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HCQ est efficace pour la COVID-19 lorsqu’elle est utilisée tôt

Dernière mise à jour le 19/12/2020

Analyse de 172 études (version du 18 décembre 2020)

⇒La HCQ est efficace pour la COVID-19. La probabilité qu’un traitement inefficace génère des résultats aussi positifs que les 172 études réalisées à ce jour est estimée à 1 sur 4 quadrillions (p = 0.00000000000000023).

⇒Le traitement précoce est le plus efficace, 100% des études faisant état d’un effet positif et une réduction estimée à 65% de l’effet mesuré (décès, hospitalisation, etc.) en utilisant une méta-analyse des essais randomisés, RR 0.35 [0.27-0.46].

⇒91% des essais contrôlés randomisés (ECR) pour le traitement précoce, la PrEP ou la PEP font état d’effets positifs, la probabilité que cela se produise pour un traitement inefficace est de 0.0059.

⇒Il existe des preuves de biais en faveur de la publication de résultats négatifs. 88% of prospective studies report positive effects, and only 78% of retrospective studies do.

⇒Un nombre nettement plus important d’études en Amérique du Nord font état de résultats négatifs par rapport au reste du monde, p = 0.00001.

LIRE AUSSI 🔖  Les études mondiales sur #HCQ #hydroxycloroquine


Figure 1. A.

Figure 1. B

Figure 1. A. Diagramme de dispersion montrant la distribution des effets signalés dans les premières études de traitement et dans toutes les études (les lignes verticales et les cases ombragées indiquent la médiane et l’intervalle interquartile). Un traitement précoce est plus efficace. B et C. Résultats des études classés par date, la ligne indiquant la probabilité que la fréquence observée de résultats positifs soit due au hasard d’un traitement inefficace.

Introduction

Nous analysons toutes les études importantes concernant l’utilisation de la HCQ (ou CQ) pour la COVID-19. Les méthodes de recherche, les critères d’inclusion, les critères d’extraction des effets (les résultats les plus graves sont prioritaires), les réponses PRISMA, les méthodes statistiques et les résultats des études individuelles sont détaillés à l’annexe 1. Nous présentons les résultats de la méta-analyse des effets randomisés pour toutes les études, pour les études au sein de chaque étape de traitement, pour les résultats de mortalité uniquement, après exclusion des études présentant un biais critique, et pour les essais contrôlés randomisés (ECR) uniquement. Les méta-analyses typiques impliquent des critères de sélection subjectifs et une évaluation des biais, ce qui nécessite une compréhension des critères et de la précision des évaluations. Toutefois, le volume des études offre la possibilité d’une analyse supplémentaire simple et transparente permettant de détecter les effets.

Si le traitement n’était pas efficace, les effets observés seraient distribués de manière aléatoire (ou plus probablement négatifs si le traitement est nocif). Nous pouvons calculer la probabilité que le pourcentage observé de résultats positifs (ou plus) puisse être dû au hasard avec un traitement inefficace (the probability of >= k heads in n coin tosses, or the one-sided sign test / binomial test). L’analyse de publication des biais est importante et des ajustements peuvent être nécessaires s’il existe un biais vers la publication de résultats positifs. Pour la HCQ, nous trouvons des preuves d’un biais vers la publication de résultats négatifs.

Figure 2. Étapes du traitement.

Figure 2. Étapes du traitement.

La figure 2 montre les étapes d’un traitement possible pour la COVID-19. La prophylaxie pré-exposition (PrEP) consiste à prendre régulièrement des médicaments avant d’être infecté, afin de prévenir ou de minimiser l’infection. Dans la prophylaxie post-exposition (PEP), les médicaments sont pris après l’exposition mais avant l’apparition des symptômes. Le traitement précoce désigne un traitement immédiat ou peu après l’apparition des symptômes, tandis que le traitement tardif désigne un traitement plus tardif.

Résultats

La figure 3, la figure 4 et le tableau 1 présentent les résultats par étape de traitement, et la figure 5 montre une parcelle de forêt pour une méta-analyse des essais randomisés de toutes les études. L’analyse des résultats relatifs à la mortalité uniquement se trouve à l’annexe 2, et l’analyse excluant les études présentant des problèmes majeurs se trouve à l’annexe 3.

Traitement précoce : 100 % des études de traitement précoce font état d’un effet positif, avec une réduction estimée de 65 % de l’effet mesuré (décès, hospitalisation, etc.) par la méta-analyse des essais randomisés, RR 0.35 [0.27-0.46].

Traitement tardif : Les études sur le traitement tardif sont mitigées, 77 % d’entre elles montrant des effets positifs, et une réduction estimée à 27 % dans la méta-analyse des essais randomisés. Les études négatives se classent principalement dans les catégories suivantes : elles montrent des signes de facteurs de confusion importants non corrigés, y compris des facteurs de confusion par indication, l’utilisation est extrêmement tardive ou le dosage est excessivement élevé.

Prophylaxie pré-exposition : 79% des études de PrEP sont positives, avec une réduction estimée de 43% dans la méta-analyse des essais randomisés. Les études négatives sont toutes des études portant sur des patients atteints de maladies auto-immunes systémiques qui soit ne tiennent pas du tout compte du risque de base différent de ces patients, soit ne tiennent pas compte du risque très variable chez ces patients.

Prophylaxie post-exposition : 83% des études PEP font état d’effets positifs, avec une réduction estimée à 33% dans la méta-analyse des essais randomisés.

Figure 3. Results by treatment stage.

Figure 4. Results by treatment stage. Study results are ordered by date, with the line showing the probability that the observed frequency of positive results occurred due to random chance from an ineffective treatment.

