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Fluvoxamine for COVID-19: real-time meta analysis of 19 studies

@CovidAnalysis, September 2023
https://c19early.org/fmeta.html
 
0 0.5 1 1.5+ All studies 38% 19 37,033 Improvement, Studies, Patients Relative Risk Mortality 41% 8 4,224 Hospitalization 26% 12 6,712 Progression 34% 8 4,158 Recovery 67% 3 1,616 Cases 25% 2 9,116 RCTs 31% 9 5,964 RCT mortality 27% 2 2,973 Prophylaxis 27% 5 29,766 Early 42% 10 4,177 Late 48% 4 3,090 Fluvoxamine for COVID-19 c19early.org Sep 2023 Favorsfluvoxamine Favorscontrol
Statistically significant lower risk is seen for mortality, hospitalization, progression, and cases. 12 studies from 12 independent teams in 7 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials.
Results are robust — in exclusion sensitivity analysis 10 of 19 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
1 RCT with 1,200 patients has not reported results (1 year late).
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective.
All data to reproduce this paper and sources are in the appendix. Other meta analyses show significant improvements with fluvoxamine for mortality Deng, hospitalization Deng, Lee, Lu, Marcec, and severity Nakhaee.
Evolution of COVID-19 clinical evidence Fluvoxamine p=0.00021 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org September 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Fluvoxamine reduces risk for COVID-19 with very high confidence for pooled analysis, high confidence for mortality, hospitalization, and progression, and low confidence for recovery and cases, however increased risk is seen with low confidence for viral clearance and very low confidence for ICU admission.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 56 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ STOP COVID Lenze (DB RCT) 93% 0.07 [0.01-0.52] 1,200mg progression 0/80 6/72 Improvement, RR [CI] Dose (4d) Treatment Control Seftel (QR) 84% 0.16 [0.01-3.29] 500mg death/ICU 0/77 2/48 STOP COVID 2 Reiersen (DB RCT) -201% 3.01 [0.12-73.6] 800mg oxygen 1/272 0/275 Seo (SB RCT) 0% 1.00 [0.15-6.57] 800mg progression 2/26 2/26 Wannigama (RCT) not reported, >1 year late 1,200 (total) COVID-OUT Bramante (DB RCT) -11% 1.11 [0.33-3.61] 400mg death/hosp. 6/329 5/324 OT​1 Pineda 94% 0.06 [0.01-0.52] 800mg death 1/594 4/63 Farahani (DB RCT) 51% 0.49 [0.24-1.02] 400mg PASC 42 (n) 43 (n) LONG COVID TOGETHER Reis (DB RCT) -200% 3.00 [0.12-73.5] 800mg death 1/738 0/738 CT​2 EFFaCo Siripongbo.. (RCT) -2% 1.02 [0.26-4.00] 400mg oxygen 4/162 4/165 Tsiakalos 67% 0.33 [0.01-7.84] 800mg ICU 0/53 1/50 Tau​2 = 0.20, I​2 = 20.9%, p = 0.087 Early treatment 42% 0.58 [0.31-1.08] 15/2,373 24/1,804 42% lower risk TOGETHER Reis (DB RCT) 30% 0.70 [0.37-1.26] 800mg death 17/741 25/756 Improvement, RR [CI] Dose (4d) Treatment Control Calusic (ICU) 42% 0.58 [0.36-0.94] 1,200mg death 30/51 39/51 ICU patients Kirenga 68% 0.32 [0.19-0.53] 800mg death 29/94 126/222 ACTIV-6 Naggie (DB RCT) 31% 0.69 [0.27-1.21] 700mg progression 14/589 21/586 Tau​2 = 0.11, I​2 = 71.2%, p = 0.0013 Late treatment 48% 0.52 [0.35-0.77] 90/1,475 211/1,615 48% lower risk Oskotsky (PSM) -58% 1.58 [0.42-5.93] n/a death 2/11 19/165 Improvement, RR [CI] Dose (1m) Treatment Control Fritz 19% 0.81 [0.26-2.22] n/a hosp./ER 4/17 1,896/20,457 Diaz (PSM) 28% 0.72 [0.63-0.81] n/a cases 4,558 (n) 4,558 (n) Trkulja (PSM) 27% 0.73 [0.35-1.55] n/a death Visos-Varela -103% 2.03 [0.24-17.4] n/a death case control Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 27% 0.73 [0.65-0.82] 6/4,586 1,915/25,180 27% lower risk All studies 38% 0.62 [0.49-0.80] 111/8,434 2,150/28,599 38% lower risk 19 fluvoxamine COVID-19 studies (+1 unreported RCT) c19early.org Sep 2023 Tau​2 = 0.09, I​2 = 51.1%, p = 0.00021 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment2 CT: study uses combined treatment Favors fluvoxamine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ STOP COVID Lenze (DB RCT) 93% progression Improvement Relative Risk [CI] Seftel (QR) 84% death/ICU STOP COVID 2 Reiersen (DB RCT) -201% oxygen therapy Seo (SB RCT) 0% progression Wannigama (RCT) unreported >1 year COVID-OUT Bramante (DB RCT) -11% death/hosp. OT​1 Pineda 94% death Farahani (DB RCT) 51% PASC LONG COVID TOGETHER Reis (DB RCT) -200% death CT​2 EFFaCo Siripongb.. (RCT) -2% oxygen therapy Tsiakalos 67% ICU admission Tau​2 = 0.20, I​2 = 20.9%, p = 0.087 Early treatment 42% 42% lower risk TOGETHER Reis (DB RCT) 30% death Calusic (ICU) 42% death ICU patients Kirenga 68% death ACTIV-6 Naggie (DB RCT) 31% progression Tau​2 = 0.11, I​2 = 71.2%, p = 0.0013 Late treatment 48% 48% lower risk Oskotsky (PSM) -58% death Fritz 19% hosp./ER Diaz (PSM) 28% case Trkulja (PSM) 27% death Visos-Varela -103% death Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 27% 27% lower risk All studies 38% 38% lower risk 19 fluvoxamine C19 studies c19early.org Sep 2023 Tau​2 = 0.09, I​2 = 51.1%, p = 0.00021 Effect extraction pre-specifiedRotate device for footnotes/details Favors fluvoxamine Favors control
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.7% of 5,019 proposed treatments show efficacy c19early.org. D. Timeline of results in fluvoxamine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, and pooled outcomes in RCTs. Efficacy based on RCTs only was delayed by 16.9 months, compared to using all studies.
