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Colchicine for COVID-19: real-time meta analysis of 48 studies
Covid Analysis, June 2023
https://c19early.org/ometa.html
 
0 0.5 1 1.5+ All studies 31% 48 32,301 Improvement, Studies, Patients Relative Risk Mortality 33% 39 29,219 Ventilation 29% 10 13,614 ICU admission 25% 7 1,073 Hospitalization 17% 16 12,305 Progression 45% 7 3,449 Recovery 21% 12 12,666 Cases -9% 4 2,559 RCTs 16% 24 26,456 RCT mortality 3% 21 26,074 Peer-reviewed 30% 46 31,880 Prophylaxis 12% 9 3,222 Early 68% 1 0 Late 33% 38 29,079 Colchicine for COVID-19 c19early.org/o Jun 2023 Favorscolchicine Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 25 studies from 25 independent teams in 15 different countries show statistically significant improvements in isolation (15 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 31% [21‑40%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 24 of 48 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 31% 48 32,301 Improvement, Studies, Patients Relative Risk Mortality 33% 39 29,219 Ventilation 29% 10 13,614 ICU admission 25% 7 1,073 Hospitalization 17% 16 12,305 Progression 45% 7 3,449 Recovery 21% 12 12,666 Cases -9% 4 2,559 RCTs 16% 24 26,456 RCT mortality 3% 21 26,074 Peer-reviewed 30% 46 31,880 Prophylaxis 12% 9 3,222 Early 68% 1 0 Late 33% 38 29,079 Colchicine for COVID-19 c19early.org/o Jun 2023 Favorscolchicine Favorscontrol after exclusions
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
No treatment, vaccine, or intervention is 100% effective and available. 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. Only 10% of colchicine studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for colchicine can be found in [Danjuma, Elshafei, Golpour, Lien, Rai, Salah, Yasmin, Zein], showing significant improvements for mortality and severity.
Evolution of COVID-19 clinical evidence Colchicine p=0.00000019 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org June 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with colchicine (more)
All studies Late treatment Prophylaxis Studies Patients Authors
All studies31% [21‑40%]
****
33% [22‑43%]
****
12% [-20‑35%] 48 32,301 878
Randomized Controlled TrialsRCTs16% [4‑26%]
*
16% [4‑26%]
*
- 24 26,456 570
Mortality33% [21‑43%]
****
32% [19‑43%]
****
18% [-46‑54%] 39 29,219 787
Highlights
Colchicine reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for ICU admission, low confidence for progression, and very low confidence for ventilation.
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 51 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hunt 68% 0.32 [0.15-0.67] death Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0006 Early treatment 68% 0.32 [0.15-0.67] 68% improvement GRECCO-19 Deftereos (RCT) 77% 0.23 [0.03-1.97] death 1/55 4/50 Improvement, RR [CI] Treatment Control Lopes (DB RCT) 80% 0.20 [0.01-4.03] death 0/36 2/36 Brunetti (PSM) 73% 0.27 [0.08-0.89] death 3/33 11/33 Scarsi 85% 0.15 [0.06-0.37] death 122 (n) 140 (n) Salehzadeh (RCT) 23% 0.77 [0.66-0.90] hosp. time 50 (n) 50 (n) Pinzón 35% 0.65 [0.34-1.21] death 14/145 23/156 Sandhu 42% 0.58 [0.40-0.85] death 16/34 