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Acetaminophen for COVID-19: real-time meta analysis of 17 studies
Covid Analysis, December 2022
https://c19early.org/acemeta.html
 
0 0.5 1 1.5+ All studies -30% 17 91,239 Improvement, Studies, Patients Relative Risk Mortality -32% 11 91,029 Ventilation -85% 2 1,318 Hospitalization -45% 2 524 Progression -710% 2 478 Cases -16% 5 17,190 Viral clearance -20% 1 122 RCTs -43% 1 210 Peer-reviewed -27% 16 49,587 Symptomatic -32% 14 91,239 Prophylaxis -17% 10 89,837 Early -17% 3 637 Late -56% 5 765 Acetaminophen for COVID-19 c19early.org/ace Dec 2022 Favorsacetaminophen Favorscontrol after exclusions
Meta analysis using the most serious outcome reported shows 30% [11‑52%] higher risk.
0 0.5 1 1.5+ All studies -30% 17 91,239 Improvement, Studies, Patients Relative Risk Mortality -32% 11 91,029 Ventilation -85% 2 1,318 Hospitalization -45% 2 524 Progression -710% 2 478 Cases -16% 5 17,190 Viral clearance -20% 1 122 RCTs -43% 1 210 Peer-reviewed -27% 16 49,587 Symptomatic -32% 14 91,239 Prophylaxis -17% 10 89,837 Early -17% 3 637 Late -56% 5 765 Acetaminophen for COVID-19 c19early.org/ace Dec 2022 Favorsacetaminophen Favorscontrol after exclusions
Concerns have been raised over the use of acetminophen for COVID-19 [Pandolfi, Sestili]. While there is limited clinical data, with the only RCT comparing with indomethacin, studies to date suggest the potential for harm.
All data to reproduce this paper and sources are in the appendix.
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 47 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% improvement 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 n/a n/a 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 Tau​2 = 0.02, I​2 = 44.2%, p < 0.0001 Late treatment -56% 1.56 [1.27-1.92] 144/501 67/264 -56% improvement Kolin -23% 1.23 [1.05-1.43] cases n/a n/a Improvement, RR [CI] Treatment Control 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 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. OT​1 Tau​2 = 0.06, I​2 = 98.5%, p = 0.099 Prophylaxis -17% 1.17 [0.97-1.42] 83/31,993 95/57,844 -17% improvement All studies -30% 1.30 [1.11-1.52] 239/32,940 164/58,299 -30% improvement Acetaminophen COVID-19 studies c19early.org/ace Dec 2022 Tau​2 = 0.06, I​2 = 97.5%, p = 0.0012 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 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% improvement Manjani -220% death Lerner -27% death Ravichand.. (RCT) -43% recovery OT​1 Lapi -75% death/hosp. Abolhassani -56% death Tau​2 = 0.02, I​2 = 44.2%, p < 0.0001 Late treatment -56% -56% improvement 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 MacFadden -48% case Campbell (PSW) -1% death Xie -5% hospitalization OT​1 Tau​2 = 0.06, I​2 = 98.5%, p = 0.099 Prophylaxis -17% -17% improvement All studies -30% -30% improvement 18 acetaminophen COVID-19 studies c19early.org/ace Dec 2022 Tau​2 = 0.06, I​2 = 97.5%, p = 0.0012 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. D. Timeline of results in acetaminophen studies.
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, 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.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, cases, viral clearance, peer reviewed studies, and non-symptomatic vs. symptomatic results.