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, studies within each treatment stage, individual outcomes, and Randomized Controlled Trials (RCTs).
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.
Table 1 shows potential mechanisms of action for the treatment of COVID-19 using fluvoxamine.
Table 1. Fluvoxamine mechanisms of action. Submit updates.
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.
An In Silico study supports the efficacy of fluvoxamine Abatematteo.
An In Vitro study supports the efficacy of fluvoxamine Abatematteo.
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 2 summarizes the results for all stages combined, after exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, and viral clearance.
Table 2. Random effects meta-analysis for all stages combined, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies38% [20‑51%]
***
19 37,033 451
Randomized Controlled TrialsRCTs31% [2‑50%]
*
9 5,964 369
Mortality41% [8‑62%]
*
8 4,224 130
ICU admissionICU-82% [-2278‑86%]2 103 13
HospitalizationHosp.26% [4‑42%]
*
12 6,712 393
Recovery67% [-9‑90%]3 1,616 264
Cases25% [11‑36%]
***
2 9,116 10
RCT mortality27% [-33‑60%]2 2,973 62
RCT hospitalizationRCT hosp.20% [-3‑37%]7 5,827 352
Table 3. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies42% [-8‑69%]48% [23‑65%]
**
27% [18‑35%]
****
Randomized Controlled TrialsRCTs30% [-17‑59%]31% [-9‑56%]
Mortality65% [-1506‑99%]51% [22‑69%]
**
4% [-78‑48%]
ICU admissionICU67% [-684‑99%]-395% [-7042‑66%]
HospitalizationHosp.36% [-2‑61%]22% [-3‑41%]3% [-116‑56%]
Recovery99% [79‑100%]
**
45% [6‑68%]
*
Cases25% [11‑36%]
***
RCT mortality-200% [-7253‑88%]30% [-26‑63%]
RCT hospitalizationRCT hosp.12% [-46‑47%]22% [-3‑41%]
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13, 14, and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 2 and Table 3.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases Jadad, and analysis of double-blind RCTs has identified extreme levels of bias Gøtzsche. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 56 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
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. 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, and may be greater when the risk of a serious outcome is overstated. 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 (B) shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see Deaton, Nichol.
Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 38 treatments with statistically significant efficacy/harm, 24 have been confirmed in RCTs, with a mean delay of 5.7 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization results.
1 fluvoxamine RCT has not reported results Wannigama. The trial reports total actual enrollment of 1,200 patients. The result is delayed over 1 year.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu report an 86% reduction in cases for post-exposure prophylaxis, Hayden show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar report only 2.5 hours improvement for inpatient treatment.
Table 4. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 16 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 56 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 16. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 56 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in López-Medina).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 17. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 months.
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Figure 17. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Publishing is often biased towards positive results, 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.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
Figure 18 shows a scatter plot of results for prospective and retrospective studies. 67% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 62% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 23% improvement, compared to 31% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy.
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Figure 18. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-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 19 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 19. Example funnel plot analysis for simulated perfect trials.
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.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
1 of the 19 studies compare against other treatments, which may reduce the effect seen. 1 of 19 studies combine treatments. The results of fluvoxamine alone may differ. 1 of 9 RCTs use combined treatment. Currently all studies are peer-reviewed. Other meta analyses show significant improvements with fluvoxamine for mortality Deng, hospitalization Deng, Lee, Lu, Marcec, and severity Nakhaee.
Fluvoxamine is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, hospitalization, progression, and cases. 12 studies from 12 independent teams in 7 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 38% [20‑51%] lower risk. Results are similar for Randomized Controlled Trials. Results are robust — in exclusion sensitivity analysis 10 of 19 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Other meta analyses show significant improvements with fluvoxamine for mortality Deng, hospitalization Deng, Lee, Lu, Marcec, and severity Nakhaee.
