Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit fromHydroxychloroquineTreatment?—The IDENTIFY Trial
et al., Journal of Clinical Medicine, doi:10.3390/jcm9123834, Nov 2020
HCQ for COVID-19
1st treatment shown to reduce risk in
March 2020, now with p < 0.00000000001 from 424 studies, used in 59 countries.
No treatment is 100% effective. Protocols
combine treatments.
6,200+ studies for
200+ treatments. c19early.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.
Standard of Care (SOC) for COVID-19 in the study country,
the USA, is very poor with very low average efficacy for approved treatments1.
Only expensive, high-profit treatments were approved for early treatment. Low-cost treatments were excluded, reducing the probability of early treatment due to access and cost barriers, and eliminating complementary and synergistic benefits seen with many low-cost treatments.
|
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.
Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial
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
is less effective
