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Sotrovimab for COVID-19: real-time meta analysis of 5 studies
Covid Analysis, May 28, 2022, DRAFT
https://c19sv.com/meta.html
0 0.5 1 1.5+ All studies 20% 5 4,524 Improvement, Studies, Patients Relative Risk Mortality 38% 4 3,596 Ventilation 89% 1 1,057 ICU admission 56% 1 94 Hospitalization 62% 1 2,085 Progression 45% 3 2,079 RCTs 10% 2 1,417 RCT mortality 10% 2 1,417 Peer-reviewed 15% 3 1,511 Early 46% 4 4,164 Late -2% 1 360 Sotrovimab for COVID-19 c19sv.com May 2022 Favorssotrovimab Favorscontrol
Statistically significant improvement is seen for hospitalization. 3 studies from 3 independent teams in 2 different countries show statistically significant improvements in isolation (1 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 20% [-96‑67%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials and worse for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment.
0 0.5 1 1.5+ All studies 20% 5 4,524 Improvement, Studies, Patients Relative Risk Mortality 38% 4 3,596 Ventilation 89% 1 1,057 ICU admission 56% 1 94 Hospitalization 62% 1 2,085 Progression 45% 3 2,079 RCTs 10% 2 1,417 RCT mortality 10% 2 1,417 Peer-reviewed 15% 3 1,511 Early 46% 4 4,164 Late -2% 1 360 Sotrovimab for COVID-19 c19sv.com May 2022 Favorssotrovimab Favorscontrol
Currently there is limited data, with only 43 control events for the most serious outcome in trials to date.
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 US EUA has been revoked. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 40% of sotrovimab studies show zero events in the treatment arm. Multiple treatments are typically used in combination, and other treatments are more effective.
No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Highlights
Sotrovimab reduces risk for COVID-19 with very high confidence for hospitalization, low confidence for ventilation, and very low confidence for ICU admission. Efficacy is variant dependent. Lower efficacy for BA.1, unlikely to be effective for BA.2. US EUA has been revoked.
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 42 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 80% 0.20 [0.01-4.16] death 0/528 2/529 Improvement, RR [CI] Treatment Control Ong 61% 0.39 [0.05-2.90] death 1/19 10/75 Aggarwal (PSM) 89% 0.11 [0.00-0.79] death 0/522 15/1,563 Zaqout -165% 2.65 [0.60-11.3] progression 4/345 3/583 Tau​2 = 1.10, I​2 = 46.9%, p = 0.44 Early treatment 46% 0.54 [0.12-2.49] 5/1,414 30/2,750 46% improvement Self (DB RCT) -2% 1.02 [0.48-2.17] death 14/182 13/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.96 Late treatment -2% 1.02 [0.48-2.17] 14/182 13/178 -2% improvement All studies 20% 0.80 [0.33-1.96] 19/1,596 43/2,928 20% improvement 5 sotrovimab COVID-19 studies c19sv.com May 2022 Tau​2 = 0.33, I​2 = 31.5%, p = 0.63 Effect extraction pre-specified(most serious outcome, see appendix) Favors sotrovimab Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
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, 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.
Variant Dependence
Efficacy is variant dependent, for example in vitro research shows that sotrovimab is not effective for the omicron BA.2 variant [Zhou].
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION PROGRESSION RCTS RCT MORTALITY PEER-REVIEWED All Early Late Sotrovimab for COVID-19 C19SV.COM MAY 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 3 4 75.0% 46% improvement
RR 0.54 [0.12‑2.49]
p = 0.44
Late treatment 0 1 0.0% -2% improvement
RR 1.02 [0.48‑2.17]
p = 0.96
All studies 3 5 60.0% 20% improvement
RR 0.80 [0.33‑1.96]
p = 0.63
Table 1. Results by treatment stage.