0 0.5 1 1.5 2+ Death/hospitalization -11% Improvement Relative Risk Progression, ER, hosp... -16% Hospitalization, day 28 2% Hospitalization, day 14 2% Progression, hypoxemia.. 5% primary Fluvoxamine  COVID-OUT  EARLY TREATMENT  DB RCT Is early treatment with fluvoxamine beneficial for COVID-19? Double-blind RCT 653 patients in the USA Control group includes metformin patients No significant difference in outcomes seen c19early.org Bramante et al., NEJM, August 2022 Favors fluvoxamine Favors control (inc..
COVID-OUT remotely operated RCT, showing no significant difference in outcomes. Results for other treatments are listed separately - metformin, ivermectin.
The "control" group includes patients receiving metformin, which is known to be beneficial for COVID-19 c19early.org (B).
Authors note that the dosage used in the trial is lower than that of other trials twitter.com.
Control arm results are very different between treatments, for example considering hospitalization/death, this was 1.0% for ivermectin vs. 2.7% for overall control, however it was 1.3% for the ivermectin-specific control. 394 control patients are shared. The rate for the non-shared 261 metformin control patients is 5%, compared to 1.3% for ivermectin control patients. The metformin arm started earlier, however it is unclear why the difference in outcomes is so large.
Results were delayed for 6 months with no explanation, with followup ending Feb 14, 2022.
Multiple outcomes are missing, for example time to recovery (where ACTIV-6 showed superiority of ivermectin).
Adherence was very low, with 77% overall reporting 70+% adherence. Numbers for 100% adherence are not provided.
Treatment was 14 days for metformin and fluvoxamine, but only 3 days for ivermectin.
Trial outcomes were changed on January 20, 2022 clinicaltrials.gov, and again on March 2, 2022 clinicaltrials.gov (B). COVIDOUT.
Medication delivery varied significantly over the trial. In this presentation vimeo.com, author indicates that delivery was initially local, later via FedEx, was much slower in August, there were delays due to team bandwidth issues, and they only realized they could use FedEx same day delivery in September.
0 0.5 1 1.5 2+ Mortality 42% Improvement Relative Risk Fluvoxamine for COVID-19  Calusic et al.  ICU PATIENTS Is very late treatment with fluvoxamine beneficial for COVID-19? PSM prospective study of 102 patients in Croatia (Apr - May 2021) Lower mortality with fluvoxamine (p=0.027) c19early.org Calusic et al., British J. Clinical Ph.., Nov 2021 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 0.5 1 1.5 2+ Case 28% Improvement Relative Risk Fluvoxamine for COVID-19  Diaz et al.  Prophylaxis Does fluvoxamine reduce COVID-19 infections? PSM retrospective 82,069 patients in the USA Fewer cases with fluvoxamine (p<0.000001) c19early.org Diaz et al., The Primary Care Companio.., Oct 2022 Favors fluvoxamine Favors control
Diaz: TriNetX PSM retrospective 82,069 OCD patients, showing lower risk of COVID-19 with fluvoxamine use.
0 0.5 1 1.5 2+ PASC, smell/taste 51% Improvement Relative Risk PASC, smell 44% PASC, taste 62% PASC, fatigue 52% primary PASC, headache 49% PASC, memory impairment 59% PASC, poor concentration -17% PASC, insomnia 8% PASC, aggression 74% PASC, depression 85% PASC, anxiety 37% PASC, myalgia 27% PASC, PCS 6% Fluvoxamine  Farahani et al.  EARLY TREATMENT  DB RCT  LONG COVID Does fluvoxamine reduce the risk of Long COVID (PASC)? Double-blind RCT 100 patients in Iran (March - June 2022) Lower PASC with fluvoxamine (not stat. sig., p=0.057) c19early.org Farahani et al., BMC Infectious Diseases, Mar 2023 Favors fluvoxamine Favors control
Farahani: RCT 100 mild/moderate COVID-19 outpatients in Iran, showing lower post COVID symptoms 12 weeks after infection, statistically significant only for fatigue with the small sample size. All symptoms may occur for non-COVID-19 reasons, smell/taste disorder may be the most likely to be related to COVID-19 infection. Fluvoxamine 100mg daily for 10 days.
0 0.5 1 1.5 2+ Hospitalization/ER, fluvo.. 19% Improvement Relative Risk Hospitalization/ER, FIA.. 12% Hospitalization/ER, SSRI 12% Hospitalization/ER, all a.. 10% Fluvoxamine for COVID-19  Fritz et al.  Prophylaxis Is prophylaxis with fluvoxamine beneficial for COVID-19? Retrospective 25,034 patients in the USA (March 2020 - May 2021) Study underpowed for fluvoxamine, only 17 patients c19early.org Fritz et al., Translational Psychiatry, Aug 2022 Favors fluvoxamine Favors control
Fritz: Retrospective 25,034 COVID+ outpatients showing significantly lower ER/hospitalization with antidepressants and FIASMA antidepressants, and a dose-dependent response.
