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Acetaminophen for COVID-19: real-time meta analysis of 25 studies

@CovidAnalysis, October 2023
https://c19early.org/acemeta.html
 
0 0.5 1 1.5+ All studies -28% 25 542,361 Improvement, Studies, Patients Relative Risk Mortality -24% 14 144,648 Ventilation -59% 3 1,642 ICU admission -31% 2 504 Hospitalization -34% 4 704 Progression -710% 2 478 Recovery -42% 2 390 Cases -19% 6 414,433 RCTs -50% 2 390 Peer-reviewed -25% 23 500,530 Symptomatic -28% 21 145,118 Prophylaxis -21% 14 532,270 Early -17% 3 637 Late -43% 8 9,454 Acetaminophen for COVID-19 c19early.org Oct 2023 after exclusions Favorsacetaminophen Favorscontrol
Meta analysis shows 24% [9‑40%] higher mortality, and pooled analysis using the most serious outcome reported shows 28% [16‑41%] higher risk.
Concerns have been raised over the use of acetaminophen (paracetamol) for COVID-19 Pandolfi, Sestili. Studies to date show increased risk. Data is limited, with RCTs to date comparing with indomethacin/ibuprofen.
Potential mechanisms of harm include glutathione depletion, fever suppression, liver toxicity, immunosuppression, cytokine disruption, prostaglandin inhibition, COX inhibition, cell/tissue injury, mitochondrial dysfunction, glycine depletion, disruption of redox balance, increased oxidative stress, trace mineral depletion, microbiome alteration, and endocannabinoid system dysfunction.
All data to reproduce this paper and sources are in the appendix. Acetaminophen is also known as paracetamol.
Evolution of COVID-19 clinical evidence Vitamin D p<0.0000000001 Acetaminophen p=0.0000018 2020 2021 2022 2023 Effective Harmful c19early.org October 2023 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
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 56 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Rinott -473% 5.73 [0.30-109] death 3/85 0/49 OT​1 Improvement, RR [CI] Treatment Control Lapi (ES) -15% 1.15 [0.92-1.43] death/hosp. n/a n/a Sharif -77% 1.77 [0.39-8.09] death 9/361 2/142 Tau​2 = 0.00, I​2 = 0.0%, p = 0.16 Early treatment -17% 1.17 [0.94-1.45] 12/446 2/191 17% higher risk Manjani -220% 3.20 [1.51-6.82] death 64/388 7/136 Improvement, RR [CI] Treatment Control Lerner -27% 1.27 [0.96-1.68] death 5,783 (all patients) Ravichandran (RCT) -43% 1.43 [1.14-1.78] no recov. 77/107 52/103 OT​1 Lapi -75% 1.75 [1.40-2.18] death/hosp. n/a n/a Abolhassani -56% 1.56 [0.58-4.18] death 3/6 8/25 Baldia (ICU) 12% 0.88 [0.72-1.07] death 1,166 (n) 1,480 (n) ICU patients Stufano -19% 1.19 [0.70-2.02] PASC 11/23 23/57 Sobhy (DB RCT) -110% 2.10 [1.05-4.20] ICU 21/90 10/90 OT​1 Tau​2 = 0.04, I​2 = 60.1%, p = 0.00035 Late treatment -43% 1.43 [1.18-1.74] 176/1,780 100/1,891 43% higher risk Blanc -51% 1.51 [0.82-2.84] cases 60 (n) 119 (n) Improvement, RR [CI] Treatment Control Kolin -23% 1.23 [1.05-1.43] cases 397,064 (all patients) Park (PSM) 25% 0.75 [0.35-1.59] death 12/397 16/397 OT​1 Gálvez-Barrón -47% 1.47 [0.66-3.33] death 43 (n) 60 (n) Reese (PSM) -61% 1.61 [1.40-1.84] death 20,826 (n) 20,826 (n) Chandan (PSM) -18% 1.18 [0.83-1.64] death 71/8,595 79/8,595 OT​1 CT​2 Oh 2% 0.98 [0.38-2.49] death 58 (n) 7,655 (n) Leal 7% 0.93 [0.91-0.96] cases n/a n/a Moreno-Martos -29% 1.29 [1.27-1.32] hosp. MacFadden -48% 1.48 [1.44-1.51] cases n/a n/a Campbell (PSW) -1% 1.01 [0.99-1.02] death 2,074 (n) 20,311 (n) Xie -5% 1.05 [0.70-1.56] hosp. population-based cohort OT​1 Kim (PSM) -71% 1.71 [0.69-4.24] death 12/162 7/162 OT​1 Ritsinger -21% 1.21 [1.17-1.25] death 24,641 (n) 20,225 (n) Tau​2 = 0.03, I​2 = 98.0%, p = 0.0013 Prophylaxis -21% 1.21 [1.08-1.37] 95/56,856 102/78,350 21% higher risk All studies -28% 1.28 [1.16-1.41] 283/59,082 204/80,432 28% higher risk Acetaminophen COVID-19 studies c19early.org October 2023 Tau​2 = 0.03, I​2 = 96.6%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment2 CT: study uses combined treatment Favors acetaminophen Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Rinott -473% death OT​1 Improvement Relative Risk [CI] Lapi (ES) -15% death/hosp. Sharif -77% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.16 Early treatment -17% 17% higher risk Manjani -220% death Lerner -27% death Ravichand.. (RCT) -43% recovery OT​1 Lapi -75% death/hosp. Abolhassani -56% death Baldia (ICU) 12% death ICU patients Stufano -19% PASC Sobhy (DB RCT) -110% ICU admission OT​1 Tau​2 = 0.04, I​2 = 60.1%, p = 0.00035 Late treatment -43% 43% higher risk Blanc -51% case Kolin -23% case Park (PSM) 25% death OT​1 Gálvez-Barrón -47% death Reese (PSM) -61% death Chandan (PSM) -18% death OT​1 CT​2 Oh 2% death Leal 7% case Moreno-Martos -29% hospitalization MacFadden -48% case Campbell (PSW) -1% death Xie -5% hospitalization OT​1 Kim (PSM) -71% death OT​1 Ritsinger -21% death Tau​2 = 0.03, I​2 = 98.0%, p = 0.0013 Prophylaxis -21% 21% higher risk All studies -28% 28% higher risk 25 acetaminophen C19 studies c19early.org Oct 2023 Tau​2 = 0.03, I​2 = 96.6%, p < 0.0001 Effect extraction pre-specifiedRotate device for footnotes/details Favors acetaminophen 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. 0.7% of 5,680 proposed treatments show efficacy c19early.org. D. Timeline of results in acetaminophen studies. The marked dates indicate the time when a harmful effect was identified with statistical significance from ≥3 studies for pooled outcomes and one or more specific outcome. Harm based on specific outcomes was delayed by 5.4 months, compared to using pooled outcomes.