Figure 5. Forest plot (random effects model). (ES) indicates the early treatment subset of a study (these are not included in the overall results).

Essais contrôlés randomisés (ECR)

Les ECR sont très précieux et minimisent les biais potentiels, mais ils ne sont ni nécessaires ni suffisants. [Concato] constatent que les études d’observation bien conçues ne surestiment pas systématiquement l’ampleur des effets du traitement par rapport aux ECR. [Anglemyer] a résumé les analyses comparant les ECR aux études observationnelles et a trouvé peu de preuves de différences significatives dans les estimations des effets. [Lee] montre que seulement 14% des lignes directrices de l’Infectious Diseases Society of America étaient basées sur des ECR. Les limites d’un ECR peuvent facilement l’emporter sur les avantages, par exemple des dosages excessifs, des délais de traitement trop longs ou un biais d’enquête sur Internet pourraient facilement avoir un effet plus important sur les résultats. Des questions éthiques peuvent empêcher la réalisation d’ECR pour des traitements dont l’efficacité est connue. Pour en savoir plus sur les problèmes liés aux ECR, voir [Deaton, Nichol]. Les résultats limités aux ECR sont présentés à la figure 6, 7 et au tableau 2. Même avec le petit nombre d’ECR à ce jour, il y a une forte indication d’efficacité. Si l’on exclut les traitements tardifs, 91 % des ECR à ce jour font état de résultats positifs.

Figure 6. Randomized Controlled Trials. The distribution of results for RCTs is similar to the distribution for all other studies.

Figure 7. RCTs excluding late treatment.

Discussion

Publication biaisée

La publication est souvent biaisée en faveur des résultats positifs, ce dont il faudrait tenir compte lors de l’analyse du pourcentage de résultats positifs. Les études qui nécessitent moins d’efforts sont considérées comme plus susceptibles de présenter un biais de publication. Les essais prospectifs qui impliquent un effort important sont susceptibles d’être publiés quel que soit le résultat, tandis que les études rétrospectives sont plus susceptibles de présenter un biais. Par exemple, les chercheurs peuvent effectuer une analyse préliminaire avec un minimum d’efforts et les résultats peuvent influencer leur décision de continuer. Les études rétrospectives offrent également plus de possibilités pour les spécificités de l’extraction des données et les ajustements pour influencer les résultats.

Pour la HCQ, 87,8% des études prospectives font état d’effets positifs, contre 77,9% des études rétrospectives, indiquant un biais vers la publication de résultats négatifs. La figure 8 montre un diagramme de dispersion des résultats des études prospectives et rétrospectives.

La figure 9 montre les résultats par région du monde, pour toutes les régions qui ont > à 5 études. Les études réalisées en Amérique du Nord sont 4,0 fois plus susceptibles de donner des résultats négatifs que les études du reste du monde combinées, 44,4 % contre 11,2 %, test z bilatéral -4,51, p = 0,00001. [Berry] a effectué une analyse indépendante qui a également montré un biais vers des résultats négatifs pour les recherches basées aux États-Unis.

Figure 8. Prospective vs. retrospective studies.

Figure 9. Results by region.

L’absence de parti pris en faveur des résultats positifs n’est pas très surprenante. Les résultats tant négatifs que positifs sont très importants étant donné l’utilisation actuelle de la HCQ pour la COVID-19 dans le monde entier, dont on peut trouver des preuves dans les études analysées ici, les protocoles gouvernementaux et les rapports de presse, par exemple [AFP, AfricaFeeds, Africanews, Afrik.com, Al Arabia, Al-bab, Anadolu Agency, Anadolu Agency (B), Archyde, Barron’s, Barron’s (B), BBC, Belayneh, A., CBS News, Challenge, Dr. Goldin, Efecto Cocuyo, Expats.cz, Face 2 Face Africa, France 24, France 24 (B), Franceinfo, Global Times, Government of China, Government of India, GulfInsider, Le Nouvel Afrik, LifeSiteNews, Medical World Nigeria, Medical Xpress, Medical Xpress (B), Middle East Eye, Ministerstva Zdravotnictví, Morocco World News, Mosaique Guinee, Nigeria News World, NPR News, Oneindia, Pan African Medical Journal, Parola, Pilot News, Pleno.News, Q Costa Rica, Rathi, Russian Government, Teller Report, The Africa Report, The Australian, The BL, The East African, The Guardian, The Indian Express, The Moscow Times, The North Africa Post, The Tico Times, Ukraine Ministry of Health Care, Ukrinform, Vanguard, Voice of America].

Nous constatons également une tendance à la publication de résultats négatifs par certaines revues et journaux, les scientifiques faisant état de difficultés à publier des résultats positifs [Boulware, Meneguesso]. Bien que 138 études montrent des résultats positifs, le New York Times, par exemple, n’a écrit que des articles pour des études qui affirment que la HCQ n’est pas efficace [The New York Times, The New York Times (B), The New York Times (C)]. Au 10 septembre 2020, le New York Times affirme toujours qu’il existe des preuves évidentes que la HCQ n’est pas efficace pour la COVID-19 [The New York Times (D)]. Au 9 octobre 2020, le United States National Institutes of Health recommande de ne pas utiliser la HCQ pour les patients hospitalisés et non hospitalisés [United States National Institutes of Health].

