Covid Analysis, May 2023
•Statistically significant improvements are seen for mortality and progression. 25 studies from 23 independent teams in 11 different countries show statistically significant improvements in isolation (20 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 11% [5‑16%] 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% [4‑16%], 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.
|All studies||Late treatment||Prophylaxis||Studies||Patients||Authors|
|All studies||11% [5‑16%]|
|Randomized Controlled TrialsRCTs||5% [-2‑11%]||5% [-2‑11%]||-||7||23,015||201|
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 51 treatments.
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, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
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.
|All studies||11% [5‑16%]|
|After exclusions||14% [8‑19%]|
|Peer-reviewed studiesPeer-reviewed||11% [6‑17%]|
|Randomized Controlled TrialsRCTs||5% [-2‑11%]||7||23,015||201|
|ICU admissionICU||4% [-13‑18%]||13||34,600||192|
|RCT mortality||5% [-2‑11%]||6||22,735||174|
|Early treatment||Late treatment||Prophylaxis|
|All studies||67% [-696‑99%]||19% [10‑27%]|
|After exclusions||67% [-696‑99%]||23% [15‑30%]|
|Peer-reviewed studiesPeer-reviewed||67% [-696‑99%]||20% [11‑28%]|
|Randomized Controlled TrialsRCTs||67% [-696‑99%]||5% [-2‑11%]||-|
|VentilationVent.||-||5% [-19‑24%]||2% [-9‑12%]|
|ICU admissionICU||-||0% [-65‑40%]||4% [-15‑19%]|
|HospitalizationHosp.||67% [-696‑99%]||17% [-19‑42%]||-2% [-8‑4%]|
|Viral||-||-2% [-61‑36%]||10% [0‑18%]
|RCT mortality||-||5% [-2‑11%]||-|
Figure 13 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 16% improvement, compared to 13% for other studies. Figure 14 and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1 and Table 2.
[Jadad], and analysis of double-blind RCTs has identified extreme levels of bias [Gøtzsche]. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
[Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[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.
Heterogeneity in COVID-19 studies arises from many factors including:
[McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 51 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 51 treatments.
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
[Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Figure 18. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
[Boulware, Meeus, Meneguesso].
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
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.
Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
[Banaser, Baral, Srinivasan], showing significant improvements for one or more of mortality and mechanical ventilation.
Statistically significant improvements are seen for mortality and progression. 25 studies from 23 independent teams in 11 different countries show statistically significant improvements in isolation (20 for the most serious outcome). Meta analysis using the most serious outcome reported shows 11% [5‑16%] 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% [4‑16%], and the 16% improvement in the REMAP-CAP RCT.
[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.
[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.
[Aidouni] Prospective study of 1,124 COVID-19 ICU patients, showing lower mortality with aspirin treatment.
[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.
[Alamdari] Retrospective 459 patients in Iran, 53 treated with aspirin, showing no significant difference with treatment.
[Ali (B)] Retrospective 1,645 hospitalized patients in the USA, showing lower mortality with aspirin use, without statistical significance.
[Ali] Retrospective 1,190 ICU patients in Egypt, showing lower mortality with aspirin treatment. 150mg daily.
[Aweimer] Retrospective 149 patients under invasive mechanical ventilation in Germany showing no significant difference in mortality with aspirin prophylaxis in unadjusted results.
[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.
[Basheer] Retrospective 390 hospitalized patients in Israel, showing higher risk of mortality with prior aspirin use. Details of the analysis are not provided.
[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.
[Bradbury] RCT 1,557 critical patients, showing significantly lower mortality with aspirin, with 97.5% posterior probability of efficacy.
[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.
[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.
[Chow] Retrospective 112,269 hospitalized COVID-19 patients in the USA, showing lower mortality with aspirin treatment.
[Chow (B)] Retrospective 412 hospitalized patients, 98 treated with aspirin, showing lower mortality, ventilation, and ICU admission with treatment.
[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.
[Drew] Retrospective 2,736,091 individuals in the U.S., U.K., and Sweden, showing lower risk of hospital/clinic visits with aspirin use.
[Eikelboom] Late (5.4 days) outpatient RCT showing no significant difference in outcomes with aspirin treatment.
[Eikelboom (B)] RCT very late stage (baseline SpO2 77%) patients, showing no significant differences with rivaroxaban and aspirin treatment.
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.