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HCQ for COVID-19: real-time meta analysis of 345 studies
https://hcqmeta.com/
0 0.5 1 1.5+ All studies 25% 345 457,932 Improvement, Studies, Patients Relative Risk Mortality 21% 212 325,877 Hospitalization 18% 51 87,448 RCTs 19% 53 24,762 RCTs Early 39% 11 3,085 RCTs Late 13% 31 12,115 Early 63% 38 56,773 Early Mortality 72% 15 52,740 Early Hosp. 41% 15 50,743 PrEP 31% 71 154,168 PEP 33% 8 6,040 Late 19% 232 245,569 HCQ for COVID-19 hcqmeta.com May 2022 FavorsHCQ Favorscontrol after exclusions
Meta analysis using the most serious outcome reported shows 63% [53‑70%] improvement for the 38 early treatment studies. Results are similar after exclusion based sensitivity analysis and after restriction to peer-reviewed studies. The 11 RCTs show 39% [8‑59%] improvement, and the 15 mortality results shows 72% [57‑81%] lower mortality.
21 early treatment studies show statistically significant improvements in isolation (15 for the most serious outcome).
Late treatment is less successful, with only 67% of the 232 studies reporting a positive effect. Very late stage treatment is not effective and may be harmful, especially when using excessive dosages.
78% of Randomized Controlled Trials (RCTs) for early, PrEP, or PEP treatment report positive effects, the probability of results as good or better for an ineffective treatment is 0.0053.
0 0.5 1 1.5+ All studies 25% 345 457,932 Improvement, Studies, Patients Relative Risk Mortality 21% 212 325,877 Hospitalization 18% 51 87,448 RCTs 19% 53 24,762 RCTs Early 39% 11 3,085 RCTs Late 13% 31 12,115 Early 63% 38 56,773 Early Mortality 72% 15 52,740 Early Hosp. 41% 15 50,743 PrEP 31% 71 154,168 PEP 33% 8 6,040 Late 19% 232 245,569 HCQ for COVID-19 hcqmeta.com May 2022 FavorsHCQ Favorscontrol after exclusions
There is evidence of bias towards publishing negative results. 76% of prospective studies report positive effects, compared to 71% of retrospective studies. Studies from North America are 2.6 times more likely to report negative results than studies from the rest of the world combined, p = 0.0000000245.
Negative meta analyses of HCQ generally choose a subset of trials, focusing on late treatment, especially trials with very late treatment and excessive dosages.
While many treatments have some level of efficacy, they do not replace vaccines and other measures to avoid infection. Only 5% of HCQ studies show zero events in the treatment arm. Multiple treatments are typically used in combination, which may be significantly more effective.
No treatment, vaccine, or intervention is 100% available and effective for all variants. All practical, effective, and safe means should be used. Denying the efficacy of treatments increases mortality, morbidity, collateral damage, and endemic risk.
All data to reproduce this paper and the sources are in the appendix. See [Ladapo, Prodromos, Risch, Risch (B)] for other meta analyses showing efficacy when HCQ is used early.
Total345 studies5,457 authors458,509 patients
Positive effects249 studies3,844 authors328,863 patients
Early treatment 63% improvement RR 0.37 [0.30-0.47]
Late treatment 19% improvement RR 0.81 [0.76-0.86]
Highlights
HCQ reduces risk for COVID-19 with very high confidence for mortality, hospitalization, cases, viral clearance, and in pooled analysis.
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 42 treatments.
    