We analyze all significant studies concerning the use of acetaminophen 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.
Table 1 shows potential mechanisms by which the treatment of COVID-19 with acetaminophen could be harmful.
Table 1. Mechanisms of action for potential harmful effects with acetaminophen treatment. Submit updates.
Glutathione depletionAcetaminophen metabolism relies on glutathione, an antioxidant that helps protect cells from damage. Higher or chronic doses of acetaminophen can deplete glutathione levels, which could lead to impaired immune cell function.
Fever suppressionFever is a natural defense mechanism that helps the body fight off infection. By reducing fever, acetaminophen could potentially prolong infections.
Liver toxicityAcetaminophen overdose can damage the liver. The liver plays an important role in immune function and inflammation.
ImmunosuppressionSome research indicates acetaminophen directly suppresses immune cells such as lymphocytes and macrophages, reducing immune defenses.
Cytokine disruptionAcetaminophen exposure has been found to alter cytokine production, such as reducing IL-6 levels. Cytokines regulate immunity, so this could impair immune responses.
Prostaglandin inhibitionAcetaminophen inhibits prostaglandins, which are signaling molecules that play a role in inflammation and immunity. Reduced prostaglandins could potentially alter immune regulation and make it more difficult for the body to fight off infection.
COX inhibitionAcetaminophen weakly inhibits COX-1/COX-2 enzymes. These generate immune-modulating prostaglandins, so inhibition could alter immunity.
Cell/tissue injuryAcetaminophen is known to cause oxidative injury to cells, even at normal doses. This low-grade damage could potentially trigger inflammatory and immune responses.
Mitochondrial dysfunctionHigh doses of acetaminophen may impair mitochondrial energy production. Mitochondria play important roles in immune cell activation and function.
Glycine depletionConjugating acetaminophen requires glycine, an amino acid that is involved in a number of important biological processes, including immune function. Depletion of glycine could reduce antioxidant production and have immunomodulatory effects.
Disruption of redox balanceAcetaminophen can disrupt the redox balance, which is the balance between antioxidants and free radicals. Free radicals are unstable molecules that can damage cells. If the redox balance is disrupted, it could lead to increased inflammation and impaired immune cell function.
Increased oxidative stressBeyond glutathione depletion, acetaminophen can increase reactive oxygen species and oxidative stress. Oxidative stress can damage cells and trigger inflammation.
Trace mineral depletionAcetaminophen increases urinary excretion of trace minerals involved in immunity like zinc, selenium, and manganese.
Microbiome alterationAcetaminophen exposure might alter the intestinal microbiota, which is the community of bacteria that live in the gut. The intestinal microbiota plays an important role in immune function, so changes to the microbiota could make it more difficult for the body to fight off infection.
Endocannabinoid system dysfunctionAcetaminophen may disrupt endocannabinoid signaling, which helps regulate immune function. This could lead to improper immune responses.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 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, peer reviewed studies, and non-symptomatic vs. symptomatic results.
Table 2. 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<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies-28% [-41‑-16%]
****
25 542,361 421
After exclusions-25% [-38‑-13%]
****
22 541,724 394
Peer-reviewed studiesPeer-reviewed-25% [-38‑-13%]
****
23 500,530 376
Randomized Controlled TrialsRCTs-50% [-93‑-16%]
**
2 390 14
Mortality-24% [-40‑-9%]
**
14 144,648 319
VentilationVent.-59% [-454‑55%]3 1,642 15
ICU admissionICU-31% [-331‑60%]2 504 10
HospitalizationHosp.-34% [-56‑-15%]
***
4 704 45
Recovery-42% [-78‑-14%]
**
2 390 14
Cases-19% [-56‑9%]6 414,433 73
Table 3. 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<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies-17% [-45‑6%]-43% [-74‑-18%]
***
-21% [-37‑-8%]
**
After exclusions-36% [-67‑-10%]
**
-21% [-37‑-8%]
**
Peer-reviewed studiesPeer-reviewed-17% [-45‑6%]-43% [-74‑-18%]
***
-17% [-32‑-3%]
*
Randomized Controlled TrialsRCTs-50% [-93‑-16%]
**
Mortality-127% [-775‑41%]-36% [-104‑9%]-21% [-41‑-5%]
*
VentilationVent.-434% [-1345‑-98%]
***
13% [-83‑58%]
ICU admissionICU-110% [-320‑-5%]
*
40% [-147‑85%]
HospitalizationHosp.-59% [-130‑-9%]
*
-29% [-37‑-20%]
****
Recovery-42% [-78‑-14%]
**
Cases-19% [-56‑9%]
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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.
