Sotrovimab for COVID-19: real-time meta analysis of 21 studies
@CovidAnalysis, September 2023
https://c19early.org/vmeta.html
•Meta analysis using the most serious outcome reported shows
26% [9‑40%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
•11 studies from 11 independent teams in 5 countries show statistically significant
improvements.
•Results are robust — in exclusion sensitivity analysis 10 of 21
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
•Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.1 Liu, Sheward, VanBlargan and no efficacy for omicron BA.2 Zhou. US EUA has been revoked. mAb use may create new variants that spread globally Focosi. mAb use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape Choudhary.
•No treatment or intervention
is 100% effective. All practical, effective, and safe means should be used
based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
are more effective.
•All data to reproduce this paper and
sources are in the appendix.
Highlights
Sotrovimab reduces
risk for COVID-19 with very high confidence for pooled analysis, low confidence for mortality, ventilation, and hospitalization, and very low confidence for ICU admission and progression.
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 56
treatments.
B
Loading..
C
Loading..
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.
0.7% of 5,019 proposed treatments show efficacy
c19early.org.
D. Timeline of results in sotrovimab studies. The marked date indicates the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes.
We analyze all significant studies
concerning the use of
sotrovimab
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and 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 sotrovimab is not effective for omicron BA.2 Zhou.
| Bamlanivimab/ etesevimab |
Casirivimab/ imdevimab |
Sotrovimab | Bebtelovimab | Tixagevimab/ cilgavimab |
|
|---|---|---|---|---|---|
| Alpha B.1.1.7 | |||||
| Beta/ Gamma BA1.351/ P.1 | |||||
| Delta B.1.617.2 | |||||
| Omicron BA.1/ BA.1.1 | |||||
| Omicron BA.2 | |||||
| Omicron BA.5 | |||||
| Omicron BA.4.6 | |||||
| Omicron BQ.1.1 |
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes.
Table 3 shows results by treatment stage.
Figure 3, 4, 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 26% [9‑40%] ** | 21 | 42,808 | 1,014 |
| After exclusions | 29% [12‑43%] ** | 19 | 34,717 | 988 |
| Peer-reviewed studiesPeer-reviewed | 31% [5‑49%] * | 17 | 27,406 | 907 |
| Randomized Controlled TrialsRCTs | 10% [-109‑61%] | 2 | 1,417 | 715 |
| Mortality | 49% [-13‑77%] | 10 | 16,383 | 798 |
| VentilationVent. | 71% [-23‑93%] | 2 | 2,745 | 75 |
| HospitalizationHosp. | 37% [-1‑61%] | 6 | 14,171 | 65 |
| RCT mortality | 10% [-109‑61%] | 2 | 1,417 | 715 |
| Early treatment | Late treatment | |
|---|---|---|
| All studies | 29% [13‑43%] ** | -40% [-214‑38%] |
| After exclusions | 33% [16‑46%] *** | -40% [-214‑38%] |
| Peer-reviewed studiesPeer-reviewed | 38% [13‑55%] ** | -40% [-214‑38%] |
| Randomized Controlled TrialsRCTs | 80% [-316‑99%] | -2% [-117‑52%] |
| Mortality | 77% [64‑85%] **** | -40% [-214‑38%] |
| VentilationVent. | 71% [-23‑93%] | |
| HospitalizationHosp. | 37% [-1‑61%] | |
| RCT mortality | 80% [-316‑99%] | -2% [-117‑52%] |
Loading..
Loading..
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.
Loading..
Loading..
Figure 4. Random effects meta-analysis for mortality results.
Loading..
Figure 5. Random effects meta-analysis for ventilation.
Loading..
Figure 6. Random effects meta-analysis for ICU admission.
Loading..
Figure 7. Random effects meta-analysis for hospitalization.
Loading..
Figure 8. Random effects meta-analysis for progression.
Loading..
Figure 9. Random effects meta-analysis for recovery.
Loading..
Figure 10. Random effects meta-analysis for peer reviewed studies.
Zeraatkar 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.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Figure 11 shows a comparison of results for RCTs and non-RCT studies.
The median effect size for
RCTs is 39% improvement,
compared to 29% for other studies.
Figure 12 shows a forest plot for random
effects meta-analysis of all Randomized Controlled Trials.
RCT results are included in Table 2 and Table 3.
