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Aspirin for COVID-19: real-time meta analysis of 23 studies
Covid Analysis, January 18, 2022, DRAFT
https://c19aspirin.com/meta.html
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 21% 23 66,304 Improvement, Studies, Patients Relative Risk With exclusions 24% 20 62,182 Mortality 19% 20 55,760 Ventilation 9% 6 33,012 ICU admission -34% 3 706 Cases 44% 3 10,749 Viral clearance 9% 2 710 RCTs 4% 1 14,892 Peer-reviewed 19% 21 65,955 Prophylaxis 15% 11 46,056 Late 27% 12 20,248 Aspirin for COVID-19 c19aspirin.com Jan 18, 2022 Favors aspirin Favors control
Statistically significant improvements are seen for mortality, recovery, and cases. 12 studies from 6 different countries show statistically significant improvements in isolation (9 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 21% [8‑33%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ All studies 21% 23 66,304 Improvement, Studies, Patients Relative Risk With exclusions 24% 20 62,182 Mortality 19% 20 55,760 Ventilation 9% 6 33,012 ICU admission -34% 3 706 Cases 44% 3 10,749 Viral clearance 9% 2 710 RCTs 4% 1 14,892 Peer-reviewed 19% 21 65,955 Prophylaxis 15% 11 46,056 Late 27% 12 20,248 Aspirin for COVID-19 c19aspirin.com Jan 18, 2022 Favors aspirin Favors control
Benefits may only be evident without the use of other anticoagulants. The RECOVERY RCT shows 4% [-4‑11%] lower mortality for all patients, however when restricting to non-LMWH patients there was 17% [-4‑34%] improvement, consistent with the mortality results from all studies, 19% [5‑31%].
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. None of the aspirin studies show zero events in the treatment arm.
Multiple treatments are typically used in combination, and other treatments are more effective. There has been no early treatment studies to date.
Elimination of COVID-19 is a race against viral evolution. No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used, including treatments, as supported by Pfizer [Pfizer]. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and sources are in the appendix.
Studies Late treatment Prophylaxis PatientsAuthors
All studies 2327% [13‑39%]15% [-12‑36%] 66,304 274
With exclusions 2028% [14‑41%]18% [-11‑40%] 62,182 250
Peer-reviewed 2127% [10‑41%]8% [-22‑31%] 65,955 244
Randomized Controlled TrialsRCTs 14% [-4‑11%] 14,892 1
Percentage improvement with aspirin treatment
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Alamdari -28% 1.28 [0.67-2.43] death 9/53 54/406 Improvement, RR [CI] Treatment Control 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 Sahai (PSM) 13% 0.87 [0.56-1.34] death 33/248 38/248 Abdelwahab -8% 1.08 [0.15-3.82] ventilation 11/31 6/36 Al Harthi (PSM) 27% 0.73 [0.56-0.97] death 98/176 107/173 Kim (PSM) 34% 0.66 [0.36-1.23] death 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] death 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Mustafa 44% 0.56 [0.21-1.51] death 4/66 41/378 Tau​2 = 0.05, I​2 = 73.3%, p = 0.00055 Late treatment 27% 0.73 [0.61-0.87] 318/9,494 493/10,754 27% improvement Huh 71% 0.29 [0.14-0.58] cases population-based cohort Improvement, RR [CI] Treatment Control 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 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 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 Tau​2 = 0.16, I​2 = 94.1%, p = 0.25 Prophylaxis 15% 0.85 [0.64-1.12] 2,147/16,680 4,243/29,376 15% improvement All studies 21% 0.79 [0.67-0.92] 2,465/26,174 4,736/40,130 21% improvement 23 aspirin COVID-19 studies c19aspirin.com Jan 18, 2022 Tau​2 = 0.09, I​2 = 89.7%, p = 0.0023 Effect extraction pre-specified, see appendix Favors aspirin Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of aspirin for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Results
Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, recovery, cases, viral clearance, and peer reviewed studies. Table 1 summarizes the results by treatment stage.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Late treatment 10 12 83.3% 27% improvement
RR 0.73 [0.61‑0.87]
p = 0.00055
Prophylaxis 6 11 54.5% 15% improvement
RR 0.85 [0.64‑1.12]
p = 0.25
All studies 16 23 69.6% 21% improvement
RR 0.79 [0.67‑0.92]
p = 0.0023
Table 1. Results by treatment stage.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Alamdari -28% 1.28 [0.67-2.43] death 9/53 54/406 Improvement, RR [CI] Treatment Control 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 Sahai (PSM) 13% 0.87 [0.56-1.34] death 33/248 38/248 Abdelwahab -8% 1.08 [0.15-3.82] ventilation 11/31 6/36 Al Harthi (PSM) 27% 0.73 [0.56-0.97] death 98/176 107/173 Kim (PSM) 34% 0.66 [0.36-1.23] death 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] death 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Mustafa 44% 0.56 [0.21-1.51] death 4/66 41/378 Tau​2 = 0.05, I​2 = 73.3%, p = 0.00055 Late treatment 27% 0.73 [0.61-0.87] 318/9,494 493/10,754 27% improvement Huh 71% 0.29 [0.14-0.58] cases population-based cohort Improvement, RR [CI] Treatment Control 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 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 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 Tau​2 = 0.16, I​2 = 94.1%, p = 0.25 Prophylaxis 15% 0.85 [0.64-1.12] 2,147/16,680 4,243/29,376 15% improvement All studies 21% 0.79 [0.67-0.92] 2,465/26,174 4,736/40,130 21% improvement 23 aspirin COVID-19 studies c19aspirin.com Jan 18, 2022 Tau​2 = 0.09, I​2 = 89.7%, p = 0.0023 Effect extraction pre-specified, see appendix Favors aspirin Favors control
Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Alamdari -28% 1.28 [0.67-2.43] 9/53 54/406 Improvement, RR [CI] Treatment Control Meizlish (PSM) 48% 0.52 [0.34-0.81] 319 (n) 319 (n) Liu (PSM) 75% 0.25 [0.07-0.87] 2/28 11/204 Mura (PSM) 15% 0.85 [0.69-1.01] 527 (n) 527 (n) Chow 47% 0.53 [0.31-0.90] 26/98 73/314 Sahai (PSM) 13% 0.87 [0.56-1.34] 33/248 38/248 Al Harthi (PSM) 27% 0.73 [0.56-0.97] 98/176 107/173 Kim (PSM) 34% 0.66 [0.36-1.23] 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] 7,351 (n) 7,541 (n) Mustafa 44% 0.56 [0.21-1.51] 4/66 41/378 Tau​2 = 0.05, I​2 = 75.5%, p = 0.00045 Late treatment 28% 0.72 [0.60-0.87] 307/9,463 487/10,718 28% improvement Wang 58% 0.42 [0.01-1.98] 1/9 13/49 Improvement, RR [CI] Treatment Control Yuan 4% 0.96 [0.47-1.72] 11/52 29/131 Osborne (PSM) 59% 0.41 [0.35-0.48] 272/6,300 661/6,300 Mulhem -14% 1.14 [0.93-1.40] 300/1,354 216/1,865 Chow (PSM) 19% 0.81 [0.76-0.87] 1,280/6,781 2,271/10,566 Kim (PSM) -700% 8.00 [1.07-59.6] 6/15 1/20 Basheer -13% 1.13 [1.05-1.21] 45/140 29/250 Sisinni -7% 1.07 [0.89-1.29] 93/253 251/731 Pérez-Segura -49% 1.49 [1.20-1.80] 66/155 183/608 Tau​2 = 0.17, I​2 = 95.0%, p = 0.79 Prophylaxis 4% 0.96 [0.70-1.31] 2,074/15,059 3,654/20,520 4% improvement All studies 19% 0.81 [0.69-0.95] 2,381/24,522 4,141/31,238 19% improvement 20 aspirin COVID-19 mortality results c19aspirin.com Jan 18, 2022 Tau​2 = 0.09, I​2 = 90.6%, p = 0.012 Favors aspirin Favors control
Figure 4. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chow 44% 0.56 [0.37-0.85] 35/98 152/314 Improvement, RR [CI] Treatment Control Abdelwahab -8% 1.08 [0.15-3.82] 11/31 6/36 Kim (PSM) -102% 2.02 [0.83-4.90] 13/124 7/135 RECOVERY (RCT) 5% 0.95 [0.87-1.05] 7,351 (n) 7,541 (n) Tau​2 = 0.12, I​2 = 79.7%, p = 0.66 Late treatment 9% 0.91 [0.59-1.38] 59/7,604 165/8,026 9% improvement Chow (PSM) 3% 0.97 [0.93-1.02] 2,122/6,781 3,403/10,566 Improvement, RR [CI] Treatment Control Kim (PSM) -433% 5.33 [0.66-43.0] 4/15 1/20 Tau​2 = 0.88, I​2 = 60.9%, p = 0.54 Prophylaxis -63% 1.63 [0.35-7.58] 2,126/6,796 3,404/10,586 -63% improvement All studies 9% 0.91 [0.76-1.07] 2,185/14,400 3,569/18,612 9% improvement 6 aspirin COVID-19 mechanical ventilation results c19aspirin.com Jan 18, 2022 Tau​2 = 0.02, I​2 = 73.7%, p = 0.26 Favors aspirin Favors control
Figure 5. Random effects meta-analysis for ventilation.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Chow 43% 0.57 [0.38-0.85] 38/98 160/314 Improvement, RR [CI] Treatment Control Kim (PSM) -91% 1.91 [0.57-6.35] 7/124 4/135 Tau​2 = 0.53, I​2 = 72.8%, p = 0.86 Late treatment 10% 0.90 [0.29-2.83] 45/222 164/449 10% improvement Kim (PSM) -433% 5.33 [0.66-43.0] 4/15 1/20 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.12 Prophylaxis -433% 5.33 [0.66-43.0] 4/15 1/20 -433% improvement All studies -34% 1.34 [0.38-4.72] 49/237 165/469 -34% improvement 3 aspirin COVID-19 ICU results c19aspirin.com Jan 18, 2022 Tau​2 = 0.86, I​2 = 74.3%, p = 0.66 Favors aspirin Favors control
Figure 6. Random effects meta-analysis for ICU admission.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ RECOVERY (RCT) 6% 0.94 [0.91-0.98] no disch. 7,351 (n) 7,541 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0063 Late treatment 6% 0.94 [0.91-0.98] 0/7,351 0/7,541 6% improvement All studies 6% 0.94 [0.90-0.98] 0/7,351 0/7,541 6% improvement 1 aspirin COVID-19 recovery result c19aspirin.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.0063 Favors aspirin Favors control
Figure 7. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Huh 71% 0.29 [0.14-0.58] cases population-based cohort Improvement, RR [CI] Treatment Control Merzon 28% 0.72 [0.53-0.99] cases 73/1,621 589/8,856 Kim (PSM) 33% 0.67 [0.30-1.36] cases 15/136 20/136 Tau​2 = 0.13, I​2 = 67.0%, p = 0.024 Prophylaxis 44% 0.56 [0.33-0.93] 88/1,757 609/8,992 44% improvement All studies 44% 0.56 [0.33-0.93] 88/1,757 609/8,992 44% improvement 3 aspirin COVID-19 case results c19aspirin.com Jan 18, 2022 Tau​2 = 0.13, I​2 = 67.0%, p = 0.024 Favors aspirin Favors control
Figure 8. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Liu (PSM) -2% 1.02 [0.64-1.61] viral time 24 (n) 24 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.94 Late treatment -2% 1.02 [0.64-1.61] 0/24 0/24 -2% improvement Merzon 10% 0.90 [0.82-1.00] viral time 73 (n) 589 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.045 Prophylaxis 10% 0.90 [0.82-1.00] 0/73 0/589 10% improvement All studies 9% 0.91 [0.83-1.00] 0/97 0/613 9% improvement 2 aspirin COVID-19 viral clearance results c19aspirin.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.052 Favors aspirin Favors control
Figure 9. Random effects meta-analysis for viral clearance.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Alamdari -28% 1.28 [0.67-2.43] death 9/53 54/406 Improvement, RR [CI] Treatment Control 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 Sahai (PSM) 13% 0.87 [0.56-1.34] death 33/248 38/248 Abdelwahab -8% 1.08 [0.15-3.82] ventilation 11/31 6/36 Kim (PSM) 34% 0.66 [0.36-1.23] death 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] death 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Mustafa 44% 0.56 [0.21-1.51] death 4/66 41/378 Tau​2 = 0.06, I​2 = 74.0%, p = 0.0026 Late treatment 27% 0.73 [0.59-0.90] 220/9,318 386/10,581 27% improvement Wang 58% 0.42 [0.01-1.98] death 1/9 13/49 Improvement, RR [CI] Treatment Control Yuan 4% 0.96 [0.47-1.72] death 11/52 29/131 Osborne (PSM) 59% 0.41 [0.35-0.48] death 272/6,300 661/6,300 Merzon 28% 0.72 [0.53-0.99] cases 73/1,621 589/8,856 Mulhem -14% 1.14 [0.93-1.40] death 300/1,354 216/1,865 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 Tau​2 = 0.16, I​2 = 94.4%, p = 0.58 Prophylaxis 8% 0.92 [0.69-1.22] 2,147/16,680 4,243/29,376 8% improvement All studies 19% 0.81 [0.69-0.96] 2,367/25,998 4,629/39,957 19% improvement 21 aspirin COVID-19 peer reviewed trials c19aspirin.com Jan 18, 2022 Tau​2 = 0.10, I​2 = 90.1%, p = 0.015 Effect extraction pre-specified, see appendix Favors aspirin Favors control
Figure 10. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 11 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Alamdari], substantial unadjusted confounding by indication likely.
