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Nigella Sativa for COVID-19: real-time meta analysis of 11 studies
Covid Analysis, December 2022
https://c19early.org/nsmeta.html
 
0 0.5 1 1.5+ All studies 53% 11 2,959 Improvement, Studies, Patients Relative Risk Mortality 73% 4 1,192 ICU admission 61% 1 381 Hospitalization 34% 5 1,410 Recovery 70% 4 811 Cases 62% 2 481 Viral clearance 69% 3 310 RCTs 68% 8 1,592 RCT mortality 73% 4 1,192 Prophylaxis 51% 3 734 Early 69% 6 1,765 Late 39% 2 460 Nigella Sativa for COVID-19 c19early.org/ns Dec 2022 Favorsnigella sativa Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, hospitalization, recovery, cases, and viral clearance. 7 studies from 6 independent teams in 5 different countries show statistically significant improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 53% [27‑70%] improvement. Results are better for Randomized Controlled Trials and similar after exclusions. Results are consistent with early treatment being more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 9 of 11 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 53% 11 2,959 Improvement, Studies, Patients Relative Risk Mortality 73% 4 1,192 ICU admission 61% 1 381 Hospitalization 34% 5 1,410 Recovery 70% 4 811 Cases 62% 2 481 Viral clearance 69% 3 310 RCTs 68% 8 1,592 RCT mortality 73% 4 1,192 Prophylaxis 51% 3 734 Early 69% 6 1,765 Late 39% 2 460 Nigella Sativa for COVID-19 c19early.org/ns Dec 2022 Favorsnigella sativa 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. Only 18% of nigella sativa studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix.
Highlights
Studies to date suggest that Nigella Sativa reduces risk for COVID-19 with very high confidence for hospitalization, recovery, viral clearance, and in pooled analysis, high confidence for mortality, and low confidence for cases.
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+ Ashraf (RCT) 82% 0.18 [0.04-0.80] death 2/157 11/156 CT​1 Improvement, RR [CI] Treatment Control Al-Haidari (RCT) 96% 0.04 [0.00-0.70] death 0/160 14/259 Aldwihi 24% 0.76 [0.54-1.03] hosp. 85/345 152/393 Koshak (RCT) 75% 0.25 [0.03-2.22] hosp. 1/91 4/92 Bencheqr.. (DB RCT) 69% 0.31 [0.01-7.19] hosp. 0/29 1/23 Said (RCT) 77% 0.23 [0.04-1.23] recovery 30 (n) 30 (n) Tau​2 = 0.58, I​2 = 51.2%, p = 0.012 Early treatment 69% 0.31 [0.12-0.77] 88/812 182/953 69% improvement Karimi (RCT) 51% 0.49 [0.09-2.66] death 2/192 4/189 CT​1 Improvement, RR [CI] Treatment Control Setayesh (RCT) -8% 1.08 [0.07-16.7] death 1/38 1/41 CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 0.61 [0.15-2.57] 3/230 5/230 39% improvement Al-Haidari 62% 0.38 [0.31-0.46] symp. case 68/188 180/188 Improvement, RR [CI] Treatment Control Shehab 0% 1.00 [0.36-2.74] severe case 4/39 22/214 Chandra (RCT) 49% 0.51 [0.16-1.59] cases 4/52 8/53 CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 0.49 [0.28-0.86] 76/279 210/455 51% improvement All studies 53% 0.47 [0.30-0.73] 167/1,321 397/1,638 53% improvement 11 nigella sativa COVID-19 studies c19early.org/ns Dec 2022 Tau​2 = 0.18, I​2 = 66.5%, p = 0.00075 Effect extraction pre-specified(most serious outcome, see appendix) 1 CT: study uses combined treatment Favors nigella sativa Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf (RCT) 82% death CT​1 Relative Risk [CI] Al-Haidari (RCT) 96% death Aldwihi 24% hospitalization Koshak (RCT) 75% hospitalization Bencheq.. (DB RCT) 69% hospitalization Said (RCT) 77% recovery Tau​2 = 0.58, I​2 = 51.2%, p = 0.012 Early treatment 69% 69% improvement Karimi (RCT) 51% death CT​1 Setayesh (RCT) -8% death CT​1 Tau​2 = 0.00, I​2 = 0.0%, p = 0.51 Late treatment 39% 39% improvement Al-Haidari 62% symp. case Shehab 0% severe case Chandra (RCT) 49% case CT​1 Tau​2 = 0.13, I​2 = 45.0%, p = 0.013 Prophylaxis 51% 51% improvement All studies 53% 53% improvement 11 nigella sativa COVID-19 studies c19early.org/ns Dec 2022 Tau​2 = 0.18, I​2 = 66.5%, p = 0.00075 Protocol pre-specified/rotate for details1 CT: study uses combined treatment Favors nigella sativa 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 nigella sativa studies.
