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0 0.5 1 1.5 2+ Recovery time 25% Improvement Relative Risk Hospitalization time -13% Viral clearance time 0% HCQ for COVID-19  Hong et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? PSM retrospective 30 patients in South Korea (Feb - Apr 2020) Faster recovery with HCQ (not stat. sig., p=0.45) Hong et al., PLOS ONE, May 2022 Favors HCQ Favors control

Use of combined treatment of 3rd-generation cephalosporin, azithromycin and antiviral agents on moderate SARs-CoV-2 patients in South Korea: A retrospective cohort study

Hong et al., PLOS ONE, doi:10.1371/journal.pone.0267645
May 2022  
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HCQ for COVID-19
1st treatment shown to reduce risk in March 2020
*, now known with p < 0.00000000001 from 422 studies, recognized in 42 countries.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
4,000+ studies for 60+ treatments.
Retrospective 25 hospitalized patients treated with cephalosporin, azithromycin, and HCQ, and 217 SOC patients in South Korea, reporting no significant differences. 5 patients receiving lopinavir/ritonavir and HCQ >5 days were excluded for unknown reasons. HCQ was typically initiated based on progression or side effects from another treatment. Conflicting results are reported. Table 2 indicates 15 CA/HCQ patients after matching, while Table S2 shows 25, and the Table 3 count is blank. S2 appears to incorrectly show before matching results, and the after matching results are missing in Table 3. 200mg HCQ bid.
Viral load measured by PCR may not accurately reflect infectious virus measured by viral culture. Porter show that viral load early in infection was correlated with infectious virus, but viral load late in infection could be high even with low or undetectable infectious virus. Assessing viral load later in infection may underestimate reductions in infectious virus with treatment.
recovery time, 24.9% lower, HR 0.75, p = 0.45, treatment 15, control 15, inverted to make HR<1 favor treatment, propensity score matching.
hospitalization time, 12.7% higher, HR 1.13, p = 0.75, treatment 15, control 15, inverted to make HR<1 favor treatment, propensity score matching.
viral clearance time, 0.5% lower, HR 1.00, p = 0.99, treatment 15, control 15, inverted to make HR<1 favor treatment, propensity score matching.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Hong et al., 4 May 2022, retrospective, South Korea, peer-reviewed, 11 authors, study period 28 February, 2020 - 28 April, 2020. Contact:,,
This PaperHCQAll
Use of combined treatment of 3rd-generation cephalosporin, azithromycin and antiviral agents on moderate SARs-CoV-2 patients in South Korea: A retrospective cohort study
Wooyoung Hong, Yu-Kyung Park, Bong-Ok Kim, Sung Kyu Park, Jiin Shin, Soon-Pyo Jang, Hea-Woon Park, Wonjong Yang, Joonyoung Jang, Soon-Woo Jang, Tae-Ho Hwang
PLOS ONE, doi:10.1371/journal.pone.0267645
Objectives To assess efficacy and safety of the combined treatment of antibiotics (3rd-generation cephalosporin and azithromycin) and antiviral agents (lopinavir/ritonavir or hydroxychloroquine) on moderate COVID-19 patients in South Korea. Methods A retrospective cohort study of the 358 laboratory-confirmed SARS-CoV-2 (COVID-19) patients was conducted. 299 patients met inclusion criteria for analysis. Propensity score matching (PSM) and Cox regression method were used to control and adjust for confounding factors. Mild to moderate COVID-19 patients were managed with either CA/LoP (cephalosporin, azithromycin, and lopinavir/ritonavir) (n = 57), CA/HQ (cephalosporin, azithromycin, and hydroxychloroquine) (n = 25) or standard supportive care (n = 217). We analyzed the association between treatment group and standard supportive group in terms of three endpoints: time to symptom resolution, time to viral clearance, and hospital stay duration. Using propensity-score matching analysis, three rounds of propensity-matching analysis were performed to balance baseline characteristics among three cohorts. Results Kaplan-Meier curves fitted using propensity score-matched data revealed no significant differences on time to symptom resolution, time to viral clearance, hospital stay duration
Supporting information S1 Author Contributions Conceptualization: Wooyoung Hong, Soon-Woo Jang. Data curation: Sung Kyu Park, Soon-Woo Jang. Formal analysis: Wooyoung Hong. Funding acquisition: Tae-Ho Hwang. Investigation: Yu-Kyung Park, Bong-Ok Kim, Sung Kyu Park, Hea-Woon Park, Wonjong Yang, Joonyoung Jang, Soon-Woo Jang. Methodology: Wooyoung Hong. Project administration: Soon-Woo Jang, Tae-Ho Hwang. Resources: Yu-Kyung Park, Bong-Ok Kim, Sung Kyu Park, Hea-Woon Park, Wonjong Yang, Joonyoung Jang. Software: Wooyoung Hong. Supervision: Soon-Woo Jang, Tae-Ho Hwang. Validation: Wooyoung Hong, Soon-Woo Jang. Visualization: Soon-Pyo Jang. Writing -original draft: Wooyoung Hong, Soon-Woo Jang. Writing -review & editing: Wooyoung Hong, Jiin Shin.
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Late treatment
is less effective
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