  
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gautret 66% 0.34 [0.17-0.68] 2.4g viral+ 6/20 14/16 Improvement, RR [CI] Dose (4d) Treatment Control Huang (RCT) 92% 0.08 [0.01-1.32] 4g (c) no recov. 0/10 6/12 OT​1 CQ​3 Esper 64% 0.36 [0.15-0.87] 2g hosp. 8/412 12/224 CT​2 Ashraf 68% 0.32 [0.10-1.10] 1.6g death 10/77 2/5 Huang (ES) 59% 0.41 [0.26-0.64] 2g (c) viral time 32 (n) 37 (n) CQ​3 Guérin 61% 0.39 [0.02-9.06] 2.4g death 0/20 1/34 CT​2 Chen (RCT) 72% 0.28 [0.11-0.74] 1.6g viral time 18 (n) 12 (n) Derwand 79% 0.21 [0.03-1.47] 1.6g death 1/141 13/377 CT​2 Mitjà (RCT) 16% 0.84 [0.35-2.03] 2g hosp. 8/136 11/157 Skipper (RCT) 37% 0.63 [0.21-1.91] 3.2g death/hosp. 5/231 8/234 Hong 65% 0.35 [0.13-0.72] n/a viral+ 42 (n) 48 (n) Bernabeu-Wittel 59% 0.41 [0.36-0.95] 2g death 189 (n) 83 (n) CT​2 Yu (ES) 85% 0.15 [0.02-1.05] 1.6g death 1/73 238/2,604 Ly 56% 0.44 [0.26-0.75] 2.4g death 18/116 29/110 CT​2 Ip 55% 0.45 [0.11-1.85] n/a death 2/97 44/970 Heras 96% 0.04 [0.02-0.09] n/a death 8/70 16/30 CT​2 Kirenga 26% 0.74 [0.47-1.17] n/a recov. time 29 (n) 27 (n) Sulaiman 64% 0.36 [0.17-0.80] 2g death 7/1,817 54/3,724 Guisado-Vasco (ES) 67% 0.33 [0.05-1.55] n/a death 2/65 139/542 Szente Fonseca 64% 0.36 [0.20-0.67] 2g hosp. 25/175 89/542 Cadegiani 81% 0.19 [0.01-3.88] 1.6g death 0/159 2/137 Simova 94% 0.06 [0.00-1.13] 2.4g hosp. 0/33 2/5 CT​2 Omrani (RCT) 12% 0.88 [0.26-2.94] 2.4g hosp. 7/304 4/152 CT​2 Agusti 68% 0.32 [0.06-1.67] 2g progression 2/87 4/55 Su 85% 0.15 [0.04-0.57] 1.6g progression n/a n/a Amaravadi (RCT) 60% 0.40 [0.13-1.28] 3.2g no recov. 3/15 6/12 Roy 2% 0.98 [0.45-2.20] n/a recov. time 14 (n) 15 (n) Mokhtari 70% 0.30 [0.20-0.45] 2g death 27/7,295 287/21,464 Corradini (ES) 67% 0.33 [0.14-0.78] n/a death 641 (n) 102 (n) Million 83% 0.17 [0.06-0.48] 2.4g death 5/8,315 11/2,114 CT​2 Sobngwi (RCT) 52% 0.48 [0.09-2.58] 1.6g no recov. 2/95 4/92 OT​1 Rodrigues (RCT) -200% 3.00 [0.13-71.6] 3.2g hosp. 1/42 0/42 CT​2 Sawanpanyalert 42% 0.58 [0.18-1.91] varies progression n/a n/a CT​2 Atipornwan.. (RCT) -150% 2.50 [0.10-59.6] 1.6g progression 1/60 0/30 OT​1 CT​2 Chechter 95% 0.05 [0.00-0.96] 2g hosp. 0/60 3/12 CT​2 Rouamba (ES) 73% 0.27 [0.09-1.02] 2.4g progression 23/399 4/33 Avezum (RCT) 1% 0.99 [0.29-3.41] 2g death 5/687 5/682 Roy-García (RCT) -100% 2.00 [0.19-20.9] 1.6g progression 2/31 1/31 Early treatment 63% 0.37 [0.30-0.47] 179/22,007 1,009/34,766 63% improvement All 38 hydroxychloroquine COVID-19 early treatment studies hcqmeta.com May 2022 Tau​2 = 0.20, I​2 = 49.9%, p < 0.0001 Effect extraction pre-specified, see appendix 1 OT: comparison with other treatment3 CQ: study uses chloroquine 2 CT: study uses combined treatment Favors HCQ Favors control
B
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Ashraf 68% 0.32 [0.10-1.10] 1.6g 10/77 2/5 Improvement, RR [CI] Dose (4d) Treatment Control Guérin 61% 0.39 [0.02-9.06] 2.4g 0/20 1/34 Derwand 79% 0.21 [0.03-1.47] 1.6g 1/141 13/377 Bernabeu-Wittel 59% 0.41 [0.36-0.95] 2g 189 (n) 83 (n) Yu (ES) 85% 0.15 [0.02-1.05] 1.6g 1/73 238/2,604 Ly 56% 0.44 [0.26-0.75] 2.4g 18/116 29/110 Ip 55% 0.45 [0.11-1.85] n/a 2/97 44/970 Heras 96% 0.04 [0.02-0.09] n/a 8/70 16/30 Sulaiman 64% 0.36 [0.17-0.80] 2g 7/1,817 54/3,724 Guisado-Vasco (ES) 67% 0.33 [0.05-1.55] n/a 2/65 139/542 Cadegiani 81% 0.19 [0.01-3.88] 1.6g 0/159 2/137 Mokhtari 70% 0.30 [0.20-0.45] 2g 27/7,295 287/21,464 Corradini (ES) 67% 0.33 [0.14-0.78] n/a 641 (n) 102 (n) Million 83% 0.17 [0.06-0.48] 2.4g 5/8,315 11/2,114 Avezum (RCT) 1% 0.99 [0.29-3.41] 2g 5/687 5/682 Early treatment 72% 0.28 [0.17-0.46] 86/19,762 841/32,978 72% improvement All 15 hydroxychloroquine COVID-19 mortality early treatment results hcqmeta.com May 2022 Tau​2 = 0.41, I​2 = 68.0%, p < 0.0001 Favors HCQ Favors control
C
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Esper 64% 0.36 [0.15-0.87] 2g hosp. 8/412 12/224 Improvement, RR [CI] Dose (4d) Treatment Control Derwand 82% 0.18 [0.07-0.54] 1.6g hosp. 4/141 58/377 Mitjà (RCT) 16% 0.84 [0.35-2.03] 2g hosp. 8/136 11/157 Skipper (RCT) 49% 0.51 [0.15-1.66] 3.2g hosp. 4/231 8/234 Ip 37% 0.63 [0.37-0.96] n/a hosp. 21/97 305/970 Sulaiman 39% 0.61 [0.52-0.72] 2g hosp. 171/1,817 617/3,724 Szente Fonseca 64% 0.36 [0.20-0.67] 2g hosp. 25/175 89/542 Cadegiani 98% 0.02 [0.00-0.27] 1.6g hosp. 0/159 27/137 Simova 94% 0.06 [0.00-1.13] 2.4g hosp. 0/33 2/5 Omrani (RCT) 12% 0.88 [0.26-2.94] 2.4g hosp. 7/304 4/152 Mokhtari 35% 0.65 [0.59-0.71] 2g hosp. 523/7,295 2,382/21,464 Million 4% 0.96 [0.71-1.29] 2.4g hosp. 214/8,315 64/2,114 Rodrigues (RCT) -200% 3.00 [0.13-71.6] 3.2g hosp. 1/42 0/42 Chechter 95% 0.05 [0.00-0.96] 2g hosp. 0/60 3/12 Avezum (RCT) 23% 0.77 [0.52-1.12] 2g hosp. 44/689 57/683 Early treatment 41% 0.59 [0.48-0.72] 1,030/19,906 3,639/30,837 41% improvement All 15 hydroxychloroquine COVID-19 hospitalization early treatment results hcqmeta.com May 2022 Tau​2 = 0.05, I​2 = 63.5%, p < 0.0001 Favors HCQ Favors control
    
