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Fluvoxamine for COVID-19: real-time meta analysis of 5 studies
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PLEASE SUBMIT FEEDBACK
Covid Analysis, December 3, 2021
https://c19fluvoxamine.com/meta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 38% 5 2,052 Improvement, Studies, Patients Relative Risk, 95% CI Mortality 36% 3 1,775 Ventilation 22% 1 1,497 Hospitalization 67% 3 1,774 Progression 93% 1 152 Recovery 99% 1 125 Viral clearance -49% 1 428 RCTs 65% 2 1,649 Prophylaxis -58% 1 176 Early 89% 2 277 Late 40% 2 1,599 Fluvoxamine for COVID-19 c19fluvoxamine.com Dec 3, 2021 Favors fluvoxamine Favors control
Meta analysis using the most serious outcome reported shows 38% [7‑58%] improvement. Results are better for Randomized Controlled Trials. Early treatment is more effective than late treatment.
Statistically significant improvements are seen for mortality and recovery. 4 studies show statistically significant improvements in isolation (1 for the most serious outcome).
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 38% 5 2,052 Improvement, Studies, Patients Relative Risk, 95% CI Mortality 36% 3 1,775 Ventilation 22% 1 1,497 Hospitalization 67% 3 1,774 Progression 93% 1 152 Recovery 99% 1 125 Viral clearance -49% 1 428 RCTs 65% 2 1,649 Prophylaxis -58% 1 176 Early 89% 2 277 Late 40% 2 1,599 Fluvoxamine for COVID-19 c19fluvoxamine.com Dec 3, 2021 Favors fluvoxamine Favors control
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 40% of fluvoxamine studies show zero events in the treatment arm.
Multiple treatments are typically used in combination, and other treatments may be more effective.
Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used, including treatments, as supported by Pfizer [Pfizer]. Denying the efficacy of treatments increases the risk of COVID-19 becoming endemic; and increases mortality, morbidity, and collateral damage.
All data to reproduce this paper and sources are in the appendix.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 589% [15‑99%]40% [23‑53%]-58% [-493‑58%] 2,052 55
Randomized Controlled TrialsRCTs 293% [-28‑100%]30% [-26‑63%] 1,649 38
Percentage improvement with fluvoxamine treatment
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 93% 0.07 [0.00-1.28] progression 0/80 6/72 Improvement, RR [CI] Treatment Control Seftel (QR) 84% 0.16 [0.01-3.29] death/ICU 0/77 2/48 Tau​2 = 0.00; I​2 = 0.0% Early treatment 89% 0.11 [0.01-0.85] 0/157 8/120 89% improvement Reis (DB RCT) 30% 0.70 [0.37-1.26] death 17/741 25/756 Improvement, RR [CI] Treatment Control Calusic (PSM) 42% 0.58 [0.36-0.94] death 30/51 39/51 Tau​2 = 0.00; I​2 = 0.0% Late treatment 40% 0.60 [0.47-0.77] 47/792 64/807 40% improvement Oskotsky (PSM) -58% 1.58 [0.42-5.93] death 2/11 19/165 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis -58% 1.58 [0.42-5.93] 2/11 19/165 -58% improvement All studies 38% 0.62 [0.42-0.93] 49/960 91/1,092 38% improvement 5 fluvoxamine COVID-19 studies c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.05; I​2 = 22.4%; Z = 2.29 Effect extraction pre-specified, see appendix Favors fluvoxamine 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 fluvoxamine 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.
