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0 0.5 1 1.5 2+ Mortality 59% Improvement Relative Risk c19hcq.org Modrák et al. HCQ for COVID-19 LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 213 patients in Czech Republic Lower mortality with HCQ (p=0.038) Modrák et al., medRxiv, doi:10.1101/2020.12.03.20239863 Favors HCQ Favors control
Detailed disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic: An exploratory analysis
Modrák et al., medRxiv, doi:10.1101/2020.12.03.20239863 (Preprint)
Modrák et al., Detailed disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic: An exploratory.., medRxiv, doi:10.1101/2020.12.03.20239863 (Preprint)
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Retrospective 213 hospitalized patients in Czech Republic showing lower mortality with HCQ. Subject to confounding by indication.
risk of death, 59.0% lower, RR 0.41, p = 0.04, treatment 108, control 105, Cox (single).
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Modrák et al., 4 Dec 2020, retrospective, Czech Republic, preprint, 26 authors.
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Abstract: medRxiv preprint doi: https://doi.org/10.1101/2020.12.03.20239863; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Detailed disease progression of 213 patients hospitalized with Covid-19 in the Czech Republic: An exploratory analysis Martin Modrák1, Paul-Christian Bürkner2, Tomáš Sieger3, Tomáš Slisz4, Martina Vašáková4, Grigorij Mesežnikov5, Luis Fernando Casas-Mendez5, Jaromír Vajter5, Jan Táborský6, Viktor Kubricht7, Daniel Suk8, Jan Horejsek8, Martin Jedlička9, Adriana Mifková9, Adam Jaroš10, Miroslav Kubiska11, Jana Váchalová11, Robin Šín11, Markéta Veverková12, Zbyšek Pospíšil13, Julie Vohryzková14, Rebeka Pokrievková15, Kristián Hrušák14, Kristína Christozova14, Vianey Leos-Barajas16, Karel Fišer17, Tomáš Hyánek10 Institute of Microbiology of the Czech Academy of Sciences, Prague, Czech Republic University of Stuttgart, Germany 3 Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic 4 Department of Respiratory Medicine, 1st Faculty of Medicine, Charles University; Thomayer Hospital, Prague, Czech Republic 5 Motol University Hospital, Prague, Czech Republic 6 AGEL Hospital Nový Jičín, Nový Jičín, Czech Republic 7 Kralovské Vinohrady University Hospital, Prague, Czech Republic 8 General University Hospital in Prague, Czech Republic 9 Military Hospital Olomouc, Olomouc, Czech Republic 10 Na Homolce Hospital, Prague, Czech Republic 11 Department of Infectious Diseases and Travel Medicine, Faculty of Medicine in Pilsen, Charles University, University Hospital in Pilsen, Pilsen, Czech Republic 12 Hořovice Hospital, Hořovice, Czech Republic 13 Třebíč Hospital, Třebíč, Czech Republic 14 2nd Faculty of Medicine, Charles University in Prague 15 3rd Faculty of Medicine, Charles University in Prague 16 Department of Statistical Sciences, University of Toronto 17 Department of Bioinformatics, 2nd Faculty of Medicine, Charles University in Prague 1 2 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.03.20239863; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license . Abstract: We collected a multi-centric retrospective dataset of patients (N = 213) who were admitted to ten hospitals in Czech Republic and tested positive for SARS-CoV-2. The dataset contains baseline patient characteristics, breathing support required, pharmacological treatment received and multiple markers on daily resolution. Patients in the dataset were treated with hydroxychloroquine (N = 108), azithromycin (N = 72), favipiravir (N = 9), convalescent plasma (N = 7), dexamethasone (N = 4) and remdesivir (N = 3), often in combination. Most patients were admitted during the first wave of the epidemic. To explore association between treatments and patient outcomes we performed multiverse analysis, observing how the conclusions change between defensible choices of statistical model, predictors included in the model and other analytical..
Late treatment
is less effective
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