Détails du traitement

Nous nous concentrons ici sur la question de savoir si la HCQ est efficace ou non pour la COVID-19. Il existe des différences significatives en fonction du stade de traitement, le traitement précoce étant le plus efficace. 100% des études de traitement précoce font état d’un effet positif, avec une réduction estimée de 65% de l’effet mesuré (décès, hospitalisation, etc.) dans la méta-analyse des effets aléatoires, RR 0.35 [0.27-0.46]. De nombreux facteurs sont susceptibles d’influencer le degré d’efficacité, notamment le schéma posologique, les médicaments concomitants tels que le zinc ou l’azithromycine, le délai précis de traitement, la charge virale initiale des patients et l’état actuel des patients.

Conclusion

La HCQ est un traitement efficace contre la COVID-19. La probabilité qu’un traitement inefficace génère des résultats aussi positifs que les 172 études réalisées à ce jour est estimée à 1 sur 4 quadrillions (p = 0,00000000000000023).

Révisions

Ce document est basé sur des données, tous les graphiques et les chiffres sont générés de manière dynamique. Nous mettrons à jour le document au fur et à mesure de la publication de nouvelles études ou avec des corrections éventuelles. (Veuillez consulter les mises à jour ici).

21/10 : Nous avons ajouté des études [Dubee, Martinez-Lopez, Solh]. Nous avons reçu un rapport que le United States National Institutes of Health recommande de ne pas utiliser la HCQ pour les patients hospitalisés et non hospitalisés à partir du 9 octobre, et nous avons ajouté une référence.

22/10 : Nous avons ajouté [Anglemyer, Ñamendys-Silva]. Nous avons mis à jour la discussion sur [Axfors] pour la deuxième version de cette étude. Nous avons ajouté un tableau résumant les résultats de l’ECR.

23/10 : Nous avons ajouté [Komissarov, Lano] . La deuxième version de la préimpression de [Komissarov] comprend une comparaison avec le groupe de contrôle (non mentionné dans la première version). Nous avons mis à jour [Lyngbakken] pour utiliser le résultat de la mortalité dans la version récente du journal (non indiqué dans la préimpression).

Nous avons ajouté :
10/26: [Coll, Goenka, Synolaki].
10/28: [Arleo, Choi].
10/30: [Berenguer, Faíco-Filho].
10/31: [Fonseca, Frontera, Tehrani].
11/1: [Trullàs].
11/4: [Behera, Cadegiani].
11/8: [Dhibar].
11/9: [Self].
11/10: [Mathai].
11/12: [Simova, Simova (B)].
11/13: [Núñez-Gil, Águila-Gordo].
11/14: [Sheshah].
11/18: [Budhiraja].
11/19: [Falcone].
11/20: [Omrani].
11/23: [Revollo].
11/24: [Boari].
11/25: [Qin], and analysis restricted to mortality results.
11/27: [van Halem].
11/28: [Lambermont].
11/30: [Abdulrahman].
12/1: [Capsoni].
12/2: [Rodriguez-Gonzalez].
12/4: [Modrák, Ozturk, Peng].
12/7: [Maldonado].
12/8: [Barnabas].
12/9: [Agusti, Guglielmetti].
12/11: [Jung].
12/13: [Bielza].
12/14: [Rivera-Izquierdo, Rodriguez-Nava].
12/15: [Kalligeros, López].
12/16: [Alqassieh, Naseem, Orioli, Sosa-García, Tan].
12/17: [Signes-Costa].

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Annexe 1. Méthodes et résultats de l’étude

Nous avons effectué des recherches continues sur PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, les listes de référence d’autres études et méta-analyses, et des soumissions sur le site c19study.com, qui reçoit régulièrement des soumissions d’études positives et négatives à la publication. Les termes de recherche étaient hydroxychloroquine ou chloroquine et COVID-19 ou SARS-CoV-2, ou simplement hydroxychloroquine ou chloroquine. Toutes les études concernant l’utilisation de la HCQ ou de la CQ pour la COVID-19 qui font état d’un effet par rapport à un groupe de contrôle sont incluses dans l’analyse principale. Il s’agit d’une analyse vivante qui sera mise à jour régulièrement.

Nous avons extrait les valeurs de l’effet et les données associées de toutes les études. Si les études font état de plusieurs types d’effets, le résultat le plus grave est utilisé dans les calculs pour cette étude. Par exemple, si les effets sur la mortalité et les cas sont tous deux signalés, l’effet sur la mortalité est utilisé, il peut être différent de l’effet sur lequel l’étude s’est concentrée. Si les résultats de la mortalité sont donnés à plusieurs reprises, nous avons utilisé la date la plus récente. La mortalité seule est préférée aux résultats combinés. Les résultats avec des événements nuls dans les deux bras n’ont pas été utilisés. Le résultat clinique est considéré comme plus important que l’état du test PCR. Lorsque les résultats fournissent un ratio de chances, nous avons calculé le risque relatif lorsque cela était possible, ou nous l’avons converti en un risque relatif selon [Zhang] Les intervalles de confiance et les valeurs p ont été utilisés lorsqu’ils étaient disponibles, en utilisant des valeurs ajustées lorsqu’elles étaient fournies. Si nécessaire, la conversion entre les valeurs p et les intervalles de confiance a été effectuée [Altman, Altman (B)], et le test exact de Fisher a été utilisé pour calculer les valeurs p des données sur les événements. Si une étude séparait HCQ et HCQ+AZ, nous avons utilisé les résultats combinés qui étaient possibles, ou les résultats pour le groupe plus large. Les résultats sont tous exprimés avec un RR < 1,0 suggérant l’efficacité. La plupart des résultats représentent le risque relatif de quelque chose de négatif. Quelques études font état de temps relatifs, où les résultats sont exprimés comme le rapport entre le temps pour le groupe HCQ et le temps pour le groupe de contrôle. Une étude rapporte le taux de réduction de la charge virale, où le résultat est basé sur le pourcentage de changement du taux. Les calculs ont été effectués en Python (3.9.0) avec scipy (1.5.4), pythonmeta (1.11), numpy (1.19.4), statsmodels (0.12.1), et plotly (4.14.1). La parcelle de forêt est calculée en utilisant PythonMeta [Deng] avec le modèle à effets aléatoires DerSimonian et Laird (l’hypothèse d’effet fixe n’est pas plausible dans ce cas). Nous n’avons reçu aucun financement, cette recherche est effectuée pendant notre temps libre. Nous n’avons aucune affiliation avec des sociétés pharmaceutiques ou des partis politiques.

