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0 0.5 1 1.5 2+ Mortality -59% Improvement Relative Risk Mortality (b) 71% Burdick et al. HCQ for COVID-19 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) Burdick et al., J. Clinical Medicine, doi:10.3390/jcm9123834 Favors HCQ Favors control
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
Burdick et al., Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit.., Journal of Clinical Medicine, doi:10.3390/jcm9123834
Nov 2020   Source   PDF  
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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.
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.
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Abstract: Journal of Clinical Medicine Article Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial Hoyt Burdick 1,2 , Carson Lam 3 , Samson Mataraso 3 , Anna Siefkas 3, * , Gregory Braden 4 , R. Phillip Dellinger 5 , Andrea McCoy 6 , Jean-Louis Vincent 7 , Abigail Green-Saxena 3 , Gina Barnes 3 , Jana Hoffman 3 , Jacob Calvert 3 , Emily Pellegrini 3 and Ritankar Das 3 1 2 3 4 5 6 7 * Cabell Huntington Hospital, Huntington, WV 25701, USA; Marshall University School of Medicine, Huntington, WV 25701, USA Dascena, Inc., San Francisco, CA 94115, USA; (C.L.); (S.M.); (A.G.-S.); (G.B.); (J.H.); (J.C.); (E.P.); (R.D.) Kidney Care and Transplant Associates of New England, Springfield, MA 01104, USA; Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School of Rowan University, Camden, NJ 08103, USA; Cape Regional Medical Center, Cape May Court House, NJ 08210, USA; Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium; Correspondence: Received: 12 October 2020; Accepted: 24 November 2020; Published: 26 November 2020   Abstract: 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. Keywords: machine learning; COVID-19; SARS-Cov-2; hydroxychloroquine; mortality; prediction; drug treatment J. Clin. Med. 2020, 9, 3834; doi:10.3390/jcm9123834 J. Clin. Med. 2020, 9, 3834 2 of 18
Late treatment
is less effective
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