0 0.5 1 1.5 2+ Mortality 68% Improvement Relative Risk Symptom resolution 53% Fluvoxamine  Kirenga et al.  LATE TREATMENT Is late treatment with fluvoxamine beneficial for COVID-19? Prospective study of 316 patients in Uganda (Dec 2021 - Feb 2022) Lower mortality (p<0.0001) and improved recovery (p=0.04) c19early.org Kirenga et al., Molecular Psychiatry, Mar 2023 Favors fluvoxamine Favors control
Kirenga: Prospective study of 316 hospitalized patients in Uganda, 94 receiving fluvoxamine, showing significantly lower mortality and improved recovery with treatment.
0 0.5 1 1.5 2+ Progression 93% Improvement Relative Risk Hospitalization 82% Fluvoxamine  STOP COVID  EARLY TREATMENT  DB RCT Is early treatment with fluvoxamine beneficial for COVID-19? Double-blind RCT 152 patients in the USA (April - August 2020) Lower progression (p=0.009) and hospitalization (p=0.009) c19early.org Lenze et al., JAMA, November 2020 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).
0 0.5 1 1.5 2+ Progression, ER, urgent ca.. 31% Improvement Relative Risk Clinical progression, day.. 34% Clinical progression, day 7 -15% Clinical progression.. (b) 6% Recovery -1% post-hoc primary Hospitalization, non-C19 49% Fluvoxamine  ACTIV-6  LATE TREATMENT  DB RCT Is late treatment with fluvoxamine beneficial for COVID-19? Double-blind RCT 1,175 patients in the USA (August 2022 - January 2023) Lower progression (p=0.34) and improved recovery (p=0.32), not sig. Multiple major issues with trial, see notes c19early.org Naggie et al., medRxiv, September 2023 Favors fluvoxamine Favors control
Naggie: Late treatment low risk population RCT showing lower progression to hospitalization or urgent care/ER visits with fluvoxamine, without statistical significance.

There was no mortality and only three hospitalizations. Authors provide no details on the cause of hospitalization, but they appear to be unrelated to COVID-19. eFigure 5 shows no COVID-19 clinical progression to hospitalization (note that a hospitalization can be seen in the equivalent plot for the low dose arm), and the text indicates that the "COVID clinical progression scale simplified into a self-reported evaluation of home levels (limited vs not)".

Note that the urgent care/ER visit outcome is also likely diluted due to inclusion of all-cause events, and could be statistically significant for only COVID-19 events.

The sustained recovery outcome, which shows no difference, was a post-hoc creation used to hide efficacy for ivermectin, and is not logical for evaluating efficacy in this trial. The definition includes any minor symptom within a three day period - e.g., any minor cough, headache, body ache, or fatigue that occurs in a three day period, regardless of cause, results in the treatment being considered a failure. For example, late treatment that is effective at minimizing progression, but has no improvement in resolution of cough, would not be detected. (Authors even use the end of the three day period to further minimize efficacy).

Late treatment - median 5 days, 75% 4+ days, 25% 7+ days, up to 12 days.

Also see Naggie (B) for many issues with this trial, and McCarthy for the lower dose arm.
0 0.5 1 1.5 2+ Mortality -58% Improvement Relative Risk Mortality, fluoxetine 26% Fluvoxamine for COVID-19  Oskotsky et al.  Prophylaxis Is prophylaxis with fluvoxamine beneficial for COVID-19? PSM retrospective 7,696 patients in the USA Study underpowed for fluvoxamine, only 11 patients c19early.org Oskotsky et al., JAMA Network Open, Nov 2021 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 0.5 1 1.5 2+ Mortality 94% Improvement Relative Risk Oxygen therapy 73% Hospitalization 51% Hospitalization time -71% Fluvoxamine  Pineda et al.  EARLY TREATMENT Is early treatment with fluvoxamine beneficial for COVID-19? Prospective study of 657 patients in Honduras (Nov 2020 - Jan 2022) Lower mortality (p=0.011) and lower oxygen therapy (p=0.00016) c19early.org Pineda et al., Frontiers in Pharmacology, Oct 2022 Favors fluvoxamine Favors control
Pineda: Prospective study of 657 COVID+ outpatients in Honduras, 594 accepting fluvoxamine treatment, showing significantly lower mortality and hospitalization with treatment.
0 0.5 1 1.5 2+ Oxygen therapy, NIV -201% Improvement Relative Risk Oxygen therapy 33% Oxygen therapy, PP 38% Hospitalization 9% Progression 12% primary Fluvoxamine  STOP COVID 2  EARLY TREATMENT  DB RCT Is early treatment with fluvoxamine beneficial for COVID-19? Double-blind RCT 547 patients in the USA (December 2020 - May 2021) Trial underpowered to detect differences c19early.org Reiersen et al., Open Forurm Infectiou.., Aug 2021 Favors fluvoxamine Favors control
Reiersen: Remote RCT 547 outpatients a median of 5 days from onset, showing no significant differences with fluvoxamine. The trial was stopped early and underpowered due to low event rates. The trial does not report outcomes that may not be underpowered like time to recovery. Authors note that treatment may have been too late.
0 0.5 1 1.5 2+ Mortality -200% Improvement Relative Risk Hospitalization 12% Hospitalization or ER >6h.. 50% primary Fluvoxamine  TOGETHER  EARLY TREATMENT  DB RCT Is early treatment with fluvoxamine + budesonide beneficial for COVID-19? Double-blind RCT 1,476 patients in Brazil (January - July 2022) Lower progression with fluvoxamine + budesonide (p=0.037) c19early.org Reis et al., Annals of Internal Medicine, Apr 2023 Favors fluvoxamine Favors control
Reis: Low-risk (1% hospitalization) outpatient RCT with 738 fluvoxamine + budesonide patients and 738 placebo patients, showing significantly lower hospitalization/ER visits with treatment.

Other arms of this trial show many critical issues Reis (C), many of which may also apply to this arm.
0 0.5 1 1.5 2+ Mortality 30% Improvement Relative Risk Mortality, PP 91% Ventilation 22% Hospitalization 22% Extended ER observation.. 32% primary Extended ER observa.. (b) 31% Extended ER observa.. (c) 66% Viral clearance -49% Fluvoxamine  TOGETHER  LATE TREATMENT  DB RCT Is late treatment with fluvoxamine beneficial for COVID-19? Double-blind RCT 1,497 patients in Brazil (January - August 2021) Fewer hosp./ER visits with fluvoxamine (p=0.0041) c19early.org Reis et al., The Lancet Global Health, Aug 2021 Favors fluvoxamine Favors control
Reis (B): 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 (B). NCT04727424.

Authors do not specify if the placebo looks identical to the film-coated Luvox tablets. Reportedly there is no registration of manufacturing for matching tablets by Abbott in Brazil, and no import license for identical placebo tablets abroad. This would be an additional reason for blinding failure if the placebo tablets are not identical in appearance.

For other issues with this trial see: twitter.com (C), twitter.com (D), twitter.com (E).

Many of the issues in the companion ivermectin trial may also apply to this trial c19ivm.org, notably the potential for significant use of an effective treatment in the placebo group doyourownresearch.substack.com, which would reduce the efficacy seen.
0 0.5 1 1.5 2+ Death/ICU 84% Improvement Relative Risk Hospitalization 94% Recovery 99% Fluvoxamine  Seftel et al.  EARLY TREATMENT Is early treatment with fluvoxamine beneficial for COVID-19? Prospective study of 125 patients in the USA Lower hospitalization (p=0.0026) and improved recovery (p<0.0001) c19early.org Seftel et al., Open Forum Infectious D.., Feb 2021 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).
0 0.5 1 1.5 2+ Progression 0% Improvement Relative Risk Progression, PP 34% Time to progression 13% Fluvoxamine  Seo et al.  EARLY TREATMENT  RCT Is early treatment with fluvoxamine beneficial for COVID-19? RCT 52 patients in South Korea (January - February 2021) Trial underpowered to detect differences c19early.org Seo et al., Infection & Chemotherapy, Mar 2022 Favors fluvoxamine Favors control
Seo: Early terminated RCT with 52 COVID+ patients in South Korea, showing no significant difference in progression with fluvoxamine treatment. There were only 2 events in each arm, and only one event for fluvoxamine in PP analysis. The trial was terminated early because the treatment center closed. 100mg fluvoxamine bid for 10 days.
0 0.5 1 1.5 2+ Oxygen therapy -2% Improvement Relative Risk Hospitalization -22% Fluvoxamine  EFFaCo  EARLY TREATMENT  RCT Is early treatment with fluvoxamine beneficial for COVID-19? RCT 327 patients in Thailand (June 2021 - February 2022) Trial underpowered to detect differences c19early.org Siripongboonsitti et al., Int. J. Infe.., Jun 2023 Favors fluvoxamine Favors control
Siripongboonsitti: RCT 327 outpatients in Thailand, showing no significant difference with 50mg fluvoxamine bid added to favipiravir. Authors note that trials showing benefit mostly used 100mg bid.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Hospitalization -37% Fluvoxamine for COVID-19  Trkulja et al.  Prophylaxis Is prophylaxis with fluvoxamine beneficial for COVID-19? PSM retrospective 32,085 patients in Croatia Lower mortality (p=0.41) and higher hospitalization (p=0.5), not sig. c19early.org Trkulja et al., European J. Clinical P.., Nov 2022 Favors fluvoxamine Favors control
Trkulja: Retrospective COVID+ patients in Croatia, showing no significant difference in outcomes with fluvoxamine prophylaxis.
