Analgesics
Antiandrogens
Bromhexine
Budesonide
Cannabidiol
Colchicine
Conv. Plasma
Curcumin
Ensovibep
Famotidine
Favipiravir
Fluvoxamine
Hydroxychlor..
Ivermectin
Lactoferrin
Lifestyle
Melatonin
Metformin
Minerals
Molnupiravir
Monoclonals
Naso/orophar..
Nigella Sativa
Nitazoxanide
Paxlovid
Peg.. Lambda
Quercetin
Remdesivir
Vitamins

Other
Feedback
Home
Top
Results
Abstract
All HCQ studies
Meta analysis
 
Feedback
Home
c19hcq.org COVID-19 treatment researchHCQHCQ (more..)
Melatonin Meta
Bromhexine Meta Metformin Meta
Budesonide Meta
Cannabidiol Meta Molnupiravir Meta
Colchicine Meta
Conv. Plasma Meta
Curcumin Meta Nigella Sativa Meta
Ensovibep Meta Nitazoxanide Meta
Famotidine Meta Paxlovid Meta
Favipiravir Meta Peg.. Lambda Meta
Fluvoxamine Meta Quercetin Meta
Hydroxychlor.. Meta Remdesivir Meta
Ivermectin Meta
Lactoferrin Meta

All Studies   Meta Analysis   Recent:  
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk Mortality (b) -84% HCQ for COVID-19  Soto-Becerra et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 3,322 patients in Peru (April - July 2020) Lower mortality with HCQ (p<0.000001) c19hcq.org Soto-Becerra et al., medRxiv, October 2020 Favors HCQ Favors control

Real-World Effectiveness of hydroxychloroquine, azithromycin, and ivermectin among hospitalized COVID-19 patients: Results of a target trial emulation using observational data from a nationwide Healthcare System in Peru