63/78 Rodriguez-Nava 6% 0.94 [0.61-1.47] death 16/52 85/261 Mahale -7% 1.07 [0.59-1.96] death 11/39 25/95 Valerio Pas.. (ICU) 23% 0.77 [0.31-1.94] death 5/35 12/30 ICU patients CT​1 COLCORONA Tardif (DB RCT) 44% 0.56 [0.19-1.67] death 5/2,235 9/2,253 Mareev 80% 0.20 [0.01-4.01] death 0/21 2/22 García-Posada 57% 0.43 [0.16-0.84] death 48/99 59/110 CT​1 Manenti (PSW) 76% 0.24 [0.09-0.67] death 71 (n) 70 (n) Mostafaie (RCT) 83% 0.17 [0.02-1.34] death 1/60 6/60 CT​1 RECOVERY Recovery C.. (RCT) -1% 1.01 [0.93-1.10] death 1,173/5,610 1,190/5,730 Hueda-Zavaleta 54% 0.46 [0.23-0.91] death 10/50 109/301 Kevorkian 96% 0.04 [0.01-0.21] progression 28 (n) 40 (n) CT​1 Gaitán-Dua.. (RCT) 22% 0.78 [0.44-1.36] death 22/153 28/161 CT​1 Pascual-Fi.. (RCT) 80% 0.20 [0.01-4.03] death 0/52 2/51 Dorward (RCT) 70% 0.30 [0.01-7.37] death 0/156 1/120 Absalón-.. (DB RCT) 29% 0.71 [0.21-2.40] death 4/56 6/60 Diaz (RCT) 12% 0.88 [0.70-1.12] death 131/640 142/639 Alsultan (RCT) 36% 0.64 [0.20-2.07] death 3/14 7/21 Karakaş 13% 0.87 [0.46-1.64] death 16/165 19/171 Pourdowlat (RCT) 73% 0.27 [0.11-0.71] hosp. 5/102 18/100 Gorial (RCT) 67% 0.33 [0.04-3.14] death 1/80 3/80 STRUCK Pimenta B.. (RCT) 79% 0.21 [0.01-4.05] death 0/14 2/16 Jalal (RCT) 24% 0.76 [0.62-0.93] hosp. time 36 (n) 44 (n) Cecconi (DB RCT) 29% 0.71 [0.28-1.79] death 7/119 10/120 ACT inpatient Eikelboom (RCT) -8% 1.08 [0.91-1.29] death 264/1,304 249/1,307 ACT outpatient Eikelboom (RCT) -9% 1.09 [0.48-2.47] death 12/1,939 11/1,942 COLVID-19 Perricone (RCT) -36% 1.36 [0.45-4.11] death 7/77 5/75 Rahman (DB RCT) 71% 0.29 [0.10-0.92] death 4/146 13/146 Kasiri (DB RCT) 7% 0.93 [0.32-2.69] death 6/55 6/51 Sunil Naik (RCT) -169% 2.69 [0.11-64.6] death 1/62 0/43 COLSTAT Shah (RCT) -75% 1.75 [0.53-5.83] death 7/125 4/125 CT​1 Villamañán 42% 0.58 [0.33-0.96] death 19/111 32/111 Tau​2 = 0.08, I​2 = 70.6%, p < 0.0001 Late treatment 33% 0.67 [0.57-0.78] 1,812/14,181 2,158/14,898 33% improvement Madrid-García -37% 1.37 [0.48-3.90] death n/a n/a Improvement, RR [CI] Treatment Control Ozcifci 4% 0.96 [0.75-1.22] cases 130/616 85/421 Monserrat .. (PSM) 80% 0.20 [0.02-0.93] death n/a n/a Topless 23% 0.77 [0.56-1.07] death population-based cohort Oztas -406% 5.06 [0.59-43.2] hosp. 5/635 1/643 Avanoglu Guler 79% 0.21 [0.04-0.83] oxygen 6/66 3/7 Correa-Rodríguez -150% 2.50 [0.10-60.6] oxygen 1/163 0/81 Sáenz-Aldea -8% 1.08 [0.76-1.53] hosp. case control Chevalier -28% 1.28 [0.51-2.35] death 5/21 111/569 Tau​2 = 0.09, I​2 = 53.7%, p = 0.44 Prophylaxis 12% 0.88 [0.65-1.20] 147/1,501 200/1,721 12% improvement All studies 31% 0.69 [0.60-0.79] 1,959/15,682 2,358/16,619 31% improvement 48 colchicine COVID-19 studies c19early.org/o Jun 2023 Tau​2 = 0.08, I​2 = 69.2%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors colchicine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Hunt 68% death Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.0006 Early treatment 68% 68% improvement GRECCO-19 Deftereos (RCT) 77% death Lopes (DB RCT) 80% death Brunetti (PSM) 73% death Scarsi 85% death Salehzadeh (RCT) 23% hospitalization Pinzón 35% death Sandhu 42% death Rodriguez-Nava 6% death Mahale -7% death Valerio Pa.. (ICU) 