We analyze all significant studies concerning the use of nigella sativa 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 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.
9 In Silico studies support the efficacy of nigella sativa [Banerjee, Bouchentouf, Duru, Hardianto, Khan, Maiti, Mir, Rizvi, Sherwani].
2 In Vitro studies support the efficacy of nigella sativa [Esharkawy, Sherwani].
[Thomas] present a phase I clinical study investigating a novel formulation of nigella sativa that may be more effective for COVID-19.
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
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, and 9 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, recovery, cases, and viral clearance.
Improvement Studies Patients Authors
All studies53% [27‑70%]11 2,959 146
After exclusions58% [32‑73%]10 2,706 139
Randomized Controlled TrialsRCTs68% [40‑83%]8 1,592 128
Mortality73% [23‑91%]4 1,192 76
HospitalizationHosp.34% [16‑47%]5 1,410 87
Cases62% [54‑68%]2 481 15
Viral69% [33‑86%]3 310 59
RCT mortality73% [23‑91%]4 1,192 76
RCT hospitalizationRCT hosp.53% [8‑76%]4 672 79
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 studies69% [23‑88%] 639% [-157‑85%] 251% [14‑72%] 3
After exclusions69% [23‑88%] 639% [-157‑85%] 262% [54‑68%] 2
Randomized Controlled TrialsRCTs82% [55‑93%] 539% [-157‑85%] 249% [-59‑84%] 1
Mortality87% [51‑96%] 239% [-157‑85%] 2-
HospitalizationHosp.25% [7‑40%] 350% [-15‑78%] 2-
Cases--62% [54‑68%] 2
Viral69% [33‑86%] 3--
RCT mortality87% [51‑96%] 239% [-157‑85%] 2-
RCT hospitalizationRCT hosp.73% [-61‑96%] 250% [-15‑78%] 2-
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 ICU admission.
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Figure 6. Random effects meta-analysis for hospitalization.
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Figure 7. Random effects meta-analysis for recovery.
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Figure 8. Random effects meta-analysis for cases.
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Figure 9. Random effects meta-analysis for viral clearance.
Figure 10 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 72% improvement, compared to 24% for other studies. Figure 11, 12, and 13 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
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Figure 10. Results for RCTs and non-RCT studies.
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Figure 11. 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.
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Figure 12. Random effects meta-analysis for RCT mortality results.
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Figure 13. Random effects meta-analysis for RCT hospitalization results.
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.
[Shehab], 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. Non-prescription supplements may show very wide variations in quality [Crawford, Crighton].
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 nigella sativa, 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.
0% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 78% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 12% improvement, compared to 69% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 17 shows a scatter plot of results for prospective and retrospective studies.
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Figure 17. Prospective vs. retrospective studies.
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 18 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 18. 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. Nigella Sativa for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 nigella sativa 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 nigella sativa 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.
4 of 11 studies combine treatments. The results of nigella sativa alone may differ. 4 of 8 RCTs use combined treatment. Currently all studies are peer-reviewed.
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.