Pour un traitement précoce, nous avons utilisé un délai de 5 jours après les symptômes, bien qu’une période plus courte puisse être préférable. Les antiviraux ne sont généralement considérés comme efficaces que lorsqu’ils sont utilisés dans un délai plus court, par exemple 0-36 ou 0-48 heures pour l’oseltamivir, les délais plus longs n’étant pas efficaces [McLean, Treanor].

Vous trouverez ci-dessous un résumé des résultats de l’étude. Il est facile de proposer d’exclure certains articles pour diverses raisons, par exemple [Fried, Kelly, Kuderer, McGrail] font état de résultats négatifs mais ne considèrent pas eux-mêmes les résultats comme comparables – ils notent que les patients traités étaient significativement plus malades et ne font pas de corrections. Pour éviter tout biais potentiel dans l’évaluation, nous incluons actuellement toutes les études. La recherche HCQ présente un biais négatif comme indiqué ci-dessus et le fait de remédier à ce biais augmentera l’efficacité observée. Étant donné l’état des discussions scientifiques sur la HCQ, nous pensons qu’une approche conservatrice est appropriée, d’autant plus que l’efficacité est claire même avec cette approche. À titre de référence, un projet d’analyse excluant les études présentant des problèmes majeurs est présenté à l’annexe 3.

Traitement précoce
Un seul résultat par étude est inclus dans les calculs, conformément aux détails ci-dessus.
[Agusti], risk of disease progression, RR 0.32, p = 0.21, pneumonia.
[Agusti], risk of no virological cure, RR 0.68,.
[Ashraf], risk of death, RR 0.32, p = 0.15.
[Cadegiani], risk of death, RR 0.19, p = 0.21, control group 1.
[Cadegiani], risk of ventilation, RR 0.05, p < 0.001, control group 1.
[Cadegiani], risk of hospitalization, RR 0.02, p < 0.001, control group 1.
[Chen], median time to PCR-, RR 0.28, p = 0.01.
[Derwand], risk of death, RR 0.21, p = 0.12.
[Derwand], risk of hospitalization, RR 0.18, p < 0.001.
[Esper], risk of hospitalization, RR 0.36, p = 0.02.
[Fonseca], HCQ vs. nothing, RR 0.36, p < 0.001.
[Fonseca], HCQ vs. anything else, RR 0.49, p = 0.006.
[Gautret], risk of no virological cure at day 6, RR 0.34, p = 0.001.
[Guisado-Vasco], risk of death, RR 0.12, p = 0.001.
[Guérin], risk of death, RR 0.39, p = 1.00.
[Guérin], risk of no recovery, RR 0.35, p < 0.001.
[Heras], risk of death, RR 0.04, p = 0.004.
[Hong], risk of prolonged viral shedding, RR 0.35, p = 0.001.
[Huang], risk of no virological cure, RR 0.41, p < 0.001.
[Huang (B)], risk of no recovery at day 14, RR 0.08, p = 0.02.
[Huang (B)], risk of no improvement in pneumonia at day 14, RR 0.17, p = 0.22.
[Ip], risk of hospitalization, RR 0.54, p = 0.03.
[Izoulet], risk of death, RR 0.15, p < 0.001.
[Kirenga], median time to recovery, RR 0.74, p = 0.20.
[Lagier], risk of death, RR 0.41, p = 0.05.
[Ly], risk of death, RR 0.44, p = 0.02.
[Mitjà], risk of hospitalization, RR 0.75, p = 0.64.
[Mitjà], risk of no recovery, RR 0.83, p = 0.38.
[Omrani], HCQ+AZ or HCQ vs. control risk of hospitalization, RR 0.88, p = 1.00.
[Omrani], HCQ+AZ or HCQ vs. control risk of symptomatic at day 21, RR 0.74, p = 0.58.
[Omrani], HCQ+AZ or HCQ vs. control risk of Ct<=40 at day 14, RR 1.10, p = 0.13.
[Simova], risk of hospitalization, RR 0.06, p = 0.01.
[Simova], risk of viral+ at day 14, RR 0.04, p = 0.001.
[Skipper], risk of hospitalization, RR 0.48, p = 0.19.
[Skipper], risk of no recovery at day 14, RR 0.80, p = 0.21.
[Sulaiman], risk of death, RR 0.36, p = 0.01.
[Sulaiman], risk of hospitalization, RR 0.61, p = 0.001.

