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Aspirin for COVID-19: real-time meta analysis of 66 studies
Covid Analysis, May 2023
https://c19early.org/emeta.html
 
0 0.5 1 1.5+ All studies 11% 66 173,818 Improvement, Studies, Patients Relative Risk Mortality 11% 58 159,420 Ventilation 5% 13 45,168 ICU admission 4% 13 34,600 Hospitalization -2% 9 12,578 Progression 11% 10 29,744 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 5% 7 23,015 RCT mortality 5% 6 22,735 Peer-reviewed 11% 57 141,897 Prophylaxis 5% 36 134,148 Early 67% 1 280 Late 19% 29 39,390 Aspirin for COVID-19 c19early.org/e May 2023 Favorsaspirin Favorscontrol after exclusions
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.
0 0.5 1 1.5+ All studies 11% 66 173,818 Improvement, Studies, Patients Relative Risk Mortality 11% 58 159,420 Ventilation 5% 13 45,168 ICU admission 4% 13 34,600 Hospitalization -2% 9 12,578 Progression 11% 10 29,744 Recovery 9% 3 16,018 Cases 10% 7 10,749 Viral clearance 9% 2 710 RCTs 5% 7 23,015 RCT mortality 5% 6 22,735 Peer-reviewed 11% 57 141,897 Prophylaxis 5% 36 134,148 Early 67% 1 280 Late 19% 29 39,390 Aspirin for COVID-19 c19early.org/e May 2023 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% [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.
Evolution of COVID-19 clinical evidence Aspirin p=0.00037 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org May 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Percentage improvement with aspirin (more)
All studies Late treatment Prophylaxis Studies Patients Authors
All studies11% [5‑16%]
***
19% [10‑27%]
***
5% [-2‑13%] 66 173,818 984
Randomized Controlled TrialsRCTs5% [-2‑11%]5% [-2‑11%]- 7 23,015 201
Mortality11% [4‑16%]
**
19% [10‑27%]
****
4% [-6‑12%] 58 159,420 880
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 51 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 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 REMAP-CAP Bradbury (RCT) 16% 0.84 [0.70-1.00] death 165/563 170/521 Chow (PSW) 13% 0.87 [0.81-0.93] death population-based cohort Santoro (PSM) 38% 0.62 [0.42-0.92] death 360 (n) 2,949 (n) RESIST 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 ACT inpatient Eikelboom (RCT) -5% 1.05 [0.86-1.28] death 193/1,063 186/1,056 CT​1 ACT outpatient 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 Aidouni (ICU) 31% 0.69 [0.54-0.88] death 202/712 165/412 ICU patients Singla (RCT) 57% 0.43 [0.04-3.27] death 3/49 5/49 CT​1 Tau​2 = 0.04, I​2 = 77.2%, p = 0.00011 Late treatment 19% 0.81 [0.73-0.90] 1,202/16,716 1,883/22,674 19% 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 Yuan 4% 0.96 [0.47-1.72] death 11/52 29/131 Ramos-Rincón -29% 1.29 [1.05-1.51] death 132/264 253/526 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) Drew 22% 0.78 [0.49-1.24] progression n/a n/a 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 Formiga (PSM) -3% 1.03 [0.94-1.13] death 1,000/3,291 874/2,885 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 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. population-based cohort 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) Zadeh 37% 0.63 [0.30-1.29] death n/a n/a Azizi 0% 1.00 [0.53-1.87] death 17/131 17/131 Aweimer -10% 1.10 [0.90-1.34] death 34/44 74/105 Intubated patients Tau​2 = 0.04, I​2 = 89.0%, p = 0.17 Prophylaxis 5% 0.95 [0.87-1.02] 3,651/36,594 6,374/97,554 5% improvement All studies 11% 0.89 [0.84-0.95] 4,853/53,454 8,258/120,364 11% improvement 66 aspirin COVID-19 studies c19early.org/e May 2023 Tau​2 = 0.04, I​2 = 86.1%, p = 0.00037 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 RECOVERY (RCT) 4% death Mustafa 44% death REMAP-CAP Bradbury (RCT) 16% death Chow (PSW) 13% death Santoro (PSM) 38% death RESIST Ghati (RCT) 22% death Karimpour-Razk.. -123% death ACT inpatient Eikelboom (RCT) -5% death CT​1 ACT outpatient Eikelboom (RCT) -9% death Ali (ICU) 40% death ICU patients Aidouni (ICU) 31% death ICU patients Singla (RCT) 57% death CT​1 Tau​2 = 0.04, I​2 = 77.2%, p = 0.00011 Late treatment 19% 19% improvement Huh 71% case Holt -34% death/ICU Wang 58% death Yuan 4% death Ramos-Rincón -29% death Osborne (PSM) 59% death Merzon 28% case Mulhem -14% death Reese (PSM) -61% death Drew 22% progression 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 Formiga (PSM) -3% death Sullerot (PSW) -10% death Monserrat.. (PSM) -31% death Levy 26% death/hosp. Nimer 4% hospitalization Gogtay -6% death 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 Zadeh 37% death Azizi 0% death Aweimer -10% death Intubated patients Tau​2 = 0.04, I​2 = 89.0%, p = 0.17 Prophylaxis 5% 5% improvement All studies 11% 11% improvement 66 aspirin COVID-19 studies c19early.org/e May 2023 Tau​2 = 0.04, I​2 = 86.1%, p = 0.00037 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors aspirin Favors control
B
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C
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D
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. 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, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. 0.9% of 3,817 proposed treatments show efficacy [c19early.org]. D. Timeline of results in aspirin studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and one or more specific outcome.
