Comment to Sands et al. — No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19
Notice bibliographique
Résumé
I read with interest the retrospective analysis of Sands et al., 2020Sands K. Wenzel R. McLean L. Korwek K. Roach J. Miller K. et al.No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19.Int J Infect Dis. 2021; 104: 34-40https://doi.org/10.1016/j.ijid.2020.12.060Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar) on the effects of hydroxychloroquine (HCQ) treatment in 1669 COVID-19 patients. The principal outcome was the association between this treatment and in-hospital deaths. The authors concluded that a significant increase in mortality was associated with the treatment. However, this study contains many biases and limitations which must be reviewed before drawing this conclusion. Data were collected at 21 health centers, so the dosage and duration of HCQ use can vary. However, the authors have not mentioned this issue. Figure 1 (Sands et al., 2020Sands K. Wenzel R. McLean L. Korwek K. Roach J. Miller K. et al.No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19.Int J Infect Dis. 2021; 104: 34-40https://doi.org/10.1016/j.ijid.2020.12.060Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar) showed that many patients started treatment at >3 days after admission to the hospital. In addition, the duration of treatment is very short (median = 5 days, range = 1–12 days). A total of 130/973 (13.4%) patients received HCQ at an advanced stage of disease (i.e. requiring intensive care and/or ventilator support), and 40/101 (39.6%) patients who died received the initial treatment at this stage; this suggests that anti-SARS-CoV-2 treatment must be introduced early. Their results showed a significant difference in mortality between 3 groups of patients: 7.2% of patients who received the first dose while at mild severity, and 21.6% and 50.0% at moderate and severe severity, respectively, P-value < 0.0000 (Chi2 test using OpenEpi [http://www.openepi.com/Menu/OE_Menu.htm]). Gautret et al. (Gautret et al., 2020Gautret P. Lagier J.C. Parola P. Hoang V.T. Meddeb L. Sevestre J. et al.Clinical and microbiological effect of a combination of hydroxychloroquine and azithromycin in 80 COVID-19 patients with at least a six-day follow up: a pilot observational study.Travel Med Infect Dis. 2020; 34: 101663Crossref PubMed Scopus (506) Google Scholar, Lagier et al., 2020Lagier J.C. Million M. Gautret P. Colson P. Cortaredona S. Giraud-Gatineau A. et al.Outcomes of 3,737 COVID-19 patients treated with hydroxychloroquine/azithromycin and other regimens in Marseille, France: a retrospective analysis.Travel Med Infect Dis. 2020; 36: 101791https://doi.org/10.1016/j.tmaid.2020.101791Crossref PubMed Scopus (186) Google Scholar) showed that patients were not considered to be under treatment if they received HCQ for <3 days. Several biomarkers which may be potential risk factors for predicting severe and fatal COVID-19 were not evaluated, such as white blood cell, lymphocyte and platelet count, d-dimer >1 μg/mL, and high lactate dehydrogenase level and serum ferritin (Henry et al., 2020Henry B.M. de Oliveira M.H.S. Benoit S. Plebani M. Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis.Clin Chem Lab Med. 2020; 58: 1021-1028https://doi.org/10.1515/cclm-2020-0369Crossref PubMed Scopus (1200) Google Scholar, Li et al., 2020Li X. Xu S. Yu M. Wang K. Tao Y. Zhou Y. et al.Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan.J Allergy Clin Immunol. 2020; 146: 110-118https://doi.org/10.1016/j.jaci.2020.04.006Abstract Full Text Full Text PDF PubMed Scopus (1438) Google Scholar). In their data, one BMI measurement was recorded as 2046.3 kg/m2. This patient should be excluded from the study because of the unrealistic nature of this value. The authors declared that they had imputed the BMI at 29.55 kg/m2 (as the median); however, in their analysis, the BMI ranged from 13.8 to 2050 (Tables 1 and 3) (Sands et al., 2020Sands K. Wenzel R. McLean L. Korwek K. Roach J. Miller K. et al.No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19.Int J Infect Dis. 2021; 104: 34-40https://doi.org/10.1016/j.ijid.2020.12.060Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). In multivariable logistic regression analysis, the BMI was introduced, and the odds ratio in both models was 1.00, 95% CI = [1.00–1.00], suggesting that their final results were not reliable. In conclusion, the quality of their work was insufficient to conclude the effect of HCQ on mortality in COVID-19 patients. None. No funding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,094 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,028 | 0,029 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».