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Spironolactone for COVID-19: real-time meta analysis of 10 studies
Covid Analysis, October 6, 2022, DRAFT
https://c19early.com/spmeta.html
 
0 0.5 1 1.5+ All studies 45% 10 3,137 Improvement, Studies, Patients Relative Risk Mortality 48% 2 198 Ventilation 48% 2 1,726 ICU admission 43% 2 1,726 Hospitalization 52% 2 272 Recovery 49% 5 800 Cases 51% 2 0 Viral clearance 46% 2 300 RCTs 44% 3 324 Peer-reviewed 22% 6 953 Prophylaxis 28% 4 2,277 Early 77% 1 270 Late 52% 5 590 Spironolactone for COVID-19 c19early.com/sp Oct 2022 Favorsspironolactone Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, progression, and recovery. 9 studies from 9 independent teams in 8 different countries show statistically significant improvements in isolation (7 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 45% [18‑63%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and worse for peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.5 1 1.5+ All studies 45% 10 3,137 Improvement, Studies, Patients Relative Risk Mortality 48% 2 198 Ventilation 48% 2 1,726 ICU admission 43% 2 1,726 Hospitalization 52% 2 272 Recovery 49% 5 800 Cases 51% 2 0 Viral clearance 46% 2 300 RCTs 44% 3 324 Peer-reviewed 22% 6 953 Prophylaxis 28% 4 2,277 Early 77% 1 270 Late 52% 5 590 Spironolactone for COVID-19 c19early.com/sp Oct 2022 Favorsspironolactone Favorscontrol after exclusions
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. None of the spironolactone studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Spironolactone reduces risk for COVID-19 with very high confidence for recovery and in pooled analysis, low confidence for mortality, ventilation, ICU admission, and progression, and very low confidence for viral clearance.
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+ Cadegiani 77% 0.23 [0.08-0.66] recov. time 8 (n) 262 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.0062 Early treatment 77% 0.23 [0.08-0.66] 0/8 0/262 77% improvement Mareev (RCT) 11% 0.89 [0.65-1.22] no recov. 33 (n) 33 (n) CT​1 Improvement, RR [CI] Treatment Control Ersoy (ICU) 46% 0.54 [0.36-0.81] death 14/30 26/30 ICU patients Davarpanah 78% 0.22 [0.08-0.55] hosp. 6/103 23/103 CT​1 Abbasi (SB RCT) 55% 0.45 [0.18-1.13] death 5/51 19/87 Wadhwa (RCT) 72% 0.28 [0.09-0.85] progression 4/74 9/46 Tau​2 = 0.21, I​2 = 70.7%, p = 0.0042 Late treatment 52% 0.48 [0.29-0.79] 29/291 77/299 52% improvement Holt -129% 2.29 [1.59-3.32] death/ICU 16/31 148/658 Improvement, RR [CI] Treatment Control Jeon 77% 0.23 [0.08-0.64] cases case control MacFadden 7% 0.93 [0.88-0.98] cases n/a n/a Cousins (PSM) 69% 0.31 [0.07-1.00] ventilation 794 (n) 794 (n) Tau​2 = 0.48, I​2 = 92.0%, p = 0.41 Prophylaxis 28% 0.72 [0.34-1.54] 16/825 148/1,452 28% improvement All studies 45% 0.55 [0.37-0.82] 45/1,124 225/2,013 45% improvement 10 spironolactone COVID-19 studies c19early.com/sp Oct 2022 Tau​2 = 0.27, I​2 = 87.0%, p = 0.0032 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors spironolactone Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Cadegiani 77% recovery Improvement Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.0062 Early treatment 77% 77% improvement Mareev (RCT) 11% recovery CT​1 Ersoy (ICU) 46% death ICU patients Davarpanah 78% hospitalization CT​1 Abbasi (SB RCT) 55% death Wadhwa (RCT) 72% progression Tau​2 = 0.21, I​2 = 70.7%, p = 0.0042 Late treatment 52% 52% improvement Holt -129% death/ICU Jeon 77% case MacFadden 7% case Cousins (PSM) 69% ventilation Tau​2 = 0.48, I​2 = 92.0%, p = 0.41 Prophylaxis 28% 28% improvement All studies 45% 45% improvement 10 spironolactone COVID-19 studies c19early.com/sp Oct 2022 Tau​2 = 0.27, I​2 = 87.0%, p = 0.0032 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors spironolactone Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of spironolactone for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION RECOVERY CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS PEER-REVIEWED After Exclusions ALL STUDIES All Prophylaxis Early Late Spironolactone for COVID-19 C19EARLY.COM/SP OCT 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 1 1 100% 77% improvement
RR 0.23 [0.08‑0.66]
p = 0.0062
Late treatment 5 5 100% 52% improvement
RR 0.48 [0.29‑0.79]
p = 0.0042
Prophylaxis 3 4 75.0% 28% improvement
RR 0.72 [0.34‑1.54]
p = 0.41
All studies 9 10 90.0% 45% improvement
RR 0.55 [0.37‑0.82]
p = 0.0032
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 1077% [34‑92%]52% [21‑71%]28% [-54‑66%] 3,137 93