Soto-Becerra et al., medRxiv, doi:10.1101/2020.10.06.20208066
Oct 2020  
  Twitter
  Facebook
Share
  Source   PDF   All Studies   Meta AnalysisMeta
Retrospective database study of 5683 patients, 692 received HCQ/CQ+AZ, 200 received HCQ/CQ, 203 received ivermectin, 1600 received AZ, 358 received ivermectin+AZ, and 2630 received standard of care.
This study includes anyone with ICD-10 COVID-19 codes which includes asymptomatic PCR+ patients, therefore many patients in the control group are likely asymptomatic with regards to SARS-CoV-2, but in the hospital for another reason. For those that had symptomatic COVID-19, there is also likely significant confounding by indication.
In this study all medications show higher mortality at day 30, which is consistent with asymptomatic (for COVID-19) or mild condition patients being more common in the control group.
For ivermectin they show 30 day mortality aHR = 1.39 [0.88 - 2.22]. KM curves show that the treatment groups were in more serious condition, and also that after about day 35 survival became better with ivermectin. The last day available for ivermectin shows RR 0.83, p = 0.01. More than the total excess mortality happened on the first day. This is consistent with treated patients being in more serious condition, and with many of the control group patients being in hospital for something unrelated to COVID-19.
Authors use a machine learning based propensity scoring system that appears over-parameterized and likely to result in significant overfitting and inaccurate results. Essentially they test for all interactions between two and three covariates. The nature and large number of covariates means many random correlations may be found. COVID-19 severity is not used.
This study also does not compare treatments with a control group not receiving the treatment - authors put patients receiving treatments after 48 hours in the control group.
Authors state that outcomes within 24 hours were excluded, however KM curves show significant mortality at day 1 (only for the treatment groups).
Several protocol violations and missing data have also been reported in this study: trialsitenews.com, trialsitenews.com (B).
See also: twitter.com.
Ivermectin dosage details: cdn.www.gob.pe This study is excluded in the after exclusion results of meta analysis: substantial unadjusted confounding by indication likely; includes PCR+ patients that may be asymptomatic for COVID-19 but in hospital for other reasons.
This study includes ivermectin and HCQ.
risk of death, 18.1% lower, HR 0.82, p < 0.001, treatment 346 of 692 (50.0%), control 1,606 of 2,630 (61.1%), NNT 9.0, day 54 (last day available) weighted KM.
risk of death, 84.0% higher, HR 1.84, p = 0.02, treatment 165 of 692 (23.8%), control 401 of 2,630 (15.2%), adjusted per study, day 30.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Soto-Becerra et al., 8 Oct 2020, retrospective, database analysis, Peru, preprint, median age 59.4, 4 authors, study period 1 April, 2020 - 19 July, 2020.
All Studies   Meta Analysis   Submit Updates or Corrections
This PaperHCQAll
Real-world effectiveness of hydroxychloroquine, azithromycin, and ivermectin among hospitalized COVID-19 patients: results of a target trial emulation using observational data from a nationwide healthcare system in Peru
Percy Soto-Becerra, Carlos Culquichicón, Yamilee Hurtado-Roca, Roger V Araujo-Castillo
doi:10.1101/2020.10.06.20208066
Introduction: In Latin America, Peru is the most impacted country due to COVID-19 pandemic. Given the authorized nationwide use of hydroxychloroquine, azithromycin, ivermectin and dexamethasone in COVID-19 patients, we aimed to evaluate their effectiveness alone or combined to prevent 30-day mortality among COVID-19 hospitalized patients without life-threatening illness. Methods : Design. Retrospective cohort study using digital clinical records to emulate a target trial. Setting. Nationwide data of mid-and high-level complexity hospitals from the Peruvian Social Health Insurance (EsSalud) between April 1 and July 19, 2020. Participants. Patients aged 18 years with confirmed SARS-CoV-2 infection by PCR, and non-severe pneumonia at admission. Interventions. We compared five treatment groups to the standard of care treatment regimen (control group) within 48 hours of admission to hospital: hydroxychloroquine/chloroquine alone (HCQ), ivermectin alone (IVM), azithromycin alone (AZIT), HCQ + AZIT group, and IVM + AZIT group with doses recommended by the Peruvian Ministry of Health. Main outcomes measures. The primary outcome was all-cause mortality rate, the secondary outcomes were survival without ICU rate, and survival without oxygen prescription rate. Analyses were adjusted for confounding factors by inverse probability of treatment weighting. A doubly-adjustment method was done for sensitivity analysis. Results: Among 5683 patients eligible for analysis, 200 received hydroxychloroquine or chloroquine within 48 hours of hospital admission, 203 received ivermectin, 1600 received azithromycin, 692 received hydroxychloroquine or chloroquine plus azithromycin, 358 received ivermectin plus azithromycin and 2630 received standard of care. AZIT + HCQ group was associated with 84% higher all-cause mortality hazard rate compared to standard care (wHR = 1.84, 95% CI: 1.12-3.02). Consistently, AZIT + HCQ treatment was associated with survival without transfer to ICU (wHR = 1.49, 95% CI: 1.01-2.19) and survival without oxygen prescription (wHR = 1.70, 95% CI: 1.07-2.69). HCQ treatment was associated with 77% higher all-cause mortality or oxygen prescription hazard rate compared to standard treatment (wHR = 1.77, 95% CI: 1.01-3.11). Surprisingly, we observed that IVM treatment was associated with less survival rate without transfer to ICU in the weighted analysis (wHR = 1.58, 95% CI:1.11-2.25) By last, we did not find evidence of effect on reduce allcause mortality or increase survival rate without transfer to ICU or survival rate without oxygen prescription for AZIT group neither AZIT + IVM group in all analyses. .
