Aspirin for COVID-19: real-time meta analysis of 69 studies

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 for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 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.

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.

Aweimer, unadjusted results with no group details.

Azizi, age matching based on only two categories, matching may be very poor given the relationship between age and COVID-19 risk; inconsistent data.

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.

Shamsi, unadjusted results with no group details.

Heterogeneity in COVID-19 studies arises from many factors including:

The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu report an 86% reduction in cases for post-exposure prophylaxis, Hayden show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar report only 2.5 hours improvement for inpatient treatment.

Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 56 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in López-Medina).

Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.

There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective Peacock, Willett.

Effectiveness may depend strongly on the dosage and treatment regimen.

The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.

We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 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.

Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 months.

The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.

All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results Boulware, Meeus, Meneguesso.

One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.

Figure 19 shows a scatter plot of results for prospective and retrospective studies. 39% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 30% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 14% improvement, compared to 13% for prospective studies, showing similar results.

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.

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.

Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.

Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.

Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.

Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.

In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.

No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.

2 of 69 studies combine treatments. The results of aspirin alone may differ. 2 of 7 RCTs use combined treatment. Other meta analyses show significant improvements with aspirin for mortality Banaser, Baral, Srinivasan and mechanical ventilation Banaser.

Aspirin is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality and progression. 26 studies from 24 independent teams in 11 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 11% [6‑17%] lower risk. Results are similar for higher quality and peer-reviewed studies and worse for Randomized Controlled Trials. 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% [4‑17%], and the 16% improvement in the REMAP-CAP RCT.

Other meta analyses show significant improvements with aspirin for mortality Banaser, Baral, Srinivasan and mechanical ventilation Banaser.

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.

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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.

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Aidouni: Prospective study of 1,124 COVID-19 ICU patients, showing lower mortality with aspirin treatment.

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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.

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Alamdari: Retrospective 459 patients in Iran, 53 treated with aspirin, showing no significant difference with treatment.

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Ali (B): Retrospective 1,645 hospitalized patients in the USA, showing lower mortality with aspirin use, without statistical significance.

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Ali: Retrospective 1,190 ICU patients in Egypt, showing lower mortality with aspirin treatment. 150mg daily.

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Aweimer: Retrospective 149 patients under invasive mechanical ventilation in Germany showing no significant difference in mortality with aspirin prophylaxis in unadjusted results.

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Azizi: Retrospective 131 COVID-19 patients with aspirin use and 131 matched controls in Iran, showing no significant difference in outcomes, however age matching used only two categories, 40-60 and 60+, therefore matching may be very poor given the relationship between age and COVID-19 risk. The percentages given for the control group death/recovery outcomes do not match the reported counts.

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Basheer: Retrospective 390 hospitalized patients in Israel, showing higher risk of mortality with prior aspirin use. Details of the analysis are not provided.

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Bejan: Retrospective 9,748 COVID-19 patients in the USA showing no signficant difference with aspirin use.

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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.

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Bradbury: RCT 1,557 critical patients, showing significantly lower mortality with aspirin, with 97.5% posterior probability of efficacy.

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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.

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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.

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Chow: Retrospective 112,269 hospitalized COVID-19 patients in the USA, showing lower mortality with aspirin treatment.

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Chow (B): Retrospective 412 hospitalized patients, 98 treated with aspirin, showing lower mortality, ventilation, and ICU admission with treatment.

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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.

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Drew: Retrospective 2,736,091 individuals in the U.S., U.K., and Sweden, showing lower risk of hospital/clinic visits with aspirin use.

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Eikelboom: Late (5.4 days) outpatient RCT showing no significant difference in outcomes with aspirin treatment.

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Eikelboom (B): RCT very late stage (baseline SpO2 77%) patients, showing no significant differences with rivaroxaban and aspirin treatment.

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Elhadi: Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.

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Formiga: Retrospective 20,641 hospitalized patients in Spain, showing no significant difference in outcomes with existing aspirin use.

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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.

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Gogtay: Retrospective 125 COVID+ hospitalized patients in the USA, showing no significant differences with aspirin prophylaxis.

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Goshua: PSM retrospective 2,785 hospitalized patients in the USA, showing lower mortality and higher ventilation and ICU admission with aspirin treatment.

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Haji Aghajani: Retrospective 991 hospitalized patients in Iran, showing lower mortality with aspirin treatment.

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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.

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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).

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Husain: Retrospective 42 patients in Bangladesh, 11 treated with aspirin, showing fewer complications with treatment.

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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.

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Karruli: Retrospective 32 ICU patients showing lower mortality with aspirin treatment, without statistical significance.

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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.

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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.

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Lal: Retrospective 21,579 hospitalized COVID-19 patients mostly in the USA, showing lower risk of mortality and severity with existing aspirin use.

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Levy: Retrospective 849 COVID-19+ patients in skilled nursing homes, showing lower risk of combined hospitalization/death with aspirin prophylaxis, not reaching statistical significance.

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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.

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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.

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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.

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