23% death ICU patients CT​1 COLCORONA Tardif (DB RCT) 44% death Mareev 80% death García-Posada 57% death CT​1 Manenti (PSW) 76% death Mostafaie (RCT) 83% death CT​1 RECOVERY Recovery .. (RCT) -1% death Hueda-Zavaleta 54% death Kevorkian 96% progression CT​1 Gaitán-Du.. (RCT) 22% death CT​1 Pascual-F.. (RCT) 80% death Dorward (RCT) 70% death Absalón.. (DB RCT) 29% death Diaz (RCT) 12% death Alsultan (RCT) 36% death Karakaş 13% death Pourdowlat (RCT) 73% hospitalization Gorial (RCT) 67% death STRUCK Pimenta .. (RCT) 79% death Jalal (RCT) 24% hospitalization Cecconi (DB RCT) 29% death ACT inpatient Eikelboom (RCT) -8% death ACT outpatient Eikelboom (RCT) -9% death COLVID-19 Perricone (RCT) -36% death Rahman (DB RCT) 71% death Kasiri (DB RCT) 7% death Sunil Naik (RCT) -169% death COLSTAT Shah (RCT) -75% death CT​1 Villamañán 42% death Tau​2 = 0.08, I​2 = 70.6%, p < 0.0001 Late treatment 33% 33% improvement Madrid-García -37% death Ozcifci 4% case Monserrat.. (PSM) 80% death Topless 23% death Oztas -406% hospitalization Avanoglu Guler 79% oxygen therapy Correa-Rodríguez -150% oxygen therapy Sáenz-Aldea -8% hospitalization Chevalier -28% death Tau​2 = 0.09, I​2 = 53.7%, p = 0.44 Prophylaxis 12% 12% improvement All studies 31% 31% improvement 48 colchicine COVID-19 studies c19early.org/o Jun 2023 Tau​2 = 0.08, I​2 = 69.2%, p < 0.0001 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors colchicine Favors control
B
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C
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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. 0.9% of 3,946 proposed treatments show efficacy [c19early.org]. D. Timeline of results in colchicine 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, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on specific outcomes in RCTs was delayed by 4.2 months, compared to using pooled outcomes in RCTs.
We analyze all significant studies concerning the use of colchicine for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Table 1 shows potential mechanisms of action for the treatment of COVID-19 using colchicine.
Table 1. Colchicine mechanisms of action. Submit updates.
Antiviral effectsDirect antiviral activity via inhibiting microtubule polymerization and viral entry.
Immunomodulatory effectsPotential prevention of an overactive immune response via modulation of immune cell functions, such as neutrophil chemotaxis, adhesion, and activation.
Anti-inflammatory effectsReduction in inflammation and severity of cytokine storm via inibition of inflammasome activation and the release of pro-inflammatory cytokines, including IL-1β, IL-6, and TNF-α.
Prevention of microvascular thrombosisReduction in the risk of clot formation via antithrombotic properties, such as inhibiting platelet aggregation.
Cardioprotective effectsMitigation of myocardial injury via reduced myocardial inflammation and oxidative stress, and inhibition of NLRP3 inflammasomes.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 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 peer reviewed studies.
Table 2. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  **** p<0.0001.