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Figure 13. Random effects meta-analysis for non-symptomatic vs. symptomatic results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 14 shows a comparison of results for RCTs and non-RCT studies. Figure 15 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. RCT results are included in Table 2 and Table 3.
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 56 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).
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. RCTs for acetaminophen 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, and may be greater when the risk of a serious outcome is overstated. This bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of the 38 treatments with statistically significant efficacy/harm, 24 have been confirmed in RCTs, with a mean delay of 5.7 months. For the 14 unconfirmed treatments, 4 have zero RCTs to date. The point estimates for the remaining 10 are all consistent with the overall results (benefit or harm), with 8 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and 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 14. Results for RCTs and non-RCT studies.
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Figure 15. 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.
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.
Lapi, substantial unadjusted confounding by indication likely.
Rinott, unadjusted differences between groups.
Sharif, 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 4. 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 56 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 56 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, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 months.
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Figure 18. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results Boulware, Meeus, Meneguesso. For acetaminophen, there is currently not enough data to evaluate publication bias with high confidence.
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 19 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 19. 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. Acetaminophen for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
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.
7 of the 24 studies compare against other treatments, which may reduce the effect seen. 1 of 24 studies combine treatments. The results of acetaminophen alone may differ. None of the RCTs use combined treatment.
Meta analysis shows 24% [9‑40%] higher mortality, and pooled analysis using the most serious outcome reported shows 28% [16‑41%] higher risk.
Concerns have been raised over the use of acetaminophen (paracetamol) for COVID-19 Pandolfi, Sestili. Studies to date show increased risk. Data is limited, with RCTs to date comparing with indomethacin/ibuprofen.
0 0.5 1 1.5 2+ Mortality -56% Improvement Relative Risk Acetaminophen  Abolhassani et al.  LATE TREATMENT Is late treatment with acetaminophen beneficial for COVID-19? Retrospective 31 patients in Iran Study underpowered to detect differences c19early.org Abolhassani et al., J. Allergy and Cli.., Sep 2022 Favors acetaminophen Favors control
Abolhassani: Retrospective 31 hospitalized patients ≤19 with pre-existing inborn errors of immunity, showing no significant difference in mortality with acetaminophen. Only 6 patients were treated with acetaminophen.
0 0.5 1 1.5 2+ Mortality, day 90 12% Improvement Relative Risk Mortality, day 30 14% Acetaminophen  Baldia et al.  ICU PATIENTS Is very late treatment with acetaminophen beneficial for COVID-19? Prospective study of 2,646 patients in multiple countries Lower mortality with acetaminophen (not stat. sig., p=0.2) c19early.org Baldia et al., BMC Geriatrics, December 2022 Favors acetaminophen Favors control
Baldia: Prospective study of 2,646 ICU patients ≥70 years old, showing no significant difference in mortality with acetaminophen use in the 10 days prior to ICU admission.
0 0.5 1 1.5 2+ Case -51% Improvement Relative Risk Acetaminophen for COVID-19  Blanc et al.  Prophylaxis Does acetaminophen reduce COVID-19 infections? Retrospective 179 patients in France (March - April 2020) More cases with acetaminophen (not stat. sig., p=0.19) c19early.org Blanc et al., MDPI AG, May 2020 Favors acetaminophen Favors control
Blanc: Retrospective 179 elderly patients in France, showing higher risk of COVID-19 cases with acetaminophen use, without statistical significance.
0 0.5 1 1.5 2+ Mortality, day 60 -1% Improvement Relative Risk Mortality, day 30 0% Acetaminophen  Campbell et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 22,385 patients in the USA (March - December 2020) No significant difference in mortality c19early.org Campbell et al., PLOS ONE, May 2022 Favors acetaminophen Favors control
Campbell: Retrospective 28,856 COVID-19 patients in the USA, showing no significant difference in mortality for chronic acetaminophen use vs. sporadic NSAID use. Since acetaminophen 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 -18% Improvement Relative Risk Case -27% Acetaminophen  Chandan et al.  Prophylaxis Is prophylaxis with acetaminophen + combined treatments beneficial for COVID-19? PSM retrospective 17,190 patients in the United Kingdom (Jan - Jul 2020) Study compares with NSAIDs, results vs. placebo may differ Higher mortality (p=0.35) and more cases (p=0.17), not sig. c19early.org Chandan et al., Arthritis & Rheuma.., Apr 2021 Favors acetaminophen Favors NSAIDs
Chandan: Retrospective 12,457 patients prescribed paracetamol with codeine/dihydrocodeine and 13,202 prescribed NSAIDs, showing no significant differences in cases and mortality. Patients prescribed codeine/dihydrocodeine may have different susceptibility to COVID-19.