Studies to date suggest that nigella sativa is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, hospitalization, recovery, cases, and viral clearance. 7 studies from 6 independent teams in 5 different countries show statistically significant improvements in isolation (3 for the most serious outcome). Meta analysis using the most serious outcome reported shows 53% [27‑70%] improvement. Results are better for Randomized Controlled Trials and similar after exclusions. Results are consistent with early treatment being more effective than late treatment. Results are robust — in exclusion sensitivity analysis 9 of 11 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5 2+ Symptomatic case 62% Improvement Relative Risk c19early.org/ns Al-Haidari et al. Nigella Sativa for COVID-19 Prophylaxis Favors nigella sativa Favors control
[Al-Haidari (B)] Prophylaxis study with 376 mostly high-risk patients, 188 treated with nigella sativa, showing significantly lower cases with treatment. Black seeds 40mg/kg orally once daily.
0 0.5 1 1.5 2+ Mortality 96% Improvement Relative Risk Severe case 93% c19early.org/ns Al-Haidari et al. Nigella Sativa for COVID-19 RCT EARLY Favors nigella sativa Favors control
[Al-Haidari] Open-label RCT with 419 patients in Iraq, 160 treated with Nigella Sativa, showing lower mortality and severe cases with treatment. Black seeds 40mg/kg orally once daily for 14 days.
0 0.5 1 1.5 2+ Hospitalization 24% Improvement Relative Risk c19early.org/ns Aldwihi et al. Nigella Sativa for COVID-19 EARLY Favors nigella sativa Favors control
[Aldwihi] Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
0 0.5 1 1.5 2+ Mortality 82% Improvement Relative Risk Mortality (b) 67% Mortality (c) 79% Recovery 84% Recovery (b) 75% Viral clearance 82% Viral clearance (b) 77% c19early.org/ns Ashraf et al. NCT04347382 HNS-COVID-PK Nigella Sativa RCT EARLY Favors nigella sativa Favors control
[Ashraf] RCT with 157 patients treated with honey and nigella sativa, and 156 control patients, showing significantly faster recovery and viral clearance.

Honey (1gm/kg/day) plus encapsulated nigella sativa seeds (80mg/kg/day) orally in 2-3 divided doses daily for up to 13 days.
0 0.5 1 1.5 2+ Hospitalization 69% Improvement Relative Risk Time to sustained clinical.. 9% Time to sustained clini.. (b) 35% Viral clearance 43% c19early.org/ns Bencheqroun et al. Nigella Sativa for COVID-19 RCT EARLY Favors nigella sativa Favors control
[Bencheqroun] 52 patient RCT in the USA with nigella sativa component thymoquinone, showing improved recovery with treatment. There was a significantly faster decline in the total symptom burden, and a significant increase in CD8+ and helper CD4+ central memory T lymphocytes. The treatment group contained 5 more vaccinated patients and 7 more overweight patients. Authors also present in vitro results showing an inhibitory effect with five SARS-CoV-2 variants including omicron.
0 0.5 1 1.5 2+ Case 49% Improvement Relative Risk Case (b) 87% Case (c) 74% c19early.org/ns Chandra et al. CTRI/2020/08/027222 Nigella Sativa RCT Prophylaxis Favors nigella sativa Favors control
[Chandra] RCT 251 high-risk individuals in India, mostly with direct contact with COVID-19 positive patients, testing polyherbal formulations Infuza, which includes nigella sativa, and Kulzam. Both formulations showed lower risk, without statisical significance, while the best results were from the combination of both.
0 0.5 1 1.5 2+ Mortality 51% Improvement Relative Risk ICU admission 61% Hospitalization time 70% primary Fever 67% Dyspnea 14% c19early.org/ns Karimi et al. Nigella Sativa for COVID-19 RCT LATE Favors nigella sativa Favors control
[Karimi] RCT 358 hospitalized patients in Iran, 184 receiving treatment with a combination of nigella sativa and several other herbal medicines, showing shorter hospitalization time and improved recovery with treatment. IR.TUMS.VCR.REC.1399.024.
0 0.5 1 1.5 2+ Hospitalization 75% Improvement Relative Risk Recovery 43% c19early.org/ns Koshak et al. NCT04401202 Nigella Sativa RCT EARLY Favors nigella sativa Favors control
[Koshak] RCT 183 mild COVID-19 outpatients in Saudi Arabia, 91 treated with Nigella Sativa, showing lower hospitalization and faster recovery with treatment. 500mg Nigella Sativa oil (MARNYS Cuminmar) twice daily for 10 days. NCT04401202.