  
D
    
  
E
    
  
F
Figure 1. A. Random effects meta-analysis of all early treatment studies. This plot shows pooled effects, analysis for individual outcomes is below, and more details on pooled effects can be found in the heterogeneity section. Effect extraction is pre-specified, using the most serious outcome reported. Simplified dosages are shown for comparison, these are the total dose in the first four days. Chloroquine is indicated with (c). For details of effect extraction and full dosage information see the appendix. B and C. Random effects meta-analysis of all early treatment mortality and hospitalization results. D. Scatter plot of the effects reported in early treatment studies and in all studies. Early treatment is more effective. E and F. Chronological history of all reported effects, with the probability that the observed or greater frequency of positive results were generated by an ineffective treatment.
Introduction
We analyze all significant studies concerning the use of HCQ (or CQ) 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 mortality results only, after exclusion of studies with critical bias, and for Randomized Controlled Trials (RCTs) only. Typical meta analyses involve subjective selection criteria and bias evaluation, requiring an understanding of the criteria and the accuracy of the evaluations. However, the volume of studies presents an opportunity for an additional simple and transparent analysis aimed at detecting efficacy.
If treatment was not effective, the observed effects would be randomly distributed (or more likely to be negative if treatment is harmful). We can compute the probability that the observed percentage of positive results (or higher) could occur due to chance with an ineffective treatment (the probability of >= k heads in n coin tosses, or the one-sided sign test / binomial test). Analysis of publication bias is important and adjustments may be needed if there is a bias toward publishing positive results. For HCQ, we find evidence of a bias toward publishing negative results.
Figure 2 shows stages of possible treatment for COVID-19. Pre-Exposure Prophylaxis (PrEP) refers to regularly taking medication before being infected, in order to prevent or minimize infection. In Post-Exposure Prophylaxis (PEP), medication is taken after exposure but before symptoms appear. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
    
