Top
Introduction
Results
Exclusions
Randomized Controlled Trials
Heterogeneity
Discussion
Conclusion
Study Notes
Methods and Data
Supplementary
References

All studies
Mortality
Ventilation
ICU admission
Hospitalization
Progression
Recovery
COVID-19 cases
Viral clearance
Peer reviewed
Exclusions
All RCTs
RCT mortality

Feedback
Home
Show Outline
Top   Intro   Results   Exc.   RCT   Heterogeneity   Discussion   Conclusion   StudyNotes   Appendix   SupplementarySupp.   ReferencesRef.
Home   COVID-19 treatment studies for Aspirin  COVID-19 treatment studies for Aspirin  C19 studies: Aspirin  Aspirin   Select treatmentSelect treatmentTreatmentsTreatments
Melatonin Meta
Bromhexine Meta Metformin Meta
Budesonide Meta Molnupiravir Meta
Cannabidiol Meta
Colchicine Meta Nigella Sativa Meta
Conv. Plasma Meta Nitazoxanide Meta
Curcumin Meta Nitric Oxide Meta
Ensovibep Meta Paxlovid Meta
Famotidine Meta Peg.. Lambda Meta
Favipiravir Meta Povidone-Iod.. Meta
Fluvoxamine Meta Quercetin Meta
Hydroxychlor.. Meta Remdesivir Meta
Iota-carragee.. Meta
Ivermectin Meta Zinc Meta
Lactoferrin Meta

Other Treatments Global Adoption
Loading...
Analgesics..
Antiandrogens..
Bromhexine
Budesonide
Cannabidiol
Colchicine
Conv. Plasma
Curcumin
Ensovibep
Famotidine
Favipiravir
Fluvoxamine
Hydroxychlor..
Iota-carragee..
Ivermectin
Lactoferrin
Lifestyle..
Melatonin
Metformin
Molnupiravir
Monoclonals..
Nigella Sativa
Nitazoxanide
Nitric Oxide
Paxlovid
Peg.. Lambda
Povidone-Iod..
Quercetin
Remdesivir
Vitamins..
Zinc
Aspirin for COVID-19: real-time meta analysis of 54 studies
Covid Analysis, October 6, 2022, DRAFT
https://c19aspirin.com/meta.html
 
0 0.5 1 1.5+ All studies 12% 54 158,773 Improvement, Studies, Patients Relative Risk Mortality 12% 46 144,375 Ventilation 4% 10 40,612 ICU admission 4% 10 31,168 Hospitalization -1% 7 5,363 Progression 11% 7 23,646 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 7% 4 16,917 RCT mortality 6% 3 16,637 Peer-reviewed 13% 47 128,766 Prophylaxis 6% 30 127,968 Early 67% 1 280 Late 21% 23 30,525 Aspirin for COVID-19 c19aspirin.com Oct 2022 Favorsaspirin Favorscontrol after exclusions
Statistically significant improvement is seen for mortality. 23 studies from 21 independent teams in 9 different countries show statistically significant improvements in isolation (18 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 12% [6‑17%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.5 1 1.5+ All studies 12% 54 158,773 Improvement, Studies, Patients Relative Risk Mortality 12% 46 144,375 Ventilation 4% 10 40,612 ICU admission 4% 10 31,168 Hospitalization -1% 7 5,363 Progression 11% 7 23,646 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 7% 4 16,917 RCT mortality 6% 3 16,637 Peer-reviewed 13% 47 128,766 Prophylaxis 6% 30 127,968 Early 67% 1 280 Late 21% 23 30,525 Aspirin for COVID-19 c19aspirin.com Oct 2022 Favorsaspirin Favorscontrol after exclusions
Studies to date do not show a significant benefit for mechanical ventilation and ICU admission. Benefit may be more likely without coadministered anticoagulants. The RECOVERY RCT shows 4% [-4‑11%] lower mortality for all patients, however when restricting to non-LMWH patients there was 17% [-4‑34%] improvement, consistent with the mortality results of all studies, 12% [5‑18%], and the 16% improvement in the REMAP-CAP RCT.
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 are significantly more effective. Only 4% of aspirin studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for aspirin can be found in [Banaser, Srinivasan], showing significant improvements for mortality and mechanical ventilation.
Highlights
Aspirin reduces risk for COVID-19 with very high confidence for mortality and in pooled analysis, low confidence for progression, recovery, and viral clearance, and very low confidence for cases. Benefit may be more likely without coadministered anticoagulants.
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 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Connors (DB RCT) 67% 0.33 [0.01-7.96] hosp. 0/144 1/136 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.5 Early treatment 67% 0.33 [0.01-7.96] 0/144 1/136 67% improvement Alamdari -28% 1.28 [0.67-2.43] death 9/53 54/406 Improvement, RR [CI] Treatment Control Husain 80% 0.20 [0.01-3.55] death 0/11 3/31 Goshua (PSM) 35% 0.65 [0.42-0.98] death 319 (n) 319 (n) Meizlish (PSM) 48% 0.52 [0.34-0.81] death 319 (n) 319 (n) Liu (PSM) 75% 0.25 [0.07-0.87] death 2/28 11/204 Mura (PSM) 15% 0.85 [0.69-1.01] death 527 (n) 527 (n) Chow 47% 0.53 [0.31-0.90] death 26/98 73/314 Haji Aghajani 25% 0.75 [0.57-0.99] death 336 (n) 655 (n) Elhadi (ICU) 10% 0.90 [0.67-1.21] death 22/40 259/425 ICU patients Sahai (PSM) 13% 0.87 [0.56-1.34] death 33/248 38/248 Pourhoseingholi -32% 1.32 [1.02-1.71] death 71/290 268/2,178 Vahedian-Azimi 22% 0.78 [0.33-1.74] death 13/337 28/250 Abdelwahab -8% 1.08 [0.15-3.82] ventilation 11/31 6/36 Karruli (ICU) 46% 0.54 [0.09-3.13] death 1/5 22/27 ICU patients Al Harthi (PSM) 27% 0.73 [0.56-0.97] death 98/176 107/173 Kim (PSM) 34% 0.66 [0.36-1.23] death 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] death 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Mustafa 44% 0.56 [0.21-1.51] death 4/66 41/378 Bradbury (RCT) 16% 0.84 [0.70-1.00] death 165/563 170/521 Chow (PSW) 13% 0.87 [0.81-0.93] death Santoro (PSM) 38% 0.62 [0.42-0.92] death 360 (n) 2,949 (n) Ghati (RCT) 22% 0.78 [0.31-1.98] death 11/442 7/219 Tau​2 = 0.02, I​2 = 64.4%, p < 0.0001 Late treatment 21% 0.79 [0.72-0.87] 601/12,197 1,250/18,328 21% improvement Huh 71% 0.29 [0.14-0.58] cases population-based cohort Improvement, RR [CI] Treatment Control Holt -34% 1.34 [0.98-1.84] death/ICU 35/116 129/573 Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Formiga (PSM) -3% 1.03 [0.94-1.13] death 1,000/3,291 874/2,885 Yuan 4% 0.96 [0.47-1.72] death 11/52 29/131 Osborne (PSM) 59% 0.41 [0.35-0.48] death 272/6,300 661/6,300 Merzon 28% 0.72 [0.53-0.99] cases 73/1,621 589/8,856 Mulhem -14% 1.14 [0.93-1.40] death 300/1,354 216/1,865 Reese (PSM) -61% 1.61 [1.31-1.99] death 4,921 (n) 4,921 (n) Pan -13% 1.13 [0.70-1.82] death 239 (n) 523 (n) Oh 1% 0.99 [0.65-1.50] death n/a n/a Son (PSM) 24% 0.76 [0.34-1.71] death case control Ma (PSM) 9% 0.91 [0.82-1.02] death Chow (PSM) 19% 0.81 [0.76-0.87] death 1,280/6,781 2,271/10,566 Kim (PSM) -700% 8.00 [1.07-59.6] death 6/15 1/20 Basheer -13% 1.13 [1.05-1.21] death 45/140 29/250 Sisinni -7% 1.07 [0.89-1.29] death 93/253 251/731 Pérez-Segura -49% 1.49 [1.20-1.80] death 66/155 183/608 Sullerot (PSW) -10% 1.10 [0.81-1.49] death 101/301 224/746 Monserrat V.. (PSM) -31% 1.31 [1.01-1.71] death n/a n/a Levy 26% 0.74 [0.49-1.10] death/hosp. 29/159 178/690 Nimer 4% 0.96 [0.69-1.33] hosp. 83/427 136/1,721 Gogtay -6% 1.06 [0.51-1.89] death 12/38 21/87 Drew 22% 0.78 [0.49-1.24] progression n/a n/a Campbell (PSW) 3% 0.97 [0.95-1.00] death 419 (n) 20,311 (n) Lal 11% 0.89 [0.82-0.97] death 4,691 (n) 16,888 (n) Botton -4% 1.04 [0.98-1.10] death/int. Malik 14% 0.86 [0.39-1.80] death 15/87 24/223 Abul 33% 0.67 [0.47-0.95] death 46/511 201/1,176 Loucera 18% 0.82 [0.74-0.92] death 2,127 (n) 13,841 (n) Tau​2 = 0.04, I​2 = 90.2%, p = 0.15 Prophylaxis 6% 0.94 [0.86-1.02] 3,468/34,007 6,030/93,961 6% improvement All studies 12% 0.88 [0.83-0.94] 4,069/46,348 7,281/112,425 12% improvement 54 aspirin COVID-19 studies c19aspirin.com Oct 2022 Tau​2 = 0.03, I​2 = 86.0%, p = 0.00015 Effect extraction pre-specified(most serious outcome, see appendix) Favors aspirin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Connors (DB RCT) 67% hospitalization Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.5 Early treatment 67% 67% improvement Alamdari -28% death Husain 80% death Goshua (PSM) 35% death Meizlish (PSM) 48% death Liu (PSM) 75% death Mura (PSM) 15% death Chow 47% death Haji Aghajani 25% death Elhadi (ICU) 10% death ICU patients Sahai (PSM) 13% death Pourhoseingholi -32% death Vahedian-Azimi 22% death Abdelwahab -8% ventilation Karruli (ICU) 46% death ICU patients Al Harthi (PSM) 27% death Kim (PSM) 34% death Zhao 43% death RECOVERY (RCT) 4% death Mustafa 44% death Bradbury (RCT) 16% death Chow (PSW) 13% death Santoro (PSM) 38% death Ghati (RCT) 22% death Tau​2 = 0.02, I​2 = 64.4%, p < 0.0001 Late treatment 21% 21% improvement Huh 71% case Holt -34% death/ICU Wang 58% death Formiga (PSM) -3% death Yuan 4% death Osborne (PSM) 59% death Merzon 28% case Mulhem -14% death Reese (PSM) -61% death Pan -13% death Oh 1% death Son (PSM) 24% death Ma (PSM) 9% death Chow (PSM) 19% death Kim (PSM) -700% death Basheer -13% death Sisinni -7% death Pérez-Segura -49% death Sullerot (PSW) -10% death Monserrat V.. (PSM) -31% death Levy 26% death/hosp. Nimer 4% hospitalization Gogtay -6% death Drew 22% progression Campbell (PSW) 3% death Lal 11% death Botton -4% death/intubation Malik 14% death Abul 33% death Loucera 18% death Tau​2 = 0.04, I​2 = 90.2%, p = 0.15 Prophylaxis 6% 6% improvement All studies 12% 12% improvement 54 aspirin COVID-19 studies c19aspirin.com Oct 2022 Tau​2 = 0.03, I​2 = 86.0%, p = 0.00015 Effect extraction pre-specifiedRotate device for details Favors aspirin Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of aspirin 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 2. Treatment stages.
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION PROGRESSION RECOVERY CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS RCT MORTALITY PEER-REVIEWED After Exclusions ALL STUDIES All Prophylaxis Early Late Aspirin for COVID-19 C19ASPIRIN.COM OCT 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 1 1 100% 67% improvement
RR 0.33 [0.01‑7.96]
p = 0.5
Late treatment 20 23 87.0% 21% improvement
RR 0.79 [0.72‑0.87]
p < 0.0001
Prophylaxis 17 30 56.7% 6% improvement
RR 0.94 [0.86‑1.02]
p = 0.15
All studies 38 54 70.4% 12% improvement
RR 0.88 [0.83‑0.94]
p = 0.00015
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 5467% [-696‑99%]21% [13‑28%]6% [-2‑14%] 158,773 811
With exclusions 4967% [-696‑99%]22% [13‑30%]8% [-0‑16%] 153,497 762
Peer-reviewed 4767% [-696‑99%]23% [15‑30%]5% [-4‑13%] 128,766 720
Randomized Controlled TrialsRCTs 467% [-696‑99%]6% [-1‑13%] 16,917 115
Table 2. Results by treatment stage for all studies and with different exclusions.
Loading..
Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Loading..
Figure 5. Random effects meta-analysis for mortality results.
Loading..
Figure 6. Random effects meta-analysis for ventilation.
Loading..
Figure 7. Random effects meta-analysis for ICU admission.
Loading..
Figure 8. Random effects meta-analysis for hospitalization.
Loading..
Figure 9. Random effects meta-analysis for progression.
Loading..
Figure 10. Random effects meta-analysis for recovery.
Loading..
Figure 11. Random effects meta-analysis for cases.
Loading..
Figure 12. Random effects meta-analysis for viral clearance.
Loading..
Figure 13. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during a 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.
Exclusions
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 14 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Alamdari], substantial unadjusted confounding by indication likely.
[Elhadi], unadjusted results with no group details.
[Holt], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely, substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Mustafa], unadjusted results with no group details.
Loading..
Figure 14. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 15 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. The median effect size for RCTs is 19% improvement, compared to 14% for other studies. Figure 16 and 17 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
Figure 15. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
Loading..
Figure 16. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Loading..
Figure 17. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 4 4 100% 7% improvement
RR 0.93 [0.87‑1.01]
p = 0.083
RCT mortality results 3 3 100% 6% improvement
RR 0.94 [0.87‑1.01]
p = 0.086
Table 3. Randomized Controlled Trial results.
Heterogeneity
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]. Figure 18 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 18. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
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.
Other 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.
Medication quality.
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.
Meta analysis.
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. While we present pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant for specific use cases.
Discussion
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 aspirin, 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.
44% 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. The median effect size for retrospective studies is 15% improvement, compared to 13% for prospective studies, showing similar results. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
Figure 19. Prospective vs. retrospective studies.
Funnel plot 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 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.
Figure 20. Example funnel plot analysis for simulated perfect trials.
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. Aspirin for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 aspirin 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 aspirin 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.
Other meta analyses for aspirin can be found in [Banaser, Srinivasan], showing significant improvements for mortality and mechanical ventilation.
Conclusion
Aspirin is an effective treatment for COVID-19. Statistically significant improvement is seen for mortality. 23 studies from 21 independent teams in 9 different countries show statistically significant improvements in isolation (18 for the most serious outcome). Meta analysis using the most serious outcome reported shows 12% [6‑17%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Studies to date do not show a significant benefit for mechanical ventilation and ICU admission. Benefit may be more likely without coadministered anticoagulants. The RECOVERY RCT shows 4% [-4‑11%] lower mortality for all patients, however when restricting to non-LMWH patients there was 17% [-4‑34%] improvement, consistent with the mortality results of all studies, 12% [5‑18%], and the 16% improvement in the REMAP-CAP RCT.
Study Notes
0 0.5 1 1.5 2+ Ventilation -8% Improvement Relative Risk c19aspirin.com Abdelwahab et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Abdelwahab] Retrospective 225 hospitalized patients in Egypt, showing significantly lower thromboembolic events with aspirin treatment, but no significant difference in the need for mechanical ventilation.
0 0.5 1 1.5 2+ Mortality, day 56 33% Improvement Relative Risk Mortality, day 30 40% Hospitalization 20% c19aspirin.com Abul et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Abul] Retrospective 1,687 nursing home residents in the USA, showing significantly lower risk of mortality with chronic low-dose aspirin use. Low dose 81mg aspirin users had treatment ≥10 of 14 days prior to the positive COVID date, control patients had no aspirin use in the prior 14 days.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Mortality (b) 14% c19aspirin.com Al Harthi et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Al Harthi] Retrospective 1,033 critical condition patients, showing lower in-hospital mortality with aspirin in PSM analysis. Patients receiving aspirin also had a higher risk of significant bleeding, although not reaching statistical significance. Authors note that the use of aspirin during an ICU stay should be tailored to each patient.
0 0.5 1 1.5 2+ Mortality -28% Improvement Relative Risk c19aspirin.com Alamdari et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Alamdari] Retrospective 459 patients in Iran, 53 treated with aspirin, showing no significant difference with treatment.
0 0.5 1 1.5 2+ Mortality -13% Improvement Relative Risk c19aspirin.com Basheer et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Basheer] Retrospective 390 hospitalized patients in Israel, showing higher risk of mortality with prior aspirin use. Details of the analysis are not provided.
0 0.5 1 1.5 2+ Death/intubation -4% Improvement Relative Risk Hospitalization -3% c19aspirin.com Botton et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Botton] Retrospective 31 million people without cardiovascular disease in France, showing no significant difference in hospitalization or combined intubation/death with low dose aspirin prophylaxis.
0 0.5 1 1.5 2+ Mortality 16% Improvement Relative Risk Discharge 17% Progression 21% Progression (b) 5% primary c19aspirin.com Bradbury et al. NCT02735707 REMAP-CAP Aspirin RCT LATE Favors aspirin Favors control
[Bradbury] RCT 1,557 critical patients, showing significantly lower mortality with aspirin, with 97.5% posterior probability of efficacy.
0 0.5 1 1.5 2+ Mortality, day 60 3% Improvement Relative Risk Mortality, day 30 2% c19aspirin.com Campbell et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Campbell] Retrospective 28,856 COVID-19 patients in the USA, showing no significant difference in mortality for chronic aspirin use vs. sporadic NSAID use. Since aspirin is available OTC and authors only tracked prescriptions, many patients classified as sporadic users may have been chronic users.
0 0.5 1 1.5 2+ Mortality 19% Improvement Relative Risk Ventilation 3% c19aspirin.com Chow et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Chow (C)] PSM retrospective 6,781 hospitalized patients ≥50 years old in the USA who were on pre-hospital antiplatelet therapy (84% aspirin), and 10,566 matched controls, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 13% Improvement Relative Risk c19aspirin.com Chow et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Chow] Retrospective 112,269 hospitalized COVID-19 patients in the USA, showing lower mortality with aspirin treatment.
0 0.5 1 1.5 2+ Mortality 47% Improvement Relative Risk Ventilation 44% ICU admission 43% c19aspirin.com Chow et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Chow (B)] Retrospective 412 hospitalized patients, 98 treated with aspirin, showing lower mortality, ventilation, and ICU admission with treatment.
0 0.5 1 1.5 2+ Hospitalization 67% Improvement Relative Risk Progression 19% Progression (b) 6% primary c19aspirin.com Connors et al. NCT04498273 Aspirin RCT EARLY TREATMENT Favors aspirin Favors control
[Connors] Early terminated RCT with 164 aspirin and 164 control patients in the USA with very few events, showing no significant difference with aspirin treatment for the combined endpoint of all-cause mortality, symptomatic venous or arterial thromboembolism, myocardial infarction, stroke, and hospitalization for cardiovascular or pulmonary indication. There was no mortality and no major bleeding events among participants that started treatment (there was one ITT placebo death). ACTIV-4B. NCT04498273.
0 0.5 1 1.5 2+ Progression 22% Improvement Relative Risk Case -3% c19aspirin.com Drew et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Drew] Retrospective 2,736,091 individuals in the U.S., U.K., and Sweden, showing lower risk of hospital/clinic visits with aspirin use.
0 0.5 1 1.5 2+ Mortality 10% Improvement Relative Risk c19aspirin.com Elhadi et al. Aspirin for COVID-19 ICU PATIENTS Favors aspirin Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
0 0.5 1 1.5 2+ Mortality -3% Improvement Relative Risk Ventilation -3% ICU admission -4% c19aspirin.com Formiga et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Formiga] Retrospective 20,641 hospitalized patients in Spain, showing no significant difference in outcomes with existing aspirin use.
0 0.5 1 1.5 2+ Mortality 22% Improvement Relative Risk Mortality (b) 58% Ventilation 9% Ventilation (b) 50% Progression 30% primary Progression (b) 60% primary c19aspirin.com Ghati et al. CTRI/2020/07/026791 RESIST Aspirin RCT LATE Favors aspirin Favors control
[Ghati] RCT hospitalized patients in India, 224 treated with atorvastatin, 225 with aspirin, and 225 with both, showing lower serum interleukin-6 levels with aspirin, but no statistically significant changes in other outcomes. Low dose aspirin 75mg daily for 10 days.
0 0.5 1 1.5 2+ Mortality -6% Improvement Relative Risk Ventilation 50% ICU admission 49% c19aspirin.com Gogtay et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Gogtay] Retrospective 125 COVID+ hospitalized patients in the USA, showing no significant differences with aspirin prophylaxis.
0 0.5 1 1.5 2+ Mortality 35% Improvement Relative Risk Ventilation -49% ICU admission -45% c19aspirin.com Goshua et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Goshua] PSM retrospective 2,785 hospitalized patients in the USA, showing lower mortality and higher ventilation and ICU admission with aspirin treatment.
0 0.5 1 1.5 2+ Mortality 25% Improvement Relative Risk c19aspirin.com Haji Aghajani et al. Aspirin for COVID-19 LATE Favors aspirin Favors control
[Haji Aghajani] Retrospective 991 hospitalized patients in Iran, showing lower mortality with aspirin treatment.
0 0.5 1 1.5 2+ Death/ICU -34% Improvement Relative Risk c19aspirin.com Holt et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Holt] Retrospective 689 hospitalized COVID-19 patients in Denmark, showing higher risk of ICU/death with aspirin use in unadjusted results subject to confounding by indication.
0 0.5 1 1.5 2+ Case 71% Improvement Relative Risk c19aspirin.com Huh et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Huh] Retrospective database analysis of 65,149 in South Korea, showing significantly lower cases with existing aspirin treatment. The journal version of this paper does not present the aspirin results (only combined results for NSAIDs).
0 0.5 1 1.5 2+ Mortality 80% Improvement Relative Risk Recovery 65% Complications 96% c19aspirin.com Husain et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Husain] Retrospective 42 patients in Bangladesh, 11 treated with aspirin, showing fewer complications with treatment.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk c19aspirin.com Karruli et al. Aspirin for COVID-19 ICU PATIENTS Favors aspirin Favors control
[Karruli] Retrospective 32 ICU patients showing lower mortality with aspirin treatment, without statistical significance.
0 0.5 1 1.5 2+ Mortality -700% Improvement Relative Risk Ventilation -433% ICU admission -433% Case 33% Mortality (b) 34% Ventilation (b) -102% ICU admission (b) -91% c19aspirin.com Kim et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Kim (B)] Retrospective database analysis of 22,660 patients tested for COVID-19 in South Korea. There was no significant difference in cases according to aspirin use. Aspirin use before COVID-19 was related to an increased death rate and aspirin use after COVID-19 was related to a higher risk of oxygen therapy.
[Kim] Retrospective database analysis of 22,660 patients tested for COVID-19 in South Korea. There was no significant difference in cases according to aspirin use. Aspirin use before COVID-19 was related to an increased death rate and aspirin use after COVID-19 was related to a higher risk of oxygen therapy.
0 0.5 1 1.5 2+ Mortality 11% Improvement Relative Risk ICU admission 22% Progression 9% c19aspirin.com Lal et al. NCT04323787 Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Lal] Retrospective 21,579 hospitalized COVID-19 patients mostly in the USA, showing lower risk of mortality and severity with existing aspirin use.
0 0.5 1 1.5 2+ Death/hospitalization 26% Improvement Relative Risk c19aspirin.com Levy et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Levy] Retrospective 849 COVID-19+ patients in skilled nursing homes, showing lower risk of combined hospitalization/death with aspirin prophylaxis, not reaching statistical significance.
0 0.5 1 1.5 2+ Mortality 75% Improvement Relative Risk Mortality (b) 81% Time to viral- -2% c19aspirin.com Liu et al. Aspirin for COVID-19 LATE TREATMENT Favors aspirin Favors control
[Liu] Retrospective PSM analysis of 232 hospitalized patients, 28 treated with aspirin, showing lower mortality with treatment. There was no significant difference in viral clearance.
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk c19aspirin.com Loucera et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ Mortality 9% Improvement Relative Risk Hospitalization 2% Symptomatic case -9% Case -7% c19aspirin.com Ma et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Ma] UK Biobank retrospective 77,271 patients aged 50-86, showing no significant differences with aspirin use. Matching lead to different results for the gender vs. overall analysis, for example the overall result for cases was OR 1.07, however both gender results are lower OR 0.97 and 1.02.
0 0.5 1