Burdick Hoyt, Lam Carson, Mataraso Samson, Siefkas Anna, Braden Gregory, Dellinger R Phillip, McCoy Andrea, Vincent Jean-Louis, Green-Saxena Abigail, Barnes Gina, Hoffman Jana, Calvert Jacob, Pellegrini Emily, Das Ritankar
Cabell Huntington Hospital, Huntington, WV 25701, USA.
Marshall University School of Medicine, Huntington, WV 25701, USA.
J Clin Med. 2020 Nov 26;9(12):3834. doi: 10.3390/jcm9123834.
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 ( = 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.
针对2019年新型冠状病毒病（COVID-19）的治疗药物已被提出，但支持其使用的证据有限。开发了一种机器学习算法，以识别羟氯喹与生存率提高相关的COVID-19患者亚群；该人群可能与临床试验研究相关。在美国的六家医院进行了一项实用试验。我们纳入了2020年3月10日至6月4日期间入院的COVID-19患者。治疗未进行随机分组。研究终点是死亡率；出院是一个竞争事件。在整个人群以及算法表明适合治疗的亚群中获得了风险比。共纳入290名患者。在算法识别出的亚群中，羟氯喹与生存率的统计学显著提高（P = 0.011）相关（调整后的风险比为0.29，95%置信区间（CI）为0.11 - 0.75）。在算法表明的患者中，治疗组的调整后生存率为82.6%，未治疗组为51.2%。在总体人群中未观察到治疗与死亡率之间的关联。在通过机器学习算法识别出的、使用羟氯喹治疗结局更好的COVID-19患者群体中，研究结束时生存率提高了31%。精准医学方法可能有助于识别在临床试验中更有可能被证明从羟氯喹治疗中获益的COVID-19患者亚群。