Studies Early treatment Late treatment PatientsAuthors
All studies 546% [-149‑88%]-2% [-117‑52%] 4,524 756
Peer-reviewed 368% [-70‑94%]-2% [-117‑52%] 1,511 725
Randomized Controlled TrialsRCTs 280% [-316‑99%]-2% [-117‑52%] 1,417 715
Table 2. Results by treatment stage for all studies and with different exclusions.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 80% 0.20 [0.01-4.16] death 0/528 2/529 Improvement, RR [CI] Treatment Control Ong 61% 0.39 [0.05-2.90] death 1/19 10/75 Aggarwal (PSM) 89% 0.11 [0.00-0.79] death 0/522 15/1,563 Zaqout -165% 2.65 [0.60-11.3] progression 4/345 3/583 Tau​2 = 1.10, I​2 = 46.9%, p = 0.44 Early treatment 46% 0.54 [0.12-2.49] 5/1,414 30/2,750 46% improvement Self (DB RCT) -2% 1.02 [0.48-2.17] death 14/182 13/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.96 Late treatment -2% 1.02 [0.48-2.17] 14/182 13/178 -2% improvement All studies 20% 0.80 [0.33-1.96] 19/1,596 43/2,928 20% improvement 5 sotrovimab COVID-19 studies c19sv.com May 2022 Tau​2 = 0.33, I​2 = 31.5%, p = 0.63 Effect extraction pre-specified(most serious outcome, see appendix) Favors sotrovimab Favors control
Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 80% 0.20 [0.01-4.16] 0/528 2/529 Improvement, RR [CI] Treatment Control Ong 61% 0.39 [0.05-2.90] 1/19 10/75 Aggarwal (PSM) 89% 0.11 [0.00-0.79] 0/522 15/1,563 Tau​2 = 0.00, I​2 = 0.0%, p = 0.053 Early treatment 76% 0.24 [0.06-1.02] 1/1,069 27/2,167 76% improvement Self (DB RCT) -2% 1.02 [0.48-2.17] 14/182 13/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.96 Late treatment -2% 1.02 [0.48-2.17] 14/182 13/178 -2% improvement All studies 38% 0.62 [0.25-1.51] 15/1,251 40/2,345 38% improvement 4 sotrovimab COVID-19 mortality results c19sv.com May 2022 Tau​2 = 0.18, I​2 = 16.3%, p = 0.3 Favors sotrovimab Favors control
Figure 5. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 89% 0.11 [0.01-2.06] 0/528 4/529 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.14 Early treatment 89% 0.11 [0.01-2.06] 0/528 4/529 89% improvement All studies 89% 0.11 [0.01-2.06] 0/528 4/529 89% improvement 1 sotrovimab COVID-19 mechanical ventilation result c19sv.com May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.14 Favors sotrovimab Favors control
Figure 6. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ong 56% 0.44 [0.11-1.73] 2/19 18/75 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.24 Early treatment 56% 0.44 [0.11-1.73] 2/19 18/75 56% improvement All studies 56% 0.44 [0.11-1.73] 2/19 18/75 56% improvement 1 sotrovimab COVID-19 ICU result c19sv.com May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.24 Favors sotrovimab Favors control
Figure 7. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Aggarwal (PSM) 62% 0.38 [0.20-0.67] hosp. 11/522 89/1,563 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0025 Early treatment 62% 0.38 [0.20-0.67] 11/522 89/1,563 62% improvement All studies 62% 0.38 [0.21-0.71] 11/522 89/1,563 62% improvement 1 sotrovimab COVID-19 hospitalization result c19sv.com May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0025 Favors sotrovimab Favors control
Figure 8. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 75% 0.25 [0.11-0.57] 7/528 28/529 Improvement, RR [CI] Treatment Control Ong 59% 0.41 [0.17-0.99] 19 (n) 75 (n) Zaqout -165% 2.65 [0.60-11.3] 4/345 3/583 Tau​2 = 0.71, I​2 = 72.9%, p = 0.31 Early treatment 45% 0.55 [0.18-1.71] 11/892 31/1,187 45% improvement All studies 45% 0.55 [0.18-1.71] 11/892 31/1,187 45% improvement 3 sotrovimab COVID-19 progression results c19sv.com May 2022 Tau​2 = 0.71, I​2 = 72.9%, p = 0.31 Favors sotrovimab Favors control
Figure 9. Random effects meta-analysis for progression.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Self (DB RCT) 11% 0.89 [0.73-1.10] no recov. 22/160 27/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.68 Late treatment 11% 0.89 [0.73-1.10] 22/160 27/178 11% improvement All studies 11% 0.89 [0.53-1.50] 22/160 27/178 11% improvement 1 sotrovimab COVID-19 recovery result c19sv.com May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.68 Favors sotrovimab Favors control
Figure 10. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 80% 0.20 [0.01-4.16] death 0/528 2/529 Improvement, RR [CI] Treatment Control Ong 61% 0.39 [0.05-2.90] death 1/19 10/75 Tau​2 = 0.00, I​2 = 0.0%, p = 0.18 Early treatment 68% 0.32 [0.06-1.70] 1/547 12/604 68% improvement Self (DB RCT) -2% 1.02 [0.48-2.17] death 14/182 13/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.96 Late treatment -2% 1.02 [0.48-2.17] 14/182 13/178 -2% improvement All studies 15% 0.85 [0.44-1.65] 15/729 25/782 15% improvement 3 sotrovimab COVID-19 peer reviewed trials c19sv.com May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.64 Effect extraction pre-specified(most serious outcome, see appendix) Favors sotrovimab Favors control
Figure 11. 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 a 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.
Randomized Controlled Trials (RCTs)
Figure 12 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 13 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. Table 3 summarizes the 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).