0 0.5 1 1.5 2+ Mortality -47% Improvement Relative Risk Severe case 23% Acetaminophen  Gálvez-Barrón et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 103 patients in Spain (March - May 2020) Higher mortality (p=0.42) and lower severe cases (p=0.55), not sig. c19early.org Gálvez-Barrón et al., Gerontology, Apr 2021 Favors acetaminophen Favors control
Gálvez-Barrón: Analysis of 103 elderly hospitalized COVID-19 patients in Spain, showing higher mortality with acetaminophen, without statistical significance.
0 0.5 1 1.5 2+ Cardiovascular complicat.. -15% Improvement Relative Risk Renal failure -92% Acetaminophen  Jeong et al.  EARLY TREATMENT Is early treatment with acetaminophen beneficial for COVID-19? Retrospective study in South Korea (January - April 2020) Study compares with NSAIDs, results vs. placebo may differ No significant difference in outcomes seen c19early.org Jeong et al., Clinical Infectious Dise.., Jul 2020 Favors acetaminophen Favors NSAIDs
Jeong: Retrospective 1,824 hospitalized COVID-19 patients in South Korea, showing higher progression to combined death, ICU, ventilation, or sepsis (4% versus 0%, group sizes not provided) with paracetamol vs. NSAIDs. Treatment time may vary - exposure was defined as 7 days before and including cohort entry in hospitalized COVID-19 patients.
0 0.5 1 1.5 2+ Mortality -71% Improvement Relative Risk Ventilation -14% ICU admission 40% Oxygen therapy -9% Acetaminophen for COVID-19  Kim et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? PSM retrospective 324 patients in South Korea Study compares with NSAIDs, results vs. placebo may differ Higher mortality with acetaminophen (not stat. sig., p=0.34) c19early.org Kim et al., Int. J. Environmental Rese.., Feb 2023 Favors acetaminophen Favors NSAIDs
Kim: PSM retrospective in South Korea, showing no significant differences in outcomes with acetaminophen use vs. NSAID use. Adherence and dosage are unknown.
0 0.5 1 1.5 2+ Case -23% Improvement Relative Risk Acetaminophen for COVID-19  Kolin et al.  Prophylaxis Does acetaminophen reduce COVID-19 infections? Retrospective 397,064 patients in the United Kingdom More cases with acetaminophen (p=0.0086) c19early.org Kolin et al., PLOS ONE, November 2020 Favors acetaminophen Favors control
Kolin: 397,064 patient UK Biobank retrospective showing higher risk of COVID-19 with acetaminophen use.
0 0.5 1 1.5 2+ Death/hospitalization -15% Improvement Relative Risk Death/hospitalization (b) -29% Death/hospitalization (c) -75% late Acetaminophen  Lapi et al.  EARLY TREATMENT Is early treatment with acetaminophen beneficial for COVID-19? Retrospective study in Italy No significant difference in death/hosp. c19early.org Lapi et al., Internal and Emergency Me.., Jul 2022 Favors acetaminophen Favors control
Lapi: Retrospective paracetamol use with a primary care database in Italy, showing no significant difference in hospitalization/death for use 0-3 and 4-7 days from diagnosis, and significantly higher risk for use >7 days from diagnosis. Confounding by indication may have a greater effect on late usage.
0 0.5 1 1.5 2+ Case 7% Improvement Relative Risk Acetaminophen for COVID-19  Leal et al.  Prophylaxis Does acetaminophen reduce COVID-19 infections? Retrospective study in the United Kingdom (March 2020 - February 2021) Fewer cases with acetaminophen (p=0.004) c19early.org Leal et al., COVID, August 2021 Favors acetaminophen Favors control
Leal: UK Biobank retrospective showing lower cases with acetaminophen use.
0 0.5 1 1.5 2+ Mortality -27% Improvement Relative Risk Acetaminophen  Lerner et al.  LATE TREATMENT Is late treatment with acetaminophen beneficial for COVID-19? Retrospective 5,783 patients in France (February 2020 - June 2021) Higher mortality with acetaminophen (not stat. sig., p=0.097) c19early.org Lerner et al., JMIR Medical Informatics, Mar 2022 Favors acetaminophen Favors control
Lerner: Retrospective 5,783 hospitalized patients in France, showing higher mortality with paracetamol use, without statistical significance.
0 0.5 1 1.5 2+ Case -48% Improvement Relative Risk Acetaminophen  MacFadden et al.  Prophylaxis Does acetaminophen reduce COVID-19 infections? Retrospective study in Canada (January - December 2020) More cases with acetaminophen (p<0.000001) c19early.org MacFadden et al., Open Forum Infectiou.., Mar 2022 Favors acetaminophen Favors control
MacFadden: Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing higher cases with chronic use of acetaminophen.
0 0.5 1 1.5 2+ Mortality -220% Improvement Relative Risk Ventilation -434% Progression -244% Progression (b) -201% Hospitalization time -100% Acetaminophen  Manjani et al.  LATE TREATMENT Is late treatment with acetaminophen beneficial for COVID-19? Retrospective 524 patients in the USA (February - June 2020) Higher mortality (p=0.001) and ventilation (p=0.001) c19early.org Manjani et al., Chest, October 2021 Favors acetaminophen Favors control
Manjani: Retrospective 524 hospitalized patients in the USA, showing higher mortality and progression with acetaminophen use.
