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0 0.5 1 1.5 2+ Mortality -5% Improvement Relative Risk Gadhiya et al. HCQ for COVID-19 LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 271 patients in the USA Study underpowered to detect differences Gadhiya et al., BMJ Open, doi:10.1136/bmjopen-2020-042549 Favors HCQ Favors control
Clinical characteristics of hospitalised patients with COVID-19 and the impact on mortality: a single-network, retrospective cohort study from Pennsylvania state
Gadhiya et al., BMJ Open, doi:10.1136/bmjopen-2020-042549
Gadhiya et al., Clinical characteristics of hospitalised patients with COVID-19 and the impact on mortality: a single-network,.., BMJ Open, doi:10.1136/bmjopen-2020-042549
Apr 2021   Source   PDF  
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Retrospective 283 patients in the USA showing higher mortality with all treatments (not statistically significant). Confounding by indication is likely. In the supplementary appendix, authors note that the treatments were usually given for patients that required oxygen therapy. Oxygen therapy and ICU admission (possibly, the paper includes ICU admission for model 2 in some places but not others) were the only variables indicating severity used in adjustments. Time based confounding is likely because HCQ became increasingly controversial and less used over the time covered (March 1 to May 31, 2020), while overall treatment protocols during this period improved dramatically, i.e., more control patients likely come later in the period when treatment protocols were greatly improved. This study is excluded in the after exclusion results of meta analysis: substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically; substantial unadjusted confounding by indication likely.
risk of death, 4.8% higher, RR 1.05, p = 0.89, treatment 22 of 55 (40.0%), control 33 of 216 (15.3%), adjusted per study, odds ratio converted to relative risk, multivariate logistic regression.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Gadhiya et al., 8 Apr 2021, retrospective, USA, peer-reviewed, 4 authors.
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Abstract: Open access Original research Kinjal P Gadhiya,1 Panupong Hansrivijit ‍ ‍,1 Mounika Gangireddy,1 John D Goldman2 To cite: Gadhiya KP, Hansrivijit P, Gangireddy M, et al. Clinical characteristics of hospitalised patients with COVID-19 and the impact on mortality: a single-­network, retrospective cohort study from Pennsylvania state. BMJ Open 2021;11:e042549. doi:10.1136/ bmjopen-2020-042549 ►► Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (http://​dx.​doi.o​ rg/​10.​1136/​ bmjopen-​2020-​042549). KPG, PH and MG contributed equally. Received 08 July 2020 Revised 07 October 2020 Accepted 18 March 2021 © Author(s) (or their employer(s)) 2021. Re-­use permitted under CC BY-­NC. No commercial re-­use. See rights and permissions. Published by BMJ. 1 Department of Internal Medicine, UPMC Pinnacle, Harrisburg, Pennsylvania, USA 2 Department of Infectious Diseases, UPMC Pinnacle, Harrisburg, Pennsylvania, USA Correspondence to Dr Panupong Hansrivijit; h​ ansrivijitp@​upmc.e​ du ABSTRACT Objective COVID-19 is a respiratory disease caused by SARS-­CoV-2 with the highest burden in the USA. Data on clinical characteristics of patients with COVID-19 in US population are limited. Thus, we aim to determine the clinical characteristics and risk factors for in-­hospital mortality from COVID-19. Design Retrospective observational study. Setting Single-­network hospitals in Pennsylvania state. Participants Patients with confirmed SARS-­CoV-2 infection who were hospitalised from 1 March to 31 May 2020. Primary and secondary outcome measures Primary outcome was in-­hospital mortality. Secondary outcomes were complications, such as acute kidney injury (AKI) and acute respiratory distress syndrome (ARDS). Results Of 283 patients, 19.4% were non-­survivors. The mean age of all patients was 64.1±15.9 years. 56.2% were male and 50.2% were white. Several factors were identified from our adjusted multivariate analyses to be associated with in-­hospital mortality: increasing age (per 1-­year increment; OR 1.07 (1.045 to 1.105)), hypoxia (oxygen saturation <95%; OR 4.630 (1.934 to 1.111)), opacity/infiltrate on imaging (OR 3.077 (1.276 to 7.407)), leucocytosis (white blood cell >10 109/µL; OR 2.732 (1.412 to 5.263)), ferritin >336 ng/mL (OR 4.016 (1.195 to 13.514)), lactate dehydrogenase >200 U/L (OR 7.752 (1.639 to 37.037)), procalcitonin >0.25 ng/ mL (OR 2.404 (1.011 to 5.714)), troponin I >0.03 ng/ mL (OR 2.242 (1.080 to 4.673)), need for advanced oxygen support other than simple nasal cannula (OR 4.608–13.889 (2.053 to 31.250)), intensive care unit admission/transfer (OR 13.699 (6.135 to 30.303)), renal replacement therapy (OR 21.277 (5.025 to 90.909)), need for vasopressor (OR 22.222 (9.434 to 52.632)), ARDS (OR 23.810 (10.204 to 55.556)), respiratory acidosis (OR 7.042 (2.915 to 16.949)), and AKI (OR 3.571 (1.715 to 7.407)). When critically ill patients were analysed independently, increasing Sequential Organ Failure Assessment score (OR 1.544 (1.168 to 2.039)), AKI (OR 2.128 (1.111 to 6.667)) and ARDS (OR 6.410 (2.237 to 18.182)) were predictive of in-­hospital mortality. Strengths and limitations of this study ►► Individual patient’s chart was reviewed. ►► Multivariate analysis (binary logistic regression model) was used to report the results. ►► Retrospective, observational design. ►► Limited sample size. ►► Only hospitalised patients were..
Late treatment
is less effective
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