Improvement Studies Patients Authors
All studies31% [21‑40%]
****
48 32,301 878
After exclusions43% [31‑52%]
****
39 14,680 631
Peer-reviewed studiesPeer-reviewed30% [20‑39%]
****
46 31,880 868
Randomized Controlled TrialsRCTs16% [4‑26%]
*
24 26,456 570
RCTs after exclusionsRCTs w/exc.26% [16‑35%]
****
19 10,896 379
Mortality33% [21‑43%]
****
39 29,219 787
VentilationVent.29% [-15‑56%]10 13,614 260
ICU admissionICU25% [3‑43%]
*
7 1,073 155
HospitalizationHosp.17% [8‑25%]
***
16 12,305 281
Recovery21% [7‑33%]
**
12 12,666 169
Cases-9% [-27‑6%]4 2,559 42
RCT mortality3% [-6‑12%]21 26,074 546
RCT hospitalizationRCT hosp.18% [7‑28%]
**
9 9,191 188
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<0.0001.
Early treatment Late treatment Prophylaxis
All studies68% [33‑85%]
**
33% [22‑43%]
****
12% [-20‑35%]
After exclusions68% [33‑85%]
**
48% [36‑58%]
****
14% [-16‑36%]
Peer-reviewed studiesPeer-reviewed68% [33‑85%]
**
33% [21‑43%]
****
12% [-20‑35%]
Randomized Controlled TrialsRCTs-16% [4‑26%]
*
-
RCTs after exclusionsRCTs w/exc.-26% [16‑35%]
****
-
Mortality68% [33‑85%]
**
32% [19‑43%]
****
18% [-46‑54%]
VentilationVent.-29% [-15‑56%]-
ICU admissionICU-25% [3‑43%]
*
-
HospitalizationHosp.-20% [11‑28%]
****
-10% [-43‑16%]
Recovery-22% [7‑34%]
**
7% [-70‑49%]
Cases---9% [-27‑6%]
RCT mortality-3% [-6‑12%]-
RCT hospitalizationRCT hosp.-18% [7‑28%]
**
-
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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 peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. Figure 13, 14, 15, 16, and 17 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCTs after exclusions, RCT mortality results, RCT mortality results after exclusions, 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 51 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 colchicine 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] 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, 36 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 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is 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 RCTs after exclusions. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 15. Random effects meta-analysis for RCT mortality results.
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Figure 16. Random effects meta-analysis for RCT mortality results after exclusions.
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Figure 17. Random effects meta-analysis for RCT hospitalization results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 18 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Diaz], very late stage, oxygen saturation <90% at baseline; very late stage, >80% on oxygen/ventilation at baseline.
[Eikelboom], very late stage, oxygen saturation <90% at baseline.
[Jalal], minimal details provided.
[Karakaş], excessive unadjusted differences between groups.
[Mahale], unadjusted results with no group details.
[Oztas], excessive unadjusted differences between groups.
[Recovery Collaborative Group], very late stage, 9 days since symptoms started, 32% baseline ventilation.
[Rodriguez-Nava], substantial unadjusted confounding by indication likely; excessive unadjusted differences between groups; unadjusted results with no group details.
[Shah], very late stage, >50% on oxygen/ventilation at baseline.
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Figure 18. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
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 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 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 20. 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, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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Figure 20. 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].
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 21 shows a scatter plot of results for prospective and retrospective studies. 59% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 46% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 38% improvement, compared to 29% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy.
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Figure 21. 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 22 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 22. 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. Colchicine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 colchicine 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 colchicine 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.
6 of 48 studies combine treatments. The results of colchicine alone may differ. 3 of 24 RCTs use combined treatment. Other meta analyses for colchicine can be found in [Danjuma, Elshafei, Golpour, Lien, Rai, Salah, Yasmin, Zein], showing significant improvements for one or more of mortality and severity.