Figure 12. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gupta (DB RCT) 80% 0.20 [0.01-4.16] death 0/528 2/529 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.3 Early treatment 80% 0.20 [0.01-4.16] 0/528 2/529 80% improvement Self (DB RCT) -2% 1.02 [0.48-2.17] death 14/182 13/178 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.96 Late treatment -2% 1.02 [0.48-2.17] 14/182 13/178 -2% improvement All studies 10% 0.90 [0.39-2.09] 14/710 15/707 10% improvement 2 sotrovimab COVID-19 Randomized Controlled Trials c19sv.com May 2022 Tau​2 = 0.06, I​2 = 4.5%, p = 0.82 Effect extraction pre-specified(most serious outcome, see appendix) Favors sotrovimab Favors control
Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 1 2 50.0% 10% improvement
RR 0.90 [0.39‑2.09]
p = 0.82
RCT mortality results 1 2 50.0% 10% improvement
RR 0.90 [0.39‑2.09]
p = 0.82
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
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]. Figure 14 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 42 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 14. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 42 treatments. Early treatment is critical.
Patient demographics.
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]).
Effect measured.
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.
Variants.
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].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
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 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 the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. 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.
Discussion
Publication bias.
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.
67% of retrospective studies report positive effects, compared to 50% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 61% improvement, compared to 39% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 15 shows a scatter plot of results for prospective and retrospective studies.
Figure 15. Prospective vs. retrospective studies.
Funnel plot analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 16 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 16. Example funnel plot analysis for simulated perfect trials.
Early/late vs. mild/moderate/severe.
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.
Conclusion
Statistically significant improvement is seen for hospitalization. 3 studies from 3 independent teams in 2 different countries show statistically significant improvements in isolation (1 for the most serious outcome). Meta analysis using the most serious outcome reported shows 20% [-96‑67%] improvement, without reaching statistical significance. Results are worse for Randomized Controlled Trials and worse for peer-reviewed studies. Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 43 control events for the most serious outcome in trials to date.
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. Monoclonal antibody use with variants can be associated with prolonged viral loads, clinical deterioration, and immune escape [Choudhary].
Study Notes
0 0.5 1 1.5 2+ Mortality 89% Improvement Relative Risk Hospitalization 62% c19sv.com Aggarwal et al. Sotrovimab for COVID-19 EARLY Favors sotrovimab Favors control
[Aggarwal] Retrospective 522 sotrovimab patients and matched controls in the USA, showing significantly lower hospitalization and mortality with treatment.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Ventilation 89% Progression 75% Hospitalization >24hrs or.. 79% primary c19sv.com Gupta et al. NCT04545060 COMET-ICE Sotrovimab RCT EARLY Favors sotrovimab Favors control
[Gupta] RCT 1,057 outpatients, 529 treated with sotrovimab, showing significantly lower hospitalization >24h or mortality with treatment.
0 0.5 1 1.5 2+ Mortality 61% Improvement Relative Risk ICU admission 56% Progression 59% c19sv.com Ong et al. Sotrovimab for COVID-19 EARLY TREATMENT Favors sotrovimab Favors control
[Ong] Retrospective 19 sotrovimab patients and 75 controls is Singapore, showing lower progression with treatment.
0 0.5 1 1.5 2+ Mortality -2% Improvement Relative Risk Recovery 11% primary Recovery (b) 7% c19sv.com Self et al. NCT04501978 Sotrovimab RCT LATE TREATMENT Favors sotrovimab Favors control
[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. TICO. NCT04501978.
0 0.5 1 1.5 2+ Progression -165% Improvement Relative Risk c19sv.com Zaqout et al. Sotrovimab for COVID-19 EARLY TREATMENT Favors sotrovimab Favors control
[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.
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 c19sv.com. 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. 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 (3.9.12) with scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
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://c19sv.com/meta.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], 4/5/2022, retrospective, USA, North America, preprint, 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.
[Gupta], 12/4/2021, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, multiple regions, peer-reviewed, 68 authors, average treatment delay 2.6 days, trial NCT04545060 (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.
[Ong], 3/5/2022, retrospective, Singapore, Asia, 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.
[Zaqout], 4/21/2022, retrospective, Qatar, Middle East, preprint, 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.
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, multiple regions, peer-reviewed, 647 authors, study period 16 December, 2020 - 1 March, 2021, average treatment delay 8.0 days, trial NCT04501978. 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, day 90, primary outcome.
risk of no recovery, 7.4% lower, RR 0.93, p = 0.69, treatment 160, control 178, pulmonary-plus ordinal outcome @day 5.
Supplementary Data
References
Please send us corrections, updates, or comments. Vaccines and treatments are both valuable and complementary. All practical, effective, and safe means should be used. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases mortality, morbidity, collateral damage, and the risk of endemic status. 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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