Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis
Authors: Yahya Mahamat-Saleh
Nathan Peiffer-Smadja
Anthony Guihur
Mathieu Edouard Rebeaud
Matthieu Mulot
Thibault Fiolet
Journal: Clinical Microbiology and Infection (2021)
Discipline: IMMUNOLOGY
Keywords: Azithromycin; Chloroquine; Coronavirus; Coronavirus disease 2019; Hydroxychloroquine
Affiliations: CESP (Centre for Research in Epidemiology and Population Health), Facult√© de M√©decine-Universit√© Paris-Sud, Facult√© de M√©decine-UVSQ, INSERM, Universit√© Paris Saclay, Villejuif, France; Gustave Roussy, Villejuif, France. Electronic address: ., Department of Plant Molecular Biology, Faculty of Biology and Medicine, University of Lausanne, Switzerland., Laboratory of Soil Biodiversity, Faculty of Science, University of Neuch√¢tel, Switzerland., Universit√© de Paris, IAME, INSERM, Paris, France; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College, London, UK; Infectious and Tropical Diseases Department, Bichat-Claude Bernard Hospital, AP-HP, Paris, France., CESP (Centre for Research in Epidemiology and Population Health), Facult√© de M√©decine-Universit√© Paris-Sud, Facult√© de M√©decine-UVSQ, INSERM, Universit√© Paris Saclay, Villejuif, France; Gustave Roussy, Villejuif, France.

Abstract:  Background: Hydroxychloroquine or chloroquine with or without azithromycin have been widely promoted to treat coronavirus disease 2019 (COVID-19) following early in vitro antiviral effects against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Objective: The aim of this systematic review and meta-analysis was to assess whether chloroquine or hydroxychloroquine with or without azithromycin decreased COVID-19 mortality compared with the standard of care. Data sources: PubMed, Web of Science, Embase Cochrane Library, Google Scholar and MedRxiv were searched up to 25 July 2020. Study eligibility criteria: We included published and unpublished studies comparing the mortality rate between patients treated with chloroquine or hydroxychloroquine with or without azithromycin and patients managed with standard of care. Participants: Patients ‚â?8 years old with confirmed COVID-19. Interventions: Chloroquine or hydroxychloroquine with or without azithromycin. Methods: Effect sizes were pooled using a random-effects model. Multiple subgroup analyses were conducted to assess drug safety. Results: The initial search yielded 839 articles, of which 29 met our inclusion criteria. All studies except one were conducted on hospitalized patients and evaluated the effects of hydroxychloroquine with or without azithromycin. Among the 29 articles, three were randomized controlled trials, one was a non-randomized trial and 25 were observational studies, including 11 with a critical risk of bias and 14 with a serious or moderate risk of bias. After excluding studies with critical risk of bias, the meta-analysis included 11 932 participants for the hydroxychloroquine group, 8081 for the hydroxychloroquine with azithromycin group and 12 930 for the control group. Hydroxychloroquine was not significantly associated with mortality: pooled relative risk (RR) 0.83 (95% CI 0.65-1.06, n = 17 studies) for all studies and RR = 1.09 (95% CI 0.97-1.24, n = 3 studies) for randomized controlled trials. Hydroxychloroquine with azithromycin was associated with an increased mortality (RR = 1.27; 95% CI 1.04-1.54, n = 7 studies). We found similar results with a Bayesian meta-analysis. Conclusion: Hydroxychloroquine alone was not associated with reduced mortality in hospitalized COVID-19 patients but the combination of hydroxychloroquine and azithromycin significantly increased mortality. 