Colchicine is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 25 studies from 25 independent teams in 15 different countries show statistically significant improvements in isolation (15 for the most serious outcome). Meta analysis using the most serious outcome reported shows 31% [21‑40%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral. Early treatment is more effective than late treatment. Results are robust — in exclusion sensitivity analysis 24 of 48 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
RCT results are less favorable, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Progression to critical or.. 17% primary Recovery -13% c19early.org/o Absalón-Aguilar et al. Colchicine for COVID-19 RCT LATE Is late treatment with colchicine beneficial for COVID-19? Double-blind RCT 116 patients in Mexico No significant difference in outcomes seen Absalón-Aguilar et al., J. General Internal Medi.., doi:10.1007/s11606-021-07203-8 Favors colchicine Favors control
[Absalón-Aguilar] Very late stage RCT with 56 colchicine and 60 control patients in Mexico, showing no significant differences.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk Hospitalization time 20% no CI c19early.org/o Alsultan et al. Colchicine for COVID-19 RCT LATE Is late treatment with colchicine beneficial for COVID-19? RCT 35 patients in Syria Trial underpowered to detect differences Alsultan et al., Interdisciplinary Perspectives .., doi:10.1155/2021/2129006 Favors colchicine Favors control
[Alsultan] Small RCT 49 severe condition hospitalized patients in Syria, showing lower mortality with colchicine and shorter hospitalization time with both colchicine and budesonide (all of these were not statistically significant).
0 0.5 1 1.5 2+ Oxygen therapy 79% Improvement Relative Risk c19early.org/o Avanoglu Guler et al. Colchicine for COVID-19 Prophylaxis Is prophylaxis with colchicine beneficial for COVID-19? Retrospective 73 patients in Turkey Lower need for oxygen therapy with colchicine (p=0.043) Avanoglu Guler et al., Modern Rheumatology, doi:10.1093/mr/roac074 Favors colchicine Favors control
[Avanoglu Guler] Retrospective 73 familial Mediterranean fever patients with COVID-19 in Turkey, showing significantly higher risk of hospitalization for respiratory support with non-adherence to colchicine treatment before the infection.
0 0.5 1 1.5 2+ Mortality 73% Improvement Relative Risk Discharge 73% c19early.org/o Brunetti et al. Colchicine for COVID-19 LATE Is late treatment with colchicine beneficial for COVID-19? PSM retrospective 66 patients in the USA Lower mortality (p=0.033) and higher discharge (p=0.033) Brunetti et al., J. Clin. Med., 9:9, 2961, doi:10.3390/jcm9092961 Favors colchicine Favors control
[Brunetti] PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 29% Improvement Relative Risk Ventilation 50% ICU admission 21% Combined NIV/ICU/vent.. 15% primary c19early.org/o Cecconi et al. Colchicine for COVID-19 RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? Double-blind RCT 240 patients in Spain (August 2020 - March 2021) Lower ventilation with colchicine (not stat. sig., p=0.29) Cecconi et al., Scientific Reports, doi:10.1038/s41598-022-13424-6 Favors colchicine Favors control
[Cecconi] RCT 240 hospitalized patients with COVID-19 pneumonia, mean 9 days from the onset of symptoms, showing no significant differences with colchicine treatment. EudraCT 2020-001841-38.
0 0.5 1 1.5 2+ Mortality -28% Improvement Relative Risk Hospitalization 8% c19early.org/o Chevalier et al. Colchicine for COVID-19 Prophylaxis Is prophylaxis with colchicine beneficial for COVID-19? Retrospective 1,213 patients in France Higher mortality with colchicine (not stat. sig., p=0.54) Chevalier et al., Frontiers in Medicine, doi:10.3389/fmed.2023.1152587 Favors colchicine Favors control
[Chevalier] Retrospective 1,213 rheumatic disease patients in France, showing no significant difference with colchicine use in univariate analysis.
0 0.5 1 1.5 2+ Oxygen therapy -150% Improvement Relative Risk Hospitalization -150% Recovery 7% Case 1% c19early.org/o Correa-Rodríguez et al. Colchicine Prophylaxis Is prophylaxis with colchicine beneficial for COVID-19? Retrospective 244 patients in Spain Study underpowered for serious outcomes Correa-Rodríguez et al., Medicina Clínica, doi:10.1016/j.medcle.2022.08.009 Favors colchicine Favors control
[Correa-Rodríguez] Retrospective 244 Behçet disease patients in Spain, showing no significant difference in outcomes with colchicine treatment. Confounding by indication may significantly affect results - colchicine may be prescribed more often for more serious cases, which may have a higher baseline risk for COVID-19.
