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 variants46,47.
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 synergistic48-58, 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.
Pooled Effects
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 75 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 = 0.0000014 to p = 0.000000005.
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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, 46 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 54% 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 < 0.0562-69. 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 alone48-58. 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.
Focosi (B) et al. present a review covering bebtelovimab for COVID-19.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors17-21, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk22, 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 7,000+ proposed treatments show efficacy71.
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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, XBB2. mAb use may create new variants that spread globally3,4, and may be associated with prolonged viral loads, clinical deterioration, and immune escape4-6.
Prescription treatments have been preferentially used by patients at lower risk7. 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.
0 0.5 1 1.5 2+ Hospitalization -27% Improvement Relative Risk Hospitalization (b) -2% Recovery time 25% no CI Viral load reduction, day 7 4% Viral load reduction, day 5 30% Viral load reduction, day 3 15% Viral clearance 39% primary Bebtelovimab  BLAZE-4  EARLY TREATMENT  RCT Is early treatment with bebtelovimab beneficial for COVID-19? RCT 714 patients in the USA (April - July 2021) Improved viral clearance with bebtelovimab (p<0.000001) c19early.org Dougan et al., medRxiv, March 2022 Favors bebtelovimab Favors control
Dougan: RCT showing improved viral clearance with bebtelovimab. Results refer to the placebo controlled portion of the trial.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Death/hospitalization 43% Hospitalization 29% Bebtelovimab  Dryden-Peterson et al.  EARLY TREATMENT Is early treatment with bebtelovimab beneficial for COVID-19? Retrospective 754 patients in the USA (March - May 2022) Lower mortality (p=0.25) and death/hosp. (p=0.14), not sig. c19early.org Dryden-Peterson et al., Open Forum Inf.., Oct 2022 Favors bebtelovimab Favors control
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.
0 0.5 1 1.5 2+ Death/hospitalization 20% Improvement Relative Risk Bebtelovimab for COVID-19  Kip et al.  EARLY TREATMENT Is early treatment with bebtelovimab beneficial for COVID-19? Retrospective 2,571 patients in the USA (December 2020 - August 2022) Study underpowered to detect differences c19early.org Kip et al., Annals of Internal Medicine, Apr 2023 Favors bebtelovimab Favors control
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 hygiene75,76, vitamin D77, 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.
0 0.5 1 1.5 2+ Mortality -151% Improvement Relative Risk Mortality (b) -201% Hospitalization -27% Hospitalization (b) -51% Hospitalization (c) -2% Viral clearance 36% primary Viral clearance (b) 38% primary Viral clearance (c) 33% primary Bebtelovimab  Lilly et al.  EARLY TREATMENT  RCT Is early treatment with bebtelovimab beneficial for COVID-19? RCT 380 patients in the USA (October 2020 - October 2021) Improved viral clearance with bebtelovimab (not stat. sig., p=0.073) c19early.org Lilly, February 2022 Favors bebtelovimab Favors control
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.
0 0.5 1 1.5 2+ Mortality 57% Improvement Relative Risk ICU admission 59% Hospitalization, COVID-19 56% Hospitalization, all cause 46% Progression -33% Bebtelovimab  Molina et al.  EARLY TREATMENT Is early treatment with bebtelovimab beneficial for COVID-19? Retrospective 9,162 patients in the USA (April - October 2022) Lower hospitalization (p<0.0001) and higher progression (p=0.001) c19early.org Molina et al., Int. J. Infectious Dise.., Apr 2023 Favors bebtelovimab Favors control
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 hygiene75,76, vitamin D77, 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.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Death/hospitalization 25% Hospitalization 11% Bebtelovimab  Sridhara et al.  EARLY TREATMENT Is early treatment with bebtelovimab beneficial for COVID-19? PSM retrospective 2,182 patients in the USA (Apr - Aug 2022) Lower mortality (p=0.25) and death/hosp. (p=0.31), not sig. c19early.org Sridhara et al., PLOS ONE, April 2023 Favors bebtelovimab Favors control
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 hygiene75,76, vitamin D77, 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 to81. 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 184. 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 (3.12.3) with scipy (1.13.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta85 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 effective35,36.
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 the bottom of this page.
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, 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.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current 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.
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