Retracted: No

PubPeer comments:
 In their review, the authors precisely described the inclusion criteria in the  Study selection section, and reads:  Inclusion criteria were 1) reports containing original data with available risk estimates (Hazard Ratios, Odds Ratios, Relative Risk and or with data on the number of deaths in HCQ CQ and control groups; 2) any publication dates; 3) comparative studies with a control group without HCQ nor CQ; and 4) COVID-19 confirmed cases by RT-PCR. It is surprising that the RECOVERY clinical trial has been selected in the meta-analysis since about 10% of the patients included in the RECOVERY trial were actually tested negative, and 1% of them not tested (see below, Table 1 of the RECOVERY preprint on HCQ).  , 
 The authors also included the RCT Skipper et al. despite the fact that only about 34% subjects were RT-PCR positive (see below, subjects characteristics from Skipper et al.)  , 
 Another inconsistency in the meta-analysis. Mortality RR for Geleris et al. is reported to be 1.04 [0.82 - 1.32]. However, in the original study, this hazard ratio is actually for  Intubation or death , it is not the mortality RR (below, Table 2 from Geleris et al.)  , 
 Dear Arses Kaupi, Thank you for your comments. 1-tRECOVERY trial mostly answered the inclusion criteria since 90% were tested. This major trial was included since there is no difference in the random-effects model between: HR for all participants = 1.09 [0.96-1.23] HR for confirmed PCR participants = 1.09 [0.96-1.24] Additionally, there are 89% confirmed test in the HCQ group and 90% confirmed test in the control group since the study was randomized. There is no imbalance for PCR-testing between the groups and therefore it will have no impact on the results 2-tSkipper et al. trial was included since the authors wrote  In subgroup analyses, participants with epidemiologic linkage or probable COVID-19 by case definition only had similar responses to those with PCR-confirmed COVID-19. PCR-confirmed cases had the least effect observed. Additionally, Skipper et al. trial had a negligible weight in the model: 0.7%. Influence analysis allowed to check the effect of the exclusion of this trial. The pooled RR did not change. 3-tGeleris et al. used a composite outcome. We preferred to use an adjusted Hazard Ratio instead of using a calculated and unadjusted Relative Risk. Statistical adjustment allows to reduce confusion and selection bias, especially in observational study such as Geleris et al. Section 6.3. Cochrane training:  For non-randomized studies: when extracting data from non-randomized studies, adjusted effect estimates may be available (e.g. adjusted odds ratios from logistic regression analyses, or adjusted rate ratios from Poisson regression analyses). These are generally preferable to analyses based on summary statistics, because they usually reduce the impact of confounding. The variables that have been used for adjustment should be recorded  https: training.cochrane.org handbook current chapter-06 Kind regards, , 
 The authors reported the mortality RR from Sbidian et al. to be: HCQ : 1.05 [0.80 - 1.38] HCQ+AZI : 1.40 [1.03 - 1.90] The original Sbidian et al. reported those 95%CI to be (below Table 3 of their study): HCQ : 1.05 [0.77 - 1.33] HCQ+AZI : 1.40 [0.98 - 1.81]  , 
 Letter to editor by Matthieu MILLION, Yanis ROUSSEL, Jean-Christophe LAGIER, Didier RAOULT https: www.mediterranee-infection.com wp-content uploads 2020 04 Response-to-Fiolet-et-al-Manuscript.pdf , 
 Les tableaux sont dans le lien ci joint []http: www.francesoir.fr opinions-tribunes-societe-science-tech covid-19-une-meta-analyse-et-le-retour-des-veterans) ANALYSE : Il y a quelques jours, une u00e9tonnante mu00e9ta-analyse de plusieurs u00e9tudiants a conclu u00e0 lu2019inefficacitu00e9 de lu2019hydroxychloroquine et u00e0 la dangerositu00e9 de la bithu00e9rapie utilisu00e9e par lu2019IHU. Quels sont les critu00e8res du2019inclusion de cette mu00e9ta-analyse Les auteurs nous pru00e9cisent ces critu00e8res : voir tableau Study selection was conducted by two investigators (TF and YM) who screened the titles and the abstracts. Discrepancies were resolved by a third investigator (AG). Inclusion criteria were 1) reports containing original data with available risk estimates (Hazard Ratios, Odds