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Fluvoxamine for COVID-19: real-time meta analysis of 6 studies
Covid Analysis, January 18, 2022, DRAFT
https://c19fluvoxamine.com/meta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 34% 6 2,599 Improvement, Studies, Patients Relative Risk Mortality 36% 3 1,775 Ventilation 22% 1 1,497 Hospitalization 34% 4 2,321 Viral clearance -49% 1 428 RCTs 30% 3 2,196 Peer-reviewed 38% 5 2,052 Prophylaxis -58% 1 176 Early 64% 3 824 Late 40% 2 1,599 Fluvoxamine for COVID-19 c19fluvoxamine.com Jan 18, 2022 Favors fluvoxamine Favors control
Statistically significant improvements are seen for mortality and recovery. 4 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation (1 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 34% [7‑53%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 34% 6 2,599 Improvement, Studies, Patients Relative Risk Mortality 36% 3 1,775 Ventilation 22% 1 1,497 Hospitalization 34% 4 2,321 Viral clearance -49% 1 428 RCTs 30% 3 2,196 Peer-reviewed 38% 5 2,052 Prophylaxis -58% 1 176 Early 64% 3 824 Late 40% 2 1,599 Fluvoxamine for COVID-19 c19fluvoxamine.com Jan 18, 2022 Favors fluvoxamine Favors control
Treatment recommendations are available from Ontario.
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 33% 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 mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 664% [-93‑93%]40% [23‑53%]-58% [-493‑58%] 2,599 56
Peer-reviewed 589% [15‑99%]40% [23‑53%]-58% [-493‑58%] 2,052 55
Randomized Controlled TrialsRCTs 362% [-310‑96%]30% [-26‑63%] 2,196 39
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 Lenze (DB RCT) 7% 0.93 [0.42-2.06] hosp. 11/272 12/275 Tau​2 = 1.11, I​2 = 47.1%, p = 0.24 Early treatment 64% 0.36 [0.07-1.93] 11/429 20/395 64% 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%, p < 0.0001 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%, p = 0.51 Prophylaxis -58% 1.58 [0.42-5.93] 2/11 19/165 -58% improvement All studies 34% 0.66 [0.47-0.93] 60/1,232 103/1,367 34% improvement 6 fluvoxamine COVID-19 studies c19fluvoxamine.com Jan 18, 2022 Tau​2 = 0.04, I​2 = 18.8%, p = 0.017 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.
Mechanisms of Action
FIASMAFluvoxamine is a functional inhibitor of acid sphingomyelinase (FIASMA). SARS-CoV-2 activates the ASM/ceramide system which may facilitate viral entry. ASM inhibition may reduce the concentration of ceramides and inhibit viral entry [Carpinteiro, Carpinteiro (B), Hashimoto, Hoertel].
Sigma-1 activationFluvoxamine may reduce clinical deterioration via σ-1 (S1R) receptor activation, which regulates cytokine production [Hashimoto, Hashimoto (B), Sukhatme].
Platelet activationPlatelet activation may contribute to COVID-19 severity. Fluvoxamine inhibits platelet activation [Battinelli, Sukhatme].
Lysosomal traffickingSARS-CoV-2 uses lysosomal trafficking to escape from infected cells. Fluvoxamine is lysosomotropic and interferes with endolysosomal viral trafficking [Hashimoto, Norinder, Sukhatme].
Heme oxygenaseCOVID-19 risk may be related to low intracellular heme oxygenase (HO-1). Fluvoxamine increases HO-1 and HO-1 has cytoprotective and anti-inflammatory properties [Almási, Hooper, Hooper (B)].
Mast cell degranulationFluvoxamine may reduce cytokine storm due to decreased mast cell degranulation [Sukhatme].
MelatoninMelatonin may be beneficial for COVID-19, and fluvoxamine may elevate melatonin levels via CYP1A2 and CYP2C19 inhibition [Anderson, Camp, Hashimoto, Ramos, Sukhatme].