Bias in clinical research may be defined as something that tends to make
conclusions differ systematically from the truth. RCTs help to make study
groups more similar and can provide a higher level of evidence, however they
are subject to many biases Jadad, and analysis of double-blind RCTs
has identified extreme levels of bias Gøtzsche.
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, 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.
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 56 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 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.
Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 38 treatments with statistically significant efficacy/harm, 24 have been confirmed in RCTs, with a mean delay of 5.7 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 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.
Loading..
Figure 11. Results for RCTs and non-RCT studies.
Loading..
Figure 12. 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.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding studies with
major issues likely to alter results, non-standard studies, and studies where
very minimal detail is currently available. Our bias evaluation is based on
analysis of each study and identifying when there is a significant chance that
limitations will substantially change the outcome of the study. We believe
this can be more valuable than checklist-based approaches such as Cochrane
GRADE, which may underemphasize serious issues not captured in the checklists,
overemphasize issues unlikely to alter outcomes in specific cases (for
example, lack of blinding for an objective mortality outcome, or certain
specifics of randomization with a very large effect size), or be easily
influenced by potential bias. However, they can also be very high
quality.
The studies excluded are as below.
Figure 13 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Brown, unadjusted results with no group details; significant unadjusted confounding possible.
Zheng, study compares against another treatment showing significant efficacy.
Loading..
Figure 13. Random effects meta-analysis for all studies after exclusions.
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.
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 delay | Result |
| Post exposure prophylaxis | 86% fewer cases Ikematsu |
| <24 hours | -33 hours symptoms Hayden |
| 24-48 hours | -13 hours symptoms Hayden |
| Inpatients | -2.5 hours to improvement Kumar |
Figure 14 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 56 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 14. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 56 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 15. 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.
Currently, 38 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 treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 months.
Loading..
Loading..
Figure 15. 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 sotrovimab, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 16 shows a scatter plot of
results for prospective and retrospective studies.
53% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
50% of prospective studies, showing similar results.
The median effect size for
retrospective studies is 29% improvement,
compared to 39% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
Loading..
Figure 16. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Studies for sotrovimab were primarily for early treatment, in contrast
with typical low cost treatments that were mostly tested with late treatment.
![]()
![]()
Figure 17. Patented treatments received mostly early treatment studies, while low cost treatments were typically tested for late treatment.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 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 < 0.05
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 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 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.
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 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.
2 of the 21
studies compare against other treatments, which may reduce the effect
seen.
Sotrovimab is
an effective treatment for COVID-19.
Meta analysis using the most serious outcome reported shows
26% [9‑40%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials.
Early treatment shows efficacy while late treatment does not, consistent with expectations for an antiviral treatment.
11 studies from 11 independent teams in 5 countries show statistically significant
improvements.
Results are robust — in exclusion sensitivity analysis 10 of 21
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Efficacy is variant dependent. In Vitro studies suggest lower efficacy for omicron BA.1 Liu, Sheward, VanBlargan and no efficacy for omicron BA.2 Zhou. US EUA has been revoked. mAb use may create new variants that spread globally Focosi. mAb use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape Choudhary.
Aggarwal:
Retrospective 30,247 outpatients in the USA, showing no significant differences with sotrovimab with omicron BA.1.
Aggarwal (B):
PSM retrospective 10,036 outpatients, 522 treated with sotrovimab, showing lower mortality and hospitalization with treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. 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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
Bell:
N3C IPTW retrospective 4,992 COVID-19 patients in the USA treated with sotrovimab and 541,325 controls, showing lower combined hospitalization or mortality.
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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. 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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
Brown:
Retrospective 186 patients in the UK treated with sotrovimab, and 222 eligible but declining treatment, showing no significant difference in hospitalization. No group details are provided and the results are subject to confounding by indication.
De Vito:
Retrospective 689 COVID-19 patients in Italy, showing lower mortality with sotrovimab treatment.
Drysdale:
Retrospective 599 high-risk sotrovimab patients and 5,191 untreated controls, showing lower hospitalization/mortality with treatment, without statistical significance in the overall cohort. Efficacy was better for those ≥65, and efficacy was lower in later time periods.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. 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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
Evans:
Retrospective high risk outpatients in the UK, showing lower hospitalization/death with sotrovimab treatment. Residual confounding is likely with adjustments having no detail on specific comorbidities.
