Covid Analysis, January 18, 2022, DRAFT
•Statistically significant improvements are seen for mortality and hospitalization. 5 studies from 4 different countries show statistically significant improvements in isolation (4 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 31% [13‑45%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and better for peer-reviewed studies.
•Results are robust — in exclusion sensitivity analysis 6 of 13 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
•RCT results are less favorable and do not show statistical significance, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
•While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 8% of colchicine studies show zero events in the treatment arm.
•Multiple treatments are typically used in combination, and other treatments may be more effective. There has been no early treatment studies to date.
•Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used, including treatments, as supported by Pfizer [Pfizer]. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
•All data to reproduce this paper and sources are in the appendix.
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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and peer reviewed studies. Table 1 summarizes the results by treatment stage.
|Treatment time||Number of studies reporting positive effects||Total number of studies||Percentage of studies reporting positive effects||Random effects meta-analysis results|
RR 0.69 [0.55‑0.87]
p = 0.002
RR 0.69 [0.55‑0.87]
p = 0.002
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 11 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Rodriguez-Nava], substantial unadjusted confounding by indication likely, excessive unadjusted differences between groups, unadjusted results with no group details.
Randomized Controlled Trials (RCTs)
Figure 12 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 13 and 14 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 2 summarizes the results.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for 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. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
|Treatment time||Number of studies reporting positive effects||Total number of studies||Percentage of studies reporting positive effects||Random effects meta-analysis results|
|Randomized Controlled Trials||6||8||75.0%||
RR 0.85 [0.70‑1.05]
p = 0.13
|RCT mortality results||5||6||83.3%||
RR 0.96 [0.82‑1.12]
p = 0.62
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 15 shows an example where efficacy declines as a function of treatment delay.
Patient demographics.Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations.
Publication bias.Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For colchicine, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
The median effect size for retrospective studies is 54% improvement, compared to 36% for prospective studies, consistent with a positive publication bias. 50% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 33% of prospective studies, consistent with a bias toward publishing positive results. Figure 16 shows a scatter plot of results for prospective and retrospective studies.
Conflicts of interest.Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. 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.
Early/late vs. mild/moderate/severe.Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Notes.1 of 13 studies combine treatments. The results of colchicine alone may differ. 1 of 8 RCTs use combined treatment. [Zein] present another meta analysis for colchicine, showing significant improvement for mortality.
Colchicine is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality and hospitalization. 5 studies from 4 different countries show statistically significant improvements in isolation (4 for the most serious outcome). Meta analysis using the most serious outcome reported shows 31% [13‑45%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and better for peer-reviewed studies. Results are robust — in exclusion sensitivity analysis 6 of 13 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
RCT results are less favorable and do not show statistical significance, however they are dominated by the very late stage RECOVERY RCT, for which the results are not generalizable to earlier usage.
[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).
[Brunetti] PSM matched analysis from consecutive hospitalized patients, with 33 colchicine and 33 control matched patients, showing lower mortality with treatment.
[Deftereos] RCT with 55 patients treated with colchicine and 50 control patients, showing lower mortality and ventilation with treatment. NCT04326790.
[Dorward] Late treatment RCT with 156 colchicine patients in the UK, showing no significant differences. ISRCTN86534580.
[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.
[Lopes] RCT with 36 colchicine and 36 control patients, showing reduced length of hospitalization and oxygen therapy with treatment.
[Pinzón] Retrospective 301 pneumonia patients in Colombia showing lower mortality with colchicine treatment.
[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. NCT04381936.
[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. There were no deaths. IRCT20200418047126N1.
[Sandhu] Prospective cohort study of hospitalized patients in the USA, 34 treated with colchicine, showing lower mortality and intubation with treatment.
[Scarsi] Retrospective 122 colchicine patients and 140 control patients in Italy, showing lower mortality with treatment.