Table 1. Fluvoxamine mechanisms of action. Submit updates.
Results
Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, viral clearance, and peer reviewed studies. Table 2 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 3 3 100% 64% improvement
RR 0.36 [0.07‑1.93]
p = 0.24
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 5 6 83.3% 34% improvement
RR 0.66 [0.47‑0.93]
p = 0.017
Table 2. 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 Lenze (DB RCT) 7% 0.93 [0.42-2.06] hosp. 11/272 12/275 Tau​2 = 1.11, I​2 = 47.1%, p = 0.24 Early treatment 64% 0.36 [0.07-1.93] 11/429 20/395 64% 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%, p < 0.0001 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%, p = 0.51 Prophylaxis -58% 1.58 [0.42-5.93] 2/11 19/165 -58% improvement All studies 34% 0.66 [0.47-0.93] 60/1,232 103/1,367 34% improvement 6 fluvoxamine COVID-19 studies c19fluvoxamine.com Jan 18, 2022 Tau​2 = 0.04, I​2 = 18.8%, p = 0.017 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%, p < 0.0001 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%, p = 0.51 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 Jan 18, 2022 Tau​2 = 0.01, I​2 = 12.5%, p = 0.0038 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%, p = 0.33 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 Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.33 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 Lenze (DB RCT) 7% 0.93 [0.42-2.06] hosp. 11/272 12/275 Tau​2 = 1.24, I​2 = 58.6%, p = 0.18 Early treatment 68% 0.32 [0.06-1.68] 12/429 23/395 68% 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%, p = 0.093 Late treatment 22% 0.78 [0.61-1.03] 75/741 97/756 22% improvement All studies 34% 0.66 [0.35-1.23] 87/1,170 120/1,151 34% improvement 4 fluvoxamine COVID-19 hospitalization results c19fluvoxamine.com Jan 18, 2022 Tau​2 = 0.16, I​2 = 41.0%, p = 0.19 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%, p = 0.073 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 Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.073 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%, p = 0.0022 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 Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0022 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%, p < 0.0001 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 Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Favors fluvoxamine Favors control
Figure 9. Random effects meta-analysis for viral clearance.
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%, p = 0.034 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%, p < 0.0001 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%, p = 0.51 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 peer reviewed trials c19fluvoxamine.com Jan 18, 2022 Tau​2 = 0.05, I​2 = 22.4%, p = 0.022 Effect extraction pre-specified, see appendix Favors fluvoxamine Favors control
Figure 10. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 11 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 12 and 13 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
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 11. 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+ Lenze (DB RCT) 93% 0.07 [0.00-1.28] progression 0/80 6/72 Improvement, RR [CI] Treatment Control Lenze (DB RCT) 7% 0.93 [0.42-2.06] hosp. 11/272 12/275 Tau​2 = 2.08, I​2 = 64.4%, p = 0.44 Early treatment 62% 0.38 [0.04-4.10] 11/352 18/347 62% 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%, p = 0.25 Late treatment 30% 0.70 [0.37-1.26] 17/741 25/756 30% improvement All studies 30% 0.70 [0.37-1.34] 28/1,093 43/1,103 30% improvement 3 fluvoxamine COVID-19 Randomized Controlled Trials c19fluvoxamine.com Jan 18, 2022 Tau​2 = 0.10, I​2 = 29.8%, p = 0.28 Effect extraction pre-specified, see appendix Favors fluvoxamine Favors control
Figure 12. 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%, p = 0.25 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 Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.25 Favors fluvoxamine Favors control
Figure 13. 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 3 3 100% 30% improvement
RR 0.70 [0.37‑1.34]
p = 0.28
RCT mortality results 1 1 100% 30% improvement
RR 0.70 [0.38‑1.28]
p = 0.25
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]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 14 shows an example where efficacy declines as a function of treatment delay.
