Covid Analysis, February 2023
•Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 24 studies from 24 independent teams in 15 different countries show statistically significant improvements in isolation (14 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 35% [24‑44%] 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 43 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.
•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 12% 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 [Rai, Yasmin, Zein], showing significant improvements for mortality and severity.
|All studies||Late treatment||Prophylaxis||Studies||Patients||Authors|
|All studies||35% [24‑44%]|
|Randomized Controlled TrialsRCTs||17% [5‑27%]|
Colchicine reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, high confidence for ICU admission, and low confidence for ventilation and progression, however increased risk is seen with low confidence for cases.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
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.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 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.
|All studies||35% [24‑44%]|
|After exclusions||42% [30‑52%]|
|Peer-reviewed studiesPeer-reviewed||34% [23‑43%]|
|Randomized Controlled TrialsRCTs||17% [5‑27%]|
|ICU admissionICU||25% [3‑43%]|
|RCT mortality||4% [-6‑13%]||20||25,824||525|
|RCT hospitalizationRCT hosp.||18% [7‑28%]|
|Early treatment||Late treatment||Prophylaxis|
|All studies||68% [33‑85%]|
|After exclusions||68% [33‑85%]|
|Peer-reviewed studiesPeer-reviewed||68% [33‑85%]|
|Randomized Controlled TrialsRCTs||-||17% [5‑27%]|
|ICU admissionICU||-||25% [3‑43%]|
|RCT mortality||-||4% [-6‑13%]||-|
|RCT hospitalizationRCT hosp.||-||18% [7‑28%]|
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 1 and Table 2.
[Jadad]. For example, 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.
[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].
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 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Diaz], 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.
[Rodriguez-Nava], substantial unadjusted confounding by indication likely; excessive unadjusted differences between groups; unadjusted results with no group details.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 48 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[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].
[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.
Figure 18. 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.
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.
[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.
63% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 50% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 54% improvement, compared to 33% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
Figure 20 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.
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.
Colchicine is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 24 studies from 24 independent teams in 15 different countries show statistically significant improvements in isolation (14 for the most serious outcome). Meta analysis using the most serious outcome reported shows 35% [24‑44%] 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 43 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.
[Absalón-Aguilar] Very late stage RCT with 56 colchicine and 60 control patients in Mexico, showing no significant differences.
[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).
[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.
[Brunetti] PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.
[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.
[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.
[Deftereos] RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment.
[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.
[Dorward] Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.
[Eikelboom] 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].
[Eikelboom (B)] RCT very late stage (baseline SpO2 80%) patients, showing no significant differences with colchicine treatment.
[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.
[García-Posada] Retrospective 209 hospitalized patients in Colombia, showing lower mortality with antibiotics + LMWH + corticosteroids + colchicine in multivariable analysis.
[Gorial] RCT with 80 colchicine and 80 control patients, showing improved recovery with treatment. SOC included vitamin C, vitamin D, and zinc.
[Hueda-Zavaleta] Retrospective 450 late stage (median oxygen saturation 86%) COVID+ hospitalized patients in Peru, showing lower mortality with colchicine treatment.
[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.
[Jalal] Open label RCT of colchicine showing improved recovery with treatment. Only the abstract is currently available. Colchicine 0.5mg bid for 14 days.
[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%).
[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.
[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.
[Lopes] RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.
[Mahale] Retrospective 134 hospitalized COVID-19 patients in India, showing no significant difference with colchicine treatment in unadjusted results.
[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.
[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.
[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.
[Mostafaie] RCT with 60 patients treated with colchicine and phenolic monoterpenes and 60 control patients in Iran, showing lower mortality with treatment. NCT04392141.
[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].
Patients with conditions leading to the use of colchicine may have significantly different baseline risk, e.g. [Topless].
[Pascual-Figal] RCT with 52 colchicine patients and 51 control patients, showing lower risk of clinical deterioration with treatment. COL-COVID. NCT04350320.
[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).
[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).
[Pinzón] Retrospective 301 pneumonia patients in Colombia showing lower mortality with colchicine treatment.
[Pourdowlat] RCT 202 patients in Iran, 102 treated with colchicine, showing lower hospitalization and improved clinical outcomes with treatment.
[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.
[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 oxygen requirements unknown (data is provided but combined with "none"). ISRCTN 50189673.
[Rodriguez-Nava] Retrospective 313 patients, mostly critical stage and mostly requiring respiratory support. Confounding by indication likely.
[Salehzadeh] Open label RCT with 100 hospitalized patients in Iran, 50 treated with colchicine, showing shorter hospitalization time with treatment. There were no deaths.
[Sandhu] Prospective cohort study of hospitalized patients in the USA, 34 treated with colchicine, showing lower mortality and intubation with treatment.