0 0.5 1 1.5 2+ Mortality 77% Improvement Relative Risk Ventilation 82% Clinical deterioration 87% c19early.org/o Deftereos et al. NCT04326790 GRECCO-19 Colchicine RCT LATE Is late treatment with colchicine beneficial for COVID-19? RCT 105 patients in Greece Lower progression with colchicine (p=0.046) Deftereos et al., JAMA Network Open, doi:10.1001/jamanetworkopen.2020.13136 Favors colchicine Favors control
[Deftereos] RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment.
0 0.5 1 1.5 2+ Mortality 12% primary Improvement Relative Risk Death/intubation 17% primary Death/intubation (b) 52% Mortality (b) 17% Death/intubation (c) 25% c19early.org/o Diaz et al. NCT04328480 Colchicine RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? RCT 1,279 patients in Argentina (April 2020 - March 2021) Lower mortality (p=0.3) and death/intubation (p=0.08), not stat. sig. Diaz et al., JAMA Network Open, doi:10.1001/jamanetworkopen.2021.41328 Favors colchicine Favors control
[Diaz] Very late stage RCT (O2 88%, 84% on oxygen) with 1,279 hospitalized patients in Argentina, showing lower mortality and lower combined mortality/ventilation, statistically significant only for the combined outcome and per-protocol analysis. NCT04328480. COLCOVID.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk Death/hospitalization -30% Death/hospitalization (b) 22% Recovery -6% c19early.org/o Dorward et al. Colchicine for COVID-19 RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? RCT 1,301 patients in the United Kingdom (March - May 2021) Lower mortality with colchicine (not stat. sig., p=0.43) Dorward et al., British J. General Practice, doi:10.3399/BJGP.2022.0083 Favors colchicine Favors control
[Dorward] Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.
0 0.5 1 1.5 2+ Mortality -9% Improvement Relative Risk Death/hospitalization -2% primary Hospitalization -2% c19early.org/o Eikelboom et al. NCT04324463 ACT outpatient Colchicine RCT LATE Is late treatment with colchicine beneficial for COVID-19? RCT 3,881 patients in Canada (August 2020 - February 2022) No significant difference in outcomes seen Eikelboom et al., The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00299-5 Favors colchicine Favors control
[Eikelboom (B)] Late (5.4 days) outpatient RCT showing no significant difference in outcomes with colchicine treatment. Authors include a meta analysis of 6 colchicine RCTs, however there were 19 RCTs as of the publication date [c19colchicine.com].
0 0.5 1 1.5 2+ Mortality -8% Improvement Relative Risk Progression -4% Progression (b) 2% c19early.org/o Eikelboom et al. NCT04324463 ACT inpatient Colchicine RCT LATE Is late treatment with colchicine beneficial for COVID-19? RCT 2,611 patients in multiple countries (October 2020 - February 2022) No significant difference in outcomes seen Eikelboom et al., The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00298-3 Favors colchicine Favors control
[Eikelboom] RCT very late stage (baseline SpO2 80%) patients, showing no significant differences with colchicine treatment.
0 0.5 1 1.5 2+ Mortality 22% Improvement Relative Risk c19early.org/o Gaitán-Duarte et al. NCT04359095 Colchicine RCT LATE Is late treatment with colchicine+rosuvastatin beneficial for COVID-19? RCT 314 patients in Colombia Lower mortality with colchicine+rosuvastatin (not stat. sig., p=0.38) Gaitán-Duarte et al., eClinicalMedicine, doi:10.1016/j.eclinm.2021.101242 Favors colchicine Favors control
[Gaitán-Duarte] RCT 633 hospitalized patients in Colombia, 153 treated with colchicine + rosuvastatin, not showing statistically significant differences in outcomes. Improved results were seen with the combination of emtricitabine/tenofovir disoproxil + rosuvastatin + colchicine. NCT04359095.
