A new machine learning study has suggested the most effective combinations of drugs to prevent the recurrence of COVID-19 following an initial infection. However, these combinations vary among patients, highlighting the need for personalized approaches.
The study led by UC Riverside utilized real-world data obtained from a hospital in China. The researchers discovered that individual characteristics, such as age, weight, and pre-existing conditions, play a crucial role in determining the drug combinations that effectively reduce the rates of COVID-19 recurrence.
Data's Origin
According to the study, the data's origin in China holds significance for two key reasons. Firstly, while patients in the United States typically receive one or two drugs for COVID-19 treatment, Chinese doctors had the flexibility to prescribe up to eight different drugs during the early stages of the pandemic. This allowed for a more extensive study of various drug combinations.
Additionally, COVID-19 patients in China were mandated to undergo post-hospitalization quarantine in government-operated hotels. This measure provided an opportunity for a more systematic assessment of reinfection rates.
The study was initiated in April 2020, during the initial stages of the pandemic. While many studies at that time primarily focused on mortality rates, doctors in the vicinity of Hong Kong, specifically in Shenzhen, expressed heightened concern regarding the rates of recurrence due to the relatively lower number of fatalities observed in their area.
To their surprise, Jiayu Liao, a co-author of the study and an associate professor of bioengineering, discovered that nearly 30% of patients tested positive again within 28 days after being discharged from the hospital.
The study included data from over 400 COVID-19 patients, with an average age of 45. Most individuals had moderate cases of the virus, and the gender distribution was relatively even. Treatment involved various combinations of antiviral, anti-inflammatory, and immune-modulating drugs, like interferon or hydroxychloroquine.
The success of different combinations among various demographic groups can be attributed to the virus's behavior. COVID-19 suppresses interferon, a protein produced by cells to impede invading viruses.
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Immune-boosting Medications
According to Liao's explanation, individuals who had weaker immune systems before contracting COVID-19 needed immune-boosting medications to effectively fight against the virus.
Conversely, younger individuals typically exhibit hyperactive immune responses to the infection, which can result in excessive inflammation of tissues and, in severe cases, even mortality. As a result, younger patients necessitate the inclusion of immune suppressants in their treatment regimen.
Liao urges reconsidering age and medical conditions when choosing treatments, as current practices often overlook variations as traditional drug trials fail to account for other medical conditions, but the researchers developed a technique to address this by matching individuals virtually.
Despite advancements in our understanding of COVID-19 and the effectiveness of vaccines in reducing mortality, there remains a significant knowledge gap regarding treatments and prevention of reinfections. Xinping Cui hopes that the findings of this study will be applied to address issues surrounding recurrence.
The study's findings were published in the Journal of Frontiers in Artificial Intelligence.
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