With exclusions 852% [21‑71%]56% [-28‑85%] 2,178 85
Peer-reviewed 634% [0‑57%]13% [-100‑62%] 953 54
Randomized Controlled TrialsRCTs 344% [-14‑73%] 324 49
Table 2. Results by treatment stage for all studies and with different exclusions.
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Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. 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 a 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.
Exclusions
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 14 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Cadegiani], significant unadjusted differences between groups.
[Holt], 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, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 15 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. Figure 16 and 17 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
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 spironolactone are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
Figure 15. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
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Figure 16. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 17. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 3 3 100% 44% improvement
RR 0.56 [0.27‑1.14]
p = 0.11
RCT mortality results 1 1 100% 55% improvement
RR 0.45 [0.18‑1.13]
p = 0.089
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Figure 18 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 18. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
Patient demographics.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Other treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
Medication quality.
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.
Meta analysis.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant for specific use cases.
Discussion
Publication bias.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For spironolactone, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.
80% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 100% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 46% improvement, compared to 72% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
Figure 19. Prospective vs. retrospective studies.
Funnel plot analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 20. Example funnel plot analysis for simulated perfect trials.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Spironolactone for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 spironolactone 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 spironolactone trials represent the optimal conditions for efficacy.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
Notes.
2 of 10 studies combine treatments. The results of spironolactone alone may differ. 1 of 3 RCTs use combined treatment.
Conclusion
Spironolactone is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, progression, and recovery. 9 studies from 9 independent teams in 8 different countries show statistically significant improvements in isolation (7 for the most serious outcome). Meta analysis using the most serious outcome reported shows 45% [18‑63%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and worse for peer-reviewed studies. Early treatment is more effective than late treatment.
Study Notes
0 0.5 1 1.5 2+ Mortality 55% Improvement Relative Risk Ventilation 34% ICU admission 19% Recovery 47% c19early.com/sp Abbasi et al. Spironolactone for COVID-19 RCT LATE Favors spironolactone Favors control
[Abbasi] RCT including 51 spironolactone patients and 87 control patients in Iran, showing improved recovery with spironolactone, sitagliptin, and the combination of both.
0 0.5 1 1.5 2+ Recovery time 77% Improvement Relative Risk Recovery time (b) 83% Time to viral- 38% c19early.com/sp Cadegiani et al. Spironolactone for COVID-19 EARLY Favors spironolactone Favors control
[Cadegiani] Prospective study of 270 female COVID-19 patients in Brazil, 75 with hyperandrogenism, of which 8 were on spironolactone. Results suggest that HA patients may be at increased risk, and that spironolactone use may reduce the risk compared to both other HA patients and non-HA patients. SOC included other treatments and there was no mortality or hospitalization.
0 0.5 1 1.5 2+ Ventilation 69% Improvement Relative Risk ICU admission 58% c19early.com/sp Cousins et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Cousins] PSM retrospective 64,349 COVID-19 patients in the USA, showing spironolactone associated with lower ICU admission.