Ratios, Relative Risk and or with data on the number of deaths in HCQ CQ and control groups; 2) any publication dates; 3) comparative studies with a control group without HCQ nor CQ; and 4) COVID-19 confirmed cases by RT-PCR. Studies reporting no deaths, reviews and meta-analyses, commentaries, editorials and in vitro and in vivo animal studies were excluded. Des rapports contenant des donnu00e9es originales avec des estimations de risque disponibles (Hazard Ratios, Odds Ratios, Relative Risk et ou avec des donnu00e9es sur le nombre de du00e9cu00e8s dans les groupes HCQ CQ et tu00e9moins ; Toutes les dates de publication ; Des u00e9tudes comparatives avec un groupe tu00e9moin sans HCQ ni CQ; Des cas confirmu00e9s de COVID-19 par RT-PCR. Les u00e9tudes ne rapportant aucun du00e9cu00e8s, les revues et mu00e9ta-analyses, les commentaires, les u00e9ditoriaux et les u00e9tudes animales in vitro et in vivo ont u00e9tu00e9 exclus. Figure 1 : Critu00e8res du2019inclusion de Fiolet et al. voir le lien en haut de l article Un premier problu00e8me important : des patients non testu00e9s ont u00e9tu00e9 inclus dans la mu00e9ta-analyse On peut su2019u00e9tonner de voir du00e8s le du00e9but de la lecture que des u00e9tudes aient u00e9tu00e9 intu00e9gru00e9es malgru00e9 lu2019entrave aux critu00e8res du2019inclusion relative u00e0 la PCR positive des patients. En effet, la RCT de lu2019u00e9tude RECOVERY (Horby et al.) contient les donnu00e9es de 10% de patients PCR nu00e9gatifs et 1% de patients dont le ru00e9sultat de la PCR est inconnu et la RCT de Skipper et al. avec seulement 73 212 patients testu00e9s par PCR soit 34% de patients confirmu00e9s par PCR. Or , le fait que les patients aient u00e9tu00e9 confirmu00e9s par PCR est un critu00e8re du2019inclusion de la mu00e9ta-analyse. Il est tru00e8s u00e9tonnant que ces 2 u00e9tudes aient u00e9tu00e9 incluses alors quu2019elles ne ru00e9pondent pas aux critu00e8res du2019inclusion. Figure 2: Caractu00e9risation des patients de lu2019u00e9tude Recovery Horby et al et figure 3 caractu00e9ristiques des patients de lu2019u00e9tude Skipper et al. voir le lien en haut de l article Cette mu00e9ta-analyse a inclus 18 publications avec une su00e9lection de 6 u00e9tudes en pru00e9publication 18 publications ont u00e9tu00e9 retenues pour lu2019analyse statistique de lu2019hydroxychloroquine et de la bithu00e9rapie. Une seule ne traite que de la bithu00e9rapie (Rivera et al.) On note lu2019utilisation de 6 u00e9tudes en pru00e9publication, alors quu2019une mu00e9ta-analyse publiu00e9e dans une revue su00e9rieuse ne devrait pas inclure de pru00e9publication. En effet, apru00e8s revue par les pairs, des ru00e9sultats peuvent changer ou disparau00eetre. Il semblerait que les auteurs ne veulent ru00e9pondre de plus qu u00e0 des pairs malgru00e9 le fait qu ils incluent des u00e9tudes pru00e9print non revues par les pairs. Pourquoi ce traitement diffu00e9renciu00e9? Figure 4 : Choix des u00e9tudes su00e9lectionnu00e9es pour la mu00e9ta-analyse; et Figure 5 choix de Fiolet et al. du2019inclure 1 3 de preprints parmi les u00e9tudes retenues. voir le lien en haut de l article Et qui plus est concernant lu2019u00e9tude en pru00e9publication de Sbidian et al., on peut voir dans le tableau S1 de la mu00e9ta-analyse, une grossiu00e8re erreur puisque les chiffres donnent plus de morts que de patients dans le groupe contru00f4le. Ceci nu2019est pas su00e9rieux. Entrons dans le du00e9tail de la mu00e9ta-analyse avec ces u00e9tudes choisies Les auteurs ont su00e9lectionnu00e9 18 u00e9tudes dont celle du statisticien J. Magagnoli aidu00e9 par ses collaborateurs, malheureusement non spu00e9cialistes en infectiologie. Fin avril, cette u00e9tude ru00e9trospective sur 807 vu00e9tu00e9rans amu00e9ricains a u00e9tonnu00e9 par ses ru00e9sultats nu00e9gatifs. Pourquoi avoir intu00e9gru00e9 cette u00e9tude si controversu00e9e dans cette mu00e9ta-analyse, nous ne le savons pas, mais toujours est-il que cette u00e9tude est celle qui donne les ru00e9sultats les plus du00e9favorables pour lu2019hydroxychloroquine dans cette mu00e9ta-analyse. Au printemps, cette u00e9tude a u00e9tu00e9 tru00e8s critiquu00e9e. A raison, puisque nous savons depuis avec les ru00e9sultats de RECOVERY quu2019un dosage infu00e9rieur u00e0 9,6g sur 10 jours nu2019entraine pas