0 0.5 1 1.5 2+ Recovery, dyspnea 77% Improvement Relative Risk Recovery, NS+D, dyspnea 89% Recovery, cough 80% Recovery, NS+D, cough 77% Recovery, fatigue 85% Recovery, NS+D, fatigue 90% Recovery, smell 85% Recovery, NS+D, smell 67% Recovery, taste 58% Recovery, NS+D, taste 58% Recovery, sore throat 82% Recovery, NS+S 86% Recovery, headache 27% Recovery, NS+D, headache 56% Recovery, diarrhea 80% Recovery, NS+D, diarrhea 90% Viral clearance, day 14 61% Viral clearance, day 7 85% Viral clearance, NS+D, d.. 91% Viral clearance, NS+.. (b) 87% c19early.org/ns Said et al. NCT04981743 Nigella Sativa RCT EARLY TREATMENT Favors nigella sativa Favors control
[Said] 120 patient RCT comparing vitamin D, nigella sativa, and combined vitamin D+nigella sativa, showing improved symptom recovery and viral clearance with both vitamin D and nigella sativa, and further improvements with the combination of both. All patients received vitamin C, zinc, and lactoferrin.
0 0.5 1 1.5 2+ Mortality -8% Improvement Relative Risk Oxygen time 27% Hospitalization time 29% c19early.org/ns Setayesh et al. IRCT20200330046899N1 Nigella Sativa RCT LATE Favors nigella sativa Favors control
[Setayesh] Small RCT 41 patients treated with nigella sativa, glycyrrhiza glabra, punica granatum, and rheum palmatum, and 41 control patients, showing shorter hospitalization with treatment.
0 0.5 1 1.5 2+ Severe case 0% unadjusted Improvement Relative Risk c19early.org/ns Shehab et al. Nigella Sativa for COVID-19 Prophylaxis Favors nigella sativa Favors control
[Shehab] Retrospective survey-based analysis of 349 COVID-19 patients, showing no significant difference with nigella sativa prophylaxis in unadjusted analysis. REC/UG/2020/03.
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 nigella sativa, 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 nigella sativa 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/nsmeta.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.
[Al-Haidari], 1/31/2021, Randomized Controlled Trial, Iraq, peer-reviewed, 3 authors. risk of death, 95.8% lower, RR 0.04, p = 0.001, treatment 0 of 160 (0.0%), control 14 of 259 (5.4%), NNT 18, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of severe case, 92.6% lower, RR 0.07, p < 0.001, treatment 2 of 160 (1.2%), control 44 of 259 (17.0%), NNT 6.4.
[Aldwihi], 5/11/2021, retrospective, Saudi Arabia, peer-reviewed, survey, mean age 36.5, 8 authors, study period August 2020 - October 2020. risk of hospitalization, 24.0% lower, RR 0.76, p = 0.09, treatment 85 of 345 (24.6%), control 152 of 393 (38.7%), NNT 7.1, adjusted per study, odds ratio converted to relative risk, multivariable.
[Ashraf], 11/3/2020, Randomized Controlled Trial, placebo-controlled, Pakistan, peer-reviewed, 29 authors, study period 30 April, 2020 - 29 July, 2020, this trial uses multiple treatments in the treatment arm (combined with honey) - results of individual treatments may vary, trial NCT04347382 (history) (HNS-COVID-PK). risk of death, 81.9% lower, RR 0.18, p = 0.01, treatment 2 of 157 (1.3%), control 11 of 156 (7.1%), NNT 17, all cases.
risk of death, 67.1% lower, RR 0.33, p = 0.49, treatment 0 of 107 (0.0%), control 1 of 103 (1.0%), NNT 103, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), moderate cases.
risk of death, 78.8% lower, RR 0.21, p = 0.03, treatment 2 of 50 (4.0%), control 10 of 53 (18.9%), NNT 6.7, severe cases.
risk of no recovery, 83.6% lower, HR 0.16, p < 0.001, treatment 107, control 103, inverted to make HR<1 favor treatment, moderate cases.
risk of no recovery, 75.2% lower, HR 0.25, p < 0.001, treatment 50, control 53, inverted to make HR<1 favor treatment, severe cases.
risk of no viral clearance, 81.9% lower, HR 0.18, p < 0.001, treatment 107, control 103, inverted to make HR<1 favor treatment, moderate cases.
risk of no viral clearance, 76.9% lower, HR 0.23, p < 0.001, treatment 50, control 53, inverted to make HR<1 favor treatment, severe cases.