0 0.5 1 1.5 2+ ICU admission 67% Improvement Relative Risk Hospitalization 84% Progression 86% Fluvoxamine  Tsiakalos et al.  EARLY TREATMENT Is early treatment with fluvoxamine beneficial for COVID-19? Retrospective 103 patients in Greece (September - December 2021) Lower progression with fluvoxamine (p=0.02) c19early.org Tsiakalos et al., Microorganisms, August 2023 Favors fluvoxamine Favors control
Tsiakalos: Retrospective 103 outpatients in Greece, showing lower risk of progression with fluvoxamine 100mg bid for 10 days. 2 patients (4%) in the fluvoxamine group had clinical deterioration compared to 8 patients (16%) in the standard care group (p<0.05). After adjusting for confounders, fluvoxamine was associated with a lower risk of clinical deterioration (adjusted OR 0.12, p=0.02). Fluvoxamine was also associated with improved lymphocyte count. Control patients were during Sep-Nov 2021, and treatment patients Nov-Dec 2021, introducing potential confounding by time due to changes in variants, although the change in risk during this period is expected to be relatively low.
0 0.5 1 1.5 2+ Mortality -103% Improvement Relative Risk ICU admission -395% Hospitalization 40% Progression 32% Case 12% Fluvoxamine  Visos-Varela et al.  Prophylaxis Is prophylaxis with fluvoxamine beneficial for COVID-19? Retrospective 86,602 patients in Spain Higher mortality (p=0.52) and ICU admission (p=0.24), not sig. c19early.org Visos-Varela et al., European Neuropsy.., Apr 2023 Favors fluvoxamine Favors control
Visos-Varela: Retrospective 86,602 patients in Spain, showing lower COVID-19 risk SSRIs citalopram and paroxetine. There were no significant difference for fluvoxamine, which few patients were taking.
Wannigama: 1,200 patient fluvoxamine early treatment RCT with results not reported over 1 year after completion.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were fluvoxamine, 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 fluvoxamine 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 (3.11.4) with scipy (1.11.1), pythonmeta (1.26), numpy (1.25.0), statsmodels (0.14.0), and plotly (5.15.0).
Forest plots are computed using PythonMeta Deng (B) with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective McLean, Treanor.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/fmeta.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.
Bramante, 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 3 authors, average treatment delay 5.0 days, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04510194 (history) (COVID-OUT). risk of death/hospitalization, 10.8% higher, RR 1.11, p = 0.88, treatment 6 of 329 (1.8%), control 5 of 324 (1.5%), odds ratio converted to relative risk.
risk of progression, 16.1% higher, RR 1.16, p = 0.68, treatment 18 of 329 (5.5%), control 15 of 324 (4.6%), odds ratio converted to relative risk, combined ER, hospitalization, death.
risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S8, day 28.
risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S7, day 14.
risk of progression, 4.6% lower, RR 0.95, p = 0.75, treatment 79 of 329 (24.0%), control 80 of 321 (24.9%), NNT 110, odds ratio converted to relative risk, combined hypoxemia, ER, hospitalization, death, primary outcome.
Farahani, 3/31/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, peer-reviewed, mean age 38.5, 3 authors, study period March 2022 - June 2022. risk of PASC, 50.8% lower, RR 0.49, p = 0.06, treatment 42, control 43, smell and taste disturbance combined.
risk of PASC, 44.2% lower, RR 0.56, p = 0.28, treatment 6 of 42 (14.3%), control 11 of 43 (25.6%), NNT 8.9, smell.
risk of PASC, 61.6% lower, RR 0.38, p = 0.20, treatment 3 of 42 (7.1%), control 8 of 43 (18.6%), NNT 8.7, taste.
risk of PASC, 51.5% lower, RR 0.48, p = 0.04, treatment 9 of 42 (21.4%), control 19 of 43 (44.2%), NNT 4.4, fatigue, primary outcome.
risk of PASC, 48.8% lower, RR 0.51, p = 0.48, treatment 3 of 42 (7.1%), control 6 of 43 (14.0%), NNT 15, headache.
risk of PASC, 59.0% lower, RR 0.41, p = 0.43, treatment 2 of 42 (4.8%), control 5 of 43 (11.6%), NNT 15, memory impairment.
risk of PASC, 17.0% higher, RR 1.17, p = 0.78, treatment 8 of 42 (19.0%), control 7 of 43 (16.3%), poor concentration.
risk of PASC, 7.9% lower, RR 0.92, p = 1.00, treatment 9 of 42 (21.4%), control 10 of 43 (23.3%), NNT 55, insomnia.
risk of PASC, 74.4% lower, RR 0.26, p = 0.36, treatment 1 of 42 (2.4%), control 4 of 43 (9.3%), NNT 14, aggression.
risk of PASC, 85.4% lower, RR 0.15, p = 0.06, treatment 1 of 42 (2.4%), control 7 of 43 (16.3%), NNT 7.2, depression.
risk of PASC, 37.0% lower, RR 0.63, p = 0.32, treatment 8 of 42 (19.0%), control 13 of 43 (30.2%), NNT 8.9, anxiety.
risk of PASC, 26.9% lower, RR 0.73, p = 0.76, treatment 5 of 42 (11.9%), control 7 of 43 (16.3%), NNT 23, myalgia.
risk of PASC, 6.4% lower, RR 0.94, p = 0.60, treatment 32 of 42 (76.2%), control 35 of 43 (81.4%), NNT 19, PCS.