Results
Figure 3, 4, 5, 6, 7, 8, and 9 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, and viral clearance. Table 1 summarizes the results by treatment stage.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 2 2 100% 89% improvement
RR 0.11 [0.01‑0.85]
p = 0.034
Late treatment 2 2 100% 40% improvement
RR 0.60 [0.47‑0.77]
p < 0.0001
Prophylaxis 0 1 0.0% -58% improvement
RR 1.58 [0.42‑5.93]
p = 0.51
All studies 4 5 80.0% 38% improvement
RR 0.62 [0.42‑0.93]
p = 0.022
Table 1. Results by treatment stage.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 93% 0.07 [0.00-1.28] progression 0/80 6/72 Improvement, RR [CI] Treatment Control Seftel (QR) 84% 0.16 [0.01-3.29] death/ICU 0/77 2/48 Tau​2 = 0.00; I​2 = 0.0% Early treatment 89% 0.11 [0.01-0.85] 0/157 8/120 89% improvement Reis (DB RCT) 30% 0.70 [0.37-1.26] death 17/741 25/756 Improvement, RR [CI] Treatment Control Calusic (PSM) 42% 0.58 [0.36-0.94] death 30/51 39/51 Tau​2 = 0.00; I​2 = 0.0% Late treatment 40% 0.60 [0.47-0.77] 47/792 64/807 40% improvement Oskotsky (PSM) -58% 1.58 [0.42-5.93] death 2/11 19/165 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis -58% 1.58 [0.42-5.93] 2/11 19/165 -58% improvement All studies 38% 0.62 [0.42-0.93] 49/960 91/1,092 38% improvement 5 fluvoxamine COVID-19 studies c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.05; I​2 = 22.4%; Z = 2.29 Effect extraction pre-specified, see appendix Favors fluvoxamine Favors control
Figure 3. 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+ Reis (DB RCT) 30% 0.70 [0.37-1.26] 17/741 25/756 Improvement, RR [CI] Treatment Control Calusic (PSM) 42% 0.58 [0.36-0.94] 30/51 39/51 Tau​2 = 0.00; I​2 = 0.0% Late treatment 40% 0.60 [0.47-0.77] 47/792 64/807 40% improvement Oskotsky (PSM) -58% 1.58 [0.42-5.93] 2/11 19/165 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Prophylaxis -58% 1.58 [0.42-5.93] 2/11 19/165 -58% improvement All studies 36% 0.64 [0.47-0.86] 49/803 83/972 36% improvement 3 fluvoxamine COVID-19 mortality results c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.01; I​2 = 12.5%; Z = 2.90 Favors fluvoxamine Favors control
Figure 4. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 22% 0.78 [0.46-1.28] 26/741 34/756 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 22% 0.78 [0.46-1.28] 26/741 34/756 22% improvement All studies 22% 0.78 [0.47-1.28] 26/741 34/756 22% improvement 1 fluvoxamine COVID-19 mechanical ventilation result c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 0.98 Favors fluvoxamine Favors control
Figure 5. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 82% 0.18 [0.02-1.50] hosp. 1/80 5/72 Improvement, RR [CI] Treatment Control Seftel (QR) 94% 0.06 [0.00-1.04] hosp. 0/77 6/48 Tau​2 = 0.00; I​2 = 0.0% Early treatment 88% 0.12 [0.02-0.67] 1/157 11/120 88% improvement Reis (DB RCT) 22% 0.78 [0.61-1.03] hosp. 75/741 97/756 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 22% 0.78 [0.61-1.03] 75/741 97/756 22% improvement All studies 67% 0.33 [0.07-1.50] 76/898 108/876 67% improvement 3 fluvoxamine COVID-19 hospitalization results c19fluvoxamine.com Dec 3, 2021 Tau​2 = 1.09; I​2 = 58.6%; Z = 1.44 Favors fluvoxamine Favors control
Figure 6. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 93% 0.07 [0.00-1.28] 0/80 6/72 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 93% 0.07 [0.00-1.28] 0/80 6/72 93% improvement All studies 93% 0.07 [0.00-1.28] 0/80 6/72 93% improvement 1 fluvoxamine COVID-19 progression result c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 1.79 Favors fluvoxamine Favors control
Figure 7. Random effects meta-analysis for progression.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Seftel (QR) 99% 0.01 [0.00-0.21] no recov. 0/77 29/48 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 99% 0.01 [0.00-0.21] 0/77 29/48 99% improvement All studies 99% 0.01 [0.00-0.21] 0/77 29/48 99% improvement 1 fluvoxamine COVID-19 recovery result c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 3.07 Favors fluvoxamine Favors control
Figure 8. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) -49% 1.49 [0.94-2.38] viral+ 167/207 163/221 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment -49% 1.49 [0.94-2.38] 167/207 163/221 -49% improvement All studies -49% 1.49 [1.35-1.65] 167/207 163/221 -49% improvement 1 fluvoxamine COVID-19 viral clearance result c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 7.61 Favors fluvoxamine Favors control
Figure 9. Random effects meta-analysis for viral clearance.