du2019effet nu00e9gatif significatif. Il y a donc un biais de su00e9lection. Un communiquu00e9 de lu2019IHU sur ce sujet nous informe que lu2019u00e9tude comporte 3 biais majeurs : La lymphopu00e9nie est deux fois plus u00e9levu00e9e dans les groupes traitu00e9s, alors quu2019il su2019agit du2019un important critu00e8re de su00e9vu00e9ritu00e9 de la maladie. Du2019ailleurs les auteurs u00e9crivent que la su00e9vu00e9ritu00e9 des patients est diffu00e9rente suivant les groupes. Mais la diffu00e9rence semble trop u00e9levu00e9e. Les ru00e9sultats sont en fonction de la su00e9vu00e9ritu00e9. Il nu2019y a pas de diffu00e9rence entre les groupes si le traitement est donnu00e9 avant intubation. Les ru00e9sultats globaux sont plombu00e9s par les mauvais chiffres des patients intubu00e9s. Une analyse propre u00e0 chaque stade est pru00e9fu00e9rable. Pru00e8s du2019un tiers du groupe contru00f4le a u00e9tu00e9 traitu00e9 avec de lu2019azithromycine. Figure 7 : Importance du stade de la maladie Covid-19 dans lu2019utilisation des traitements Sur cette base, la logique serait du2019exclure cette u00e9tude de la mu00e9ta-analyse. Ce problu00e8me est tru00e8s important, car le rejet de cette u00e9tude entrau00eene l inversion totale des conclusions de la mu00e9ta-analyse : les auteurs auraient du00fb ainsi conclure u00e0 lu2019existence du2019un effet positif significatif de lu2019hydroxychloroquine (21% de ru00e9duction de mortalitu00e9), et u00e0 une absence de toxicitu00e9 de la bithu00e9rapie. Si un biais de croyance peut empu00eacher certains de comprendre la partie scientifique pru00e9cu00e9dente, une erreur statistique cruciale est u00e0 pointer. En effet, mu00eame sans retirer cette u00e9tude, si cette erreur statistique est corrigu00e9e, les conclusions de la mu00e9ta-analyse su2019inversent u00e9galement. La mu00e9ta-analyse u00e9value (seulement) la mortalitu00e9. Rappelons que le but du2019une mu00e9ta-analyse est du2019obtenir une estimation robuste de la taille du2019un effet, et pour ce faire, il faut que les effets de chaque u00e9tude retenue soient eux-mu00eames estimu00e9s de la maniu00e8re moins biaisu00e9e possible. Or lu2019u00e9tude de Magagnoli et al. comporte un sous-groupe de 425 patients, qui contient les premiers patients u00e0 avoir u00e9tu00e9 hospitalisu00e9s (ceux inclus dans les 4 premiu00e8res semaines de lu2019u00e9tude). Ce sous-groupe a justement u00e9tu00e9 cru00e9u00e9 par les auteurs pour connaitre lu2019influence du traitement sur la mortalitu00e9, un critu00e8re quu2019il est essentiel du2019u00e9valuer de maniu00e8re non biaisu00e9e, sur le long terme. Sur lu2019analyse des 807 patients, certains u00e9taient encore hospitalisu00e9s ; aux issues encore inconnues au moment de lu2019u00e9tude. Cette u00e9tude comporte donc deux analyses : la premiu00e8re sur les 807 inclus, la seconde sur les 425 premiers patients u00e0 avoir u00e9tu00e9 hospitalisu00e9s et dont les auteurs connaissaient lu2019ultime issue (sortie du2019hospitalisation ou du00e9cu00e8s). Figure 9 : Un sous-groupe pour une meilleure analyse de la mortalitu00e9 sur le long terme Lu2019erreur la plus importante de la mu00e9ta-analyse est du2019utiliser les donnu00e9es incomplu00e8tes des 807 patients car pour un quart du groupe contru00f4le, lu2019issue nu2019u00e9tait pas connue au moment de lu2019analyse, alors quu2019on a beaucoup moins du2019outcomes manquants dans les groupes avec HCQ (ce qui u00e9tait attendu vu que les patients dans les groupes HCQ et HCQ+AZI u00e9taient dans des u00e9tats beaucoup plus su00e9vu00e8res u00e0 lu2019inclusion). Figure 10 : La proportion du2019outcomes inconnus est significativement diffu00e9rente entre les bras, ce qui rend lu2019utilisation du aHR sur la cohorte globale (censuru00e9e u00e0 droite) statistiquement incorrecte pour estimer un RR mortalitu00e9. Si le poids liu00e9 u00e0 la taille du groupe peut paraitre intu00e9ressant, il est faussu00e9 par une issue incertaine. Figure 11 : Bilan statistique de la mu00e9ta-analyse sur lu2019hydroxychloroquine Figure 12 : Bilan statistique de lu2019u00e9tude Magagnoli et al. sur les 807 patients Le chiffre le plus fiable, peu importe le poids de lu2019u00e9chantillon est donc celui des 425 premiers patients. On su2019aperu00e7oit alors que les HR sont totalement diffu00e9rents. Pour