Traitement tardif
Un seul résultat par étude est inclus dans les calculs, conformément aux détails ci-dessus.
[Abd-Elsalam], risk of death, RR 1.20, p = 1.00.
[Abd-Elsalam], risk of no recovery at day 28, RR 0.70, p = 0.009.
[Abdulrahman], risk of death, RR 0.83, p = 1.00, PSM.
[Abdulrahman], risk of combined intubation/death, RR 1.75, p = 0.24, PSM.
[Alamdari], risk of death, RR 0.45, p = 0.03.
[Alberici], risk of death, RR 0.57, p = 0.12.
[Almazrou], risk of ventilation, RR 0.35, p = 0.16.
[Almazrou], risk of ICU admission, RR 0.79, p = 0.78.
[Alqassieh], risk of hospitalization, RR 0.82, p = 0.11.
[An], time to viral clearance, RR 0.97, p = 0.92.
[Annie], risk of death, RR 0.96, p = 0.83.
[Annie], risk of death, RR 1.21, p = 0.46.
[Aparisi], risk of death, RR 0.37, p = 0.008.
[Arshad], risk of death, RR 0.49, p = 0.009.
[Ashinyo], risk of hospitalization, RR 0.67, p = 0.03.
[Ayerbe], risk of death, RR 0.48, p < 0.001.
[Barbosa], risk of death, RR 2.47, p = 0.58.
[Berenguer], risk of death, RR 0.38, p < 0.001.
[Bernaola], risk of death, RR 0.83, p < 0.001.
[Bielza], risk of death, RR 0.78, p = 0.09.
[Boari], risk of death, RR 0.45, p < 0.001.
[Bousquet], risk of death, RR 0.57, p = 0.15.
[Budhiraja], risk of death, RR 0.35, p < 0.001.
[Capsoni], risk of ventilation, RR 0.60, p = 0.30.
[Catteau], risk of death, RR 0.68, p < 0.001.
[Cavalcanti], HCQ+HCQ/AZ risk of death, RR 0.84, p = 0.77.
[Cavalcanti], HCQ+HCQ/AZ risk of hospitalization, RR 1.28, p = 0.30.
[Chen (B)], risk of no virological cure, RR 0.76, p = 0.71.
[Chen (B)], median time to PCR-, RR 0.50, p = 0.40.
[Chen (C)], risk of no virological cure, RR 1.29, p = 0.70.
[Chen (D)], risk of no improvement in pneumonia at day 6, RR 0.43, p = 0.04.
[Chen (E)], risk of radiological progression, RR 0.71, p = 0.57.
[Chen (E)], risk of viral+ at day 7, RR 2.00, p = 1.00.
[Choi], median time to PCR-, RR 1.22, p < 0.001.
[Coll], risk of death, RR 0.54, p < 0.001.
[Cravedi], risk of death, RR 1.53, p = 0.17.
[D’Arminio Monforte], risk of death, RR 0.66, p = 0.12.
[Davido], risk of combined intubation/hospitalization, RR 0.45, p = 0.04.
[Di Castelnuovo], risk of death, RR 0.70, p < 0.001.
[DISCOVERY], 29 day mortality estimated from graph, RR 0.69, p = 0.35.
[DISCOVERY], risk of 7-point scale status, RR 0.83, p = 0.40.
[Dubee], mortality at day 28, RR 0.54, p = 0.21.
[Dubee], combined mortality/intubation at day 28, RR 0.74, p = 0.82.
[Dubee], HCQ+AZ from day 0 subgroup combined mortality/intubation, RR 0.15, p = 0.21.
[Dubernet], risk of ICU admission, RR 0.12, p = 0.008.
[Falcone], risk of death, RR 0.35, p = 0.20, PSM.
[Falcone], risk of death, RR 0.75, p = 0.36, multivariate Cox regression.
[Falcone], risk of death, RR 0.43, p < 0.001, univariate Cox regression.
[Faíco-Filho], Δt7-12 ΔCt improvement, RR 0.19, p = 0.40.
[Faíco-Filho], Δt<7 ΔCt improvement, RR 0.76, p = 0.36. [Faíco-Filho], Δt>12 ΔCt improvement, RR 1.15, p = 0.52.
[Fontana], risk of death, RR 0.50, p = 0.53.
[Fried], risk of death, RR 1.27, p < 0.001.
[Frontera], PSM, RR 0.63, p = 0.01.
[Frontera], regression, RR 0.76, p = 0.02.
[Geleris], risk of combined intubation/death, RR 1.04, p = 0.76.
[Goldman], risk of death, RR 0.78, p = 0.46.
[Gonzalez], risk of death, RR 0.73, p = 0.06.
[Guglielmetti], risk of death, RR 0.65, p = 0.22, multivariable Cox.
[Guisado-Vasco (B)], risk of death, RR 0.80, p = 0.36.
[Gupta], risk of death, RR 1.06, p = 0.41.
[Heberto], risk of death, RR 0.46, p = 0.04.
[Heberto], risk of ventilation, RR 0.34, p = 0.008.
[Huang (C)], risk of no virological cure, RR 0.33, p < 0.001.
[Ip (B)], risk of death, RR 0.99, p = 0.93.
[Kalligeros], risk of death, RR 1.67, p = 0.57.
[Kamran], risk of disease progression, RR 0.95, p = 1.00.
[Kamran], with comorbidities, RR 0.45, p = 0.30.
[Kamran], risk of viral+ at day 14, RR 1.10, p = 0.52.
[Kelly], risk of death, RR 2.43, p = 0.03.
[Kim], risk of hospitalization, RR 0.49, p = 0.01.
[Kim], risk of no virological cure, RR 0.44, p = 0.005.
[Komissarov], risk of viral load, RR 1.25, p = 0.45.
[Kuderer], risk of death, RR 2.34, p < 0.001, HCQ+AZ.
[Lambermont], risk of death, RR 0.68, p = 0.46.
[Lammers], risk of combined death/ICU, RR 0.68, p = 0.02.
[Lano], risk of death, RR 0.67, p = 0.28.
[Lano], risk of combined death/ICU, RR 0.61, p = 0.23.
[Lano], not requiring O2 on diagnosis, RR 0.31, p = 0.11.
[Lauriola], risk of death, RR 0.27, p < 0.001.
[Lecronier], risk of death, RR 0.58, p = 0.24, HCQ vs. control.
[Lecronier], risk of treatment escalation, RR 0.94, p = 0.73, HCQ vs. control.
[Lecronier], risk of viral+ at day 7, RR 0.85, p = 0.61, HCQ vs. control.
[Luo], risk of death, RR 1.02, p = 0.99.
[Lyngbakken], risk of death, RR 0.96, p = 1.00.
[Lyngbakken], improvement in viral load reduction rate, RR 0.29, p = 0.51.
[López], risk of disease progression, RR 0.36, p = 0.02.
[Magagnoli], HCQ+AZ w/dispositions, RR 0.89, p = 0.74.
[Magagnoli], HCQ w/dispositions, RR 0.99, p = 0.98.
[Magagnoli], risk of death, RR 1.31, p = 0.28, HCQ+AZ.
[Magagnoli], risk of death, RR 1.83, p = 0.009, HCQ.
[Mahévas], risk of death, RR 1.20, p = 0.75.
[Maldonado], risk of death, RR 0.09, p = 0.17.
[Martinez-Lopez], risk of death, RR 0.67, p = 0.20.
[McGrail], risk of death, RR 1.70, p = 0.69.
[Membrillo de Novales], risk of death, RR 0.45, p = 0.002.
[Mikami], risk of death, RR 0.53, p < 0.001.
[Modrák], risk of death, RR 0.41, p = 0.04, Cox (single).
[Nachega], risk of death, RR 0.72, p = 0.17.
[Nachega], risk of no improvement, RR 0.74, p = 0.13.
[Naseem], risk of death, RR 0.67, p = 0.34, multivariate Cox.
[Núñez-Gil], risk of death, RR 0.92, p = 0.005.
[Orioli], risk of death, RR 0.87, p = 1.00.
[Ozturk], risk of death, RR 0.56, p = 0.14, CQ/HCQ.
[Paccoud], risk of death, RR 0.89, p = 0.88.
[Peng], risk of disease progression, RR 0.89, p = 0.63, CQ/HCQ risk of AKI.
[Peters], risk of death, RR 1.09, p = 0.57.
[Pinato], risk of death, RR 0.41, p < 0.001.
[Qin], risk of death, RR 0.66, p = 0.61.
[RECOVERY], risk of death, RR 1.09, p = 0.15.
[Rivera], risk of death, RR 1.02, p = 0.90.
[Rivera-Izquierdo], risk of death, RR 0.81, p = 0.75.
[Rodriguez-Gonzalez], risk of death, RR 0.77, p = 0.26.
[Rodriguez-Nava], risk of death, RR 1.06, p = 0.77, unadjusted.
[Roomi], risk of death, RR 1.38, p = 0.54.
[Rosenberg], risk of death, RR 1.35, p = 0.31.
[Saleemi], median time to PCR-, RR 1.21, p < 0.05.
[Sbidian], risk of death, RR 1.05, p = 0.74, whole population HCQ AIPTW adjusted.
[Sbidian], risk of no hospital discharge, RR 0.80, p = 0.002, whole population HCQ AIPTW adjusted.
[Self], risk of death, RR 0.93, p = 0.84.
[Serrano], risk of death, RR 0.57, p = 0.14.
[Shabrawishi], risk of no virological cure at day 5, RR 0.85, p = 0.66.
[Sheshah], risk of death, RR 0.20, p < 0.001.
[Shoaibi], risk of death, RR 0.85, p < 0.001.
[Signes-Costa], risk of death, RR 0.53, p < 0.001.
[Singh], risk of death, RR 0.95, p = 0.72.
[Singh], risk of ventilation, RR 0.81, p = 0.26.
[Solh], risk of death, RR 1.18, p = 0.17.
[SOLIDARITY], risk of death, RR 1.19, p = 0.23.
[Sosa-García], risk of death, RR 1.11, p = 1.00.
[Soto-Becerra], risk of death, RR 0.82, p < 0.001, day 54 (last day available) weighted KM.
[Soto-Becerra], risk of death, RR 1.84, p = 0.02, day 30.
[Synolaki], risk of death, RR 0.76, p = 0.27.
[Sánchez-Álvarez], risk of death, RR 0.54, p = 0.005.
[Tan], risk of hospitalization, RR 0.65, p = 0.04.
[Tang], risk of no virological cure at day 21, RR 0.79, p = 0.51.
[Tehrani], risk of death, RR 0.87, p = 0.63.
[Trullàs], risk of death, RR 0.64, p = 0.12.
[Ulrich], risk of death, RR 1.06, p = 1.00.
[van Halem], risk of death, RR 0.68, p = 0.05.
[Wang], risk of death, RR 0.94, p = 0.63.
[Xia], risk of no virological cure, RR 0.62, p = 0.17.
[Yu], risk of death, RR 0.40, p = 0.002.
[Zhong], risk of no virological cure at day 10, RR 0.20, p < 0.001.
[Águila-Gordo], risk of death, RR 0.33, p = 0.10.
[Ñamendys-Silva], HCQ+AZ vs. neither HCQ or CQ, RR 0.68, p = 0.18.
[Ñamendys-Silva], CQ vs. neither HCQ or CQ, RR 0.63, p = 0.09.
[Ñamendys-Silva], HCQ+AZ or CQ, RR 0.66, p = 0.006.