Goodwin:
Retrospective 604 outpatients in the UK, showing lower risk of hospitalization with sotrovimab treatment, without statistical significance due to the small number of hospitalizations.
Gupta:
RCT 1,057 outpatients, 529 treated with sotrovimab, showing significantly lower hospitalization >24h or mortality with treatment.
Kip:
Retrospective 2,571 patients treated with mAbs in the USA, and 5,135 control patients, showing lower combined mortality/hospitalization for bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab, and bebtelovimab, with statistical significance only for casirivimab/imdevimab.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. 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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
Kneidinger:
Retrospective 218 COVID+ lung transplant patients in Germany, showing no significant difference in severe cases with early sotrovimab use.
Miyashita:
Retrospective 844 patients treated with sotrovimab and matched controls in Japan, showing lower risk of oxygen therapy with treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. 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 receive additional treatments that are not tracked in the data (e.g., nasal/oral hygiene c19early.org (B), c19early.org (C), vitamin D c19early.org (D), etc.) — either because the physician recommending sotrovimab also recommended them, or because the patient seeking out sotrovimab is more likely to be familiar with the efficacy of additional treatments. Therefore, these kind of studies may overestimate the efficacy of treatments.
Ong:
Retrospective 19 sotrovimab patients and 75 controls is Singapore, showing lower progression with treatment.
Piccicacco:
Retrospective high-risk outpatients in the USA, 82 treated with remdesivir, 88 with sotrovimab, and 90 control patients, showing significantly lower combined hospitalization/ER visits with both treatments in unadjusted results. The dominant variant was omicron B.1.1.529.
Self:
RCT with 182 sotrovimab patients and 178 control patients, median 8 days from symptom onset, showing no significant differences and terminated early due to futility.
Suzuki:
Retrospective 1,921 patients in Japan, showing no significant difference in progression with sotrovimab use.
Tazare:
OpenSAFELY retrospective 75,048 outpatients in the UK, using the clone-censor-weight approach to address immortal time bias, showing lower combined mortality/hospitalization with sotrovimab treatment.
Woo:
PSM retrospective 1,254 hospitalized patients in Germany, 147 treated with sotrovimab, showing higher mortality with sotrovimab, without statistical significance.
Zaqout:
Retrospective 345 sotrovimab treated patients in Qatar matched with 583 patients that opted not to receive treatment, showing higher progression with treatment, without statistical significance.
Zheng:
OpenSAFELY retrospective 7,683 outpatients in the UK, showing no significant difference in hospitalization/death between paxlovid and sotrovimab.
Zheng (B):
Retrospective 3,331 sotrovimab and 2,689 molnupiravir patients in the UK, showing lower risk of combined hospitalization/death with sotrovimab.
We 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 sotrovimab, 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 sotrovimab for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days 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
(3.11.4) with
scipy (1.11.1), pythonmeta (1.26), numpy (1.25.0), statsmodels (0.14.0), and plotly (5.15.0).
