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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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HCQ for COVID-19

1st treatment shown to reduce risk in March 2020

^*, now known with p < 0.00000000001 from 422 studies, recognized in 42 countries.

Lower risk for mortality, hospitalization, recovery, cases, and viral clearance.

No treatment is 100% effective. Protocols combine complementary and synergistic treatments. ^* >10% efficacy in meta analysis with ≥3 clinical studies.

4,100+ studies for 60+ 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.

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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 Paper HCQ All

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
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