  
Figure 2. Treatment stages.
Preclinical and Phase I Research
5 In Silico studies support the efficacy of hydroxychloroquine [Baildya, Hussein, Noureddine, Tarek, Yadav].
13 In Vitro studies support the efficacy of hydroxychloroquine [Andreani, Clementi, Dang, Delandre, Faísca, Hoffmann, Liu, Ou, Purwati, Sheaff, Wang, Wang (B), Yao].
An In Vivo animal study supports the efficacy of hydroxychloroquine [Maisonnasse].
3 studies investigate novel formulations of hydroxychloroquine that may be more effective for COVID-19 [Faísca, Klimke, Zelenko].
[Kavanagh] present a phase I clinical study investigating a novel formulation of hydroxychloroquine 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.
Results
Figure 3 shows a visual overview of the results. Figure 4, Figure 5, and Table 1 show results by treatment stage, and Figure 6 shows a forest plot for a random effects meta-analysis of all studies. Figure 7 and Figure 8 show forest plots restricted to mortality and hospitalization results only.
Early treatment.
92% of early treatment studies report a positive effect, with an estimated reduction of 63% in the effect measured (death, hospitalization, etc.) from the random effects meta-analysis, RR 0.37 [0.30-0.47].
Late treatment.
Late treatment studies are mixed, with 67% showing positive effects, and an estimated reduction of 19% in the random effects meta-analysis. Negative studies mostly fall into the following categories: they show evidence of significant unadjusted confounding, including confounding by indication; usage is extremely late; or they use an excessively high dosage.
Pre-Exposure Prophylaxis.
78% of PrEP studies show positive effects, with an estimated reduction of 31% in the random effects meta-analysis. Negative studies are all studies of systemic autoimmune disease patients which either do not adjust for the different baseline risk of these patients at all, or do not adjust for the highly variable risk within these patients.
Post-Exposure Prophylaxis.
88% of PEP studies report positive effects, with an estimated reduction of 33% in the random effects meta-analysis.
0 0.5 1 1.5+ ALL STUDIES MORTALITY HOSPITALIZATION RANDOMIZED CONTROLLED TRIALS EARLY HOSP. After Exclusions ALL STUDIES All PrEP PEP Early Late HCQ for COVID-19 HCQMETA.COM MAY 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive results Total number of studiesPercentage of studies reporting positive results Probability of an equal or greater percentage of positive results from an ineffective treatmentRandom effects meta-analysis results
Early treatment 35 38 92.1% 1 in 30 million 63% improvement
RR 0.37 [0.30‑0.47]
p < 0.0001
Late treatment 156 233 67.0% 1 in 8 million 19% improvement
RR 0.81 [0.76‑0.86]
p < 0.0001
Pre‑Exposure Prophylaxis 56 72 77.8% 1 in 834 thousand 31% improvement
RR 0.69 [0.60‑0.80]
p < 0.0001
Post‑Exposure Prophylaxis 7 8 87.5% 1 in 28 33% improvement
RR 0.67 [0.53‑0.84]
p = 0.0005
All studies 249 345 72.2% 1 in 24 quadrillion 25% improvement
RR 0.75 [0.72‑0.79]
p < 0.0001
Table 1. Results by treatment stage. 6 studies report results for a subset with early treatment, these are not included in the overall results.
    
  
Figure 4. Results by treatment stage.
    
  
    
  
    
  
    
  
Figure 5. Chronological history of results by treatment stage, with the probability that the observed or greater frequency of positive results were generated by an ineffective treatment.
    