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 https://c19early.org/vmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Aggarwal, 6/18/2022, retrospective, USA, peer-reviewed, 10 authors, study period 26 December, 2021 - 10 March, 2022. | risk of death, 38.0% lower, RR 0.62, p = 0.62, treatment 1 of 1,542 (0.1%), control 7 of 3,663 (0.2%), odds ratio converted to relative risk. |
| risk of hospitalization, 17.5% lower, RR 0.82, p = 0.32, treatment 39 of 1,542 (2.5%), control 116 of 3,663 (3.2%), NNT 157, odds ratio converted to relative risk, primary outcome. | |
| risk of progression, 2.8% higher, RR 1.03, p = 0.83, treatment 93 of 1,542 (6.0%), control 224 of 3,663 (6.1%), NNT 1189, odds ratio converted to relative risk, ED visit. | |
| Aggarwal (B), 4/5/2022, retrospective, USA, peer-reviewed, 14 authors, study period 1 October, 2021 - 11 December, 2021. | risk of death, 88.9% lower, RR 0.11, p = 0.048, treatment 0 of 522 (0.0%), control 15 of 1,563 (1.0%), NNT 104, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28. |
| risk of hospitalization, 61.6% lower, RR 0.38, p = 0.002, treatment 11 of 522 (2.1%), control 89 of 1,563 (5.7%), NNT 28, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28, primary outcome. | |
| ED visit, 11.0% higher, RR 1.11, p = 0.55, treatment 44 of 522 (8.4%), control 119 of 1,563 (7.6%), adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, day 28. | |
| Bell, 6/12/2023, retrospective, USA, peer-reviewed, 11 authors, study period 26 May, 2021 - 30 April, 2022. | risk of death/hospitalization, 25.1% lower, RR 0.75, p = 0.004, NNT 107, odds ratio converted to relative risk, propensity score weighting, day 29. |
| Brown, 10/6/2022, retrospective, United Kingdom, peer-reviewed, 17 authors, excluded in exclusion analyses: unadjusted results with no group details; significant unadjusted confounding possible. | risk of hospitalization, 258.1% higher, RR 3.58, p = 0.15, treatment 6 of 186 (3.2%), control 2 of 222 (0.9%). |
| De Vito, 8/17/2023, retrospective, Italy, peer-reviewed, 12 authors, study period 1 January, 2022 - 31 December, 2022, average treatment delay 1.0 days. | risk of death, 81.1% lower, RR 0.19, p < 0.001, treatment 18 of 341 (5.3%), control 63 of 348 (18.1%), NNT 7.8, odds ratio converted to relative risk. |
| risk of oxygen therapy, 91.8% lower, RR 0.08, p < 0.001, treatment 17 of 341 (5.0%), control 144 of 348 (41.4%), NNT 2.7, odds ratio converted to relative risk. | |
| Drysdale, 7/27/2023, retrospective, United Kingdom, preprint, 14 authors, study period August 2020 - March 2021. | risk of death, 29.0% lower, HR 0.71, p = 0.65, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. |
| risk of death/hospitalization, 50.0% lower, HR 0.50, p = 0.07, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. | |
| risk of hospitalization, 57.0% lower, HR 0.43, p = 0.05, treatment 599, control 5,191, propensity score weighting, Cox proportional hazards. | |
| Evans, 1/25/2023, retrospective, United Kingdom, peer-reviewed, 11 authors, study period 16 December, 2021 - 22 April, 2022. | risk of death/hospitalization, 27.0% lower, HR 0.73, p = 0.03, treatment 1,079, control 4,973, Cox proportional hazards. |
| Goodwin, 3/15/2023, retrospective, United Kingdom, peer-reviewed, 3 authors, study period 22 December, 2021 - 20 February, 2022. | risk of death, 75.0% lower, RR 0.25, p = 0.55, treatment 0 of 169 (0.0%), control 2 of 336 (0.6%), NNT 168, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of hospitalization, 60.2% lower, RR 0.40, p = 0.35, treatment 2 of 169 (1.2%), control 10 of 336 (3.0%), NNT 56, COVID-19 related. | |
| risk of hospitalization, 21.5% higher, RR 1.21, p = 0.69, treatment 11 of 169 (6.5%), control 18 of 336 (5.4%), all cause. | |
