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Ibuprofen for COVID-19: real-time meta analysis of 12 studies
Covid Analysis, December 2022
https://c19early.org/ibmeta.html
 
0 0.5 1 1.5+ All studies -1% 12 54,527 Improvement, Studies, Patients Relative Risk Mortality -2% 8 50,525 Ventilation -12% 1 403 ICU admission -40% 1 403 Hospitalization -13% 2 397 Progression -9% 3 4,160 Cases -1% 2 0 Peer-reviewed -3% 10 27,085 Symptomatic -3% 11 54,527 Prophylaxis -1% 10 53,727 Early -52% 2 800 Ibuprofen for COVID-19 c19early.org/ib Dec 2022 Favorsibuprofen Favorscontrol after exclusions
Meta analysis using the most serious outcome reported shows 1% [-8‑10%] higher risk, without reaching statistical significance.
0 0.5 1 1.5+ All studies -1% 12 54,527 Improvement, Studies, Patients Relative Risk Mortality -2% 8 50,525 Ventilation -12% 1 403 ICU admission -40% 1 403 Hospitalization -13% 2 397 Progression -9% 3 4,160 Cases -1% 2 0 Peer-reviewed -3% 10 27,085 Symptomatic -3% 11 54,527 Prophylaxis -1% 10 53,727 Early -52% 2 800 Ibuprofen for COVID-19 c19early.org/ib Dec 2022 Favorsibuprofen Favorscontrol after exclusions
Concerns have been raised over potential harm from the use of ibuprofen for COVID-19 [Day], due to ACE2 upregulation; disrupting normal and beneficial action of the immune system; and delayed diagnosis. There is very limited clinical data currently, especially with regard to acute usage at onset of inefection, and there are no RCTs. Current studies do not show a significant difference in outcomes.
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 -21% 1.21 [0.33-4.38] death 3/87 9/316 Improvement, RR [CI] Treatment Control Abu Esba -170% 2.70 [0.33-22.0] death 1/40 11/357 Tau​2 = 0.00, I​2 = 0.0%, p = 0.45 Early treatment -52% 1.52 [0.52-4.51] 4/127 20/673 -52% improvement Choi (PSM) -240% 3.40 [0.64-18.1] progression case control Improvement, RR [CI] Treatment Control Samimagham -100% 2.00 [1.33-3.02] death 63 (n) 95 (n) Kragholm 4% 0.96 [0.72-1.23] progression 264 (n) 3,738 (n) Wong -23% 1.23 [0.90-1.68] death Reese (PSM) 9% 0.91 [0.62-1.35] death 5,737 (n) 5,737 (n) Drake 10% 0.90 [0.71-1.13] death n/a n/a Leal 3% 0.97 [0.94-1.00] cases n/a n/a Campbell (PSW) 0% 1.00 [0.99-1.01] death 1,814 (n) 20,311 (n) Xie -12% 1.12 [0.92-1.38] hosp. OT​1 Loucera 48% 0.52 [0.34-0.78] death 519 (n) 15,449 (n) Tau​2 = 0.01, I​2 = 68.3%, p = 0.9 Prophylaxis -1% 1.01 [0.92-1.10] 0/8,397 0/45,330 -1% improvement All studies -1% 1.01 [0.92-1.10] 4/8,524 20/46,003 -1% improvement 12 ibuprofen COVID-19 studies c19early.org/ib Dec 2022 Tau​2 = 0.01, I​2 = 62.6%, p = 0.86 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors ibuprofen Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Rinott -21% death Relative Risk [CI] Abu Esba -170% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.45 Early treatment -52% -52% improvement Choi (PSM) -240% progression Samimagham -100% death Kragholm 4% progression Wong -23% death Reese (PSM) 9% death Drake 10% death Leal 3% case Campbell (PSW) 0% death Xie -12% hospitalization OT​1 Loucera 48% death Tau​2 = 0.01, I​2 = 68.3%, p = 0.9 Prophylaxis -1% -1% improvement All studies -1% -1% improvement 12 ibuprofen COVID-19 studies c19early.org/ib Dec 2022 Tau​2 = 0.01, I​2 = 62.6%, p = 0.86 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors ibuprofen Favors control
B
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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 ibuprofen studies.
We analyze all significant studies concerning the use of ibuprofen 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, 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, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, cases, peer reviewed studies, and non-symptomatic vs. symptomatic results.
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.
Improvement Studies Patients Authors
All studies-1% [-10‑8%]12 54,527 479
After exclusions-1% [-10‑8%]11 54,130 473
Peer-reviewed studiesPeer-reviewed-3% [-11‑5%]10 27,085 448
Mortality-2% [-26‑17%]8 50,525 444
HospitalizationHosp.-13% [-37‑7%]2 397 15
Cases-1% [-11‑9%]2 0 14
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.