[Bencheqroun], 5/7/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 45.0, 25 authors, study period 27 May, 2021 - 27 September, 2021. risk of hospitalization, 69.3% lower, RR 0.31, p = 0.44, treatment 0 of 29 (0.0%), control 1 of 23 (4.3%), NNT 23, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
time to sustained clinical response, 9.1% lower, HR 0.91, p = 0.78, treatment 28, control 23, inverted to make HR<1 favor treatment, Kaplan–Meier.
time to sustained clinical response, 35.5% lower, HR 0.65, p = 0.25, treatment 28, control 23, inverted to make HR<1 favor treatment, Kaplan–Meier, high-risk patients.
risk of no viral clearance, 43.5% lower, RR 0.57, p = 0.31, treatment 5 of 21 (23.8%), control 8 of 19 (42.1%), NNT 5.5, day 14.
[Koshak], 8/15/2021, Randomized Controlled Trial, Saudi Arabia, peer-reviewed, 10 authors, trial NCT04401202 (history). risk of hospitalization, 74.7% lower, RR 0.25, p = 0.37, treatment 1 of 91 (1.1%), control 4 of 92 (4.3%), NNT 31.
risk of no recovery, 42.7% lower, RR 0.57, p < 0.001, treatment 34 of 91 (37.4%), control 60 of 92 (65.2%), NNT 3.6.
[Said], 11/8/2022, Randomized Controlled Trial, Egypt, peer-reviewed, 5 authors, trial NCT04981743 (history). risk of no recovery, 77.0% lower, OR 0.23, p = 0.09, treatment 30, control 30, adjusted per study, multivariable, dyspnea, RR approximated with OR.
risk of no recovery, 89.0% lower, OR 0.11, p = 0.01, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, dyspnea, RR approximated with OR.
risk of no recovery, 80.0% lower, OR 0.20, p = 0.003, treatment 30, control 30, adjusted per study, multivariable, cough, RR approximated with OR.
risk of no recovery, 77.0% lower, OR 0.23, p = 0.01, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, cough, RR approximated with OR.
risk of no recovery, 85.0% lower, OR 0.15, p = 0.003, treatment 30, control 30, adjusted per study, multivariable, fatigue, RR approximated with OR.
risk of no recovery, 90.0% lower, OR 0.10, p < 0.001, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, fatigue, RR approximated with OR.
risk of no recovery, 85.0% lower, OR 0.15, p = 0.04, treatment 30, control 30, adjusted per study, multivariable, smell, RR approximated with OR.
risk of no recovery, 67.0% lower, OR 0.33, p = 0.23, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, smell, RR approximated with OR.
risk of no recovery, 58.0% lower, OR 0.42, p = 0.28, treatment 30, control 30, adjusted per study, multivariable, taste, RR approximated with OR.
risk of no recovery, 58.0% lower, OR 0.42, p = 0.28, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, taste, RR approximated with OR.
risk of no recovery, 82.0% lower, OR 0.18, p = 0.08, treatment 30, control 30, sore throat, RR approximated with OR.
risk of no recovery, 86.0% lower, OR 0.14, p = 0.03, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, sore throat, RR approximated with OR.
risk of no recovery, 27.0% lower, OR 0.73, p = 0.62, treatment 30, control 30, headache, RR approximated with OR.
risk of no recovery, 56.0% lower, OR 0.44, p = 0.21, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, headache, RR approximated with OR.
risk of no recovery, 80.0% lower, OR 0.20, p = 0.06, treatment 30, control 30, diarrhea, RR approximated with OR.
risk of no recovery, 90.0% lower, OR 0.10, p = 0.03, treatment 30, control 30, adjusted per study, vitamin D and nigella sativa, multivariable, diarrhea, RR approximated with OR.