Authors also present In Vitro research showing dose-dependent inhibition in a human lung epithelial cell line.
0 0.5 1 1.5 2+ Hospitalization 78% Improvement Relative Risk Recovery time 64% c19early.com/sp Davarpanah et al. Spironolactone for COVID-19 LATE Favors spironolactone Favors control
[Davarpanah] Prospective study of 206 outpatients in Iran, 103 treated with spironolactone and sitagliptin, showing lower hospitalization and faster recovery with treatment. spironolactone 100mg and sitagliptin 100mg daily.
0 0.5 1 1.5 2+ Mortality 46% Improvement Relative Risk c19early.com/sp Ersoy et al. Spironolactone for COVID-19 ICU Favors spironolactone Favors control
[Ersoy] Retrospective 30 COVID-19 ARDS ICU patients and 30 control patients, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Death/ICU -129% Improvement Relative Risk c19early.com/sp Holt et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Holt] Retrospective 689 hospitalized COVID-19 patients in Denmark, showing higher risk of ICU/death with spironolactone use in unadjusted results subject to confounding by indication.
0 0.5 1 1.5 2+ Case 77% Improvement Relative Risk c19early.com/sp Jeon et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[Jeon] Retrospective 6,462 liver cirrhosis patients in South Korea, with 67 COVID+ cases, showing significantly lower cases with spironolactone treatment. Death and ICU results per group are not provided.
0 0.5 1 1.5 2+ Case 7% Improvement Relative Risk c19early.com/sp MacFadden et al. Spironolactone for COVID-19 Prophylaxis Favors spironolactone Favors control
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing lower cases with chronic use of spironolactone.
0 0.5 1 1.5 2+ SHOKS-COVID score 11% Improvement Relative Risk PCR+ on day 10 or hospi.. 39% Hospitalization time 8% Viral clearance 87% c19early.com/sp Mareev et al. Spironolactone for COVID-19 RCT LATE Favors spironolactone Favors control
[Mareev] Prospective 103 PCR+ patients in Russia, 33 treated with bromexhine+spironolactone, showing lower PCR+ at day 10 or hospitalization >10 days with treatment. Bromhexine 8mg 4 times daily, spironolactone 25-50 mg/day for 10 days.
0 0.5 1 1.5 2+ Progression 72% Improvement Relative Risk Discharge 49% Recovery time 18% c19early.com/sp Wadhwa et al. CTRI/2021/03/031721 Spironolactone RCT LATE Favors spironolactone Favors control
[Wadhwa] RCT 120 hospitalized patients in India, 74 treated with spironolactone and dexamethasone, and 46 with dexamethasone, showing lower progression with treatment. Spironolactone 50mg once daily day 1, 25mg once daily until day 21.
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.com. Search terms were spironolactone, 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 spironolactone 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 (3.10.7) with scipy (1.9.1), pythonmeta (1.26), numpy (1.23.3), statsmodels (0.13.2), and plotly (5.10.0).
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.com/spmeta.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.
[Cadegiani], 10/6/2020, prospective, Brazil, preprint, 4 authors, average treatment delay 3.0 days, excluded in exclusion analyses: significant unadjusted differences between groups. recovery time, 76.7% lower, relative time 0.23, p = 0.006, treatment 8, control 262, excluding anosmia.
recovery time, 82.8% lower, relative time 0.17, p = 0.002, treatment 8, control 262, including anosmia.
time to viral-, 37.9% lower, relative time 0.62, p = 0.02, treatment 8, control 262.
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.
[Abbasi], 2/7/2022, Single Blind Randomized Controlled Trial, Iran, peer-reviewed, 11 authors, study period December 2020 - April 2021. risk of death, 55.1% lower, RR 0.45, p = 0.10, treatment 5 of 51 (9.8%), control 19 of 87 (21.8%), NNT 8.3, day 5.
risk of mechanical ventilation, 33.7% lower, RR 0.66, p = 0.36, treatment 7 of 51 (13.7%), control 18 of 87 (20.7%), NNT 14, day 5.
risk of ICU admission, 18.8% lower, RR 0.81, p = 0.67, treatment 10 of 51 (19.6%), control 21 of 87 (24.1%), NNT 22, day 5.
risk of no recovery, 47.3% lower, RR 0.53, p < 0.001, treatment mean 1.64 (±0.81) n=51, control mean 3.11 (±2.45) n=87, relative clinical score, day 5.