Randomized Controlled Trials (RCTs)
Figure 10 and 11 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 2 summarizes the results.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for fluvoxamine are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
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].
Figure 12. Randomized Controlled Trials. The distribution of results for RCTs and other studies.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 93% 0.07 [0.00-1.28] progression 0/80 6/72 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Early treatment 93% 0.07 [0.00-1.28] 0/80 6/72 93% improvement Reis (DB RCT) 30% 0.70 [0.37-1.26] death 17/741 25/756 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 30% 0.70 [0.37-1.26] 17/741 25/756 30% improvement All studies 65% 0.35 [0.05-2.68] 17/821 31/828 65% improvement 2 fluvoxamine COVID-19 Randomized Controlled Trials c19fluvoxamine.com Dec 3, 2021 Tau​2 = 1.43; I​2 = 56.2%; Z = 1.00 Effect extraction pre-specified, see appendix Favors fluvoxamine Favors control
Figure 10. 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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Reis (DB RCT) 30% 0.70 [0.37-1.26] 17/741 25/756 Improvement, RR [CI] Treatment Control Tau​2 = 0.00; I​2 = 0.0% Late treatment 30% 0.70 [0.37-1.26] 17/741 25/756 30% improvement All studies 30% 0.70 [0.38-1.28] 17/741 25/756 30% improvement 1 fluvoxamine COVID-19 RCT mortality result c19fluvoxamine.com Dec 3, 2021 Tau​2 = 0.00; I​2 = 0.0%; Z = 1.16 Favors fluvoxamine Favors control
Figure 11. Random effects meta-analysis for RCT mortality results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 2 2 100% 65% improvement
RR 0.35 [0.05‑2.68]
p = 0.32
RCT mortality results 1 1 100% 30% improvement
RR 0.70 [0.38‑1.28]
p = 0.25
Table 2. 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]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 13 shows an example where efficacy declines as a function of treatment delay.
Figure 13. Effectiveness may depend critically on treatment delay.
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 thousands of different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study.
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, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For fluvoxamine, there is currently not enough data to evaluate publication bias with high confidence.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Fluvoxamine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 fluvoxamine trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all fluvoxamine trials represent the optimal conditions for efficacy.
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
Fluvoxamine is an effective treatment for COVID-19. Meta analysis using the most serious outcome reported shows 38% [7‑58%] improvement. Results are better for Randomized Controlled Trials. Early treatment is more effective than late treatment. Statistically significant improvements are seen for mortality and recovery. 4 studies show statistically significant improvements in isolation (1 for the most serious outcome).
