Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit fromHydroxychloroquineTreatment?—The IDENTIFY Trial
Burdick et al.
, Is Machine Learning a Better Way to IdentifyCOVID-19 Patients Who Might Benefit..
, Journal of Clinical Medicine, doi:10.3390/jcm9123834
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.
Abstract: Journal of
Is Machine Learning a Better Way to Identify
COVID-19 Patients Who Might Benefit from
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
Cabell Huntington Hospital, Huntington, WV 25701, USA; Hoyt.Burdick@chhi.org
Marshall University School of Medicine, Huntington, WV 25701, USA
Dascena, Inc., San Francisco, CA 94115, USA; email@example.com (C.L.); firstname.lastname@example.org (S.M.);
email@example.com (A.G.-S.); firstname.lastname@example.org (G.B.); email@example.com (J.H.);
firstname.lastname@example.org (J.C.); email@example.com (E.P.); firstname.lastname@example.org (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; Dellinger-Phil@cooperhealth.edu
Cape Regional Medical Center, Cape May Court House, NJ 08210, USA; email@example.com
Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, 1050 Brussels,
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;
J. Clin. Med. 2020, 9, 3834; doi:10.3390/jcm9123834
J. Clin. Med. 2020, 9, 3834
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is less effective
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