Early treatment Prophylaxis
All studies-52% [-351‑48%] 2-1% [-10‑8%] 10
After exclusions-21% [-338‑67%] 1-1% [-10‑8%] 10
Peer-reviewed studiesPeer-reviewed-52% [-351‑48%] 2-3% [-11‑5%] 8
Mortality-52% [-351‑48%] 2-0% [-25‑19%] 6
HospitalizationHosp.-18% [-136‑41%] 1-12% [-38‑8%] 1
Cases--1% [-11‑9%] 2
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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 cases.
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Figure 10. 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 11. 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.
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 12 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Abu Esba], substantial unadjusted confounding by indication likely.
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Figure 12. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 13 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 13. 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 14. 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 14. 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 ibuprofen, 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 15 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 15. 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. Ibuprofen for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 ibuprofen trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all ibuprofen trials represent the optimal conditions for efficacy.
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.
1 of the 12 studies compare against other treatments, which may reduce the effect seen.
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 1% [-8‑10%] higher risk, without reaching statistical significance.
Concerns have been raised over potential harm from the use of ibuprofen for COVID-19 [Day], due to ACE2 upregulation; disrupting normal and beneficial action of the immune system; and delayed diagnosis. There is very limited clinical data currently, especially with regard to acute usage at onset of inefection, and there are no RCTs. Current studies do not show a significant difference in outcomes.
0 0.5 1 1.5 2+ Mortality -170% Improvement Relative Risk Mortality (b) 37% Oxygen therapy -45% Hospitalization -18% Severe case -85% c19early.org/ib Abu Esba et al. Ibuprofen for COVID-19 EARLY TREATMENT Favors ibuprofen Favors control
[Abu Esba] Prospective study of 503 COVID-19 cases in Saudi Arabia, 40 using ibuprofen during infection, and 357 not using NSAIDs, showing no significant differences in outcomes. Results are subject to confounding by indication.
0 0.5 1 1.5 2+ Mortality, day 60 0% Improvement Relative Risk Mortality, day 30 1% c19early.org/ib Campbell et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Campbell] Retrospective 28,856 COVID-19 patients in the USA, showing no significant difference in mortality for chronic ibuprofen use vs. sporadic NSAID use. Since ibuprofen 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+ Progression -240% Improvement Relative Risk c19early.org/ib Choi et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Choi] Retrospective 293 patients in South Korea, showing higher risk of progression with ibuprofen use, without statistical significance.
0 0.5 1 1.5 2+ Mortality 10% Improvement Relative Risk c19early.org/ib Drake et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Drake] Prospective study of 78,674 COVID-19 patients, showing no significant difference in mortality with ibuprofen use.
0 0.5 1 1.5 2+ Progression 4% Improvement Relative Risk c19early.org/ib Kragholm et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Kragholm] Retrospective 4,002 COVID-19 patients in Denmark, 264 with ibuprofen prescriptions, showing no significant difference for COVID-19 severity.
0 0.5 1 1.5 2+ Case 3% Improvement Relative Risk c19early.org/ib Leal et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Leal] UK Biobank retrospective showing no significant difference in cases with ibuprofen use.
0 0.5 1 1.5 2+ Mortality 48% Improvement Relative Risk c19early.org/ib Loucera et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ Mortality 9% Improvement Relative Risk Severe case -303% c19early.org/ib Reese et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Reese] N3C retrospective 250,533 patients showing higher COVID-19 severity with ibuprofen use. Note that results for individual treatments are not included in the journal version or v2 of this preprint.
0 0.5 1 1.5 2+ Mortality -21% Improvement Relative Risk Ventilation -12% ICU admission -40% c19early.org/ib Rinott et al. Ibuprofen for COVID-19 EARLY TREATMENT Favors ibuprofen Favors control
[Rinott] Retrospective 403 COVID-19 cases in Israel, showing no significant difference in outcomes with ibuprofen use. Patients were asked about ibuprofen use starting a week before diagnosis of COVID-19 - treatment time may have been early, late, or prophylactic.
0 0.5 1 1.5 2+ Mortality -100% Improvement Relative Risk Severe case -428% Progression -13% c19early.org/ib Samimagham et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Samimagham] Retrospective 158 COVID-19 patients in Iran, showing higher risk of mortality with ibuprofen use.
0 0.5 1 1.5 2+ Mortality -23% Improvement Relative Risk Mortality (b) 17% c19early.org/ib Wong et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors control
[Wong] Retrospective 2,463,707 people in the UK, showing no significant difference in COVID-19 mortality with NSAID use. Current NSAID users were defined as those ever prescribed an NSAID in the 4 months prior to study start, and non-users were those with no record of NSAID prescription in the same time period.
0 0.5 1 1.5 2+ Hospitalization -12% Improvement Relative Risk Case -8% c19early.org/ib Xie et al. Ibuprofen for COVID-19 Prophylaxis Favors ibuprofen Favors other NSAIDs
[Xie] PSM retrospective 1,697,522 osteoarthritis or back pain patients in the US, showing no significant differences in COVID-19 cases and hospitalization for ibuprofen vs. other NSAIDs.