[Tardif] RCT for relatively low risk outpatients, 2235 treated with colchicine a mean of 5.3 days after the onset of symptoms, and 2253 controls, showing lower mortality, ventilation, and hospitalization with treatment. NCT04322682.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19colchicine.com. Search terms were colchicine, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of colchicine for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.9.10) with scipy (1.7.3), pythonmeta (1.26), numpy (1.21.4), statsmodels (0.14.0), and plotly (5.4.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment, and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at https://c19colchicine.com/meta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. Only the first (most serious) outcome is used in pooled analysis, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
|[Alsultan], 12/31/2021, Randomized Controlled Trial, Syria, Middle East, peer-reviewed, 11 authors.||risk of death, 35.7% lower, RR 0.64, p = 0.70, treatment 3 of 14 (21.4%), control 7 of 21 (33.3%), NNT 8.4.|
|[Brunetti], 9/14/2020, retrospective, propensity score matching, USA, North America, peer-reviewed, 7 authors.||risk of death, 72.7% lower, RR 0.27, p = 0.03, treatment 3 of 33 (9.1%), control 11 of 33 (33.3%), NNT 4.1, PSM.|
|risk of no hospital discharge, 72.7% lower, RR 0.27, p = 0.03, treatment 3 of 33 (9.1%), control 11 of 33 (33.3%), NNT 4.1, PSM.|
|[Deftereos], 6/24/2020, Randomized Controlled Trial, Greece, Europe, peer-reviewed, 49 authors.||risk of death, 77.3% lower, RR 0.23, p = 0.19, treatment 1 of 55 (1.8%), control 4 of 50 (8.0%), NNT 16.|
|risk of mechanical ventilation, 81.8% lower, RR 0.18, p = 0.10, treatment 1 of 55 (1.8%), control 5 of 50 (10.0%), NNT 12.|
|risk of clinical deterioration, 87.4% lower, RR 0.13, p = 0.046, treatment 1 of 55 (1.8%), control 7 of 50 (14.0%), NNT 8.2, odds ratio converted to relative risk.|
|[Dorward], 9/23/2021, Randomized Controlled Trial, United Kingdom, Europe, preprint, 21 authors.||risk of death/hospitalization, 29.8% higher, RR 1.30, p = 0.66, treatment 6 of 156 (3.8%), control 4 of 133 (3.0%), odds ratio converted to relative risk, concurrent randomisation.|
|risk of death/hospitalization, 22.1% lower, RR 0.78, p = 0.59, treatment 6 of 156 (3.8%), control 119 of 1,145 (10.4%), NNT 15, odds ratio converted to relative risk, including control patients before the colchicine arm started.|
|risk of progression, 6.0% lower, RR 0.94, p = 0.45, treatment 102 of 156 (65.4%), control 83 of 120 (69.2%), NNT 26.|
|risk of no recovery, 6.0% lower, RR 0.94, p = 0.67, treatment 156, control 133, time to alleviation of symptoms.|
|[Gaitán-Duarte], 7/10/2021, Randomized Controlled Trial, Colombia, South America, preprint, 17 authors, average treatment delay 10.0 days, this trial uses multiple treatments in the treatment arm (combined with rosuvastatin) - results of individual treatments may vary.||risk of death, 19.0% lower, RR 0.81, p = 0.43, treatment 22 of 153 (14.4%), control 28 of 161 (17.4%), NNT 33, adjusted per study, 28 days.|
|risk of mechanical ventilation, 15.0% lower, RR 0.85, p = 0.15, treatment 27 of 136 (19.9%), control 27 of 140 (19.3%), adjusted per study.|
|risk of ICU admission, 1.0% lower, RR 0.99, p = 0.97, treatment 19 of 113 (16.8%), control 18 of 114 (15.8%), adjusted per study.|
|[Lopes], 8/12/2020, Double Blind Randomized Controlled Trial, Brazil, South America, peer-reviewed, 34 authors, average treatment delay 9.5 (treatment) 8.0 (control) days.||risk of death, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 36 (0.0%), control 2 of 36 (5.6%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).|
|risk of ICU admission, 50.0% lower, RR 0.50, p = 0.67, treatment 2 of 36 (5.6%), control 4 of 36 (11.1%), NNT 18.|
|[Pinzón], 10/23/2020, retrospective, Colombia, South America, preprint, 9 authors.||risk of death, 34.5% lower, RR 0.65, p = 0.18, treatment 14 of 145 (9.7%), control 23 of 156 (14.7%), NNT 20, odds ratio converted to relative risk.|
|[Recovery Collaborative Group], 5/18/2021, Randomized Controlled Trial, United Kingdom, Europe, peer-reviewed, 35 authors, average treatment delay 9.0 days.||risk of death, 1.0% higher, RR 1.01, p = 0.77, treatment 1,173 of 5,610 (20.9%), control 1,190 of 5,730 (20.8%).|
|risk of mechanical ventilation, 18.0% higher, RR 1.18, p = 0.06, treatment 259 of 3,815 (6.8%), control 228 of 3,962 (5.8%).|
|risk of death/intubation, 2.0% higher, RR 1.02, p = 0.47, treatment 1,344 of 5,342 (25.2%), control 1,343 of 5,469 (24.6%).|
|risk of no hospital discharge, 2.0% higher, RR 1.02, p = 0.44, treatment 1,709 of 5,610 (30.5%), control 1,698 of 5,730 (29.6%).|
|[Rodriguez-Nava], 11/5/2020, retrospective, USA, North America, peer-reviewed, median age 68.0, 8 authors, excluded in exclusion analyses: substantial unadjusted confounding by indication likely, excessive unadjusted differences between groups, unadjusted results with no group details.||risk of death, 5.5% lower, RR 0.94, p = 0.87, treatment 16 of 52 (30.8%), control 85 of 261 (32.6%), NNT 56, unadjusted.|
|[Salehzadeh], 9/21/2020, Randomized Controlled Trial, Iran, Middle East, preprint, 3 authors.||hospitalization time, 22.7% lower, relative time 0.77, p = 0.001, treatment 50, control 50.|
|[Sandhu], 10/27/2020, prospective, USA, North America, peer-reviewed, 4 authors.||risk of death, 41.7% lower, RR 0.58, p < 0.001, treatment 16 of 34 (47.1%), control 63 of 78 (80.8%), NNT 3.0.|
|risk of mechanical ventilation, 52.9% lower, RR 0.47, p < 0.001, treatment 16 of 34 (47.1%), control 68 of 68 (100.0%), NNT 1.9.|
|risk of no hospital discharge, 41.7% lower, RR 0.58, p < 0.001, treatment 16 of 34 (47.1%), control 63 of 78 (80.8%), NNT 3.0.|
|[Scarsi], 9/14/2020, retrospective, Italy, Europe, peer-reviewed, 28 authors.||risk of death, 84.9% lower, RR 0.15, p < 0.001, treatment 122, control 140.|
|[Tardif], 1/27/2021, Double Blind Randomized Controlled Trial, Canada, North America, peer-reviewed, 44 authors, average treatment delay 5.3 days.||risk of death, 43.9% lower, RR 0.56, p = 0.30, treatment 5 of 2,235 (0.2%), control 9 of 2,253 (0.4%), NNT 569, odds ratio converted to relative risk.|
|risk of mechanical ventilation, 46.8% lower, RR 0.53, p = 0.09, treatment 11 of 2,235 (0.5%), control 21 of 2,253 (0.9%), NNT 227, odds ratio converted to relative risk.|
|risk of hospitalization, 20.0% lower, RR 0.80, p = 0.09, treatment 101 of 2,235 (4.5%), control 128 of 2,253 (5.7%), NNT 86, odds ratio converted to relative risk.|
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