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.
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 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.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies11% [5‑16%]
***
66 173,818 984
After exclusions14% [8‑19%]
****
58 167,678 901
Peer-reviewed studiesPeer-reviewed11% [6‑17%]
***
57 141,897 862
Randomized Controlled TrialsRCTs5% [-2‑11%]7 23,015 201
Mortality11% [4‑16%]
**
58 159,420 880
VentilationVent.5% [-6‑15%]13 45,168 174
ICU admissionICU4% [-13‑18%]13 34,600 192
HospitalizationHosp.-2% [-8‑4%]9 12,578 122
Recovery9% [-1‑18%]3 16,018 78
Cases10% [-6‑24%]7 10,749 69
Viral9% [-0‑17%]2 710 16
RCT mortality5% [-2‑11%]6 22,735 174
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval. * p<0.05  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies67% [-696‑99%]19% [10‑27%]
***
5% [-2‑13%]
After exclusions67% [-696‑99%]23% [15‑30%]
****
8% [-0‑15%]
Peer-reviewed studiesPeer-reviewed67% [-696‑99%]20% [11‑28%]
****
5% [-3‑13%]
Randomized Controlled TrialsRCTs67% [-696‑99%]5% [-2‑11%]-
Mortality-19% [10‑27%]
****
4% [-6‑12%]
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%]
Recovery-9% [-1‑18%]-
Cases--10% [-6‑24%]
Viral--2% [-61‑36%]10% [0‑18%]
*
RCT mortality-5% [-2‑11%]-
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. 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 preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the 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. 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.
Bias in clinical research may be defined as something that tends to make conclusions differ systematically from the truth. RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases [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.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 51 treatments we have analyzed, 64% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments (they may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration).
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].
Currently, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 36 treatments with statistically significant efficacy/harm, 23 have been confirmed in RCTs, with a mean delay of 4.4 months. For the 13 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 9 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatment showing >10% efficacy for all studies, but <10% for RCTs is aspirin.
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 off-patent medications, 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, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. 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.
[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.
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Figure 16. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. 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.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
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]
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.
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, 36 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 97% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.1 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 2.9 months.
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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].
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.
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Figure 19. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 20. Example funnel plot analysis for simulated perfect trials.
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 66 studies combine treatments. The results of aspirin alone may differ. 2 of 7 RCTs use combined treatment. Other meta analyses for aspirin can be found in [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.
0 0.5 1 1.5 2+ Ventilation -8% Improvement Relative Risk c19early.org/e Abdelwahab et al. Aspirin for COVID-19 LATE TREATMENT Is late treatment with aspirin beneficial for COVID-19? Retrospective 67 patients in Egypt Study underpowered to detect differences Abdelwahab et al., Clinical Drug Investigation, doi:10.1007/s40261-021-01061-2 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 Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 1,687 patients in the USA (December 2020 - September 2021) Lower mortality with aspirin (p=0.025) Abul et al., medRxiv, doi:10.1101/2022.08.03.22278392 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 31% Improvement Relative Risk Ventilation 10% c19early.org/e Aidouni et al. Aspirin for COVID-19 ICU PATIENTS Is very late treatment with aspirin beneficial for COVID-19? Prospective study of 1,124 patients in Morocco (Mar 2020 - Mar 2022) Lower mortality with aspirin (p=0.003) Aidouni et al., Research Square, doi:10.21203/rs.3.rs-2313880/v1 Favors aspirin Favors control
[Aidouni] Prospective study of 1,124 COVID-19 ICU patients, showing lower mortality with aspirin treatment.