Marcy: Estimated 600 patient acetaminophen early treatment RCT with results expected soon (estimated completion over 2 months ago).
0 0.5 1 1.5 2+ Hospitalization, combined -29% Improvement Relative Risk Hospitalization, CU-AMC -52% Hospitalization, CUIMC -5% Hospitalization, HealthVer.. 22% Hospitalization, OpenCl.. -16% Hospitalization, Optum -57% Hospitalization, VA-OMOP -47% Acetaminophen  Moreno-Martos et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 99,890 patients in multiple countries (Jan - Jun 2020) Higher hospitalization with acetaminophen (p<0.000001) c19early.org Moreno-Martos et al., Wellcome Open Re.., Jan 2022 Favors acetaminophen Favors control
Moreno-Martos: Aanlysis of prescriptions in multiple databases showing higher risk of COVID-19 hospitalization with acetaminophen use for COPD patients. Acetaminophen use was more prevalent in hospitalized patients compared to diagnosed patients (data from tables 1, 5, and S3).
0 0.5 1 1.5 2+ Mortality 2% Improvement Relative Risk Acetaminophen for COVID-19  Oh et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 7,713 patients in South Korea (Jan - Jun 2020) No significant difference in mortality c19early.org Oh et al., Int. J. Environmental Resea.., Jun 2021 Favors acetaminophen Favors control
Oh: Retrospective 7,713 COVID-19 patients in Korea, showing no significant difference in mortality with paracetamol use.
0 0.5 1 1.5 2+ Mortality 25% Improvement Relative Risk Ventilation 38% Acetaminophen for COVID-19  Park et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? PSM retrospective 794 patients in South Korea Study compares with NSAIDs, results vs. placebo may differ Lower mortality (p=0.46) and ventilation (p=0.42), not sig. c19early.org Park et al., Scientific Reports, March 2021 Favors acetaminophen Favors NSAIDs
Park: Retrospective 2,365 patients prescribed acetaminophen and 398 prescribed NSAIDs in South Korea, showing no significant differences.
0 0.5 1 1.5 2+ Recovery -43% Improvement Relative Risk Progression -3925% Recovery time -133% Recovery time (b) -75% Recovery time (c) -75% Viral clearance -20% Acetaminophen  Ravichandran et al.  LATE TREATMENT  RCT Is late treatment with acetaminophen beneficial for COVID-19? RCT 210 patients in India Trial compares with indomethacin, results vs. placebo may differ Worse recovery (p=0.0018) and higher progression (p<0.0001) c19early.org Ravichandran et al., Scientific Reports, Apr 2022 Favors acetaminophen Favors indomethacin
Ravichandran: RCT with 107 paracetamol and 103 indomethacin patients, showing higher progression and worse recovery with paracetamol.
0 0.5 1 1.5 2+ Mortality -61% Improvement Relative Risk Severe case -816% Acetaminophen for COVID-19  Reese et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? PSM retrospective 41,652 patients in the USA Higher mortality (p<0.0001) and severe cases (p<0.0001) c19early.org Reese et al., medRxiv, April 2021 Favors acetaminophen Favors control
Reese: N3C retrospective 250,533 patients showing significantly higher mortality with acetaminophen use. Note that acetaminophen results were not included in the journal version or v2 of this preprint, which focuses on NSAID analysis.
0 0.5 1 1.5 2+ Mortality -473% Improvement Relative Risk Oxygen therapy -534% Acetaminophen  Rinott et al.  EARLY TREATMENT Is early treatment with acetaminophen beneficial for COVID-19? Retrospective 134 patients in Israel (March - April 2020) Study compares with ibuprofen, results vs. placebo may differ Higher mortality (p=0.3) and higher oxygen therapy (p=0.055), not sig. c19early.org Rinott et al., Clinical Microbiology a.., Sep 2020 Favors acetaminophen Favors ibuprofen
Rinott: Retrospective 89 febrile COVID-19 patients in Israel taking paracetamol and 49 taking ibuprofen, showing higher need for respiratory support with paracetamol. Although not statistically significant, patients in the paracetamol group were older.
0 0.5 1 1.5 2+ Mortality -21% Improvement Relative Risk Acetaminophen  Ritsinger et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 44,866 patients in Sweden (January 2020 - September 2021) Higher mortality with acetaminophen (p<0.000001) c19early.org Ritsinger et al., BMJ Open, April 2023 Favors acetaminophen Favors control
Ritsinger: Retrospective 44,866 hospitalized COVID-19 patients in Sweden, showing higher mortality with vitamin D deficiency and with acetaminophen use.

The study focuses on cardiorenal disease, finding higher risk of mortality with CRD. Authors also show that COVID-19 mortality was about 1.5x higher when compared with influenza in the first two pandemic waves, but there was no significant difference in the third wave (HR 1.53 [1.45-1.62] and 1.52 [1.44-1.61] in the first two waves and 1.07 [0.99-1.14] in the third).
0 0.5 1 1.5 2+ Mortality -77% unadjusted Improvement Relative Risk Acetaminophen  Sharif et al.  EARLY TREATMENT Is early treatment with acetaminophen beneficial for COVID-19? Retrospective 503 patients in Bangladesh (December 2020 - February 2021) Study underpowered to detect differences c19early.org Sharif et al., Nutrients, November 2022 Favors acetaminophen Favors control
Sharif: Retrospective COVID-19 patients in Bangladesh, showing higher mortality with acetaminophen use in unadjusted results.