Improvement Studies Patients Authors
All studies-30% [-52‑-11%]17 91,239 172
After exclusions-26% [-48‑-7%]15 90,736 150
Peer-reviewed studiesPeer-reviewed-27% [-50‑-8%]16 49,587 149
Randomized Controlled TrialsRCTs-43% [-78‑-14%]1 210 8
Mortality-32% [-68‑-3%]11 91,029 129
VentilationVent.-85% [-1419‑77%]2 1,318 11
HospitalizationHosp.-45% [-172‑23%]2 524 15
Cases-16% [-54‑13%]5 17,190 51
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies-17% [-45‑6%] 3-56% [-92‑-27%] 5-17% [-42‑3%] 10
After exclusions-28% [-178‑41%] 2-49% [-95‑-14%] 4-17% [-42‑3%] 10
Peer-reviewed studiesPeer-reviewed-17% [-45‑6%] 3-56% [-92‑-27%] 5-12% [-37‑9%] 9
Randomized Controlled TrialsRCTs--43% [-78‑-14%] 1-
Mortality-127% [-775‑41%] 2-74% [-216‑4%] 3-18% [-58‑12%] 6
VentilationVent.--434% [-1345‑-98%] 138% [-89‑81%] 1
HospitalizationHosp.--100% [-202‑-33%] 1-5% [-56‑30%] 1
Cases---16% [-54‑13%] 5
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
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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 hospitalization.
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Figure 7. Random effects meta-analysis for progression.
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Figure 8. Random effects meta-analysis for recovery.
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Figure 9. Random effects meta-analysis for cases.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during 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 12. 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 13 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. 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 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. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
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Figure 13. 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 14 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Lapi], substantial unadjusted confounding by indication likely.
[Sharif], unadjusted results with no group details.
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Figure 14. 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.
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]
Table 3. Early treatment is more effective for baloxavir and influenza.
Figure 15 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 15. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 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 16. 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.
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Figure 16. 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 17 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 17. 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.
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.
5 of the 17 studies compare against other treatments, which may reduce the effect seen. 1 of 17 studies combine treatments. The results of acetaminophen alone may differ. None of the RCTs use combined treatment.
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.
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.
Meta analysis using the most serious outcome reported shows 30% [11‑52%] higher risk.
Concerns have been raised over the use of acetminophen for COVID-19 [Pandolfi, Sestili]. While there is limited clinical data, with the only RCT comparing with indomethacin, studies to date suggest the potential for harm.
0 0.5 1 1.5 2+ Mortality -56% Improvement Relative Risk c19early.org/ace Abolhassani et al. Acetaminophen for COVID-19 LATE 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 use.
0 0.5 1 1.5 2+ Mortality, day 60 -1% Improvement Relative Risk Mortality, day 30 0% c19early.org/ace Campbell et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Chandan et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Gálvez-Barrón et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Jeong et al. Acetaminophen for COVID-19 EARLY 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+ Case -23% Improvement Relative Risk c19early.org/ace Kolin et al. Acetaminophen for COVID-19 Prophylaxis 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 c19early.org/ace Lapi et al. Acetaminophen for COVID-19 EARLY TREATMENT 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 c19early.org/ace Leal et al. Acetaminophen for COVID-19 Prophylaxis 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 c19early.org/ace Lerner et al. Acetaminophen for COVID-19 LATE 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 c19early.org/ace MacFadden et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Manjani et al. Acetaminophen for COVID-19 LATE Favors acetaminophen Favors control
[Manjani] Retrospective 524 hospitalized patients in the USA, showing higher mortality and progression with acetaminophen use.
0 0.5 1 1.5 2+ Mortality 2% Improvement Relative Risk c19early.org/ace Oh et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Park et al. Acetaminophen for COVID-19 Prophylaxis 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% c19early.org/ace Ravichandran et al. CTRI/2021/05/033544 Acetaminophen RCT LATE 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% c19early.org/ace Reese et al. Acetaminophen for COVID-19 Prophylaxis 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.
0 0.5 1 1.5 2+ Mortality -473% Improvement Relative Risk Oxygen therapy -534% c19early.org/ace Rinott et al. Acetaminophen for COVID-19 EARLY 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.
0 0.5 1 1.5 2+ Mortality -77% unadjusted Improvement Relative Risk c19early.org/ace Sharif et al. Acetaminophen for COVID-19 EARLY 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+ Hospitalization -5% Improvement Relative Risk Case 3% c19early.org/ace Xie et al. Acetaminophen for COVID-19 Prophylaxis 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 (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
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.
[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. 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%).
[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.
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.
[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.
[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.
[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.
[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.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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