risk of no viral clearance, 61.0% lower, OR 0.39, p = 0.08, treatment 30, control 30, day 14, RR approximated with OR.
risk of no viral clearance, 85.0% lower, OR 0.15, p = 0.004, treatment 30, control 30, day 7, RR approximated with OR.
risk of no viral clearance, 91.0% lower, OR 0.09, p < 0.001, treatment 30, control 30, vitamin D and nigella sativa, day 14, RR approximated with OR.
risk of no viral clearance, 87.0% lower, OR 0.13, p = 0.003, treatment 30, control 30, vitamin D and nigella sativa, day 7, RR approximated with OR.
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.
[Karimi], 10/4/2021, Randomized Controlled Trial, Iran, peer-reviewed, 37 authors, study period March 2020 - July 2020, this trial uses multiple treatments in the treatment arm (combined with several herbal medicines) - results of individual treatments may vary. risk of death, 50.8% lower, RR 0.49, p = 0.45, treatment 2 of 192 (1.0%), control 4 of 189 (2.1%), NNT 93.
risk of ICU admission, 60.6% lower, RR 0.39, p = 0.28, treatment 2 of 192 (1.0%), control 5 of 189 (2.6%), NNT 62.
hospitalization time, 70.0% lower, HR 0.30, p < 0.001, treatment 184, control 174, Cox proportional hazards, primary outcome.
fever, 66.5% lower, OR 0.33, p = 0.001, treatment 184, control 174, inverted to make OR<1 favor treatment, RR approximated with OR.
dyspnea, 13.7% lower, OR 0.86, p < 0.001, treatment 184, control 174, inverted to make OR<1 favor treatment, RR approximated with OR.
[Setayesh], 6/3/2022, Randomized Controlled Trial, Iran, peer-reviewed, mean age 59.1, 7 authors, study period June 2020 - September 2020, this trial uses multiple treatments in the treatment arm (combined with glycyrrhiza glabra, punica granatum, and rheum palmatum) - results of individual treatments may vary, trial IRCT20200330046899N1. risk of death, 7.9% higher, RR 1.08, p = 1.00, treatment 1 of 38 (2.6%), control 1 of 41 (2.4%).
oxygen time, 26.8% lower, relative time 0.73, p = 0.007, treatment mean 3.0 (±1.6) n=38, control mean 4.1 (±1.9) n=41.
hospitalization time, 28.7% lower, relative time 0.71, p < 0.001, treatment mean 5.7 (±1.9) n=38, control mean 8.0 (±1.8) n=41.
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.
[Al-Haidari (B)], 1/31/2021, prospective, Iraq, peer-reviewed, 3 authors. risk of symptomatic case, 62.2% lower, RR 0.38, p < 0.001, treatment 68 of 188 (36.2%), control 180 of 188 (95.7%), NNT 1.7.
[Chandra], 7/5/2022, Randomized Controlled Trial, India, peer-reviewed, 12 authors, study period 18 September, 2020 - 21 May, 2021, this trial uses multiple treatments in the treatment arm (combined with Infuza polyherbal formulation) - results of individual treatments may vary, trial CTRI/2020/08/027222. risk of case, 49.0% lower, RR 0.51, p = 0.36, treatment 4 of 52 (7.7%), control 8 of 53 (15.1%), NNT 14, Infuza.
risk of case, 87.0% lower, RR 0.13, p = 0.03, treatment 1 of 51 (2.0%), control 8 of 53 (15.1%), NNT 7.6, Infuza and Kulzam.
risk of case, 74.0% lower, RR 0.26, p = 0.09, treatment 2 of 51 (3.9%), control 8 of 53 (15.1%), NNT 9.0, Kulzam.
[Shehab], 2/28/2022, retrospective, multiple countries, peer-reviewed, survey, 7 authors, study period September 2020 - March 2021, excluded in exclusion analyses: unadjusted results with no group details. risk of severe case, 0.2% lower, RR 1.00, p = 1.00, treatment 4 of 39 (10.3%), control 22 of 214 (10.3%), NNT 4173, unadjusted, severe vs. mild cases.
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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