0 0.5 1 1.5 2+ Mortality 57% Improvement Relative Risk c19early.org/o García-Posada et al. Colchicine for COVID-19 LATE Is late treatment with colchicine+combined treatments beneficial for COVID-19? Retrospective 209 patients in Colombia Lower mortality with colchicine+combined treatments (p=0.014) García-Posada et al., J. Infection and Public He.., doi:10.1016/j.jiph.2021.02.013 Favors colchicine Favors control
[García-Posada] Retrospective 209 hospitalized patients in Colombia, showing lower mortality with antibiotics + LMWH + corticosteroids + colchicine in multivariable analysis.
0 0.5 1 1.5 2+ Mortality 67% Improvement Relative Risk Recovery 63% c19early.org/o Gorial et al. Colchicine for COVID-19 RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? RCT 160 patients in Iraq Improved recovery with colchicine (p=0.001) Gorial et al., Annals of Medicine and Surgery, doi:10.1016/j.amsu.2022.103593 Favors colchicine Favors control
[Gorial] RCT with 80 colchicine and 80 control patients, showing improved recovery with treatment. SOC included vitamin C, vitamin D, and zinc.
0 0.5 1 1.5 2+ Mortality 54% Improvement Relative Risk c19early.org/o Hueda-Zavaleta et al. Colchicine for COVID-19 LATE Is late treatment with colchicine beneficial for COVID-19? Retrospective 351 patients in Peru Lower mortality with colchicine (p=0.025) Hueda-Zavaleta et al., Revista Peruana de Medici.., doi:10.17843/rpmesp.2021.382.7158 Favors colchicine Favors control
[Hueda-Zavaleta] Retrospective 450 late stage (median oxygen saturation 86%) COVID+ hospitalized patients in Peru, showing lower mortality with colchicine treatment.
0 0.5 1 1.5 2+ Mortality 68% Improvement Relative Risk c19early.org/o Hunt et al. Colchicine for COVID-19 EARLY TREATMENT Is early treatment with colchicine beneficial for COVID-19? Retrospective 26,508 patients in the USA (March - September 2020) Lower mortality with colchicine (p=0.0029) Hunt et al., J. General Internal Medicine, doi:10.1007/s11606-022-07701-3 Favors colchicine Favors control
[Hunt] Retrospective 26,508 consecutive COVID+ veterans in the USA, showing lower mortality with multiple treatments including colchicine. Treatment was defined as drugs administered ≥50% of the time within 2 weeks post-COVID+, and may be a continuation of prophylactic treatment in some cases, and may be early or late treatment in other cases. Further reduction in mortality was seen with combinations of treatments.
0 0.5 1 1.5 2+ Hospitalization time 24% Improvement Relative Risk c19early.org/o Jalal et al. NCT04867226 Colchicine RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? RCT 80 patients in Iraq (May - June 2021) Shorter hospitalization with colchicine (p=0.009) Jalal et al., Indian J. Rheumatology, doi:10.4103/injr.injr_264_21 Favors colchicine Favors control
[Jalal] Open label RCT of colchicine showing improved recovery with treatment. Only the abstract is currently available. Colchicine 0.5mg bid for 14 days.
0 0.5 1 1.5 2+ Mortality 13% Improvement Relative Risk ICU admission 16% Hospitalization time 25% c19early.org/o Karakaş et al. Colchicine for COVID-19 LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? Retrospective 336 patients in Turkey Shorter hospitalization with colchicine (p=0.0001) Karakaş et al., The J. Infection in Developing C.., doi:10.3855/jidc.14924 Favors colchicine Favors control
[Karakaş] Retrospective 356 hospitalized COVID-19 patients, shorter hospitalization time with colchicine treatment. There were no statistically significant differences for mortality or ICU admission. Significantly lower mortality was seen with higher dosage (1mg/day vs 0.5mg/day). More control patients were on oxygen at baseline (65% vs. 54%).