  
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Gautret 66% 0.34 [0.17-0.68] viral+ 6/20 14/16 Improvement, RR [CI] Treatment Control Huang (RCT) 92% 0.08 [0.01-1.32] no recov. 0/10 6/12 Esper 64% 0.36 [0.15-0.87] hosp. 8/412 12/224 Ashraf 68% 0.32 [0.10-1.10] death 10/77 2/5 Huang (ES) 59% 0.41 [0.26-0.64] viral time 32 (n) 37 (n) Guérin 61% 0.39 [0.02-9.06] death 0/20 1/34 Chen (RCT) 72% 0.28 [0.11-0.74] viral time 18 (n) 12 (n) Derwand 79% 0.21 [0.03-1.47] death 1/141 13/377 Mitjà (RCT) 16% 0.84 [0.35-2.03] hosp. 8/136 11/157 Skipper (RCT) 37% 0.63 [0.21-1.91] death/hosp. 5/231 8/234 Hong 65% 0.35 [0.13-0.72] viral+ 42 (n) 48 (n) Bernabeu-Wittel 59% 0.41 [0.36-0.95] death 189 (n) 83 (n) Yu (ES) 85% 0.15 [0.02-1.05] death 1/73 238/2,604 Ly 56% 0.44 [0.26-0.75] death 18/116 29/110 Ip 55% 0.45 [0.11-1.85] death 2/97 44/970 Heras 96% 0.04 [0.02-0.09] death 8/70 16/30 Kirenga 26% 0.74 [0.47-1.17] recov. time 29 (n) 27 (n) Sulaiman 64% 0.36 [0.17-0.80] death 7/1,817 54/3,724 Guisado-Vasco (ES) 67% 0.33 [0.05-1.55] death 2/65 139/542 Szente Fonseca 64% 0.36 [0.20-0.67] hosp. 25/175 89/542 Cadegiani 81% 0.19 [0.01-3.88] death 0/159 2/137 Simova 94% 0.06 [0.00-1.13] hosp. 0/33 2/5 Omrani (RCT) 12% 0.88 [0.26-2.94] hosp. 7/304 4/152 Agusti 68% 0.32 [0.06-1.67] progression 2/87 4/55 Su 85% 0.15 [0.04-0.57] progression n/a n/a Amaravadi (RCT) 60% 0.40 [0.13-1.28] no recov. 3/15 6/12 Roy 2% 0.98 [0.45-2.20] recov. time 14 (n) 15 (n) Mokhtari 70% 0.30 [0.20-0.45] death 27/7,295 287/21,464 Corradini (ES) 67% 0.33 [0.14-0.78] death 641 (n) 102 (n) Million 83% 0.17 [0.06-0.48] death 5/8,315 11/2,114 Sobngwi (RCT) 52% 0.48 [0.09-2.58] no recov. 2/95 4/92 Rodrigues (RCT) -200% 3.00 [0.13-71.6] hosp. 1/42 0/42 Sawanpanyalert 42% 0.58 [0.18-1.91] progression n/a n/a Atipornwan.. (RCT) -150% 2.50 [0.10-59.6] progression 1/60 0/30 Chechter 95% 0.05 [0.00-0.96] hosp. 0/60 3/12 Rouamba (ES) 73% 0.27 [0.09-1.02] progression 23/399 4/33 Avezum (RCT) 1% 0.99 [0.29-3.41] death 5/687 5/682 Roy-García (RCT) -100% 2.00 [0.19-20.9] progression 2/31 1/31 Tau​2 = 0.20, I​2 = 49.9%, p < 0.0001 Early treatment 63% 0.37 [0.30-0.47] 179/22,007 1,009/34,766 63% improvement Xia 38% 0.62 [0.32-1.22] viral+ 5/10 12/15 Improvement, RR [CI] Treatment Control Chen (RCT) 29% 0.71 [0.29-1.74] progression 5/15 7/15 Zhong 80% 0.20 [0.08-0.52] viral+ 5/115 17/82 Chen (RCT) 57% 0.43 [0.19-0.97] pneumonia 6/31 14/31 Barbosa -147% 2.47 [0.24-25.0] death 2/17 1/21 Tang (RCT) 21% 0.79 [0.38-1.62] viral+ 11/75 14/75 Magagnoli 11% 0.89 [0.45-1.77] death 39/148 18/163 Auld -3% 1.03 [0.67-1.57] death 33/114 29/103 Sánchez-Álvarez 46% 0.54 [0.34-0.84] death 322 (n) 53 (n) Mallat -203% 3.03 [1.11-7.69] viral time 23 (n) 11 (n) Membrillo de No.. 55% 0.45 [0.29-0.71] death 27/123 