Figure 14. 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 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, 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. Statistically significant improvements are seen for mortality and recovery. 4 studies from 4 independent teams in 3 different countries show statistically significant improvements in isolation (1 for the most serious outcome). Meta analysis using the most serious outcome reported shows 34% [7‑53%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Treatment recommendations are available from Ontario.
Study Notes
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 42% Improvement Relative Risk 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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hospitalization 7% Improvement Relative Risk c19fluvoxamine.com/lenze2.html Favors fluvoxamine Favors control
[Lenze] Presentation noting that STOP COVID 2 was terminated early for futility with only 30/551 cases of detioration and no significant treatment effect. The main results are not available yet, however partial results presented suggest that early treatment was more effective. NCT04668950. Hospitalization results are from [medrxiv.org].
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Progression 93% Improvement Relative Risk Hospitalization 82% c19fluvoxamine.com/lenze.html Favors fluvoxamine Favors control
[Lenze (B)] RCT 152 outpatients, 80 treated with fluvoxamine showing lower progression with treatment (0 of 80 versus 6 of 72 control). STOP COVID trial. NCT04342663.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality -58% Improvement Relative Risk Mortality (b) 26% 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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 30% Improvement Relative Risk Mortality (b) 91% Mechanical ventilation 22% Hospitalization 22% Extended ER observatio.. 32% primary Extended ER observatio.. (b) 31% Extended ER observatio.. (c) 66% Virological cure -49% 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 [science.sciencemag.org, thelancet.com]. Per-protocol analysis shows significantly improved results in this trial, however this may be subject to bias - the probability of adherence may be related to the probability of the outcome.

Regarding the combined hospitalization/extended ER observation outcome, authors have noted that at the study sites, extended medical observation was essentially equivalent to being hospitalized. “These were not standard emergency rooms but instead were COVID-19 emergency centers that were set up due to hospitals being overloaded,” Reiersen noted in an email to The Scientist. “A stay in these centers >6 hours was an indication that the patient was receiving care equivalent to hospitalization.”

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 [twitter.com]. NCT04727424.

For other issues with this trial see: [twitter.com (B), twitter.com (C)].
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Death/ICU 84% Improvement Relative Risk Hospitalization 94% Recovery 99% 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).
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.
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. 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 (3.9.10) with scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.14.0), 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.
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 pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Lenze], 8/20/2021, Double Blind Randomized Controlled Trial, USA, North America, preprint, 1 author, average treatment delay 5.0 days. risk of hospitalization, 7.3% lower, RR 0.93, p = 1.00, treatment 11 of 272 (4.0%), control 12 of 275 (4.4%), NNT 313.
[Lenze (B)], 11/12/2020, Double Blind Randomized Controlled Trial, USA, North America, peer-reviewed, 11 authors, average treatment delay 4.0 days. risk of progression, 92.7% lower, RR 0.07, p = 0.009, treatment 0 of 80 (0.0%), control 6 of 72 (8.3%), NNT 12, 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%), NNT 18, 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%), NNT 24, 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%), NNT 8.0, 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%), NNT 1.7, 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 pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Calusic], 11/1/2021, prospective, propensity score matching, Croatia, Europe, peer-reviewed, 7 authors, study period 1 April, 2021 - 31 May, 2021. risk of death, 42.0% lower, RR 0.58, p = 0.03, treatment 30 of 51 (58.8%), control 39 of 51 (76.5%), NNT 5.7, 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%), NNT 99, 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%), NNT 57, 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%), NNT 101, 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%), NNT 37, odds ratio converted to relative risk, ITT.
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%), NNT 20, 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%), NNT 21, mITT.
extended ER observation or hospitalization, 66.0% lower, RR 0.34, p < 0.001, treatment 541, control 609, per protocol.
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 pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[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%), NNT 31, fluoxetine.
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. 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 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. WCH and FLCCC provide treatment protocols.
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