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

11/26 Late treatment study

Burdick et al., Journal of Clinical Medicine, doi:10.3390/jcm9123834 (Peer Reviewed)
Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit fromHydroxychloroquineTreatment?—The IDENTIFY Trial

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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.

Burdick et al., 11/26/2020, prospective, USA, North America, peer-reviewed, 14 authors.

risk of death, 59.0% higher, RR 1.59, p = 0.12, treatment 142, control 148, adjusted per study, all patients.

risk of death, 71.0% lower, RR 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. For an individual study the most serious outcome may have a smaller number of events and lower statistical signficance, however this provides the strongest evidence for the most serious outcomes when combining the results of many trials.

All 344 studies Meta Analysis

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