0 0.5 1 1.5 2+ ICU admission -110% Improvement Relative Risk Oxygen therapy -110% Hospitalization time -36% Recovery, dyspnea -33% Recovery, fever -75% Recovery, lymphopenia -92% Recovery, cough -70% Acetaminophen  Sobhy et al.  LATE TREATMENT  DB RCT Is late treatment with acetaminophen beneficial for COVID-19? Double-blind RCT 180 patients in Egypt (January - May 2022) Trial compares with ibuprofen, results vs. placebo may differ Higher ICU admission (p=0.047) and higher oxygen therapy (p=0.047) c19early.org Sobhy et al., The Open Anesthesia J., Apr 2023 Favors acetaminophen Favors ibuprofen
Sobhy: RCT 180 moderate hospitalized COVID-19 patients in Egypt, showing higher ICU admission and longer hospitalization with acetaminophen compared with ibuprofen.
0 0.5 1 1.5 2+ PASC -19% Improvement Relative Risk Acetaminophen  Stufano et al.  LATE TREATMENT Is late treatment with acetaminophen beneficial for COVID-19? Retrospective 80 patients in Italy No significant difference in PASC c19early.org Stufano et al., Int. J. Molecular Scie.., Apr 2023 Favors acetaminophen Favors control
Stufano: Retrospective 80 mild COVID-19 patients in Italy, showing no significant difference in long COVID with acetaminophen use during infection.
0 0.5 1 1.5 2+ Hospitalization -5% Improvement Relative Risk Case 3% Acetaminophen for COVID-19  Xie et al.  Prophylaxis Is prophylaxis with acetaminophen beneficial for COVID-19? Retrospective 1,370,600 patients in the USA (Feb - Oct 2020) Study compares with ibuprofen, results vs. placebo may differ No significant difference in outcomes seen c19early.org Xie et al., Drugs, July 2022 Favors acetaminophen Favors ibuprofen
Xie: PSM retrospective 1,370,600 osteoarthritis or back pain patients in the US, showing no significant differences in COVID-19 cases and hospitalization for paracetamol vs. ibuprofen.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were acetaminophen, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of acetaminophen for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to Zhang. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 Sweeting. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.11.5) with scipy (1.11.1), pythonmeta (1.26), numpy (1.25.0), statsmodels (0.14.0), and plotly (5.15.0).
Forest plots are computed using PythonMeta Deng with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective McLean, Treanor.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/acemeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Lapi, 7/30/2022, retrospective, Italy, peer-reviewed, 8 authors, early treatment subset. risk of death/hospitalization, 15.0% higher, OR 1.15, p = 0.22, adjusted per study, early use, RR approximated with OR.
risk of death/hospitalization, 29.0% higher, OR 1.29, p = 0.52, adjusted per study, mid-term use, RR approximated with OR.
Marcy, 8/1/2023, Randomized Controlled Trial, multiple countries, trial NCT04920838 (history) (COVERAGE-A). Estimated 600 patient RCT with results missing over 2 months.
Rinott, 9/30/2020, retrospective, Israel, peer-reviewed, median age 45.0, 5 authors, study period 15 March, 2020 - 15 April, 2020, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: unadjusted differences between groups. risk of death, 472.9% higher, RR 5.73, p = 0.30, treatment 3 of 85 (3.5%), control 0 of 49 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
risk of oxygen therapy, 534.1% higher, RR 6.34, p = 0.06, treatment 11 of 85 (12.9%), control 1 of 49 (2.0%).
Sharif, 11/26/2022, retrospective, Bangladesh, peer-reviewed, 14 authors, study period 13 December, 2020 - 4 February, 2021, excluded in exclusion analyses: unadjusted results with no group details. risk of death, 77.0% higher, RR 1.77, p = 0.74, treatment 9 of 361 (2.5%), control 2 of 142 (1.4%), unadjusted, ACE.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Abolhassani, 9/13/2022, retrospective, Iran, peer-reviewed, 23 authors. risk of death, 56.2% higher, RR 1.56, p = 0.64, treatment 3 of 6 (50.0%), control 8 of 25 (32.0%).
Baldia, 12/27/2022, prospective, multiple countries, peer-reviewed, median age 75.0, 178 authors, trial NCT04321265 (history). risk of death, 12.0% lower, OR 0.88, p = 0.20, treatment 1,166, control 1,480, adjusted per study, multivariable, day 90, RR approximated with OR.
risk of death, 14.0% lower, OR 0.86, p = 0.20, treatment 1,166, control 1,480, adjusted per study, multivariable, day 30, RR approximated with OR.
Lapi, 7/30/2022, retrospective, Italy, peer-reviewed, 8 authors, excluded in exclusion analyses: substantial unadjusted confounding by indication likely. risk of death/hospitalization, 75.0% higher, OR 1.75, p < 0.001, adjusted per study, late use, RR approximated with OR, late treatment result.
Lerner, 3/30/2022, retrospective, France, peer-reviewed, median age 69.2, 7 authors, study period 1 February, 2020 - 15 June, 2021. risk of death, 26.9% higher, RR 1.27, p = 0.10, odds ratio converted to relative risk, weighted and trimmed, day 28, control prevalance approximated with overall prevalence.