Conflicts of interest: The authors declare no conflicts of interest regarding the subject of this scientific article. Origin of the health care center
References
Abella, Jolkovsky, Biney, Uspal, Hyman et al., Efficacy and Safety of Hydroxychloroquine vs Placebo for Pre-exposure SARS-CoV-2 Prophylaxis Among Health Care Workers: A Randomized Clinical Trial, JAMA Internal Medicine
Alizargar, Dangers of the Use of hydroxychloroquine and azithromycin combination in COVID-19 patients. Travel medicine and infectious disease
Arshad, Kilgore, Chaudhry, Jacobsen, Wang et al., Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19
Austin, An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies, Multivariate Behav Res
Austin, Stuart, Moving towards best practice when using inverse probability of weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies, Statistics in medicine
Baby, Maity, Mehta, Suresh, Nayak et al., Targeting SARS-CoV-2
Carvallo, Hirsch, Farinella, Safety and Efficacy of the combined use of ivermectin, dexamethasone, enoxaparin and aspirin against COVID-19, medRxiv
Coffman, Zhou, Cai, Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure, BMC medical research methodology
Desai, Franklin, Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners, BMJ
Fiolet, Guihur, Rebeaud, Mulot, Peiffer-Smadja et al., Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis, Society of Clinical Microbiology and Infectious Diseases
Furtado, Berwanger, Fonseca, Corrêa, Ferraz et al., Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial, The Lancet
Gorial, Mashhadani, Sayaly, Dakhil, Almashhadani et al., Effectiveness of Ivermectin as add-on Therapy in COVID-19 Management, Pilot Trial
Hcq, Hydroxychloroquine or chloroquine, IVM: ivermectin , AZIT: azithromycin , AZIT + HCQ,: azithromycin plus hydroxychloroquine, AZIT + IVM: azithromycin plus ivermectin, ACEI: angiotensin converting enzyme inhibitor, ARB: angiotensin receptor blocker, SD: standard deviation; %: Percentage of column
Hernán, Robins, Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available, American journal of epidemiology
Juul, Nielsen, Feinberg, Siddiqui, Jørgensen et al., Interventions for treatment of COVID-19: A living systematic review with meta-analyses and trial sequential analyses (The LIVING Project), PLOS Medicine
Labrecque, Swanson, Target trial emulation: teaching epidemiology and beyond
Lauriola, Pani, Ippoliti, Mortara, Milighetti et al., Effect of combination therapy of hydroxychloroquine and azithromycin on mortality in COVID-19 patients, Clinical and Translational Science
Lee, Lessler, Stuart, Improving propensity score weighting using machine learning, Statistics in medicine
Li, Zhao, Zhan, Quantitative proteomics reveals a broad-spectrum antiviral property of ivermectin, benefiting for COVID-19 treatment, Journal of Cellular Physiology
Mahévas, Tran, Roumier, Chabrol, Paule et al., Clinical efficacy of hydroxychloroquine in patients with covid-19 pneumonia who require oxygen: observational comparative study using routine care data, BMJ
Mccaffrey, Griffin, Almirall, Slaughter, Ramchand et al., A tutorial on propensity score estimation for multiple treatments using generalized boosted models, Statistics in medicine
Parast, Mccaffrey, Burgette, De La Guardia, Golinelli et al., Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores, Health Serv Outcomes Res Methodol
Pearl, Invited Commentary: Understanding Bias Amplification, American Journal of Epidemiology
Platt, Zucker, Baldwin, Hripcsak, Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19, The New England journal of medicine
Prevencion, diagnostico y tratamiento de personas afectadas por COVID-19 en el Peru: Peruvian Ministry of Health
Rajter, Sherman, Fatteh, Vogel, Sacks et al., ICON (Ivermectin in COvid Nineteen) study: Use of Ivermectin is Associated with Lower Mortality in Patients with COVID19, medRxiv
Richman, Antiviral Drug Discovery To Address the COVID-19 Pandemic, mBio
Ridgeway, Mccaffrey, Morral, Griffin, Burgette, Toolkit for Weighting and Analysis of Nonequivalent Groups: CRAN
Rodríguez-Molinero, Pérez-López, Gálvez-Barrón, Miñarro, Macho et al., Observational study of azithromycin in hospitalized patients with COVID-19, PloS one
Sato, Matsuyama, Marginal Structural Models as a Tool for Standardization, Epidemiology
Sekhavati, Jafari, Seyedalinaghi, Jamalimoghadamsiahkali, Sadr et al., Safety and effectiveness of azithromycin in patients with COVID-19: An open-label randomised trial, International journal of antimicrobial agents
Sohag, Hannan, Rahman, Hossain, Hasan et al., Revisiting potential druggable targets against SARS-CoV-2 and repurposing therapeutics under preclinical study and clinical trials: A comprehensive review, Drug Dev Res
Soto-Becerra, Efectividad Clínica Comparativa de Hidroxicloroquina en el Tratamiento de COVID-19 utilizando Datos Observacionales de Registros Clínicos Electrónicos de EsSalud, PRISA
Thomas, Lardenois, Lacrosse, Sarrazin, Azithromycin and Hydroxychloroquine Accelerate Recovery of Outpatients with Mild/Moderate COVID-19, AJMAH
Yu, Li, Wang, Duan, Yang et al., Techniques and strategies for the potential protein-targets discovery and active pharmaceutical molecules screening in Pandemic, Journal of Proteome Research
Late treatment
is less effective
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
  or use drag and drop   
Submit