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; Hoyt.Burdick@chhi.org Marshall University School of Medicine, Huntington, WV 25701, USA Dascena, Inc., San Francisco, CA 94115, USA; clam@dascena.com (C.L.); samson@dascena.com (S.M.); abigail@dascena.com (A.G.-S.); gbarnes@dascena.com (G.B.); jana@dascena.com (J.H.); jake@dascena.com (J.C.); emilypellegrini@dascena.com (E.P.); ritankar@dascena.com (R.D.) Kidney Care and Transplant Associates of New England, Springfield, MA 01104, USA; Gregory.Braden@baystatehealth.org 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; amccoy@caperegional.com Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium; jlvincen@ulb.ac.be Correspondence: anna@Dascena.com 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 www.mdpi.com/journal/jcm J. Clin. Med. 2020, 9, 3834 2 of 18