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Tixagevimab/cilgavimab for COVID-19: real-time meta analysis of 8 studies
Covid Analysis, December 2022
https://c19early.org/tcmeta.html
 
0 0.5 1 1.5+ All studies 50% 8 26,460 Improvement, Studies, Patients Relative Risk Mortality 41% 4 15,579 Hospitalization 59% 3 12,436 Cases 51% 6 24,140 RCTs 32% 5 9,734 RCT mortality 29% 3 7,492 Peer-reviewed 50% 6 17,252 Prophylaxis 58% 6 24,140 Early 0% 1 903 Late 30% 1 1,417 Tixagevimab/cilgavimab for COVID-19 c19early.org/tc Dec 2022 Favorstixagevimab/ci.. Favorscontrol
Statistically significant improvements are seen for mortality, hospitalization, and cases. 6 studies from 6 independent teams in 2 different countries show statistically significant improvements in isolation (4 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 50% [30‑64%] improvement. Results are slightly worse for Randomized Controlled Trials and similar for peer-reviewed studies.
0 0.5 1 1.5+ All studies 50% 8 26,460 Improvement, Studies, Patients Relative Risk Mortality 41% 4 15,579 Hospitalization 59% 3 12,436 Cases 51% 6 24,140 RCTs 32% 5 9,734 RCT mortality 29% 3 7,492 Peer-reviewed 50% 6 17,252 Prophylaxis 58% 6 24,140 Early 0% 1 903 Late 30% 1 1,417 Tixagevimab/cilgavimab for COVID-19 c19early.org/tc Dec 2022 Favorstixagevimab/ci.. Favorscontrol
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.1 [Planas]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 25% of tixagevimab/cilgavimab studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Tixagevimab/cilgavimab reduces risk for COVID-19 with very high confidence for hospitalization, cases, and in pooled analysis, high confidence for mortality, and very low confidence for recovery. Efficacy is variant dependent.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgom.. (DB RCT) 0% 1.00 [0.32-3.07] death 6/452 6/451 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 1.00 [0.32-3.07] 6/452 6/451 0% improvement Holland (DB RCT) 30% 0.70 [0.50-0.97] death 61/710 86/707 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.024 Late treatment 30% 0.70 [0.50-0.97] 61/710 86/707 30% improvement FDA (DB RCT) 40% 0.60 [0.35-1.03] symp. case 28/749 23/372 Improvement, RR [CI] Treatment Control Levin (DB RCT) 86% 0.14 [0.01-2.98] death 0/3,441 2/1,731 Young-Xu (PSM) 64% 0.36 [0.18-0.73] death 1,733 (n) 6,354 (n) Immunocompromised Kertes 92% 0.08 [0.01-0.54] death/hosp. 1/825 63/4,299 Immunocompromised Najjar-Debbiny 59% 0.41 [0.19-0.89] hosp. 72/703 377/2,812 Levin (DB RCT) 75% 0.25 [0.01-6.10] severe case 0/749 1/372 Tau​2 = 0.01, I​2 = 4.6%, p < 0.0001 Prophylaxis 58% 0.42 [0.33-0.53] 101/8,200 466/15,940 58% improvement All studies 50% 0.50 [0.36-0.70] 168/9,362 558/17,098 50% improvement 8 tixagevimab/cilgavimab COVID-19 studies c19early.org/tc Dec 2022 Tau​2 = 0.08, I​2 = 50.1%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) Favors tixagevimab/ci.. Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Montgo.. (DB RCT) 0% death Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 1. Early treatment 0% 0% improvement Holland (DB RCT) 30% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.024 Late treatment 30% 30% improvement FDA (DB RCT) 40% symp. case Levin (DB RCT) 86% death Young-Xu (PSM) 64% death Immunocompromised Kertes 92% death/hosp. Immunocompromised Najjar-Debbiny 59% hospitalization Levin (DB RCT) 75% severe case Tau​2 = 0.01, I​2 = 4.6%, p < 0.0001 Prophylaxis 58% 58% improvement All studies 50% 50% improvement 8 tixagevimab/cilgavimab COVID-19 studies c19early.org/tc Dec 2022 Tau​2 = 0.08, I​2 = 50.1%, p < 0.0001 Effect extraction pre-specifiedRotate device for details Favors tixagevimab/ci.. 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, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in tixagevimab/cilgavimab studies.