| Gupta, 12/4/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 68 authors, study period 27 August, 2020 - 2 September, 2021, average treatment delay 2.6 days, trial NCT04545060 (history) (COMET-ICE), conflicts of interest: research funding from the drug patent holder, employee of the drug patent holder. | risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 528 (0.0%), control 2 of 529 (0.4%), NNT 264, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. |
| risk of mechanical ventilation, 88.9% lower, RR 0.11, p = 0.12, treatment 0 of 528 (0.0%), control 4 of 529 (0.8%), NNT 132, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. | |
| risk of progression, 75.0% lower, RR 0.25, p < 0.001, treatment 7 of 528 (1.3%), control 28 of 529 (5.3%), NNT 25, day 29. | |
| risk of hospitalization >24hrs or death, 79.0% lower, RR 0.21, p < 0.001, treatment 6 of 528 (1.1%), control 30 of 529 (5.7%), NNT 22, day 29, ITT, primary outcome. | |
| Kip, 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. | risk of death/hospitalization, 30.0% lower, RR 0.70, p = 0.14, treatment 22 of 500 (4.4%), control 63 of 999 (6.3%), NNT 52, delta and omicron variants, day 28. |
| Kneidinger, 9/9/2022, retrospective, Germany, peer-reviewed, 11 authors, study period 1 January, 2022 - 20 March, 2022, lung transplant patients. | risk of severe case, 20.2% higher, RR 1.20, p = 0.79, treatment 21 of 125 (16.8%), control 13 of 93 (14.0%). |
| Miyashita, 5/31/2023, retrospective, Japan, peer-reviewed, 7 authors, study period December 2021 - July 2022. | risk of mechanical ventilation, 60.0% lower, RR 0.40, p = 0.45, treatment 2 of 844 (0.2%), control 5 of 844 (0.6%), NNT 281, all. |
| risk of mechanical ventilation, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 642 (0.3%), control 3 of 642 (0.5%), NNT 642, BA.1. | |
| risk of mechanical ventilation, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 202 (0.0%), control 2 of 202 (1.0%), NNT 101, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), BA.2. | |
| risk of oxygen therapy, 55.3% lower, RR 0.45, p < 0.001, treatment 34 of 844 (4.0%), control 76 of 844 (9.0%), NNT 20, all. | |
| risk of oxygen therapy, 53.6% lower, RR 0.46, p < 0.001, treatment 26 of 642 (4.0%), control 56 of 642 (8.7%), NNT 21, BA.1. | |
| risk of oxygen therapy, 60.0% lower, RR 0.40, p = 0.03, treatment 8 of 202 (4.0%), control 20 of 202 (9.9%), NNT 17, BA.2. | |
| Ong, 3/5/2022, retrospective, Singapore, peer-reviewed, 10 authors, average treatment delay 2.0 days. | risk of death, 60.5% lower, RR 0.39, p = 0.45, treatment 1 of 19 (5.3%), control 10 of 75 (13.3%), NNT 12. |
| risk of ICU admission, 56.1% lower, RR 0.44, p = 0.35, treatment 2 of 19 (10.5%), control 18 of 75 (24.0%), NNT 7.4. | |
| risk of progression, 59.0% lower, HR 0.41, p = 0.047, treatment 19, control 75, Cox proportional hazards. | |
| Piccicacco, 8/1/2022, retrospective, USA, peer-reviewed, 7 authors, study period 27 December, 2021 - 4 February, 2022, average treatment delay 4.4 days. | risk of death, 66.4% lower, RR 0.34, p = 1.00, treatment 0 of 88 (0.0%), control 1 of 90 (1.1%), NNT 90, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 29. |
| risk of hospitalization, 34.9% lower, RR 0.65, p = 0.46, treatment 7 of 88 (8.0%), control 11 of 90 (12.2%), NNT 23, day 29. | |
| risk of hospitalization/ER, 66.3% lower, RR 0.34, p = 0.01, treatment 7 of 88 (8.0%), control 21 of 90 (23.3%), NNT 6.5, odds ratio converted to relative risk, day 29. | |
| risk of progression, 89.8% lower, RR 0.10, p = 0.009, treatment 1 of 88 (1.1%), control 10 of 90 (11.1%), NNT 10, ER visit, day 29. | |
| Suzuki, 10/5/2022, retrospective, Japan, preprint, 53 authors. | risk of progression, 8.3% higher, OR 1.08, p = 0.73, treatment 672, control 1,257, adjusted per study, multivariable, RR approximated with OR. |
| Tazare, 5/16/2023, retrospective, United Kingdom, preprint, 31 authors, study period 16 December, 2021 - 21 May, 2022. | risk of death/hospitalization, 16.0% lower, HR 0.84, p = 0.002, treatment 6,408, control 65,568. |