lu2019hydroxychloroquine le HR passe de 1,83 (1,16 ; 2,89) u00e0 0,99 (0,50 ; 1,92). Et pour la bithu00e9rapie, le HR passe de 1,31 (0,80 ; 2,14) u00e0 0,89 (0,45 ; 1,77). Les conclusions de la mu00e9ta-analyse sont inversu00e9es. Figure 13 : Bilan statistique de lu2019u00e9tude Magagnoli et al. sur la sous-cohorte non biaisu00e9e de 425 patients La mu00e9ta-analyse se sert du Risk Ratio (RR) pour comparer les u00e9tudes. Mais dans lu2019u00e9tude Magagnoli et al. la mesure est le Hazard Ratio (HR): ces deux quantitu00e9s sont diffu00e9rentes. Les auteurs de la meta-analyse transforment le HR global en RR sur la mortalitu00e9 et se sont trompu00e9s en choisissant ce HR qui est complu00e8tement trompeur car il contient une grande partie de patients hospitalisu00e9s, ce qui biaise lu2019estimation du RR ru00e9el. Le HR sur la sous-cohorte traitu00e9e dans les quatre premiu00e8res semaines (patients dont on connau00eet l issue) estime beaucoup mieux le RR sur la mortalitu00e9. A minima les auteurs devraient reprendre les intervalles liu00e9s aux 425 patients. Cette correction modifie les conclusions de lu2019u00e9tude. Magagnoli et al. reconnaissent eux-mu00eames cette limitation et ce u00ab biais de taux de risque diffu00e9rentiel u00bb du HR ajustu00e9 sur la cohorte globale incluant encore une bonne partie de patients hospitalisu00e9s (donc ne devant pas compter dans l estimation d un RR mortalitu00e9 ). Figure 14 : Extrait de Magagnoli et al. expliquant que lu2019estimation sous-cohorte est beaucoup moins biaisu00e9e. Ces critiques sont cohu00e9rentes avec le biais de su00e9lection. Il est logique que les du00e9cu00e8s dans les bras avec traitement surviennent plus tu00f4t car les patients dans les bras avec HCQ sont beaucoup plus malades u00e0 lu2019inclusion donc meurent plus rapidement. Ceci explique le fait que le HR global avec patients encore hospitalisu00e9s au moment de l u00e9tude soit biaisu00e9 en du00e9faveur des bras traitu00e9s et ne doit pas u00eatre celui retenu. Comme quoi il faut u00eatre attentif u00e0 lu2019interpru00e9tation statistique et au cherry picking (picorage) pratiquu00e9s par ces auteurs militants, convaincus depuis des mois que le Pr Raoult ment et manipule les donnu00e9es. En fait, manipuler les donnu00e9es, cu2019est savoir quu2019il y a des mu00e9dicaments adaptu00e9s aux diffu00e9rents stades de la maladie tout en mu00e9langeant sciemment des u00e9tudes, peu importe les stades, pru00e9coces ou tardifs, mais aussi les su00e9vu00e9ritu00e9s des patients (par exemple toujours pour lu2019u00e9tude Magagnoli et al., les donnu00e9es des patients traitu00e9s avant intubation sont positives, alors quu2019elles sont nu00e9gatives pour les patients traitu00e9s apru00e8s intubation). Notons u00e9galement la problu00e9matique des diffu00e9rentes doses de mu00e9dicaments utilisu00e9s dans les diffu00e9rents protocoles (RECOVERY, 9,6g du2019hydroxychloroquine en 10 jours, dont 2,4g u00e0 H24 et ce pour des patients de 40kg ou plus) ou le non-respect du protocole de su00e9curisation de lu2019IHU. On peut u00e9galement su2019u00e9tonner de lu2019utilisation dans la mu00e9ta-analyse du2019une u00e9tude qui nu2019u00e9value pas la mortalitu00e9 (Geleris et al.) alors que justement cette mu00e9ta-analyse nu2019u00e9tudie que ce point. RR, HR et OR Autre erreur statistique relevu00e9e : les auteurs confondent RR (Risk Ratio), HR (Hazard Ratio) et Odds Ratio (RR): La mu00e9ta-analyse se sert du Risque Relatif (RR) pour comparer les u00e9tudes. Mais dans lu2019u00e9tude Magagnoli et al. la mesure est le Hazard Ratio (HR, proche du RR, mais avec une dimension temporelle en plus): ces deux choses sont diffu00e9rentes. Figure 15 : Fiolet et al. du00e9cident du2019u00e9galiser les diffu00e9rents types de risque Au lieu de faire les conversions appropriu00e9es : Figure 16 : source forum de statistiques stack exchange Or lu2019u00e9galitu00e9 approximative des ratios : HR = RR = OR nu2019est mathu00e9matiquement valide que dans le cas ou00f9 le taux du2019incidence du groupe non traitu00e9 est faible (typiquement 2%). Avec des taux de mortalitu00e9 de plus de 10% u00e0 plus de 20% dans Magagnoli et al., le HR est tru00e8s diffu00e9rent du RR et il aurait fallu lu2019estimer u00e0 partir de la bonne formule. Mu00eame remarque par