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All Studies   Meta Analysis    Recent:   

Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit fromHydroxychloroquineTreatment?—The IDENTIFY Trial

Burdick et al., Journal of Clinical Medicine, doi:10.3390/jcm9123834
Nov 2020  
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Mortality -59% Improvement Relative Risk Mortality (b) 71% HCQ for COVID-19  Burdick et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Prospective study of 290 patients in the USA Higher mortality with HCQ (not stat. sig., p=0.12) c19hcq.org Burdick et al., J. Clinical Medicine, Nov 2020 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
290 patient observational trial in the USA, not showing a significant difference with HCQ treatment overall, but showing significantly lower mortality in a subgroup of patients where HCQ is expected to be beneficial based on a machine learning algorithm.
risk of death, 59.0% higher, HR 1.59, p = 0.12, treatment 142, control 148, adjusted per study, all patients.
risk of death, 71.0% lower, HR 0.29, p = 0.01, treatment 26, control 17, adjusted per study, subgroup of patients where treatment is predicted to be beneficial.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Burdick et al., 26 Nov 2020, prospective, USA, peer-reviewed, 14 authors.
This PaperHCQAll
Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial
Hoyt Burdick, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R Phillip Dellinger, Andrea Mccoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini, Ritankar Das
Journal of Clinical Medicine, doi:10.3390/jcm9123834
Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11-0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.
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Late treatment
is less effective
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