The in vitro efficacy of synthetic antimalarial agents against SARS coronavirus 2 (SARS-CoV-2) has been described [1], [2], as well as a potential in vivo effect, especially on nasopharyngeal PCR [3]. The effect on SARS-CoV-2 infection of treatment with hydroxychloroquine (HCQ), used as a disease-modifying anti-rheumatic drug (DMARD) with long-term impregnation, is unknown. The objective of this study was to evaluate the impact of HCQ, used as DMARD, on the rate of positivity of the SARS-CoV-2 nasopharyngeal PCR, and on the clinical signs of infection with SARS-CoV-2, its severity and evolution in the French iRMD (inflammatory rheumatic and musculoskeletal diseases) -COVID-19 cohort.
We conducted a multicentre retrospective study between 13 April 2020 and 29 July 2020 based on the French iRMD-COVID-19 cohort [4]. From the cohort, we selected all subjects aged over 18 years and macthed subjects with ongoing HCQ treatment with patients without at the time of infection with SARS-CoV-2. Matching criteria were age (±5 years), gender, comorbidities considered as associated with poor prognosis (at least one vs. none), an immunosuppressive treatment, and use of nasopharyngeal PCR to diagnose COVID-19. (statistical analysis in Online material, supplementary data S1).
We analysed 871 patients and finally matched 71 HCQ-treated patients with 191 controls. (Table 1 , Table 2 and Online material, supplementary data S2). The 71 HCQ-treated patients (94% women) had a mean age of 53 ± 16 years. The patients were receiving HCQ for systemic lupus (n = 42), Sjögren's syndrome (n = 6), other systemic iRMDs (n = 9) or for chronic inflammatory rheumatism: rheumatoid arthritis (n = 11), other (n = 3).
Table 1.
Patient characteristics and COVID-19 assessment of HCQ-treated patients and their matched controls.
| HCQ-treated patients (n = 71) |
Matched controlsa (n = 191) |
ASD (%) | |
|---|---|---|---|
| Age (years) | |||
| 18–54 | 41 (57.7) | 100 (52.4) | |
| 55–64 | 12 (16.9) | 33 (17.3) | |
| 65–74 | 10 (14.1) | 29 (15.2) | |
| ≥ 75 | 8 (11.3) | 29 (15.2) | |
| Mean ± SD | 53 ± 16 | 55 ± 16 | 12.6 |
| Female gender | 67 (94.4) | 179 (93.7) | 2.7 |
| Comorbidities | |||
| Respiratory disease (all) | 9 (12.7) | 33 (17.3) | 12.9 |
| Interstitial lung disease | 2 (2.8) | 9 (4.7) | 10.0 |
| COPD | 2 (2.8) | 8 (4.2) | 7.5 |
| Asthma | 5 (7.0) | 19 (9.9) | 10.4 |
| Cardiac disease (all) | 10 (14.1) | 21 (11.0) | 9.3 |
| Coronary heart diseases | 6 (8.5) | 19 (9.9) | 5.2 |
| Stroke | 4 (5.6) | 4 (2.1) | 18.4 |
| Diabetes | 5 (7.0) | 19 (9.9) | 10.4 |
| Obesityb | 16.4 | ||
| < 30 | 48 (76.2) | 112 (69.1) | |
| 30–39.9 | 13 (20.6) | 45 (27.8) | |
| ≥ 40 | 2 (3.2) | 5 (3.1) | |
| Hypertension | 19 (26.8) | 56 (29.3) | 5.7 |
| Past history of cancer | 7 (9.9) | 8 (4.2) | 22.3 |
| Chronic renal failure | 12 (16.9) | 7 (3.7) | 44.7 |
| No. of patients with at least 1 comorbidity | 60 (84.5) | 163 (85.3) | 2.3 |
| Disease history | 138 | ||
| Systemic auto-immune diseases | 55 (77.5) | 41 (21.5) | |
| Chronic inflammatory arthritis | 14 (19.7) | 117 (61.3) | |
| Vasculitis | 0 (0.0) | 23 (12.0) | |
| Auto-inflammatory diseases | 1 (1.4) | 2 (1.0) | |
| Other | 1 (1.4) | 8 (4.2) | |
| Rheumatic disease or AI2D treatments | |||
| Corticosteroid | 31 (43.7) | 61 (31.9) | 24.4 |
| Daily prednisone doses ≥ 10 mg or equivalent | 10 (32.3) | 28 (47.5) | 31.4 |
| NSAIDs | 5 (7.0) | 17 (8.9) | 6.9 |
| Colchicine | 2 (2.8) | 10 (5.2) | 12.3 |
| Methotrexate | 19 (26.8) | 48 (25.1) | 3.7 |
| Leflunomide | 1 (1.4) | 12 (6.3) | 25.6 |
| Salazopyrine | 2 (2.8) | 0 (0.0) | ND |
| Mycophenolate mofetil/mycophenolic acid | 11 (15.5) | 4 (2.1) | 48.7 |
| Azathioprine | 3 (4.2) | 1 (0.5) | ND |
| IVIG | 1 (1.4) | 3 (1.6) | ND |
| Biologics | |||
| Anti-TNF | 3 (4.2) | 46 (24.1) | 59.4 |
| Anti-IL6 | 0 (0.0) | 4 (2.1) | ND |
| Anti-CD20 | 2 (2.8) | 11 (5.8) | 14.6 |
| Anti-IL17a | 0 (0.0) | 4 (2.1) | ND |
| Anti-IL1 | 0 (0.0) | 1 (0.5) | ND |
| Abatacept | 0 (0.0) | 6 (3.1) | 25.5 |
| JAK inhibitor | 0 (0.0) | 3 (1.6) | ND |
| Belimumab | 2 (2.8) | 0 (0.0) | ND |
| Other biologics | 3 (4.2) | 7 (3.7) | 2.9 |
| No. of patients with at least 1 immunosuppressive drug | 37 (52.1) | 107 (56.0) | 7.9 |
| COVID-19 assessment | |||
| PCR test performed | 49/71 (69.0) | 137/191 (71.7) | 6.0 |
| Positive PCR test | 41/48 (85.4) | 111/137 (81.0) | 11.8 |
| CT-scan performed | 33/68 (48.5) | 75/179 (41.9) | 13.4 |
| Positive CT-scan | 28/33 (84.8) | 60/75 (80.0) | 12.8 |
COPD: chronic obstructive pulmonary disease; AI2D: autoimmune and autoinflammatory diseases; NSAIDs: non-steroidal anti-inflammatory drugs; HCQ: hydroxychloroquine; IVIG: intravenous immunoglobulins; ASD: absolute standardised difference; SD: standard deviation. Values are presented as frequency (percentage) unless otherwise indicated. ND indicates not done for binary variables with frequency < 5 in the matched sample.