0 0.5 1 1.5 2+ Mortality 7% Improvement Relative Risk Ventilation 7% ICU admission -24% Recovery, day 14 28% Recovery, day 7 12% Recovery time 14% c19early.org/o Kasiri et al. IRCT20190804044429N5 Colchicine RCT LATE Is late treatment with colchicine beneficial for COVID-19? Double-blind RCT 110 patients in Iran (February - May 2021) Improved recovery with colchicine (not stat. sig., p=0.59) Kasiri et al., J. Investigative Medicine, doi:10.1177/10815589221141815 Favors colchicine Favors control
[Kasiri] Very late treatment (10 days from onset) RCT 110 patients in Iran, showing no significant difference in outcomes with colchicine. Colchicine 2mg loading dose followed by 0.5mg bid for 7 days.
0 0.5 1 1.5 2+ Mortality, ventilation, or.. 96% Improvement Relative Risk c19early.org/o Kevorkian et al. Colchicine for COVID-19 LATE Is late treatment with colchicine+combined treatments beneficial for COVID-19? Retrospective 68 patients in France (January - November 2020) Lower progression with colchicine+combined treatments (p=0.0005) Kevorkian et al., J. Infection, doi:10.1016/j.jinf.2021.02.008 Favors colchicine Favors control
[Kevorkian] Observational study in France with 28 hospitalized patients treated with prednisone/furosemide/colchicine/salicylate/direct anti-Xa inhibitor, and 40 control patients, showing lower combined mortality, ventilation, or high-flow oxygen therapy with treatment.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk ICU admission 50% Hospitalization time 22% c19early.org/o Lopes et al. Colchicine for COVID-19 RCT LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? Double-blind RCT 72 patients in Brazil Shorter hospitalization with colchicine (p=0.01) Lopes et al., RMD Open, doi:10.1136/rmdopen-2020-001455 Favors colchicine Favors control
[Lopes] RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.
0 0.5 1 1.5 2+ Mortality -37% Improvement Relative Risk Hospitalization -137% c19early.org/o Madrid-García et al. Colchicine for COVID-19 Prophylaxis Is prophylaxis with colchicine beneficial for COVID-19? Retrospective study in Spain (March - May 2020) Higher mortality (p=0.57) and hospitalization (p=0.2), not stat. sig. Madrid-García et al., Therapeutic Advances in Mu.., doi:10.1177/1759720x211002684 Favors colchicine Favors control
[Madrid-García] Retrospective 9,379 patients attending a rheumatology outpatient clinic in Spain, showing higher mortality and hospitalization with colchicine use, without statistical significance.
0 0.5 1 1.5 2+ Mortality -7% Improvement Relative Risk c19early.org/o Mahale et al. Colchicine for COVID-19 LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? Retrospective 134 patients in India (March - May 2020) Study underpowered to detect differences Mahale et al., Indian J. Critical Care Medicine, doi:10.5005/jp-journals-10071-23599 Favors colchicine Favors control
[Mahale] Retrospective 134 hospitalized COVID-19 patients in India, showing no significant difference with colchicine treatment in unadjusted results.
0 0.5 1 1.5 2+ Mortality 76% Improvement Relative Risk Recovery 44% c19early.org/o Manenti et al. Colchicine for COVID-19 LATE TREATMENT Is late treatment with colchicine beneficial for COVID-19? Retrospective 141 patients in Italy (March - April 2020) Lower mortality with colchicine (p=0.0054) Manenti et al., PLOS ONE, doi:10.1371/journal.pone.0248276 Favors colchicine Favors control
[Manenti] IPTW retrospective 141 COVID-19 patients (83% hospitalized), 71 treated with colchicine and 70 matched control patients, showing lower mortality and faster recovery with treatment.
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk ΔSHOCS-COVID 50% primary SHOCS-COVID 71%