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All Studies   Meta Analysis    Recent:   

Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis

Sammartino et al., PLOS One, doi:10.1371/journal.pone.0251262
May 2021  
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Mortality -240% Improvement Relative Risk HCQ for COVID-19  Sammartino et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? PSM retrospective 328 patients in the USA Higher mortality with HCQ (p=0.002) c19hcq.org Sammartino et al., PLOS One, May 2021 FavorsHCQ Favorscontrol 0 0.5 1 1.5 2+
HCQ for COVID-19
1st treatment shown to reduce risk in March 2020
 
*, now with p < 0.00000000001 from 411 studies, recognized in 46 countries.
No treatment is 100% effective. Protocols combine treatments. * >10% efficacy, ≥3 studies.
4,300+ studies for 75 treatments. c19hcq.org
Retrospective 1,108 hospitalized patients in New York showing significantly higher mortality with HCQ treatment.
Time based confounding is very likely because HCQ became increasingly controversial and less used over the time covered (Mar - Jun 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. Authors note that for every week or month later that a person was admitted, their risk of death dropped by 16% and 49%, respectively, yet they do not consider time based confounding.
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.
risk of death, 240.0% higher, OR 3.40, p = 0.002, treatment 137, control 191, PSM, model 1a, RR approximated with OR.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Sammartino et al., 10 May 2021, retrospective, propensity score matching, USA, peer-reviewed, 7 authors.
This PaperHCQAll
Predictors for inpatient mortality during the first wave of the SARS-CoV-2 pandemic: A retrospective analysis
Daniel Sammartino, Farrukh Jafri, Brennan Cook, Lisa La, Hyemin Kim, John Cardasis, Joshua Raff
PLOS ONE, doi:10.1371/journal.pone.0251262
Background The coronavirus disease 2019 (COVID-19) pandemic overwhelmed healthcare systems, highlighting the need to better understand predictors of mortality and the impact of medical interventions. Methods This retrospective cohort study examined data from every patient who tested positive for COVID-19 and was admitted to White Plains Hospital between March 9, 2020, and June 3, 2020. We used binomial logistic regression to analyze data for all patients, and propensity score matching for those treated with hydroxychloroquine and convalescent plasma (CP). The primary outcome of interest was inpatient mortality. Results 1,108 admitted patients with COVID-19 were available for analysis, of which 124 (11.2%) were excluded due to incomplete data. Of the 984 patients included, 225 (22.9%) died. Risk for death decreased for each day later a patient was admitted [OR 0.970, CI 0.955 to 0.985; p < 0.001]. Elevated initial C-reactive protein (CRP) value was associated with a higher risk for death at 96 hours [OR 1.007, 1.002 to 1.012; p = 0.006]. Hydroxychloroquine and CP administration were each associated with increased mortality [OR 3.4, CI 1.614 to 7.396; p = 0.002, OR 2.8560, CI 1.361 to 6.160; p = 0.006 respectively]. Conclusions Elevated CRP carried significant odds of early death. Hydroxychloroquine and CP were each associated with higher risk for death, although CP was without titers and was administered at a median of five days from admission. Randomized or controlled studies will better describe the impact of CP. Mortality decreased as the pandemic progressed, suggesting
Author Contributions Conceptualization: Daniel Sammartino, Farrukh Jafri, Lisa La, Hyemin Kim, John Cardasis. Data curation: Daniel Sammartino, Brennan Cook, Lisa La, Hyemin Kim, Joshua Raff. Formal analysis: Daniel Sammartino, Farrukh Jafri, Brennan Cook, Lisa La, Hyemin Kim, John Cardasis, Joshua Raff. Investigation: Daniel Sammartino. Methodology: Daniel Sammartino, Lisa La, Joshua Raff. Project administration: Daniel Sammartino, Farrukh Jafri, Lisa La, John Cardasis, Joshua Raff. Supervision: Farrukh Jafri, John Cardasis, Joshua Raff. Validation: Brennan Cook, Joshua Raff. Visualization: Joshua Raff. Writing -original draft: Daniel Sammartino, Farrukh Jafri, Brennan Cook, Hyemin Kim, Joshua Raff. Writing -review & editing: Daniel Sammartino, Farrukh Jafri, Brennan Cook, Hyemin Kim, Joshua Raff.
References
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Late treatment
is less effective
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