We analyze all significant studies concerning the use of tixagevimab/cilgavimab for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Efficacy is variant dependent, for example in vitro research suggests that tixagevimab/cilgavimab is not effective for omicron BA.4.6 and BQ.1.1 [Planas].
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 1. Predicted efficacy by variant from [Davis].    : likely effective    : likely ineffective    : unknown. Submit updates.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, and 8 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, recovery, cases, and peer reviewed studies.
Improvement Studies Patients Authors
All studies50% [30‑64%]8 26,460 194
Peer-reviewed studiesPeer-reviewed50% [20‑69%]6 17,252 183
Randomized Controlled TrialsRCTs32% [12‑48%]5 9,734 169
Mortality41% [6‑63%]4 15,579 157
HospitalizationHosp.59% [49‑67%]3 12,436 35
Cases51% [29‑66%]6 24,140 71
RCT mortality29% [5‑48%]3 7,492 147
Table 2. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies0% [-207‑68%] 130% [3‑50%] 158% [47‑67%] 6
Peer-reviewed studiesPeer-reviewed0% [-207‑68%] 130% [3‑50%] 162% [46‑73%] 4
Randomized Controlled TrialsRCTs0% [-207‑68%] 130% [3‑50%] 143% [5‑66%] 3
Mortality0% [-207‑68%] 130% [3‑50%] 166% [32‑83%] 2
HospitalizationHosp.57% [25‑75%] 1-65% [25‑83%] 2
Cases--51% [29‑66%] 6
RCT mortality0% [-207‑68%] 130% [3‑50%] 186% [-198‑99%] 1
Table 3. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction 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 hospitalization.
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Figure 6. Random effects meta-analysis for recovery.
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Figure 7. Random effects meta-analysis for cases.
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Figure 8. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 9 shows a comparison of results for RCTs and non-RCT studies. Figure 10 and 11 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows 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 could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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Figure 9. Results for RCTs and non-RCT studies.
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Figure 10. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 11. Random effects meta-analysis for RCT mortality results.
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 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Table 4. Early treatment is more effective for baloxavir and influenza.
Figure 12 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 12. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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 (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 13. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 13. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For tixagevimab/cilgavimab, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
100% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 60% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 64% improvement, compared to 40% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 14 shows a scatter plot of results for prospective and retrospective studies.
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Figure 14. Prospective vs. retrospective studies.
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 15 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 < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. 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 15. Example funnel plot analysis for simulated perfect trials.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that 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.
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 by 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.
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 affiliated with special interests 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 alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
Studies to date show that tixagevimab/cilgavimab is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, hospitalization, and cases. 6 studies from 6 independent teams in 2 different countries show statistically significant improvements in isolation (4 for the most serious outcome). Meta analysis using the most serious outcome reported shows 50% [30‑64%] improvement. Results are slightly worse for Randomized Controlled Trials and similar for peer-reviewed studies.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BA.2.75.2, BA.4.6, and BQ.1.1 [Planas]. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
0 0.5 1 1.5 2+ Symptomatic case, day.. 40% Improvement Relative Risk Symptomatic case 33% c19early.org/tc FDA et al. NCT04625972 Tixagev../c.. for COVID-19 RCT Prophylaxis Favors tixagevimab/ci.. Favors control
[FDA] PEP RCT with 749 tixagevimab/cilgavimab patients and 372 control patients, showing lower risk of symptomatic cases with treatment, without statistical significance. STORM CHASER. NCT04625972.