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 ibuprofen, 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 ibuprofen 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/ibmeta.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.
[Abu Esba], 11/2/2020, prospective, Saudi Arabia, peer-reviewed, 6 authors, study period 12 April, 2020 - 1 June, 2020, excluded in exclusion analyses: substantial unadjusted confounding by indication likely. risk of death, 169.5% higher, RR 2.70, p = 0.35, treatment 1 of 40 (2.5%), control 11 of 357 (3.1%), adjusted per study, multivariable.
risk of death, 36.8% lower, HR 0.63, p = 0.68, treatment 40, control 357, Cox proportional hazards.
risk of oxygen therapy, 44.8% higher, RR 1.45, p = 0.64, treatment 40, control 357, adjusted per study, multivariable.
risk of hospitalization, 18.2% higher, RR 1.18, p = 0.64, treatment 40, control 357, adjusted per study, multivariable.
risk of severe case, 84.8% higher, RR 1.85, p = 0.42, treatment 40, control 357, adjusted per study, multivariable.
[Rinott], 9/30/2020, retrospective, Israel, peer-reviewed, median age 45.0, 5 authors, study period 15 March, 2020 - 15 April, 2020. risk of death, 21.1% higher, RR 1.21, p = 0.73, treatment 3 of 87 (3.4%), control 9 of 316 (2.8%).
risk of mechanical ventilation, 11.8% higher, RR 1.12, p = 0.77, treatment 4 of 87 (4.6%), control 13 of 316 (4.1%).
risk of ICU admission, 39.7% higher, RR 1.40, p = 0.56, treatment 5 of 87 (5.7%), control 13 of 316 (4.1%).
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, no change, OR 1.00, p = 0.54, treatment 1,814, control 20,311, adjusted per study, propensity score weighting, multivariable, day 60, RR approximated with OR.
risk of death, 1.0% lower, OR 0.99, p = 0.23, treatment 1,814, control 20,311, adjusted per study, propensity score weighting, multivariable, day 30, RR approximated with OR.
[Choi], 6/23/2020, retrospective, South Korea, peer-reviewed, median age 29.0, 8 authors, study period 5 March, 2020 - 18 March, 2020. risk of progression, 240.0% higher, OR 3.40, p = 0.26, treatment 6 of 36 (16.7%) cases, 2 of 36 (5.6%) controls, case control OR, propensity score matching.
[Drake], 7/31/2021, prospective, United Kingdom, peer-reviewed, 362 authors, study period 17 January, 2020 - 10 August, 2020. risk of death, 10.0% lower, OR 0.90, p = 0.36, adjusted per study, multivariable, RR approximated with OR.
[Kragholm], 10/21/2020, retrospective, Denmark, peer-reviewed, 13 authors, study period 1 January, 2020 - 30 April, 2020. risk of progression, 4.0% lower, RR 0.96, p = 0.78, treatment 264, control 3,738.
[Leal], 8/16/2021, retrospective, United Kingdom, peer-reviewed, 5 authors, study period 16 March, 2020 - 1 February, 2021. risk of case, 3.0% lower, OR 0.97, p = 0.29, RR approximated with OR.
[Loucera], 8/16/2022, retrospective, Spain, preprint, 8 authors, study period January 2020 - November 2020. risk of death, 48.3% lower, HR 0.52, p = 0.002, treatment 519, control 15,449, Cox proportional hazards, day 30.
[Reese], 4/20/2021, retrospective, USA, preprint, 23 authors. risk of death, 9.0% lower, HR 0.91, p = 0.65, treatment 5,737, control 5,737, propensity score matching, Cox proportional hazards, Table S56.
risk of severe case, 303.0% higher, OR 4.03, p < 0.001, treatment 5,737, control 5,737, propensity score matching, Table S48, RR approximated with OR.
[Samimagham], 7/13/2020, retrospective, Iran, peer-reviewed, 4 authors. risk of death, 100% higher, OR 2.00, p < 0.001, treatment 63, control 95, adjusted per study, multivariable, RR approximated with OR.
risk of severe case, 427.8% higher, RR 5.28, p < 0.001, treatment 14 of 63 (22.2%), control 4 of 95 (4.2%).
risk of progression, 13.1% higher, RR 1.13, p = 0.04, treatment 60 of 63 (95.2%), control 80 of 95 (84.2%), moderate or severe.
[Wong], 1/21/2021, retrospective, United Kingdom, peer-reviewed, median age 53.0, 32 authors, study period 1 March, 2020 - 14 June, 2020. risk of death, 23.0% higher, HR 1.23, p = 0.19, adjusted per study, general population, multivariable.
risk of death, 17.0% lower, HR 0.83, p = 0.37, adjusted per study, rheumatoid arthritis/osteoarthritis patients, multivariable.
[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, 12.5% higher, HR 1.12, p = 0.26, Open Claims, PharMetrics Plus, both periods combined.
risk of case, 7.6% higher, HR 1.08, p = 0.25, Open Claims, PharMetrics Plus, both periods combined.
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