[Davarpanah], 1/21/2022, prospective, Iran, preprint, 9 authors, study period July 2021 - September 2021, average treatment delay 5.74 days, this trial uses multiple treatments in the treatment arm (combined with sitagliptin) - results of individual treatments may vary. risk of hospitalization, 78.3% lower, RR 0.22, p < 0.001, treatment 6 of 103 (5.8%), control 23 of 103 (22.3%), NNT 6.1, odds ratio converted to relative risk.
recovery time, 64.4% lower, relative time 0.36, p < 0.001, treatment 103, control 103.
[Ersoy], 10/13/2021, retrospective, Turkey, peer-reviewed, 7 authors. risk of death, 46.2% lower, RR 0.54, p = 0.002, treatment 14 of 30 (46.7%), control 26 of 30 (86.7%), NNT 2.5.
[Mareev], 12/3/2020, Randomized Controlled Trial, Russia, peer-reviewed, 20 authors, this trial uses multiple treatments in the treatment arm (combined with bromhexine) - results of individual treatments may vary. relative SHOKS-COVID score, 11.3% better, RR 0.89, p = 0.47, treatment mean 2.12 (±1.39) n=33, control mean 2.39 (±1.59) n=33.
risk of PCR+ on day 10 or hospitalization >10 days, 38.8% lower, RR 0.61, p = 0.02, treatment 14 of 24 (58.3%), control 20 of 21 (95.2%), NNT 2.7, odds ratio converted to relative risk.
hospitalization time, 8.2% lower, relative time 0.92, p = 0.35, treatment 33, control 33.
risk of no viral clearance, 87.4% lower, RR 0.13, p = 0.08, treatment 0 of 17 (0.0%), control 3 of 13 (23.1%), NNT 4.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 10.
[Wadhwa], 7/2/2022, Randomized Controlled Trial, placebo-controlled, India, preprint, 18 authors, study period 1 February, 2021 - 30 April, 2021, trial CTRI/2021/03/031721. risk of progression, 72.4% lower, RR 0.28, p = 0.03, treatment 4 of 74 (5.4%), control 9 of 46 (19.6%), NNT 7.1, progression to WHO >4.
risk of no hospital discharge, 49.5% lower, RR 0.51, p = 0.048, treatment 13 of 74 (17.6%), control 16 of 46 (34.8%), NNT 5.8.
recovery time, 18.2% lower, relative time 0.82, p = 0.06, treatment 74, control 46.
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.
[Cousins], 7/6/2022, retrospective, USA, preprint, 8 authors. risk of mechanical ventilation, 69.0% lower, OR 0.31, p = 0.05, treatment 794, control 794, propensity score matching, RR approximated with OR.
risk of ICU admission, 58.0% lower, OR 0.42, p = 0.004, treatment 794, control 794, propensity score matching, RR approximated with OR.
[Holt], 5/7/2020, retrospective, Denmark, peer-reviewed, median age 70.0, 4 authors, study period 1 March, 2020 - 1 April, 2020, excluded in exclusion analyses: unadjusted results with no group details. risk of death/ICU, 129.5% higher, RR 2.29, p < 0.001, treatment 16 of 31 (51.6%), control 148 of 658 (22.5%).
[Jeon], 2/23/2021, retrospective, South Korea, peer-reviewed, 3 authors. risk of case, 77.0% lower, OR 0.23, p = 0.005, treatment 6 of 49 (12.2%) cases, 89 of 245 (36.3%) controls, NNT 6.5, case control OR, model 2, within 3 months.
[MacFadden], 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. risk of case, 7.0% lower, OR 0.93, p = 0.008, RR approximated with OR.
Supplementary Data
References
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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