0 0.5 1 1.5 2+ Mortality 30% Improvement Relative Risk Recovery 7% primary c19early.org/tc Holland et al. NCT04501978 ACTIV-3-TICO Tixagev../c.. RCT LATE Favors tixagevimab/ci.. Favors control
[Holland] RCT with 710 hospitalized patients treated with tixagevimab/cilgavimab, and 707 placebo patients, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Death/hospitalization 92% Improvement Relative Risk Case 47% c19early.org/tc Kertes et al. Tixagev../c.. for COVID-19 Prophylaxis Favors tixagevimab/ci.. Favors control
[Kertes] Retrospective 825 immunocompromised individuals treated with tixagevimab-cilgavimab and 4229 untreated in Israel, showing significantly lower infection and hospitalization/death with treatment. Omicron was the dominant variant.
0 0.5 1 1.5 2+ Severe case 75% Improvement Relative Risk Symptomatic case 42% Symptomatic case (b) 33% primary c19early.org/tc Levin et al. NCT04625972 Tixagev../c.. RCT Prophylaxis Favors tixagevimab/ci.. Favors control
[Levin] 1,121 patient PEP RCT showing lower symptomatic cases with tixagevimab/cilgavimab, without statistical significance.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Symptomatic case 82% Symptomatic case (b) 76% c19early.org/tc Levin et al. NCT04625725 PROVENT Tixagev../c.. RCT Prophylaxis Favors tixagevimab/ci.. Favors control
[Levin (B)] PrEP RCT with 3,441 tixagevimab/cilgavimab patients and 1,731 control patients, showing lower risk of symptomatic cases with treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Severe case 50% primary Hospitalization 57% c19early.org/tc Montgomery et al. NCT04723394 TACKLE Tixagev../c.. RCT EARLY Favors tixagevimab/ci.. Favors control
[Montgomery] RCT 910 outpatients in the USA, 456 treated with tixagevimab/cilgavimab, showing significantly lower combined severe COVID-19/death with treatment.
0 0.5 1 1.5 2+ Hospitalization 59% Improvement Relative Risk Case 25% c19early.org/tc Najjar-Debbiny et al. Tixagev../c.. for COVID-19 Prophylaxis Favors tixagevimab/ci.. Favors control
[Najjar-Debbiny] Retrospective 732 immunocompromised patients in Israel treated with tixagevimab/cilgavimab, and 2,812 matched controls, showing significantly lower cases and hospitalization with treatment.
0 0.5 1 1.5 2+ Mortality 64% Improvement Relative Risk Death/hospitalization/c.. 69% Hospitalization 87% Case 66% c19early.org/tc Young-Xu et al. Tixagev../c.. for COVID-19 Prophylaxis Favors tixagevimab/ci.. Favors control
[Young-Xu] PSM retrospective 1,848 immunocompromised patients given tixagevimab/cilgavimab prophylaxis, showing lower mortality, hospitalization, and cases.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were tixagevimab, cilgavimab, Evusheld, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of tixagevimab/cilgavimab for COVID-19 that report a comparison with a control group are included in the main analysis. 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 1 [Sweeting]. 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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 effective [McLean, Treanor].
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/tcmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Montgomery], 6/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 20 authors, study period 28 January, 2021 - 22 July, 2021, trial NCT04723394 (history) (TACKLE). risk of death, 0.2% lower, RR 1.00, p = 1.00, treatment 6 of 452 (1.3%), control 6 of 451 (1.3%), NNT 33975, all cause mortality.
risk of severe case, 50.4% lower, RR 0.50, p = 0.010, treatment 18 of 407 (4.4%), control 37 of 415 (8.9%), NNT 22, primary outcome.