exemple pour lu2019u00e9tude de Rivera et al. ou00f9 ils ont confondu un OR avec un RR sans faire la conversion mathu00e9matique adu00e9quate. En fait, il est mu00eame connu que lu2019OR surestime fortement le RR quand la pru00e9valence est importante, donc cu2019est une grave erreur de ne pas avoir fait la conversion appropriu00e9e dans ce cas. Une u00e9tude avec des donnu00e9es contradictoires incluse dans la mu00e9ta-analyse On peut su2019u00e9tonner enfin quu2019une u00e9tude qui a deux analyses statistiques identiques, mais qui rapporte des ru00e9sultats diffu00e9rents soit incorporu00e9e, alors quu2019il su2019agit des mu00eames donnu00e9es, des mu00eames groupes et du mu00eame modu00e8le. Cela concerne l u00e9tude de Cavalcanti et al.. Figure 17 Donnu00e9es contradictoires de Cavalcanti et al. Versus figure 18 les graphes sont dans le lien ci joint [](http: ) Le HR pour la bithu00e9rapie est-il de 0,83 (0,25 ; 2,72) ou 0,64 (0,18 ; 2,21)? Le HR pour lu2019hydroxychloroquine est-il de 1,28 (0,43 ; 3,80) ou 1,47 (0,48 ; 4,53)? La diffu00e9rence est forte. http: www.francesoir.fr opinions-tribunes-societe-science-tech covid-19-une-meta-analyse-et-le-retour-des-veterans , 
 The authors include Rivera et Al : this study does not mention any RR for HCQ+AZI. The risk was taken from HCQ + any other exposure which includes other treatments than AZI. So this interval is misleading and the study should not have been selected. (Also Rivera has critical indication bias, even the authors admit it as a possibility, and it is indeed clearly the case when noticing that aOR for corticosteroids is 2.8, huge inconsistent difference from Recovery trial : statistical models for adjustments are False). , 
 This post comes as a supplement to the letter to the editor by Vu00e9drines et al. ([link to the letter] ) and presents the results of the reanalysis of the study by Fiolet et al. accounting for the inaccuracies listed in the letter (further details about the inaccuracies provided below). Our reanalysis of the data demonstrates that both findings presented in Fiolet et al., namely HCQ alone regimen does not reduce mortality and HCQ+AZI regimen significantly increases mortality, could not be reproduced after correcting for inaccuracies. On the contrary, we have found a significant reduction of mortality associated with HCQ alone, whereas no significant difference of mortality has been observed for HCQ+AZI regimen. **Consequently, our results lead us to challenge the authorsu2019 conclusions.** ## Corrected meta-analysis As mentioned in Vu00e9drines et al., our meticulous review of Fiolet et al. has identified four reported relative risks (RR) and 95% confidence interval (CI) that misrepresented original work. In brief (see below for more details): 1. Fiolet et al. interpreted the RR in Geleris et al. [2] as that of mortality. In the original work, this RR was actually that of the composite endpoint u201cintubation or deathu201d 2. Fiolet et al. reported the RR associated with the full cohort of patients in Magagnoli et al. [3]. This RR was affected by length-biased sampling, suffered from a large amount of missing endpoints that was unbalanced across the groups (18% patients on HCQ, 12% patients on HCQ+AZI and 26% of controls were still hospitalized). Magagnoli et al. conducted a subanalysis to account for *u201cthe issue of length-biased sampling and differential rates of right-censored observations among the groupsu201d* and provide a better estimate of the RR. Note that this misrepresentation of Magagnoli et al. affected **two** RR and corresponding CIs in the meta-analysis, one for the HCQ alone regimen and one for HCQ+AZI regimen. 3. Fiolet et al. mistakenly interpreted the RR in Rivera et al. [4] to be associated with HCQ+AZI regimen. The original work indicates this RR to be associated with u201cHCQ plus any other therapyu201d, which was not limited to AZI since about 22% of these patients were not treated with AZI. Using the same statistical framework as in Fiolet et al., we conducted a meta-analysis with corrected data. We excluded Geleris et al. from the HCQ meta-analysis, Rivera et al. from the HCQ+AZI meta-analysis, and used the bias-corrected RR and CI from Magagnoli et al. in both meta-analyses (Figure 1). Our results show substantial deviation from the original study as the effect of HCQ on mortality was found to be RR = 0.79 (95% CI 0.63 u2013 0.99) and the effect of HCQ+AZI regimen to be RR = 1.15 (95% CI 0.97 u2013 1.36) (random effects models). These findings lead us to challenge the conclusions drawn by Fiolet et al. as **we demonstrate a significant reduction of mortality associated with the use of HCQ, and failed to demonstrate significant mortality increase associated with HCQ+AZI treatment.