Study Notes
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 42% Imp. Relative Risk, 95% CI Calusic: Safety and efficacy of fluvoxamine in COVID-19 ICU patie.. c19fluvoxamine.com/calusic.html Favors fluvoxamine Favors control
[Calusic] Prospective PSM study of 51 COVID-19 ICU patients in Croatia and 51 matched controls, showing significantly lower mortality with treatment. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Disease progression 93% Imp. Relative Risk, 95% CI Hospitalization 82% Lenze: Fluvoxamine vs Placebo and Clinical Deterioration in Out.. c19fluvoxamine.com/lenze.html Favors fluvoxamine Favors control
[Lenze] RCT 152 outpatients, 80 treated with fluvoxamine showing lower progression with treatment (0 of 80 versus 6 of 72 control). Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality -58% Imp. Relative Risk, 95% CI Mortality (b) 26% Oskotsky: Mortality Risk Among Patients With COVID-19 Prescribed S.. c19fluvoxamine.com/oskotsky.html Favors fluvoxamine Favors control
[Oskotsky] Retrospective database analysis of 83,584 patients in the USA, showing lower mortality with existing fluoxetine use in PSM analysis. There were 11 fluvoxamine patients, showing non-statistically significant higher mortality. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 30% Imp. Relative Risk, 95% CI Mortality (b) 91% Mechanical ventilation 22% Hospitalization 22% Extended ER observatio.. 66% Extended ER observatio.. (b) 32% primary Extended ER observatio.. (c) 31% Virological cure -49% Reis: Effect of early treatment with fluvoxamine on risk of em.. c19fluvoxamine.com/reis2.html Favors fluvoxamine Favors control
[Reis] Together Trial showing significantly lower hospitalization/extended ER visits with fluvoxamine treatment. Adherence was only 73.2%. Symptom onset was unspecified or >= 4 days for 57% of patients. The schedule of study activities specifies treatment administration only one day after randomization, adding an additional day delay. Overall mortality is high for the patient population. Results may be impacted by late treatment, poor SOC, and may be specific to local variants https://science.sciencemag.org/content/372/6544/815 https://www.thelancet.com/article/S0140-6736(21)00183-5/fulltext. Per-protocol analysis shows significantly improved results in this trial. Authors note that ITT analysis provides more real-world evidence, however RCTs do not provide real-world evidence. For a drug like fluvoxamine, which is widely available, and a condition like COVID-19, which has a significant risk of death and must be treated immediately for best results, RCTs are biased toward participants that do not need help to recover, or do not believe that the drug will help. (Because patients can decline participation and take the drug). Notably, the remaining patients that choose to participate are less likely to believe adherence is important. In real-world use, including patients that decline RCT participation, and with patients informed of the benefit, adherence is likely to be much better. Note that per-protocol analysis could affect randomization. The primary outcome changed in the March 21 clinicaltrials.gov update (observation >12hrs changed to >6hrs). This is not explained or mentioned in the paper. Authors state "this study is only the second study to show an important treatment benefit for a repurposed drug in the early treatment population", however the actual number is at least 66 based on our database at the time of publication, using a conservative definition of at least 10% benefit (with statistical significance). The total dose used is less than half of that in Lenze et al. There is an unusual amount of missing data - age is unknown for 6.5% of patients according to the sub-group analysis. Both age <=50 and >50 show better results on the primary outcome than the overall result. The number of placebo patients changed significantly between the preprint and journal version. The number of treatment patients with viral clearance results reduced significantly between the preprint and journal version. Also see https://twitter.com/Covid19Crusher/status/1430170252575395843. NCT04727424. For other issues with this trial see: https://twitter.com/Covid19Crusher/status/1453726471499894787 https://twitter.com/Covid19Crusher/status/1453803654608269318. Submit Corrections or Updates.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Death/ICU 84% Imp. Relative Risk, 95% CI Hospitalization 94% Recovery 99% Seftel: Prospective cohort of fluvoxamine for early treatment of.. c19fluvoxamine.com/seftel.html Favors fluvoxamine Favors control
[Seftel] Prospective quasi-randomized (patient choice) study with 125 outpatients, 77 treated with fluvoxamine, showing lower death/ICU admission (0 of 77 vs. 2 of 48), lower hospitalization (0 of 77 vs. 6 of 48), and faster recovery with treatment. Note that 12 treatment patients were added but are not reflected in the table in the paper (because the numbers had been previously published and the IRB did not allow updating the table). Submit Corrections or Updates.
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 c19fluvoxamine.com. Search terms were fluvoxamine, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of fluvoxamine 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 calculations for that study. 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. 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 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 (3.9.9) with scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.13.1), and plotly (5.4.0).