21/43 Geleris -4% 1.04 [0.82-1.32] death/int. 262/811 84/565 Alberici 43% 0.57 [0.24-1.13] death 17/72 9/22 Rosenberg -35% 1.35 [0.76-2.40] death 189/735 28/221 Shabrawishi 15% 0.85 [0.45-1.62] viral+ 12/45 15/48 Mahévas -20% 1.20 [0.40-3.30] death 9/84 8/89 Yu 60% 0.40 [0.22-0.72] death 9/48 238/502 Kim 51% 0.49 [0.28-0.87] hosp. time 22 (n) 40 (n) Singh 5% 0.95 [0.74-1.22] death 104/910 109/910 Luo 32% 0.68 [0.08-5.88] death 19 (n) 264 (n) Hraiech (ICU) 65% 0.35 [0.08-1.56] death 2/17 5/15 Ip 1% 0.99 [0.80-1.22] death 432/1,914 115/598 Goldman 22% 0.78 [0.40-1.52] death 10/109 34/288 Huang 67% 0.33 [0.19-0.57] viral time 197 (n) 176 (n) Kuderer -134% 2.34 [1.62-3.21] death 45/181 121/928 Rogado 92% 0.08 [0.00-0.87] death 1/8 7/9 RECOVERY (RCT) -9% 1.09 [0.97-1.23] death 421/1,561 790/3,155 Wang 6% 0.94 [0.75-1.19] death 1,866 (n) 5,726 (n) Luo -2% 1.02 [0.39-2.65] death 11/35 4/13 Paccoud 11% 0.89 [0.23-3.47] death 21/38 26/46 Sbidian -5% 1.05 [0.77-1.33] death 111/623 830/3,792 Faíco-Filho 81% 0.19 [0.00-8.66] viral rate 34 (n) 32 (n) Fontana 50% 0.50 [0.16-1.55] death 4/12 2/3 Bousquet 43% 0.57 [0.24-1.36] death 5/27 23/81 Lagier 59% 0.41 [0.27-0.62] death 35/3,119 58/618 Sosa-García (ICU) -11% 1.11 [0.32-3.78] death 7/38 3/18 Komissarov -25% 1.25 [0.71-2.21] viral load 26 (n) 10 (n) Mikami 47% 0.53 [0.41-0.68] death 575/2,077 231/743 Martinez-Lopez 33% 0.67 [0.39-1.14] death 47/148 9/19 Arshad 51% 0.49 [0.39-0.60] death 162/1,202 108/409 An 3% 0.97 [0.57-1.67] viral+ 31 (n) 195 (n) Rivera-Izquierdo 19% 0.81 [0.24-2.76] death 215 (n) 23 (n) Chen -29% 1.29 [0.58-2.86] viral+ 16/28 4/9 Chen (RCT) 24% 0.76 [0.20-2.84] viral+ 4/21 3/12 Cravedi -53% 1.53 [0.84-2.80] death 36/101 10/43 Lecronier (ICU) 42% 0.58 [0.27-1.24] death 9/38 9/22 Trullàs 36% 0.64 [0.39-1.07] death 20/66 16/34 Gupta -6% 1.06 [0.92-1.22] death 631/1,761 153/454 Lyngbakken (RCT) 4% 0.96 [0.06-14.6] death 1/27 1/26 McGrail -70% 1.70 [0.41-7.07] death 4/33 3/42 Krishnan 20% 0.80 [0.52-1.21] death 86/144 6/8 Bernaola 17% 0.83 [0.77-0.89] death 236/1,498 28/147 Kelly -143% 2.43 [1.06-5.56] death 23/82 6/52 Rivera -2% 1.02 [0.67-1.53] death 44/179 59/327 Cavalcanti (RCT) 16% 0.84 [0.28-2.53] death 8/331 5/173 D'Arminio Monfo.. 34% 0.66 [0.39-1.11] death 53/197 47/92 Davido 55% 0.45 [0.23-0.89] int./hosp. 12/80 13/40 Yu 83% 0.17 [0.02-1.27] progression 1/231 32/1,291 Berenguer 18% 0.82 [0.74-0.90] death 681/2,618 438/1,377 Kamran 5% 0.95 [0.34-2.69] progression 11/349 5/151 Kalligeros -67% 1.67 [0.29-9.36] death 36 (n) 72 (n) Saleemi -21% 1.21 [1.00-1.46] viral time 65 (n) 20 (n) Roomi -38% 1.38 [0.40-2.76] death 13/144 6/32 Abd-Elsalam (RCT) -20% 1.20 [0.38-3.80] death 6/97 5/97 Peters -9% 1.09 [0.81-1.47] death 419/1,596 53/353 Pinato 59% 0.41 [0.29-0.58] death 30/182