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 Is late treatment with aspirin beneficial for COVID-19? PSM retrospective 351 patients in Saudi Arabia Lower mortality with aspirin (p=0.03) Al Harthi et al., J. Intensive Care Medicine, doi:10.1177/08850666221093229 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 Is late treatment with aspirin beneficial for COVID-19? Retrospective 459 patients in Iran Higher mortality with aspirin (not stat. sig., p=0.52) Alamdari et al., Tohoku J. Exp. Med., 2020, 252,.., doi:10.1620/tjem.252.73 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 Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 1,645 patients in the USA Lower mortality with aspirin (not stat. sig., p=0.067) Ali et al., Chest, doi:10.1016/j.chest.2022.11.013 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 Is very late treatment with aspirin beneficial for COVID-19? Retrospective 1,190 patients in Egypt Lower mortality (p<0.0001) and ARDS (p=0.0011) with aspirin Ali et al., Egyptian J. Anaesthesia, doi:10.1080/11101849.2022.2139104 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 -10% Improvement Relative Risk c19early.org/e Aweimer et al. Aspirin for COVID-19 INTUBATED PATIENTS Prophylaxis Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 149 patients in Germany (March 2020 - August 2021) No significant difference in mortality Aweimer et al., Scientific Reports, doi:10.1038/s41598-023-31944-7 Favors aspirin Favors control
[Aweimer] Retrospective 149 patients under invasive mechanical ventilation in Germany showing no significant difference in mortality with aspirin prophylaxis in unadjusted results.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk c19early.org/e Azizi et al. Aspirin for COVID-19 Prophylaxis Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 262 patients in Iran No significant difference in mortality Azizi et al., J. Nephropharmacology, doi:10.34172/npj.2023.10506 Favors aspirin Favors control
[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.
0 0.5 1 1.5 2+ Mortality -13% Improvement Relative Risk c19early.org/e Basheer et al. Aspirin for COVID-19 Prophylaxis Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 390 patients in Israel Higher mortality with aspirin (p=0.0003) Basheer et al., Metabolites, doi:10.3390/metabo11100679 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 Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 31,072,642 patients in France No significant difference in outcomes seen Botton et al., Research and Practice in Thrombos.., doi:10.1002/rth2.12743 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 Is late treatment with aspirin beneficial for COVID-19? RCT 1,084 patients in multiple countries (October 2020 - June 2021) Lower progression with aspirin (p=0.018) Bradbury et al., JAMA, doi:10.1001/jama.2022.2910 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 Is prophylaxis with aspirin beneficial for COVID-19? Retrospective 20,730 patients in the USA (March - December 2020) No significant difference in mortality Campbell et al., PLOS ONE, doi:10.1371/journal.pone.0267462 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 Is prophylaxis with aspirin beneficial for COVID-19? PSM retrospective 17,347 patients in the USA Lower mortality with aspirin (p=0.005) Chow et al., J. Thrombosis and Haemostasis, doi:10.1111/jth.15517 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 Is late treatment with aspirin beneficial for COVID-19? Retrospective 112,070 patients in the USA Lower mortality with aspirin (p=0.00004) Chow et al., JAMA Network Open, doi:10.1001/jamanetworkopen.2022.3890 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 Is late treatment with aspirin beneficial for COVID-19? Retrospective 412 patients in the USA Lower mortality (p=0.02) and ventilation (p=0.007) with aspirin Chow et al., Anesthesia & Analgesia, doi:10.1213/ANE.0000000000005292 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 Is early treatment with aspirin beneficial for COVID-19? Double-blind RCT 280 patients in the USA Lower hospitalization with aspirin (not stat. sig., p=0.49) Connors et al., JAMA, doi:10.1001/jama.2021.1727283 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 Is prophylaxis with aspirin beneficial for COVID-19? Retrospective study in multiple countries (March - May 2020) Lower progression with aspirin (not stat. sig., p=0.3) Drew et al., medRxiv, doi:10.1101/2021.04.28.21256261 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 Is late treatment with aspirin beneficial for COVID-19? RCT 3,881 patients in Canada (August 2020 - February 2022) Lower progression (p=0.21) and hospitalization (p=0.31), not stat. sig. Eikelboom et al., The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00299-5 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 Is late treatment with aspirin+rivaroxaban beneficial for COVID-19? RCT 2,119 patients in multiple countries (October 2020 - February 2022) No significant difference in outcomes seen Eikelboom et al., The Lancet Respiratory Medicine, doi:10.1016/S2213-2600(22)00298-3 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 Is very late treatment with aspirin beneficial for COVID-19? Prospective study of 465 patients in Libya (May - December 2020) No significant difference in mortality Elhadi et al., PLOS ONE, doi:10.1371/journal.pone.0251085 Favors aspirin Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.