Prophylaxie pré-exposition
Un seul résultat par étude est inclus dans les calculs, conformément aux détails ci-dessus.
[Abella], risk of COVID-19 case, RR 0.95, p = 1.00.
[Arleo], all patients, RR 0.50, p = 0.67.
[Arleo], inpatients, RR 0.48, p = 0.64.
[Behera], risk of COVID-19 case, RR 0.72, p = 0.29.
[Bhattacharya], risk of COVID-19 case, RR 0.19, p = 0.001.
[Cassione], risk of COVID-19 case, RR 1.50, p = 0.59.
[Chatterjee], full course vs. unused risk of COVID-19 case, RR 0.33, p < 0.001.
[de la Iglesia], risk of hospitalization, RR 1.50, p = 1.00.
[de la Iglesia], suspected COVID-19, RR 1.43, p = 0.15.
[de la Iglesia], confirmed COVID-19, RR 0.92, p = 0.84.
[Ferreira], risk of COVID-19 case, RR 0.53, p < 0.001.
[Ferri], risk of COVID-19 case, RR 0.37, p = 0.01.
[Gendebien], risk of COVID-19 case, RR 0.96, p = 0.93.
[Gendelman], risk of COVID-19 case, RR 0.92, p = 0.88.
[Gentry], risk of death, RR 0.13, p = 0.10.
[Gentry], risk of COVID-19 case, RR 0.79, p = 0.27.
[Gianfrancesco], risk of hospitalization, RR 0.97, p = 0.82.
[Goenka], risk of IgG positive, RR 0.13, p = 0.03.
[Grau-Pujol], risk of COVID-19 case, RR 0.32, p = 0.47.
[Huang (D)], risk of hospitalization, RR 0.20, p < 0.001.
[Huh], risk of COVID-19 case, RR 1.48, p = 0.09.
[Jung], risk of death, RR 0.41, p = 1.00.
[Jung], risk of COVID-19 case, RR 1.13, p = 0.86.
[Khurana], risk of COVID-19 case, RR 0.49, p = 0.02.
[Konig], risk of hospitalization, RR 0.97, p = 0.88.
[Laplana], risk of COVID-19 case, RR 1.56, p = 0.24.
[Macias], risk of hospitalization, RR 0.74, p = 1.00.
[Macias], risk of COVID-19 case, RR 1.49, p = 0.53.
[Mathai], risk of COVID-19 case, RR 0.10, p < 0.001.
[Mathai], risk of COVID-19 case, RR 0.12, p < 0.001, symptomatic.
[Mitchell], risk of death, RR 0.01, p < 0.001.
[Rajasingham], risk of hospitalization, RR 0.50, p = 1.00.
[Rajasingham], risk of COVID-19 case, RR 0.73, p = 0.12.
[Rentsch], risk of death, RR 1.03, p = 0.83.
[Revollo], PSM risk of PCR+, RR 0.77, p = 0.52.
[Revollo], PSM risk of IgG+, RR 1.43, p = 0.42.
[Singer], risk of COVID-19 case, RR 1.09, p = 0.62.
[Zhong (B)], risk of COVID-19 case, RR 0.09, p = 0.04.