Lenze, 11/12/2020, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 11 authors, study period 10 April, 2020 - 5 August, 2020, average treatment delay 4.0 days, trial NCT04342663 (history) (STOP COVID). 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.
Pineda, 10/4/2022, prospective, Honduras, peer-reviewed, mean age 48.1, 24 authors, study period November 2020 - January 2022. risk of death, 94.0% lower, RR 0.06, p = 0.01, treatment 1 of 594 (0.2%), control 4 of 63 (6.3%), NNT 16, adjusted per study.
risk of oxygen therapy, 73.0% lower, RR 0.27, p < 0.001, treatment 15 of 594 (2.5%), control 13 of 63 (20.6%), NNT 5.5, adjusted per study.
risk of hospitalization, 51.0% lower, RR 0.49, p = 0.04, treatment 23 of 594 (3.9%), control 11 of 63 (17.5%), NNT 7.4, adjusted per study.
hospitalization time, 71.4% higher, relative time 1.71, p = 0.08, treatment 23, control 11.
Reiersen, 8/20/2021, Double Blind Randomized Controlled Trial, USA, peer-reviewed, median age 47.0 (treatment) 48.0 (control), 24 authors, study period 22 December, 2020 - 21 May, 2021, average treatment delay 5.0 days, trial NCT04668950 (history) (STOP COVID 2). risk of oxygen therapy, 201.1% higher, RR 3.01, p = 0.50, treatment 1 of 272 (0.4%), control 0 of 275 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), non-invasive ventilation.
risk of oxygen therapy, 32.6% lower, RR 0.67, p = 0.60, treatment 6 of 272 (2.2%), control 9 of 275 (3.3%), NNT 94.
risk of oxygen therapy, 37.5% lower, RR 0.62, p = 0.74, treatment 3 of 164 (1.8%), control 6 of 205 (2.9%), NNT 91, per-protocol.
risk of hospitalization, 9.0% lower, RR 0.91, p = 1.00, treatment 9 of 272 (3.3%), control 10 of 275 (3.6%), NNT 305.
risk of progression, 12.4% lower, RR 0.88, p = 0.85, treatment 13 of 272 (4.8%), control 15 of 275 (5.5%), NNT 148, primary outcome.
Reis, 4/17/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 35 authors, study period 15 January, 2022 - 6 July, 2022, average treatment delay 3.0 days, this trial uses multiple treatments in the treatment arm (combined with budesonide) - results of individual treatments may vary, trial NCT04727424 (history) (TOGETHER). risk of death, 200.0% higher, RR 3.00, p = 1.00, treatment 1 of 738 (0.1%), control 0 of 738 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
risk of hospitalization, 12.5% lower, RR 0.88, p = 1.00, treatment 7 of 738 (0.9%), control 8 of 738 (1.1%), NNT 738.
hospitalization or ER >6hrs, 50.0% lower, RR 0.50, p = 0.04, treatment 13 of 738 (1.8%), control 27 of 738 (3.7%), NNT 53, adjusted per study, day 28, primary outcome.
Seftel, 2/1/2021, prospective quasi-randomized (patient choice), USA, 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).
Seo, 3/3/2022, Single Blind Randomized Controlled Trial, placebo-controlled, South Korea, peer-reviewed, median age 53.5, 14 authors, study period 15 January, 2021 - 19 February, 2021. risk of progression, no change, RR 1.00, p = 1.00, treatment 2 of 26 (7.7%), control 2 of 26 (7.7%).
risk of progression, 34.2% lower, RR 0.66, p = 1.00, treatment 1 of 19 (5.3%), control 2 of 25 (8.0%), NNT 37, PP.
time to progression, 13.3% lower, relative time 0.87, p = 0.16, treatment mean 6.5 (±0.7) n=26, control mean 7.5 (±3.5) n=26.
Siripongboonsitti, 6/29/2023, Randomized Controlled Trial, Thailand, peer-reviewed, 9 authors, study period 26 June, 2021 - 22 February, 2022, EFFaCo trial. risk of oxygen therapy, 1.9% higher, RR 1.02, p = 1.00, treatment 4 of 162 (2.5%), control 4 of 165 (2.4%).
risk of hospitalization, 22.2% higher, RR 1.22, p = 0.77, treatment 6 of 162 (3.7%), control 5 of 165 (3.0%), day 28.
Tsiakalos, 8/12/2023, retrospective, Greece, peer-reviewed, 5 authors, study period 1 September, 2021 - 31 December, 2021. risk of ICU admission, 67.3% lower, RR 0.33, p = 0.49, treatment 0 of 53 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 84.3% lower, RR 0.16, p = 0.06, treatment 1 of 53 (1.9%), control 6 of 50 (12.0%), NNT 9.9.
risk of progression, 86.0% lower, RR 0.14, p = 0.02, treatment 2 of 53 (3.8%), control 8 of 50 (16.0%), NNT 8.2, adjusted per study, odds ratio converted to relative risk, multivariable.