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. None
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, 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://c19fluvoxamine.com/meta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in calculations, which may differ from the effect a paper focuses on.
[Lenze], 11/12/2020, Double Blind Randomized Controlled Trial, USA, North America, peer-reviewed, 11 authors. risk of disease progression, 92.7% lower, RR 0.07, p = 0.009, treatment 0 of 80 (0.0%), control 6 of 72 (8.3%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), clinical deterioration over 15 days.
risk of hospitalization, 82.0% lower, RR 0.18, p = 0.009, treatment 1 of 80 (1.2%), control 5 of 72 (6.9%), COVID-19 hospitalization within 15 days, see supplemental appendix for details.
[Seftel], 2/1/2021, prospective quasi-randomized (patient choice), USA, North America, peer-reviewed, 2 authors. risk of death/ICU, 83.9% lower, RR 0.16, p = 0.15, treatment 0 of 77 (0.0%), control 2 of 48 (4.2%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 94.0% lower, RR 0.06, p = 0.003, treatment 0 of 77 (0.0%), control 6 of 48 (12.5%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no recovery, 98.7% lower, RR 0.01, p < 0.001, treatment 0 of 77 (0.0%), control 29 of 48 (60.4%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in calculations, which may differ from the effect a paper focuses on.
[Calusic], 11/1/2021, prospective, propensity score matching, Croatia, Europe, peer-reviewed, 7 authors. risk of death, 42.0% lower, RR 0.58, p = 0.03, treatment 30 of 51 (58.8%), control 39 of 51 (76.5%), adjusted per study, propensity score matching.
[Reis], 8/23/2021, Double Blind Randomized Controlled Trial, Brazil, South America, peer-reviewed, 27 authors. risk of death, 30.3% lower, RR 0.70, p = 0.24, treatment 17 of 741 (2.3%), control 25 of 756 (3.3%), odds ratio converted to relative risk, ITT.
risk of death, 90.8% lower, RR 0.09, p = 0.02, treatment 1 of 548 (0.2%), control 12 of 618 (1.9%), odds ratio converted to relative risk, per protocol.
risk of mechanical ventilation, 22.2% lower, RR 0.78, p = 0.33, treatment 26 of 741 (3.5%), control 34 of 756 (4.5%), odds ratio converted to relative risk, ITT.
risk of hospitalization, 21.6% lower, RR 0.78, p = 0.10, treatment 75 of 741 (10.1%), control 97 of 756 (12.8%), odds ratio converted to relative risk, ITT.
extended ER observation or hospitalization, 66.0% lower, RR 0.34, p < 0.001, treatment 541, control 609, per protocol.
extended ER observation or hospitalization, 32.0% lower, RR 0.68, p = 0.004, treatment 79 of 741 (10.7%), control 119 of 756 (15.7%), ITT.
extended ER observation or hospitalization, 31.0% lower, RR 0.69, p = 0.006, treatment 78 of 740 (10.5%), control 115 of 752 (15.3%), mITT.
risk of no virological cure, 49.3% higher, RR 1.49, p = 0.09, treatment 167 of 207 (80.7%), control 163 of 221 (73.8%), adjusted per study.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in calculations, which may differ from the effect a paper focuses on.
[Oskotsky], 11/15/2021, retrospective, propensity score matching, USA, North America, peer-reviewed, 8 authors. risk of death, 57.9% higher, RR 1.58, p = 0.62, treatment 2 of 11 (18.2%), control 19 of 165 (11.5%), fluvoxamine.
risk of death, 26.0% lower, RR 0.74, p = 0.04, treatment 48 of 481 (10.0%), control 956 of 7,215 (13.3%), fluoxetine.
References
Please send us corrections, updates, or comments. Vaccines and treatments are both extremely valuable and complementary. All practical, effective, and safe means should be used. Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases the risk of COVID-19 becoming endemic; and increases mortality, morbidity, and collateral damage. 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. Treatment protocols for physicians are available from the FLCCC.
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