Prophylaxie post-exposition
Un seul résultat par étude est inclus dans les calculs, conformément aux détails ci-dessus.
[Barnabas], risk of hospitalization, RR 1.04, p = 1.00.
[Barnabas], day 14 symptomatic mITT PCR+ AIM, RR 1.27, p = 0.33.
[Barnabas], day 14 symptomatic mITT PCR+ IDWeek, RR 1.23, p = 0.41.
[Barnabas], day 14 PCR+ mITT AIM, RR 1.10, p = 0.66.
[Barnabas], day 14 PCR+ mITT IDWeek, RR 0.99, p = 0.97.
[Barnabas], day 14 PCR+ ITT AIM, RR 0.81, p = 0.23.
[Boulware (B)], risk of COVID-19 case, RR 0.83, p = 0.35.
[Boulware (B)], probable COVID-19 case, RR 0.75, p = 0.22.
[Dhibar], risk of COVID-19 case, RR 0.59, p = 0.03.
[Dhibar], risk of COVID-19 case, RR 0.50, p = 0.04, PCR+.
[Dhibar], risk of symptomatic case, RR 0.56, p = 0.21.
[Mitjà (B)], risk of death, RR 0.68, p = 0.58.
[Mitjà (B)], baseline pcr- risk of cases, RR 0.68, p = 0.27.
[Polat], risk of COVID-19 case, RR 0.43, p = 0.03.
[Simova (B)], risk of COVID-19 case, RR 0.07, p = 0.01.

Annexe 2. Analyse des résultats de la mortalité

La figure 10 montre une parcelle de forêt limitée aux seuls résultats de mortalité.

Figure 10. Forest plot (random effects model) for mortality results only. (ES) indicates the early treatment subset of a study (these are not included in the overall results).

Annexe 3. Analyse avec exclusions

De nombreuses méta-analyses ont été rédigées pour la HCQ, dont la plupart sont devenues quelque peu obsolètes en raison du flux continu d’études plus récentes. Parmi les analyses récentes dont les conclusions sont positives, on peut citer [IHU Marseille], qui considère les biais significatifs résultant de la compréhension de chaque essai, et [Garcia-Albeniz, Ladapo, Prodromos], qui se concentrent sur les études d’utilisation précoce ou prophylactique.

