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.
Highlights
There are currently limited treatment options available for individuals that are infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2), the etiological agent of the novel coronavirus disease 2019 (COVID-19) [1,2]
290 patients enrolled in our study, 142 of whom received hydroxychloroquine and 43 of whom were indicated by the algorithm as more likely to have better outcomes when treated with hydroxychloroquine
We identified a subset of approximately 15% of the overall COVID-19 population who were predicted to have better outcomes when treated with hydroxychloroquine
Summary
There are currently limited treatment options available for individuals that are infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2), the etiological agent of the novel coronavirus disease 2019 (COVID-19) [1,2]. Several studies have suggested a variety of COVID-19 phenotypes, including phenotypes of more severe, rapidly progressing disease that is associated with higher rates of mortality [16,21], and hyperinflammatory phenotypes that are associated with organ damage outside the respiratory system [21] These phenotypes may have important implications for treatment effectiveness. The widespread adoption of electronic health records (EHRs) [30] represents an valuable and largely untapped source of data for use in precision medicine studies seeking to identify digital biomarkers that can be used in order to predict patient responsiveness to treatment options. Towards the end of a precision medicine approach, this study presents a pragmatic clinical trial [31] otfreaatmmaecnhtiniseaslesaorcniaintegdawlgitohriptrhemdicfoterdtshuervidiveanlt.iTfihcaistimonetohfodpoaltoiegnytsmfaoyr lweahdotmo bheyttderropxaytciehnlot rsoelqeucitnioen trceraittemrieanftoirs calsinsoiccaialtterdiawl ditehsipgrne.dicted survival This methodology may lead to better patient selection c2ri.tEerxipaefroirmcelinntiaclalSterciatilodnesign. The machine learning algorithm was developed while using gradient boosting with XGBoost, and it was developed on independent data prior to implementation in the IDENTIFY trial
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
