A machine-learning study has found the best drug combinations to prevent COVID-19 recurrence, which affects as many as one-third of patients. The researchers found these drug combos are not the same for every patient. Individual characteristics, including age, weight, and additional illness determine which combinations most effectively reduce recurrence.
Using real-world data from a hospital in China, the UC Riverside-led study has been published in the journal Frontiers in Artificial Intelligence. The lead author is Song Zhai, senior researcher at Merck, in Rahway.
That the data came from China is significant for two reasons. First, when patients are treated for COVID-19 in the U.S. they may receive one or two drugs. Early in the pandemic, doctors in China could prescribe as many as eight different drugs, enabling analysis of more drug combinations. Second, COVID-19 patients in China must quarantine in a government-run hotel after being discharged from the hospital, which allows researchers to learn about reinfection rates in a more systematic way.
“That makes this study unique and interesting. You can’t get this kind of data anywhere else in the world,” said Xinping Cui, UCR statistics professor and one of the study’s authors.
The study project began in April 2020, about a month into the pandemic. At the time, most studies were focused on death rates. However, doctors in Shenzhen, near Hong Kong, were more concerned about recurrence rates because fewer people there were dying.
“Surprisingly, nearly 30% of patients became positive again within 28 days of being released from the hospital,” said Jiayu Liao, associate professor of bioengineering and study co-author.
Data for more than 400 COVID patients was included in the study. Their average age was 45, most were infected with moderate cases of the virus, and the group was evenly divided by gender. Most were treated with one of various combinations of an antiviral, an anti-inflammatory, and an immune-modulating drug, such as interferon or hydroxychloroquine.
The fact that various demographic groups had better success with different combinations can be traced to the way the virus operates.
For example, people who had weaker immune systems prior to COVID infection required an immune-boosting drug to fight the infection effectively. Younger peoples’ immune systems can become overactive, the researchers say. To prevent this, younger people require an immunosuppressant as part of their treatment.
“When we get treatment for diseases, many doctors tend to offer one solution for people 18 and up. We should now reconsider age differences, as well as other disease conditions, such as diabetes and obesity,” Liao said.
Because this study used real-world data, the researchers had to adjust for factors that could affect the outcomes they observed. For example, if a certain drug combination was given mostly to older people and proved ineffective, it would not be clear whether the drug is to blame or the person’s age.
“For this study, we pioneered a technique to attack the challenge of confounding factors by virtually matching people with similar characteristics who were undergoing different treatment combinations,” Cui said. “In this way, we could generalize the efficacy of treatment combinations in different subgroups.”