| Zaqout, 4/21/2022, retrospective, Qatar, peer-reviewed, median age 40.0, 17 authors, study period 20 October, 2021 - 28 February, 2022. | risk of progression, 164.7% higher, RR 2.65, p = 0.19, treatment 4 of 345 (1.2%), control 3 of 583 (0.5%), adjusted per study, odds ratio converted to relative risk, progression to severe/critical disease or mortality. |
| Zheng, 1/22/2023, retrospective, United Kingdom, preprint, mean age 54.3, 9 authors, study period 11 February, 2022 - 1 October, 2022, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: study compares against another treatment showing significant efficacy. | risk of death/hospitalization, 3.8% lower, HR 0.96, p = 0.91, treatment 2,847, control 4,836, inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 60, model 4. |
| risk of death/hospitalization, 13.6% higher, HR 1.14, p = 0.70, treatment 19 of 2,847 (0.7%), control 33 of 4,836 (0.7%), inverted to make HR<1 favor treatment, COVID-19 related, propensity score weighting, Cox proportional hazards, day 28, model 4. | |
| Zheng (B), 11/16/2022, retrospective, United Kingdom, peer-reviewed, mean age 52.0, 33 authors, study period 16 December, 2021 - 10 February, 2022, this trial compares with another treatment - results may be better when compared to placebo. | risk of death/hospitalization, 50.0% lower, HR 0.50, p = 0.005, treatment 34 of 3,331 (1.0%), control 61 of 2,689 (2.3%), NNT 80, adjusted per study, multivariable, Cox proportional hazards, day 60, model 4. |
| risk of death/hospitalization, 46.0% lower, HR 0.54, p = 0.01, treatment 32 of 3,331 (1.0%), control 55 of 2,689 (2.0%), NNT 92, adjusted per study, multivariable, Cox proportional hazards, day 28, model 4. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Self, 12/23/2021, Double Blind Randomized Controlled Trial, multiple countries, peer-reviewed, 647 authors, study period 16 December, 2020 - 1 March, 2021, average treatment delay 8.0 days, trial NCT04501978 (history) (TICO). | risk of death, 2.0% higher, RR 1.02, p = 0.96, treatment 14 of 182 (7.7%), control 13 of 178 (7.3%), day 90. |
| risk of no recovery, 10.7% lower, RR 0.89, p = 0.29, treatment 22 of 160 (13.8%), control 27 of 178 (15.2%), NNT 70, inverted to make RR<1 favor treatment, day 90, primary outcome. | |
| risk of no recovery, 7.4% lower, RR 0.93, p = 0.69, treatment 160, control 178, inverted to make RR<1 favor treatment, pulmonary-plus ordinal outcome @day 5. | |
| Woo, 12/8/2022, retrospective, Germany, peer-reviewed, 13 authors. | risk of death, 140.0% higher, RR 2.40, p = 0.12, treatment 4 of 60 (6.7%), control 10 of 360 (2.8%), non-ICU, propensity score matching. |
| risk of death, 50.0% higher, RR 1.50, p = 0.08, treatment 36 of 87 (41.4%), control 24 of 87 (27.6%), ICU, propensity score matching. |
Aggarwal et al., Change in Effectiveness of Sotrovimab for Preventing Hospitalization and Mortality for At-risk COVID-19 Outpatients During an Omicron BA.1 and BA.1.1-Predominant Phase, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2022.10.002.
Aggarwal (B) et al., Real-World Evidence of the Neutralizing Monoclonal Antibody Sotrovimab for Preventing Hospitalization and Mortality in COVID-19 Outpatients, The Journal of Infectious Diseases, doi:10.1093/infdis/jiac206.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:/10.1016/j.micpath.2020.104228.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Bell et al., Real-World Effectiveness of Sotrovimab for the Early Treatment of COVID-19: Evidence from the National COVID Cohort Collaborative (N3C), Value in Health, doi:10.1016/j.jval.2023.03.176.
Biancatelli et al., Quercetin and Vitamin C: An Experimental, Synergistic Therapy for the Prevention and Treatment of SARS-CoV-2 Related Disease (COVID-19), Frontiers in Immunology, doi:10.3389/fimmu.2020.01451.
Brown et al., Demographics and outcomes of initial phase of COVID-19 Medicines Delivery Units across 4 UK centres during peak B1.1.529 omicron epidemic: a service evaluation, Open Forum Infectious Diseases, doi:10.1093/ofid/ofac527.
Choudhary et al., Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy, medRxiv, doi:10.1101/2021.09.03.21263105.