Manjani, 10/31/2021, retrospective, USA, peer-reviewed, 6 authors, study period February 2020 - June 2020. risk of death, 220.5% higher, RR 3.20, p = 0.001, treatment 64 of 388 (16.5%), control 7 of 136 (5.1%).
risk of mechanical ventilation, 434.5% higher, RR 5.34, p < 0.001, treatment 388, control 136.
risk of progression, 244.0% higher, OR 3.44, p < 0.005, treatment 132, control 136, triaged to higher level of care, high exposure, RR approximated with OR.
risk of progression, 201.0% higher, OR 3.01, p < 0.007, treatment 256, control 136, triaged to higher level of care, moderate exposure, RR approximated with OR.
hospitalization time, 100% higher, relative time 2.00, p < 0.001, treatment 388, control 136.
Ravichandran, 4/19/2022, Randomized Controlled Trial, India, peer-reviewed, 8 authors, this trial compares with another treatment - results may be better when compared to placebo, trial CTRI/2021/05/033544. risk of no recovery, 42.5% higher, RR 1.43, p = 0.002, treatment 77 of 107 (72.0%), control 52 of 103 (50.5%), day 14.
risk of progression, 3925.2% higher, RR 40.25, p < 0.001, treatment 20 of 107 (18.7%), control 0 of 103 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), SpO2 ≤93.
recovery time, 133.3% higher, relative time 2.33, p < 0.001, treatment median 7.0 IQR 2.75 n=107, control median 3.0 IQR 1.0 n=103, fever.
recovery time, 75.0% higher, relative time 1.75, p < 0.001, treatment median 7.0 IQR 2.0 n=107, control median 4.0 IQR 2.0 n=103, myalgia.
recovery time, 75.0% higher, relative time 1.75, p < 0.001, treatment median 7.0 IQR 3.0 n=107, control median 4.0 IQR 1.0 n=103, cough.
risk of no viral clearance, 20.1% higher, RR 1.20, p = 0.19, treatment 43 of 60 (71.7%), control 37 of 62 (59.7%), day 7.
Sobhy, 4/19/2023, Double Blind Randomized Controlled Trial, Egypt, peer-reviewed, 6 authors, study period January 2022 - May 2022, this trial compares with another treatment - results may be better when compared to placebo, trial PACTR202202880140319. risk of ICU admission, 110.0% higher, RR 2.10, p = 0.047, treatment 21 of 90 (23.3%), control 10 of 90 (11.1%).
risk of oxygen therapy, 110.0% higher, RR 2.10, p = 0.047, treatment 21 of 90 (23.3%), control 10 of 90 (11.1%).
hospitalization time, 35.7% higher, relative time 1.36, p = 0.01, treatment 90, control 90.
risk of no recovery, 33.3% higher, RR 1.33, p = 1.00, treatment 4 of 90 (4.4%), control 3 of 90 (3.3%), day 4, dyspnea.
risk of no recovery, 75.0% higher, RR 1.75, p = 0.25, treatment 14 of 90 (15.6%), control 8 of 90 (8.9%), day 4, fever.
risk of no recovery, 92.3% higher, RR 1.92, p = 0.04, treatment 25 of 90 (27.8%), control 13 of 90 (14.4%), day 4, lymphopenia.
risk of no recovery, 70.0% higher, RR 1.70, p = 0.03, treatment 34 of 90 (37.8%), control 20 of 90 (22.2%), day 4, cough.
Stufano, 4/18/2023, retrospective, Italy, peer-reviewed, 7 authors. risk of PASC, 18.5% higher, RR 1.19, p = 0.62, treatment 11 of 23 (47.8%), control 23 of 57 (40.4%).
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Blanc, 5/2/2020, retrospective, France, preprint, mean age 84.1, 22 authors, study period 2 March, 2020 - 8 April, 2020. risk of case, 51.4% higher, OR 1.51, p = 0.19, treatment 60, control 119, RR approximated with OR.
Campbell, 5/5/2022, retrospective, USA, peer-reviewed, 4 authors, study period 2 March, 2020 - 14 December, 2020. risk of death, 1.0% higher, OR 1.01, p = 0.43, treatment 2,074, control 20,311, adjusted per study, propensity score weighting, multivariable, day 60, RR approximated with OR.
risk of death, no change, OR 1.00, p = 0.86, treatment 2,074, control 20,311, adjusted per study, propensity score weighting, multivariable, day 30, RR approximated with OR.
Chandan, 4/29/2021, retrospective, United Kingdom, peer-reviewed, mean age 65.4, 24 authors, study period 30 January, 2020 - 31 July, 2020, this trial compares with another treatment - results may be better when compared to placebo, this trial uses multiple treatments in the treatment arm (combined with codeine or dihydrocodeine) - results of individual treatments may vary. risk of death, 17.6% higher, HR 1.18, p = 0.35, treatment 71 of 8,595 (0.8%), control 79 of 8,595 (0.9%), adjusted per study, inverted to make HR<1 favor treatment, propensity score matching, multivariable.
risk of case, 26.6% higher, HR 1.27, p = 0.17, treatment 8,595, control 8,595, adjusted per study, inverted to make HR<1 favor treatment, propensity score matching, multivariable.
