COVID-19 Recurrent Varies with Different Combinatorial Medical Treatments Determined by Machine Learning Approaches
Posted 01 Aug 2020
medRxiv DOI: 10.1101/2020.07.29.20164699
Posted 01 Aug 2020
Various medical treatments for COVID-19 are attempted. After patients are discharged, SARS-CoV-2 recurring cases are reported and the recurrence could profoundly impact patient healthcare and social economics. To date, no data on the effects of medical treatments on recurrence has been published. We analyzed the treatment data of combinations of ten different drugs for the recurring cases in a single medical center, Shenzhen, China. A total of 417 patients were considered and 414 of them were included in this study (3 deaths) with mild-to-critical COVID-19. Patients were treated by 10 different drug combinations and followed up for recurrence for 28 days quarantine after being discharged from the medical center between February and May, 2020. We applied the Synthetic Minority Oversampling Technique (SMOTE) to overcome the rare recurring events in certain age groups and performed Virtual Twins (VT) analysis facilitated by random forest regression for medical treatment-recurrence classification. Among those drug combinations, Methylprednisolone/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate (0.133) as compared to the average recurring rate (0.203). For the younger group (age 20-27) or the older group (age 60-70), the optimal drug combinations are different, but the above combination is still the second best. For obese patients, the combination of Ribavirin/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate for age group of 20-50, whereas the combination of Interferon/Lopinavir/Ritonavir/Arbidol led to lowest recurring rate for age group of 50-70. The insights into combinatorial therapy we provided here shed lights on the use of a combination of (biological and chemical) anti-virus therapy and/or anti-cytokine storm as a potentially effective therapeutic treatment for COVID-19.
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