Davis et al., The Promise and Peril of Anti-SARS-CoV-2 Monoclonal Antibodies, Clinical Infectious Diseases, doi:10.1093/cid/ciac902.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
De Vito et al., What Is the Efficacy of Sotrovimab in Reducing Disease Progression and Death in People with COVID-19 during the Omicron Era? Answers from a Real-Life Study, Viruses, doi:10.3390/v15081757.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Drysdale et al., Comparative effectiveness of sotrovimab versus no treatment in non-hospitalised high-risk patients with COVID-19 in North West London: a retrospective cohort study using the Discover dataset, medRxiv, doi:10.1101/2023.07.26.23293188.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Evans et al., Real-world effectiveness of molnupiravir, nirmatrelvir-ritonavir, and sotrovimab on preventing hospital admission among higher-risk patients with COVID-19 in Wales: A retrospective cohort study, Journal of Infection, doi:10.1016/j.jinf.2023.02.012.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Focosi et al., Analysis of SARS-CoV-2 mutations associated with resistance to therapeutic monoclonal antibodies that emerge after treatment, Drug Resistance Updates, doi:10.1016/j.drup.2023.100991.
Gasmi et al., Quercetin in the Prevention and Treatment of Coronavirus Infections: A Focus on SARS-CoV-2, Pharmaceuticals, doi:10.3390/ph15091049.
Goodwin et al., Evaluation of outpatient treatment for non-hospitalised patients with COVID-19: The experience of a regional centre in the UK, PLOS ONE, doi:10.1371/journal.pone.0281915.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Gupta et al., Effect of Sotrovimab on Hospitalization or Death Among High-risk Patients With Mild to Moderate COVID-19, JAMA, doi:10.1001/jama.2022.2832.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Kip et al., Evolving Real-World Effectiveness of Monoclonal Antibodies for Treatment of COVID-19, Annals of Internal Medicine, doi:10.7326/M22-1286.
Kneidinger et al., Outcome of lung transplant recipients infected with SARS-CoV-2/Omicron/B.1.1.529: a Nationwide German study, Infection, doi:10.1007/s15010-022-01914-8.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Liu et al., Striking Antibody Evasion Manifested by the Omicron Variant of SARS-CoV-2, bioRxiv, doi:10.1101/2021.12.14.472719.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Miyashita et al., Clinical Efficacy of the Neutralizing Antibody Therapy Sotrovimab in Patients with SARS-CoV-2 Omicron BA.1 and BA.2 Subvariant Infections, Viruses, doi:10.3390/v15061300.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Ong et al., Real-World Use of Sotrovimab for Pre-Emptive Treatment in High-Risk Hospitalized COVID-19 Patients: An Observational Cross-Sectional Study, Antibiotics, doi:10.3390/antibiotics11030345.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Piccicacco et al., Real-world effectiveness of early remdesivir and sotrovimab in the highest-risk COVID-19 outpatients during the Omicron surge, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkac256.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Self et al., Efficacy and safety of two neutralising monoclonal antibody therapies, sotrovimab and BRII-196 plus BRII-198, for adults hospitalised with COVID-19 (TICO): a randomised controlled trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00751-9.
Sheward et al., Variable loss of antibody potency against SARS-CoV-2 B.1.1.529 (Omicron), bioRxiv, doi:10.1101/2021.12.19.473354.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Suzuki et al., Real-world clinical outcomes of treatment with molnupiravir for patients with mild- to-moderate coronavirus disease 2019 during the Omicron variant pandemic, Research Square, doi:10.21203/rs.3.rs-2118653/v1.
Sweeting et al., What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data, Statistics in Medicine, doi:10.1002/sim.1761.
Tazare et al., Effectiveness of Sotrovimab and Molnupiravir in community settings in England across the Omicron BA.1 and BA.2 sublineages: emulated target trials using the OpenSAFELY platform, medRxiv, doi:10.1101/2023.05.12.23289914.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
VanBlargan et al., An infectious SARS-CoV-2 B.1.1.529 Omicron virus escapes neutralization by several therapeutic monoclonal antibodies, bioRxiv, doi:10.1101/2021.12.15.472828.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Woo et al., Sotrovimab in Hospitalized Patients with SARS-CoV-2 Omicron Variant Infection: a Propensity Score-Matched Retrospective Cohort Study, Microbiology Spectrum, doi:10.1128/spectrum.04103-22.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Zaqout et al., Effectiveness of the neutralizing antibody sotrovimab among high-risk patients with mild-to-moderate SARS-CoV-2 in Qatar, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2022.09.023.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Zhang et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Zheng et al., Comparative effectiveness of Paxlovid versus sotrovimab and molnupiravir for preventing severe COVID-19 outcomes in non-hospitalised patients: observational cohort study using the OpenSAFELY platform, medRxiv, doi:10.1101/2023.01.20.23284849.
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.