risk of hospitalization, 56.7% lower, RR 0.43, p = 0.002, treatment 17 of 413 (4.1%), control 40 of 421 (9.5%), NNT 19.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Holland], 7/8/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 103 authors, study period 10 February, 2021 - 30 September, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (ACTIV-3-TICO). risk of death, 30.0% lower, RR 0.70, p = 0.03, treatment 61 of 710 (8.6%), control 86 of 707 (12.2%), NNT 28, day 90.
risk of no recovery, 7.4% lower, RR 0.93, p = 0.21, treatment 710, control 707, inverted to make RR<1 favor treatment, sustained recovery, day 90, primary outcome.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[FDA], 12/8/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, preprint, 1 author, trial NCT04625972 (history). risk of symptomatic case, 39.5% lower, RR 0.60, p = 0.07, treatment 28 of 749 (3.7%), control 23 of 372 (6.2%), NNT 41, from graph, day 180.
risk of symptomatic case, 32.8% lower, RR 0.67, p = 0.23, treatment 23 of 749 (3.1%), control 17 of 372 (4.6%), NNT 67.
[Kertes], 7/29/2022, retrospective, Israel, peer-reviewed, 10 authors. risk of death/hospitalization, 91.9% lower, RR 0.08, p = 0.01, treatment 1 of 825 (0.1%), control 63 of 4,299 (1.5%), NNT 74, adjusted per study, odds ratio converted to relative risk, multivariable.
risk of case, 47.1% lower, RR 0.53, p = 0.01, treatment 29 of 825 (3.5%), control 308 of 4,299 (7.2%), NNT 27, adjusted per study, odds ratio converted to relative risk, multivariable.
[Levin], 11/22/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 46.0, 21 authors, study period 2 December, 2020 - 19 March, 2021, trial NCT04625972 (history). risk of severe case, 75.1% lower, RR 0.25, p = 0.33, treatment 0 of 749 (0.0%), control 1 of 372 (0.3%), NNT 372, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of symptomatic case, 41.7% lower, RR 0.58, p = 0.06, treatment 27 of 749 (3.6%), control 23 of 372 (6.2%), NNT 39, extended data cutoff.
risk of symptomatic case, 32.8% lower, RR 0.67, p = 0.23, treatment 23 of 749 (3.1%), control 17 of 372 (4.6%), NNT 67, primary outcome.
[Levin (B)], 4/20/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 24 authors, study period 21 November, 2020 - 22 March, 2021, trial NCT04625725 (history) (PROVENT). risk of death, 85.7% lower, RR 0.14, p = 0.11, treatment 0 of 3,441 (0.0%), control 2 of 1,731 (0.1%), NNT 866, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of symptomatic case, 82.1% lower, RR 0.18, p < 0.001, treatment 11 of 3,441 (0.3%), control 31 of 1,731 (1.8%), NNT 68, 6 months.
risk of symptomatic case, 76.3% lower, RR 0.24, p < 0.001, treatment 8 of 3,441 (0.2%), control 17 of 1,731 (1.0%), NNT 133, median 83 days followup.
[Najjar-Debbiny], 10/31/2022, retrospective, Israel, peer-reviewed, 5 authors. risk of hospitalization, 59.0% lower, HR 0.41, p = 0.02, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), Cox proportional hazards.
risk of case, 25.0% lower, HR 0.75, p = 0.03, treatment 72 of 703 (10.2%), control 377 of 2,812 (13.4%), NNT 32, Cox proportional hazards.
[Young-Xu], 5/29/2022, retrospective, propensity score matching, USA, preprint, 10 authors. risk of death, 64.0% lower, HR 0.36, p = 0.004, treatment 1,733, control 6,354.
risk of death/hospitalization/cases, 69.0% lower, HR 0.31, p < 0.001, treatment 17 of 1,733 (1.0%), control 206 of 6,354 (3.2%), NNT 44.
risk of hospitalization, 87.0% lower, HR 0.13, p = 0.04, treatment 1,733, control 6,354.
risk of case, 66.0% lower, HR 0.34, p = 0.03, treatment 1,733, control 6,354.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, 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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