[Mulhem], substantial unadjusted confounding by indication likely, substantial time varying confounding 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.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Meizlish (PSM) 48% 0.52 [0.34-0.81] death 319 (n) 319 (n) Improvement, RR [CI] Treatment Control 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 Sahai (PSM) 13% 0.87 [0.56-1.34] death 33/248 38/248 Abdelwahab -8% 1.08 [0.15-3.82] ventilation 11/31 6/36 Al Harthi (PSM) 27% 0.73 [0.56-0.97] death 98/176 107/173 Kim (PSM) 34% 0.66 [0.36-1.23] death 14/124 23/135 Zhao 43% 0.57 [0.41-0.78] death 121/473 140/473 RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Tau​2 = 0.05, I​2 = 76.9%, p = 0.00039 Late treatment 28% 0.72 [0.59-0.86] 305/9,375 398/9,970 28% improvement Huh 71% 0.29 [0.14-0.58] cases population-based cohort Improvement, RR [CI] Treatment Control 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 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 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 Tau​2 = 0.18, I​2 = 94.0%, p = 0.2 Prophylaxis 18% 0.82 [0.60-1.11] 1,847/15,326 4,027/27,511 18% improvement All studies 24% 0.76 [0.64-0.89] 2,152/24,701 4,425/37,481 24% improvement 20 aspirin COVID-19 studies after exclusions c19aspirin.com Jan 18, 2022 Tau​2 = 0.09, I​2 = 90.1%, p = 0.00098 Effect extraction pre-specified, see appendix Favors aspirin Favors control
Figure 11. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 12 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. Table 2 summarizes the results. Currently there is only one RCT.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for aspirin are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ RECOVERY (RCT) 4% 0.96 [0.89-1.04] death 7,351 (n) 7,541 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.35 Late treatment 4% 0.96 [0.89-1.04] 0/7,351 0/7,541 4% improvement All studies 4% 0.96 [0.88-1.04] 0/7,351 0/7,541 4% improvement 1 aspirin COVID-19 Randomized Controlled Trials c19aspirin.com Jan 18, 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.35 Effect extraction pre-specified, see appendix Favors aspirin Favors control
Figure 12. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 1 1 100% 4% improvement
RR 0.96 [0.88‑1.04]
p = 0.35
RCT mortality results 1 1 100% 4% improvement
RR 0.96 [0.88‑1.04]
p = 0.35
Table 2. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Other medications might be beneficial for late stage complications, while early use may not be effective or may even be harmful. Figure 13 shows an example where efficacy declines as a function of treatment delay.
Figure 13. Effectiveness may depend critically on treatment delay.
Patient demographics.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations.
Discussion
Publication bias.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For aspirin, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
The median effect size for retrospective studies is 27% improvement, compared to 4% for prospective studies, consistent with a positive publication bias. 52% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 100% of prospective studies, consistent with a bias toward publishing negative results. Figure 14 shows a scatter plot of results for prospective and retrospective studies.
Figure 14. Prospective vs. retrospective studies.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Aspirin for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 aspirin trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all aspirin trials represent the optimal conditions for efficacy.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Conclusion
Aspirin is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, recovery, and cases. 12 studies from 6 different countries show statistically significant improvements in isolation (9 for the most serious outcome). Meta analysis using the most serious outcome reported shows 21% [8‑33%] improvement. Results are worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Benefits may only be evident without the use of other anticoagulants. The RECOVERY RCT shows 4% [-4‑11%] lower mortality for all patients, however when restricting to non-LMWH patients there was 17% [-4‑34%] improvement, consistent with the mortality results from all studies, 19% [5‑31%].
Study Notes
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mechanical ventilation -8% Improvement Relative Risk c19aspirin.com/abdelwahab.html 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 27% Improvement Relative Risk Mortality (b) 14% c19aspirin.com/alharthi.html 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality -28% Improvement Relative Risk c19aspirin.com/alamdarie.html Favors aspirin Favors control
[Alamdari] Retrospective 459 patients in Iran, 53 treated with aspirin, showing no significant difference with treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality -13% Improvement Relative Risk c19aspirin.com/basheer.html 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 19% Improvement Relative Risk Mechanical ventilation 3% c19aspirin.com/chow2.html Favors aspirin Favors control
[Chow (B)] 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 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Mortality 47% Improvement Relative Risk Mechanical ventilation 44% ICU admission 43% c19aspirin.com/chow.html Favors aspirin Favors control
[Chow] Retrospective 412 hospitalized patients, 98 treated with aspirin, showing lower mortality, ventilation, and ICU admission with treatment.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Case 71% Improvement Relative Risk c19aspirin.com/huh3.html Favors aspirin Favors control
[Huh] Retrospective database analysis of 65,149 in South Korea, showing significantly lower cases with existing aspirin treatment. The journal version of this paper does not present the aspirin results (only combined results for NSAIDs).