** In the light of these elements, our reanalysis does not support the conclusion of Fiolet et al. *u201cthis meta-analysis clearly shows that hydroxychloroquine alone is not effective for the treatment of people with COVID-19 and that the combination of hydroxychloroquine and azithromycin increases the risk of mortalityu201d*. **We therefore ask the authors to clarify these points and or reconsider their conclusion.**  **Figure 1.** Results of the corrected meta-analysis. ## Details about corrected data **Geleris et al.** : The primary endpoint of the study was u201cintubation or deathu201d. Since it is impossible to dissociate the mortality risk from the intubation risk, this RR does not meet the first inclusion criterion defined by Fiolet et al. *u201cInclusion criteria were (a) reports containing original data with available risk estimates (hazard ratios (HR), odds ratio (OR), relative risk (RR)) and or with data on the number of deaths in hydroxychloroquine chloroquine and control groupsu201d*. Fiolet et al. assessed the quality of this study as u201cmoderate risk of biasu201d, the highest quality they assigned to observational studies. **Rivera et al.** : Fiolet et al. reported a RR associated with *u201cHCQ plus any other therapyu201d* as a RR associated with u201cHCQ + AZIu201d despite the fact that 22% of the patients in this group did not receive AZI. It is therefore impossible to disentangle the mortality associated specifically with HCQ + AZI from the mortality associated with HCQ + a therapy other than AZI. Fiolet et al. also assigned this study to their highest level of quality for observational studies. **Magagnoli et al.** : Fiolet et al. reported the RR and corresponding CI associated with the full cohorts of patients for the HCQ alone regimen and HCQ+AZI regimen. However, as mentioned by the authors of the original work, these RR were affected by *u201clength-biased sampling and differential rates of right-censored observations among the groupsu201d*. In other words, the outcome (death or discharge from the hospital) of a large number of patients in each group (HCQ alone, HCQ+AZI, controls) was missing due to differential admission at the hospital. Table 1 summarizes the missing outcomes in the three groups. | Group | Known outcome | Missing outcome | % of total missing | |:-------------:|:-------------:|:-----:|:---------:| | HCQ alone | 162 | 36 | 18 | | HCQ+AZI | 187 | 27 | 12 | | Controls | 292 | 103 | 26 | **Table 1.** Known outcome count, missing outcome count and percentage of missing outcome in each group of the full cohort of patients. Importantly, **the amount of missing outcomes differs critically across the groups of patients** ($chi^2$ test: $chi^2$ = 16.30, **p 0.0003**). The problem of missing outcomes falls into the u201cmissing datau201d type of bias. In ROBINS-I, Section 4.5.1, *u201cIn NRSI, data may be missing for baseline characteristics (including interventions received or baseline confounders), for **outcome measurements**, for other variables involved in the analysis or a combination of theseu201d*. According to the risk of bias guidelines (ROBINS-I, Section 4.5, Table 9), *u201ccritical differences between interventions in participants with missing datau201d* qualify a study for *u201ccritical risk of biasu201d*. Very well aware of this bias, Magagnoli et al. conducted a subanalysis to account for *u201cthe issue of length-biased sampling and differential rates of right-censored observations among the groupsu201d*. In this subanalysis, Magagnoli et al. only included the patients who were admitted during the first 4 weeks of the study. This subcohort included 425 patients and the outcome was known for all of them, therefore totally eliminating the bias induced by missing outcomes. As mentioned in the risk of bias guidelines (ROBINS-I, Section 4.5, Table 9), an