Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial
| Autor: | Gina Barnes, Anna Siefkas, Hoyt Burdick, Gregory Braden, Abigail Green-Saxena, Jacob Calvert, Carson Lam, R. Phillip Dellinger, Ritankar Das, Jana Hoffman, Jean Louis Vincent, Emily Pellegrini, Andrea McCoy, Samson Mataraso |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
hydroxychloroquine
Coronavirus disease 2019 (COVID-19) SARS-Cov-2 Population lcsh:Medicine Machine learning computer.software_genre Article 03 medical and health sciences 0302 clinical medicine Medicine 030212 general & internal medicine education 0303 health sciences Entire population education.field_of_study 030306 microbiology business.industry Hazard ratio lcsh:R COVID-19 drug treatment Hydroxychloroquine General Medicine prediction Precision medicine mortality Confidence interval Clinical trial machine learning Artificial intelligence business computer medicine.drug |
| Zdroj: | Journal of Clinical Medicine Volume 9 Issue 12 Journal of Clinical Medicine, Vol 9, Iss 3834, p 3834 (2020) |
| ISSN: | 2077-0383 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |