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Aspirin for COVID-19: real-time meta analysis of 60 studies
Covid Analysis, November 2022
https://c19early.org/emeta.html
 
0 0.5 1 1.5+ All studies 11% 60 171,395 Improvement, Studies, Patients Relative Risk Mortality 11% 52 156,997 Ventilation 4% 11 43,946 ICU admission 2% 11 34,502 Hospitalization -2% 9 12,578 Progression 11% 9 29,646 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 5% 6 22,917 RCT mortality 5% 5 22,637 Peer-reviewed 12% 53 141,388 Prophylaxis 7% 32 132,947 Early 67% 1 280 Late 17% 27 38,168 Aspirin for COVID-19 c19early.org/e Nov 2022 Favorsaspirin Favorscontrol after exclusions
Statistically significant improvements are seen for mortality and progression. 24 studies from 22 independent teams in 10 different countries show statistically significant improvements in isolation (19 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 11% [5‑17%] 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 11% 60 171,395 Improvement, Studies, Patients Relative Risk Mortality 11% 52 156,997 Ventilation 4% 11 43,946 ICU admission 2% 11 34,502 Hospitalization -2% 9 12,578 Progression 11% 9 29,646 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 5% 6 22,917 RCT mortality 5% 5 22,637 Peer-reviewed 12% 53 141,388 Prophylaxis 7% 32 132,947 Early 67% 1 280 Late 17% 27 38,168 Aspirin for COVID-19 c19early.org/e Nov 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, comparable with the mortality results of all studies, 11% [5‑17%], 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 3% 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, Baral, 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, high confidence for progression, low confidence for 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 Karimpour-Razke.. -123% 2.23 [1.26-3.38] death 39/90 64/363 Eikelboom (RCT) -5% 1.05 [0.86-1.28] death 193/1,063 186/1,056 CT​1 Eikelboom (RCT) -9% 1.09 [0.48-2.46] death 12/1,945 11/1,936 Ali (ICU) 40% 0.60 [0.51-0.72] death 152/660 202/530 ICU patients Tau​2 = 0.04, I​2 = 77.5%, p = 0.00048 Late treatment 17% 0.83 [0.74-0.92] 997/15,955 1,713/22,213 17% 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 .. (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) Morrison (PSM) 8% 0.92 [0.73-1.18] death 1,667 (n) 1,667 (n) Ali 28% 0.72 [0.51-1.03] death 481 (n) 1,164 (n) Tau​2 = 0.04, I​2 = 89.6%, p = 0.091 Prophylaxis 7% 0.93 [0.86-1.01] 3,468/36,155 6,030/96,792 7% improvement All studies 11% 0.89 [0.83-0.95] 4,465/52,254 7,744/119,141 11% improvement 60 aspirin COVID-19 studies c19early.org/e Nov 2022 Tau​2 = 0.03, I​2 = 86.4%, p = 0.00031 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors aspirin Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Connors (DB RCT) 67% hospitalization 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 Karimpour-Razk.. -123% death Eikelboom (RCT) -5% death CT​1 Eikelboom (RCT) -9% death Ali (ICU) 40% death ICU patients Tau​2 = 0.04, I​2 = 77.5%, p = 0.00048 Late treatment 17% 17% 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.. (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 Morrison (PSM) 8% death Ali 28% death Tau​2 = 0.04, I​2 = 89.6%, p = 0.091 Prophylaxis 7% 7% improvement All studies 11% 11% improvement 60 aspirin COVID-19 studies c19early.org/e Nov 2022 Tau​2 = 0.03, I​2 = 86.4%, p = 0.00031 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors aspirin Favors control
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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 most serious outcome in all studies, along with the result of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in aspirin studies.
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.
An In Vitro study supports the efficacy of aspirin [Geiger].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results by treatment stage and with different exclusions. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for 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.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 6067% [-696‑99%]17% [8‑26%]7% [-1‑14%] 171,395 894
After exclusions 5467% [-696‑99%]22% [13‑29%]9% [1‑16%] 165,666 836
Peer-reviewed 5367% [-696‑99%]19% [10‑28%]6% [-3‑13%] 141,388 803
Randomized Controlled TrialsRCTs 667% [-696‑99%]5% [-2‑11%] 22,917 175
Table 1. Random effects meta-analysis results by treatment stage.
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Figure 3. 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.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for progression.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. 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.
Figure 13 shows a comparison of results for RCTs and non-RCT studies. Figure 14 and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality 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 aspirin 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].
In summary, we need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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Figure 13. Results for RCTs and non-RCT studies.
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Figure 14. 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.
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Figure 15. Random effects meta-analysis for RCT mortality results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 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.
[Karimpour-Razkenari], substantial unadjusted confounding by indication likely.
[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.
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Figure 16. 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.
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.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Table 2. Early treatment is more effective for baloxavir and influenza.
Figure 17 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 17. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
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 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.
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Figure 18. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. 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.
42% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 25% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 15% improvement, compared to 7% 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.
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Figure 19. Prospective vs. retrospective studies.
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.
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.
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.
1 of 60 studies combine treatments. The results of aspirin alone may differ. 1 of 6 RCTs use combined treatment. Other meta analyses for aspirin can be found in [Banaser, Baral, Srinivasan], showing significant improvements for mortality and mechanical ventilation.
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.
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.
Aspirin is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality and progression. 24 studies from 22 independent teams in 10 different countries show statistically significant improvements in isolation (19 for the most serious outcome). Meta analysis using the most serious outcome reported shows 11% [5‑17%] 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, comparable with the mortality results of all studies, 11% [5‑17%], and the 16% improvement in the REMAP-CAP RCT.
0 0.5 1 1.5 2+ Ventilation -8% Improvement Relative Risk c19early.org/e 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% c19early.org/e 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% c19early.org/e 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 c19early.org/e 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 28% Improvement Relative Risk c19early.org/e Ali et al. Aspirin for COVID-19 Prophylaxis Favors aspirin Favors control
[Ali (B)] Retrospective 1,645 hospitalized patients in the USA, showing lower mortality with aspirin use, without statistical significance.
0 0.5 1 1.5 2+ Mortality 40% Improvement Relative Risk ARDS 37% c19early.org/e Ali et al. Aspirin for COVID-19 ICU PATIENTS Favors aspirin Favors control
[Ali] Retrospective 1,190 ICU patients in Egypt, showing lower mortality with aspirin treatment. 150mg daily.
0 0.5 1 1.5 2+ Mortality -13% Improvement Relative Risk c19early.org/e 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% c19early.org/e 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 c19early.org/e 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% c19early.org/e 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% c19early.org/e 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 c19early.org/e 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% c19early.org/e 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 c19early.org/e 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% c19early.org/e 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 -9% Improvement Relative Risk Progression 20% primary Hospitalization 17% c19early.org/e Eikelboom et al. NCT04324463 ACT outpatient Aspirin RCT LATE Favors aspirin Favors control
[Eikelboom] Late (5.4 days) outpatient RCT showing no significant difference in outcomes with aspirin treatment.
0 0.5 1 1.5 2+ Mortality -5% Improvement Relative Risk Progression 8% Progression (b) 11% c19early.org/e Eikelboom et al. NCT04324463 ACT inpatient Aspirin RCT LATE Favors aspirin Favors control
[Eikelboom (B)] RCT very late stage (baseline SpO2 77%) patients, showing no significant differences with rivaroxaban and aspirin treatment.
0 0.5 1 1.5 2+ Mortality 10% Improvement Relative Risk c19early.org/e 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% c19early.org/e 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 c19early.org/e 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% c19early.org/e 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% c19early.org/e 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 c19early.org/e 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 c19early.org/e 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 c19early.org/e 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% c19early.org/e 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 -123% Improvement Relative Risk c19early.org/e Karimpour-Razkenari et al. Aspirin LATE TREATMENT Favors aspirin Favors control
[Karimpour-Razkenari] Retrospective 478 moderate to severe hospitalized patients in Iran, showing higher mortality with aspirin treatment. Authors note confounding by indication for aspirin treatment.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk c19early.org/e