Matched on age (±5 yrs), gender, comorbidity (at least one vs. none), use of immunosuppressive drugs (at least one vs. none), and use of PCR test for COVID-19.
37 missing values (n = 8 in HCQ-treated patients).
Table 2.
COVID-19 outcome in HCQ-treated patients and their matched controls.
| HCQ-treated patients (n = 71) |
Matched controlsa (n = 191) |
OR (95%CI)b | |
|---|---|---|---|
| Severity of disease outcome | |||
| Ambulatory | 39/71 (54.9) | 120/191 (62.8) | Reference category |
| Hospitalisation | 24/71 (33.8) | 53/191 (27.7) | 1.75 (0.86 to 3.56) |
| Death/intensive care unit | 8/71 (11.3) | 18/191 (9.4) | 1.94 (0.69 to 5.41) |
| Death | 4/68 (5.9) | 12/183 (6.6) | 1.18 (0.32 to 4.31) |
OR: odds ratio; CI: confidence interval; NS: non-significant; Values are presented as no. /No. (%).
Matched on age (±5 yrs), gender, comorbidity (at least one vs. none), use of immunosuppressive drugs (at least one vs. none), and use of PCR test for COVID-19.
Calculated using multinomial or binary logistic regression models adjusted for matching factors.
We report a comparative study of COVID-19 patients receiving HCQ for an iRMD versus controls, derived from a population of 871 iRMD COVID-19 patients. We found no difference in terms of either clinical symptoms, severity or proportion of positive nasopharyngal PCR in patients on HCQ compared to matched controls. Matching criteria have been described as risk factors for COVID-19-related death [5]. The present findings are derived from observational retrospective analyses, which are subject to well-known limitations. No information was available on HCQ treatment duration, dose and blood concentration, useful for verifying adherence [6]. It should be noted, however, that HCQ has a long half-life [7] and its concentration would remain within a sufficient range over the course of COVID-19. The frequency of corticosteroids treatment was higher in the HCQ group and its influence is questionable. The percentage of positive nasopharyngeal PCR was not lower in the HCQ group. Our in vivo data do not support the described potential in vitro effect [1]. Numerous studies have used HCQ to treat or to prevent COVID-19, with no significant effect [8], [9], [10]. One could argue that the targeted blood concentration of HCQ would only be reached after several weeks [6], which could have blunted its effects. A strength of our study is that the chronic impregnation of HCQ probably eliminates this putative bias.
Funding
This study was not supported by research funding, but FAI2R is funded by the French Ministry of Social Affairs and Health (ministère des solidarités et de la santé).
Disclosure of interest
The authors declare that they have no competing interest.
Acknowledgements
We thank the members of the scientific committee of the FAI2R/SFR/SNFMI/SOFREMIP/CRI/IMIDIATE consortium for their regular and extensive work and discussions: Eric Hachulla, Alexandre Belot, Hélène Maillard, Sophie Georgin–Lavialle and Christophe Richez for FAI2R; Thierry Thomas for the SFR; Jacques Pouchot and Patrice Cacoub for the SNFMI; Aurélia Carbasse and Ulrich Meinzer for the SOFREMIP; Christophe Richez for the CRI; and Bruno Fautrel for IMIDIATE.
We thank Samira Plassart, Laurent Schwarb, Muriel Herasse, Samira Plassard, Virginie Lucas, Sarahe Dehimat, Mélanie Romier, Alexandra Willems, Fanny Fernandes, and Anna Kabala played a major role in collecting the missing data of the cohort.
We thank Elodie Drumez, Julien Labreuche (biostatistician, CHU-Lille), and Thomas Barnetche (project manager, CHU-Bordeaux), for the statistical analysis and for help in the preparation of this manuscript.
Nick Barton assisted in the preparation of this manuscript, in accordance with Good Publication Practice (GPP3) guidelines.
Footnotes
Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jbspin.2021.105226.
Online material. Supplementary data
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