Next Article in Journal
Cerebrospinal Fluid and Serum d-Serine Levels in Patients with Alzheimer’s Disease: A Systematic Review and Meta-Analysis
Next Article in Special Issue
“Go To Travel” Campaign and Travel-Associated Coronavirus Disease 2019 Cases: A Descriptive Analysis, July–August 2020
Previous Article in Journal
Evaluation of Systemic Renin and Angiotensin II Levels in Normal Tension Glaucoma
Previous Article in Special Issue
A Comparison of Case Fatality Risk of COVID-19 between Singapore and Japan
Article

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial

1
Cabell Huntington Hospital, Huntington, WV 25701, USA
2
Marshall University School of Medicine, Huntington, WV 25701, USA
3
Dascena, Inc., San Francisco, CA 94115, USA
4
Kidney Care and Transplant Associates of New England, Springfield, MA 01104, USA
5
Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School of Rowan University, Camden, NJ 08103, USA
6
Cape Regional Medical Center, Cape May Court House, NJ 08210, USA
7
Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, 1050 Brussels, Belgium
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(12), 3834; https://doi.org/10.3390/jcm9123834
Received: 12 October 2020 / Revised: 20 November 2020 / Accepted: 24 November 2020 / Published: 26 November 2020
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. View Full-Text
Keywords: machine learning; COVID-19; SARS-Cov-2; hydroxychloroquine; mortality; prediction; drug treatment machine learning; COVID-19; SARS-Cov-2; hydroxychloroquine; mortality; prediction; drug treatment
Show Figures

Figure 1

MDPI and ACS Style

Burdick, H.; Lam, C.; Mataraso, S.; Siefkas, A.; Braden, G.; Dellinger, R.P.; McCoy, A.; Vincent, J.-L.; Green-Saxena, A.; Barnes, G.; Hoffman, J.; Calvert, J.; Pellegrini, E.; Das, R. Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. J. Clin. Med. 2020, 9, 3834. https://doi.org/10.3390/jcm9123834

AMA Style

Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger RP, McCoy A, Vincent J-L, Green-Saxena A, Barnes G, Hoffman J, Calvert J, Pellegrini E, Das R. Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. Journal of Clinical Medicine. 2020; 9(12):3834. https://doi.org/10.3390/jcm9123834

Chicago/Turabian Style

Burdick, Hoyt, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R. P. Dellinger, Andrea McCoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini, and Ritankar Das. 2020. "Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial" Journal of Clinical Medicine 9, no. 12: 3834. https://doi.org/10.3390/jcm9123834

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop