Colchicine for COVID-19: real-time meta analysis of 49 studies

Table 1 shows potential mechanisms of action for the treatment of COVID-19 using colchicine.

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.

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.

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.

Figure 19 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.

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, 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.

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 44% 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.

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.

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 49 studies combine treatments. The results of colchicine alone may differ. 3 of 25 RCTs use combined treatment. Other meta analyses show significant improvements with colchicine for mortality Danjuma, Elshafei, Golpour, Lien, Rai, Salah, Zein and severity Yasmin.

Colchicine is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, ICU admission, hospitalization, and recovery. 25 studies from 25 independent teams in 15 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 30% [20‑39%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids the immune system or recovery but may have limited antiviral effects. Results are robust — in exclusion sensitivity analysis 24 of 49 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 which is not generalizable to earlier usage.

Other meta analyses show significant improvements with colchicine for mortality Danjuma, Elshafei, Golpour, Lien, Rai, Salah, Zein and severity Yasmin.

Absalón-Aguilar: Very late stage RCT with 56 colchicine and 60 control patients in Mexico, showing no significant differences.

Submit Corrections or Updates.

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).

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Brunetti: PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Chevalier: Retrospective 1,213 rheumatic disease patients in France, showing no significant difference with colchicine use in univariate analysis.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Deftereos: RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Dorward: Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Eikelboom: RCT very late stage (baseline SpO2 80%) patients, showing no significant differences with colchicine treatment.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

García-Posada: Retrospective 209 hospitalized patients in Colombia, showing lower mortality with antibiotics + LMWH + corticosteroids + colchicine in multivariable analysis.

Submit Corrections or Updates.

Gorial: RCT with 80 colchicine and 80 control patients, showing improved recovery with treatment. SOC included vitamin C, vitamin D, and zinc.

Submit Corrections or Updates.

Hassan: RCT 150 patients in Egypt showing no significant difference in outcomes with colchicine. SOC included vitamin C, D, and zinc. Colchicine 0.5mg tid days 1-3, bid days 4-7.

Submit Corrections or Updates.

Hueda-Zavaleta: Retrospective 450 late stage (median oxygen saturation 86%) COVID+ hospitalized patients in Peru, showing lower mortality with colchicine treatment.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Jalal: Open label RCT of colchicine showing improved recovery with treatment. Only the abstract is currently available. Colchicine 0.5mg bid for 14 days.

Submit Corrections or Updates.

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%).

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Lopes: RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.

Submit Corrections or Updates.

Madrid-García: Retrospective 9,379 patients attending a rheumatology outpatient clinic in Spain, showing higher mortality and hospitalization with colchicine use, without statistical significance.

Submit Corrections or Updates.

Mahale: Retrospective 134 hospitalized COVID-19 patients in India, showing no significant difference with colchicine treatment in unadjusted results.

Submit Corrections or Updates.

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.

Submit Corrections or Updates.

Mareev: Small trial with 21 colchicine patients and 22 control patients in Russia, showing improved recovery with treatment. The trial was originally an RCT, however randomization to the control arm was stopped after 5 patients, and 17 retrospective patients were added for comparison.

Submit Corrections or Updates.

Monserrat Villatoro: PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate, and higher mortality with acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol.

Submit Corrections or Updates.

Mostafaie: RCT with 60 patients treated with colchicine and phenolic monoterpenes and 60 control patients in Iran, showing lower mortality with treatment. NCT04392141.

Submit Corrections or Updates.

Ozcifci: Prospective analysis of 1,047 Behçet’s syndrome patients in Turkey, showing no significant difference in cases with colchicine use.

Submit Corrections or Updates.

Oztas: Retrospective 635 HCQ users and 643 household contacts, showing higher risk with colchicine in unadjusted results.

Patients with conditions leading to the use of colchicine may have significantly different baseline risk, e.g. Topless.

Submit Corrections or Updates.

Pascual-Figal: RCT with 52 colchicine patients and 51 control patients, showing lower risk of clinical deterioration with treatment. COL-COVID. NCT04350320.

Submit Corrections or Updates.

Perricone: RCT 152 hospitalized patients in Italy, showing no significant difference in outcomes with colchicine treatment. Table 2 shows 13% of patients treated with antivirals in the colchicine arm, however 16.9% were treated with one specific antiviral (HCQ).

Submit Corrections or Updates.

Pimenta Bonifácio: Open label RCT late stage hospitalized patients in Brazil with 14 colchicine and 16 SOC patients, showing lower mortality and improved recovery with treatment, without statistical significance. Authors note that the colchicine group had one patient with SOFA ≥7 vs. zero for SOC, however both groups had one patient intubated and SOC had more patients not requiring high-flow oxygen (12 vs. 8).

The journal version of this paper falsely states: "Ixekizumab, colchicine, and IL-2 were demonstrated to be safe but ineffective".

The pre-print more accurately represents the improved but not statistically significant results:

"The colchicine arm presented the lowest mortality rate (0%), while the low dose IL-2 had the highest (21.4%) by day 28 post-enrollment. The frequency of adverse events was lowest in the colchicine group (7.3%). None of the differences observed was statistically significant. Interpretation: Colchicine added to SOC performed better than Ixekizumab, low-dose IL-2, or SOC alone for hospitalized patients with moderate to critical Covid-19 in this exploratory study. Larger studies are needed to confirm these findings."

Submit Corrections or Updates.

Pinzón: Retrospective 301 pneumonia patients in Colombia showing lower mortality with colchicine treatment.

Submit Corrections or Updates.

Pourdowlat: RCT 202 patients in Iran, 102 treated with colchicine, showing lower hospitalization and improved clinical outcomes with treatment.

Submit Corrections or Updates.

Rahman: RCT 300 patients in Bangladesh, published 2 years after completion, showing significantly lower mortality with treatment at 28 days (not significant at 14 days). 1.2mg colchicine on day 1 followed by 0.6mg for 13 days.

Submit Corrections or Updates.

Recovery Collaborative Group: RCT with 5,610 colchicine and 5,730 control patients showing mortality RR 1.01 [0.93-1.10]. Very late stage treatment, median 9 days after symptom onset, baseline 32% ventilation (5% invasive). ISRCTN 50189673.

Dose frequency was halved for patients receiving a moderate CYP3A4 inhibitor, patients with an estimated glomerular filtration rate of less than 30 mL/min per 1·73m², and those with an estimated bodyweight of less than 70kg.

Submit Corrections or Updates.

Rodriguez-Nava: Retrospective 313 patients, mostly critical stage and mostly requiring respiratory support. Confounding by indication likely.

Submit Corrections or Updates.

Salehzadeh: Open label RCT with 100 hospitalized patients in Iran, 50 treated with colchicine, showing shorter hospitalization time with treatment. There were no deaths.

Submit Corrections or Updates.

Sandhu: Prospective cohort study of hospitalized patients in the USA, 34 treated with colchicine, showing lower mortality and intubation with treatment.

Submit Corrections or Updates.