Les méta-analyses faisant état de conclusions négatives se concentrent sur les études de traitement tardives, ont tendance à ne pas tenir compte du retard de traitement, ont tendance à suivre des évaluations basées sur des formules qui négligent les principaux problèmes des différentes études, et finissent par avoir une pondération disproportionnée par rapport à une analyse raisonnée de la contribution de chaque étude. Par exemple, [Axfors] attribue 87% de valeur à un seul essai, l’essai RECOVERY [RECOVERY], produisant ainsi le même résultat. Cependant, l’essai RECOVERY est peut-être l’étude la plus biaisée de toutes celles qu’ils ont incluses, en raison du dosage excessif utilisé, proche du niveau montré comme très dangereux dans [Borba] (OR 2.8), et avec des patients en phase terminale extrêmement malades (60% nécessitant de l’oxygène, 17% une ventilation/ECMO, et un taux de mortalité très élevé dans les deux bras). Il y a peu de raisons de penser que les résultats de cet essai sont applicables à des dosages plus typiques ou à un traitement plus précoce (10/22 : la deuxième version de cette étude publiée 10/22 attribue 74% à RECOVERY et 15% à SOLIDARITY [SOLIDARITY] , qui est le seul autre essai utilisant un dosage excessif similaire).

Nous incluons toutes les études dans l’analyse principale, mais plusieurs d’entre elles présentent des problèmes majeurs qui pourraient modifier sensiblement les résultats. Nous présentons ici un projet d’analyse excluant les études présentant des problèmes importants, notamment l’indication de différences significatives entre groupes non ajustées ou la confusion par indication, l’utilisation à un stade extrêmement avancé >14 jours après les symptômes ou >50% sur l’oxygène au départ, la fourniture de très peu de détails, les dosages excessifs qui se sont révélés dangereux, les problèmes importants avec des ajustements qui pourraient raisonnablement faire des différences substantielles, et le recours à la PCR qui peut être inexacte et moins indicative de la gravité que les symptômes. Nous vous invitons à nous faire part de vos commentaires sur les améliorations ou les corrections à apporter à ce sujet. Les études exclues sont les suivantes, et la parcelle de forêt qui en résulte est illustrée à la figure 11.

[Alamdari], substantial unadjusted confounding by indication.
[An], results only for PCR status which may be significantly different to symptoms.
[Annie], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Barbosa], excessive unadjusted differences between groups.
[Budhiraja], excessive unadjusted differences between groups.
[Cassione], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Chen], results only for PCR status which may be significantly different to symptoms.
[Chen (B)], results only for PCR status which may be significantly different to symptoms.
[Chen (C)], results only for PCR status which may be significantly different to symptoms.
[Choi], excessive unadjusted differences between groups.
[Cravedi], substantial unadjusted confounding by indication.
[de la Iglesia], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Fried], excessive unadjusted differences between groups, substantial unadjusted confounding by indication.
[Gautret], excessive unadjusted differences between groups, results only for PCR status which may be significantly different to symptoms.
[Geleris], significant issues found with adjustments.
[Gendebien], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Gendelman], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Gianfrancesco], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Gupta], >50% on oxygen/ventilation at baseline.
[Hong], results only for PCR status which may be significantly different to symptoms.
[Huang (D)], significant unadjusted confounding possible.
[Huang], results only for PCR status which may be significantly different to symptoms.
[Huang (C)], results only for PCR status which may be significantly different to symptoms.
[Huh], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Izoulet], excessive unadjusted differences between groups.
[Kamran], excessive unadjusted differences between groups.
[Kelly], substantial unadjusted confounding by indication.
[Konig], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Kuderer], substantial unadjusted confounding by indication.
[Laplana], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Lecronier], >50% on oxygen/ventilation at baseline.
[Luo], substantial unadjusted confounding by indication.
[Lyngbakken], results only for PCR status which may be significantly different to symptoms.
[Macias], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[McGrail], excessive unadjusted differences between groups.
[Mitchell], excessive unadjusted differences between groups.
[Peters], excessive unadjusted differences between groups.
[RECOVERY], excessive dosage, results do not apply to typical dosages.
[Rentsch], not fully adjusting for the baseline risk differences within systemic autoimmune patients, medication adherence unknown and may significantly change results.
[Rodriguez-Nava], substantial unadjusted confounding by indication, excessive unadjusted differences between groups.
[Roomi], substantial unadjusted confounding by indication.
[Saleemi], results only for PCR status which may be significantly different to symptoms, substantial unadjusted confounding by indication.
[Sbidian], significant issues found with adjustments.
[Shabrawishi], results only for PCR status which may be significantly different to symptoms.
[Singer], not fully adjusting for the baseline risk differences within systemic autoimmune patients.
[Singh], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Solh], >50% on oxygen/ventilation at baseline, substantial unadjusted confounding by indication.
[SOLIDARITY], excessive dosage, results do not apply to typical dosages, >50% on oxygen/ventilation at baseline.
[Sosa-García], >50% on oxygen/ventilation at baseline, substantial unadjusted confounding by indication.
[Soto-Becerra], substantial unadjusted confounding by indication, includes PCR+ patients that may be asymptomatic for COVID-19 but in hospital for other reasons.
[Tang], results only for PCR status which may be significantly different to symptoms.
[Tehrani], substantial unadjusted confounding by indication.
[Ulrich], >50% on oxygen/ventilation at baseline.
[Wang], confounding by indication is likely and adjustments do not consider COVID-19 severity.
[Xia], detail too minimal.
[Zhong], results only for PCR status which may be significantly different to symptoms.

Figure 11. Forest plot (random effects model) excluding studies with significant issues. (ES) indicates the early treatment subset of a study (these are not included in the overall results).

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