analysis that *u201caddressed missing data and is likely to have removed any risk of biasu201d* qualifies for *u201clow risk of biasu201d* regarding the problem of missing data. The results of the subanalysis differed substantially from the full cohort as no significant difference in risk of death was found in the HCQ group (RR = 0.99; CI, 0.50u20131.92) or in the HCQ+AZI group (RR = 0.89; CI, 0.45u20131.77), when compared to controls. The fact that the results of the subanalysis critically deviate from the results of the full cohort provides further evidence that the results of the full cohort analysis were not robust to the presence of missing data (ROBINS-I, Section 4.5, Box 8, Signalling question 5.5). In their risk of bias assessment, Fiolet et al. noted the following bias related to missing data: *u201cMissing data (no imputation) were more present in control group (no HCQ) for immunological and inflammation baseline variablesu201d*, **completely disregarding the problem of missing outcomes**. **References** 1. Vu00e9drines et al.,  Re -Effect of hydroxychloroquine with or without azithromycin on the mortality of COVID-19 patients: a systematic review and meta-analysis . CMI (2020). 2. Geleris et al.,  Observational study of hydroxychloroquine in hospitalized patients with Covid-19. New England Journal of Medicine (2020). 3. Magagnoli et al.,  Outcomes of hydroxychloroquine usage in United States veterans hospitalized with Covid-19. Med (2020). 4. Rivera et al.,  Utilization of COVID-19 treatments and clinical outcomes among patients with cancer: a COVID-19 and Cancer Consortium (CCC19) cohort study. Cancer Discovery (2020). , 
 We answered to the comments in two letters to the editor : 1- https: www.clinicalmicrobiologyandinfection.com article S1198-743X(20)30613-3 fulltext 2- https: www.clinicalmicrobiologyandinfection.com article S1198-743X(20)30662-5 fulltext A recent editorial from Clinical Microbiology and Infection confirmed our methods and results: https: www.clinicalmicrobiologyandinfection.com article S1198-743X(20)30644-3 fulltext. I quote  *The authors of the present review made a good case showing that new studies were published; the methods of their review were sound; and the chosen outcomes were those that matter to patients * **Previous comment from #9 Limnonectes Paramacrodon is misleading**. We answered to this comment in our letter:  *Using the HR from the subgroup cohort and the new HR for Geleris et al., it leads to similar results with the pooled RR=0.82 (95%CI: 0.64-1.04) for hydroxychloroquine (HCQ) (Supplementary Figure S1) and RR=1.23 (95%CI 1-1.53) for HCQ + azithromycin (AZI) (Supplementary Figure S2) indicating no benefit from HCQ with or without AZI. We thank Vu00e9drines et al. for leaving us the opportunity to illustrate again the robustness of our conclusion.* 9 meta-analyses on hydroxychloroquine and mortality confirmed our findings, indicating no benefit for hospitalized COVID-19 patients: Juul : https: journals.plos.org plosmedicine article?id=10.1371 journal.pmed.1003293 Pathak : https: www.sciencedirect.com science article pii S1871402120303362?via%3Dihub#! Mega : https: pubmed.ncbi.nlm.nih.gov 33082891 Kashour : https: pubmed.ncbi.nlm.nih.gov 33031488 Tzu-han Yang : https: pubmed.ncbi.nlm.nih.gov 32947506 Misra : https: pubmed.ncbi.nlm.nih.gov 32810285 Elavarasi : https: pubmed.ncbi.nlm.nih.gov 32885373 Patel : https: pubmed.ncbi.nlm.nih.gov 32519281 Ullah : https: pubmed.ncbi.nlm.nih.gov 32849936 Another great systematic review wrote * There is insufficient evidence to support the effectiveness or safety of hydroxychloroquine or chloroquine for the treatment of COVID-19 in hospitalized patients. * https: www.acpjournals.org doi 10.7326 L20-0945 **Overall, RECOVERY Trial, SOLIDARITY trial, and recent meta-analyses confirmed our conclusion hydroxychloroquine was not associated with a decrease in mortality for hospitalized patients.** Axfors et al. meta-analysis, based on published and unpublished RCTs (including SOLIDARITY and RECOVERY trials) found a significant increased risk for mortality. Thus, there is no need to reconsider our conclusion. We advise Vu00e9drines et al. to consider the last published articles and evidence. Vu00e9drines et al. stated that reality is complex. We agree, and we add that