Wannigama, 6/21/2022, Randomized Controlled Trial, Thailand, trial NCT05087381 (history). 1,200 patient RCT with results missing over 1 year.
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.
Calusic, 11/1/2021, prospective, propensity score matching, Croatia, peer-reviewed, 7 authors, study period 1 April, 2021 - 31 May, 2021. risk of death, 42.0% lower, HR 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.
Kirenga, 3/3/2023, prospective, Uganda, peer-reviewed, 19 authors, study period December 2021 - February 2022. risk of death, 68.0% lower, HR 0.32, p < 0.001, treatment 29 of 94 (30.9%), control 126 of 222 (56.8%), NNT 3.9, adjusted for unbalanced covariates, propensity score weighting, Cox proportional hazards.
symptom resolution, 53.1% lower, HR 0.47, p = 0.04, treatment 94, control 222, inverted to make HR<1 favor treatment, propensity score weighting, Cox proportional hazards, RR approximated with OR.
Naggie, 9/13/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 243 authors, study period 5 August, 2022 - 20 January, 2023, average treatment delay 5.0 days, trial NCT04885530 (history) (ACTIV-6). risk of progression, 31.0% lower, RR 0.69, p = 0.34, treatment 14 of 589 (2.4%), control 21 of 586 (3.6%), NNT 83, adjusted per study, urgent or emergency care visits, hospitalizations, or death.
clinical progression, 34.0% lower, OR 0.66, p = 0.32, treatment 589, control 586, mid-recovery, day 14, RR approximated with OR.
clinical progression, 15.0% higher, OR 1.15, p = 0.68, treatment 589, control 586, day 7, RR approximated with OR.
clinical progression, 6.0% lower, OR 0.94, p = 0.90, treatment 589, control 586, day 28, RR approximated with OR.
risk of no recovery, 1.0% higher, HR 1.01, p = 0.86, treatment 589, control 586, inverted to make HR<1 favor treatment, post-hoc primary outcome.
risk of hospitalization, 49.0% lower, RR 0.51, p = 0.59, treatment 1 of 589 (0.2%), control 2 of 586 (0.3%), NNT 583, non-COVID-19 hospitalization, day 28.
Reis (B), 8/23/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 27 authors, study period 20 January, 2021 - 5 August, 2021, trial NCT04727424 (history) (TOGETHER). 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, primary outcome.
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 viral clearance, 49.3% higher, RR 1.49, p = 0.09, treatment 167 of 207 (80.7%), control 163 of 221 (73.8%), adjusted per study, inverted to make RR<1 favor treatment.
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.
Diaz, 10/6/2022, retrospective, USA, peer-reviewed, 2 authors. risk of case, 28.0% lower, OR 0.72, p < 0.001, treatment 4,558, control 4,558, propensity score matching, RR approximated with OR.
Fritz, 8/22/2022, retrospective, USA, peer-reviewed, 5 authors, study period 1 March, 2020 - 16 May, 2021. risk of hospitalization/ER, 19.4% lower, RR 0.81, p = 0.69, treatment 4 of 17 (23.5%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, fluvoxamine, multivariable.
risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.03, treatment 707 of 3,414 (20.7%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, FIASMA, multivariable.
risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.04, treatment 559 of 2,744 (20.4%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, SSRI, multivariable.
risk of hospitalization/ER, 10.1% lower, RR 0.90, p = 0.04, treatment 971 of 4,577 (21.2%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, all antidepressants, multivariable.
Oskotsky, 11/15/2021, retrospective, propensity score matching, USA, 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.
Trkulja, 11/7/2022, retrospective, Croatia, peer-reviewed, 2 authors. risk of death, 27.0% lower, RR 0.73, p = 0.41, treatment 749, control 31,336, cohort A vs. B, propensity score matching.
risk of hospitalization, 37.0% higher, RR 1.37, p = 0.50, treatment 749, control 31,336, cohort A vs. B, COVID-related, propensity score matching.
Visos-Varela, 4/23/2023, retrospective, Spain, peer-reviewed, 8 authors. risk of death, 103.0% higher, OR 2.03, p = 0.52, treatment 1 of 413 (0.2%) cases, 7 of 7,408 (0.1%) controls, adjusted per study, case control OR.
risk of ICU admission, 395.0% higher, OR 4.95, p = 0.24, treatment 1 of 228 (0.4%) cases, 2 of 4,398 (0.0%) controls, adjusted per study, case control OR.
risk of hospitalization, 40.0% lower, OR 0.60, p = 0.39, treatment 3 of 3,060 (0.1%) cases, 69 of 56,785 (0.1%) controls, adjusted per study, case control OR.
risk of progression, 32.0% lower, OR 0.68, p = 0.56, treatment 3 of 3,060 (0.1%) cases, 25 of 26,757 (0.1%) controls, adjusted per study, case control OR.
risk of case, 12.0% lower, OR 0.88, p = 0.60, treatment 28 of 29,817 (0.1%) cases, 69 of 56,785 (0.1%) controls, adjusted per study, case control OR.
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