Gálvez-Barrón, 4/14/2021, retrospective, Spain, peer-reviewed, mean age 86.8, 13 authors, study period 12 March, 2020 - 2 May, 2020. risk of death, 47.0% higher, OR 1.47, p = 0.42, treatment 43, control 60, RR approximated with OR.
risk of severe case, 23.0% lower, OR 0.77, p = 0.55, treatment 43, control 60, RR approximated with OR.
Kim, 2/21/2023, retrospective, South Korea, peer-reviewed, mean age 55.8, 4 authors, this trial compares with another treatment - results may be better when compared to placebo. risk of death, 71.4% higher, RR 1.71, p = 0.34, treatment 12 of 162 (7.4%), control 7 of 162 (4.3%), propensity score matching.
risk of mechanical ventilation, 14.3% higher, RR 1.14, p = 1.00, treatment 8 of 162 (4.9%), control 7 of 162 (4.3%), propensity score matching.
risk of ICU admission, 40.0% lower, RR 0.60, p = 0.72, treatment 3 of 162 (1.9%), control 5 of 162 (3.1%), NNT 81, propensity score matching.
risk of oxygen therapy, 9.1% higher, RR 1.09, p = 0.87, treatment 24 of 162 (14.8%), control 22 of 162 (13.6%), propensity score matching.
Kolin, 11/17/2020, retrospective, United Kingdom, peer-reviewed, 4 authors. risk of case, 23.0% higher, RR 1.23, p = 0.009.
Leal, 8/16/2021, retrospective, United Kingdom, peer-reviewed, 5 authors, study period 16 March, 2020 - 1 February, 2021. risk of case, 7.0% lower, OR 0.93, p = 0.004, RR approximated with OR.
MacFadden, 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. risk of case, 48.0% higher, OR 1.48, p < 0.001, RR approximated with OR.
Moreno-Martos, 1/24/2022, retrospective, multiple countries, peer-reviewed, 24 authors, study period January 2020 - June 2020. risk of hospitalization, 29.3% higher, RR 1.29, p < 0.001, treatment 10,367, control 89,523, meta analysis of all databases combined.
risk of hospitalization, 51.7% higher, RR 1.52, p < 0.001, treatment 103 of 178 (57.9%), control 196 of 514 (38.1%), US CU-AMC.
risk of hospitalization, 5.1% higher, RR 1.05, p = 0.57, treatment 87 of 144 (60.4%), control 360 of 626 (57.5%), US CUIMC.
risk of hospitalization, 21.8% lower, RR 0.78, p = 0.02, treatment 64 of 319 (20.1%), control 1,585 of 6,181 (25.6%), NNT 18, US HealthVerity.
risk of hospitalization, 16.5% higher, RR 1.16, p < 0.001, treatment 2,597 of 4,983 (52.1%), control 28,320 of 63,279 (44.8%), US IQVIA OpenClaims.
risk of hospitalization, 57.0% higher, RR 1.57, p < 0.001, treatment 1,090 of 1,868 (58.4%), control 3,414 of 9,188 (37.2%), US Optum EHR.
risk of hospitalization, 47.2% higher, RR 1.47, p < 0.001, treatment 1,397 of 2,875 (48.6%), control 3,214 of 9,735 (33.0%), US VA-OMOP.
Oh, 6/24/2021, retrospective, South Korea, peer-reviewed, 5 authors, study period 1 January, 2020 - 4 June, 2020. risk of death, 1.9% lower, RR 0.98, p = 0.97, treatment 58, control 7,655, adjusted per study, odds ratio converted to relative risk, multivariable, control prevalance approximated with overall prevalence.
Park, 3/3/2021, retrospective, South Korea, peer-reviewed, 5 authors, this trial compares with another treatment - results may be better when compared to placebo. risk of death, 24.8% lower, HR 0.75, p = 0.46, treatment 12 of 397 (3.0%), control 16 of 397 (4.0%), NNT 99, inverted to make HR<1 favor treatment, propensity score matching.
risk of mechanical ventilation, 37.5% lower, HR 0.62, p = 0.42, treatment 5 of 397 (1.3%), control 8 of 397 (2.0%), NNT 132, inverted to make HR<1 favor treatment, propensity score matching.
Reese, 4/20/2021, retrospective, USA, preprint, 23 authors. risk of death, 61.0% higher, HR 1.61, p < 0.001, treatment 20,826, control 20,826, propensity score matching, Cox proportional hazards, Table S58.
risk of severe case, 816.0% higher, OR 9.16, p < 0.001, treatment 20,826, control 20,826, propensity score matching, Table S50, RR approximated with OR.
Ritsinger, 4/28/2023, retrospective, Sweden, peer-reviewed, mean age 79.8, 8 authors, study period 1 January, 2020 - 9 September, 2021. risk of death, 21.0% higher, HR 1.21, p < 0.001, treatment 24,641, control 20,225.
Xie, 7/13/2022, retrospective, USA, peer-reviewed, 9 authors, study period 1 February, 2020 - 31 October, 2020, this trial compares with another treatment - results may be better when compared to placebo. risk of hospitalization, 4.8% higher, HR 1.05, p = 0.83, inverted to make HR<1 favor treatment, Open Claims, PharMetrics Plus, both periods combined.
risk of case, 3.5% lower, HR 0.97, p = 0.82, inverted to make HR<1 favor treatment, Open Claims, PharMetrics Plus, both periods combined.
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