Comment to Sands et al. — No clinical benefit in mortality associated with hydroxychloroquine treatment in patients with COVID-19
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.028 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".