Predictive Model using AI may detect Hyperuricemia Risk Early among those on low dose Aspirin
Written By : Aditi
Medically Reviewed By : Dr. Kamal Kant Kohli
Published On 2024-02-09 12:45 GMT | Update On 2024-02-10 06:59 GMT
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Hyperuricemia is primarily attributed to increased serum uric acid (SUA) concentration, considered the primary gout factor. In the US, approximately 38 million adults, up to 16.9% of the population, are affected by hyperuricemia.
According to an original research article published in Frontiers in Pharmacology, researchers concluded that a predictive model established by XGBoost algorithms could help clinicians detect hyperuricemia risk early in people taking low-dose aspirin.
Hyperuricemia is a severe condition related to gout and cardiovascular diseases. Low-dose aspirin was reported to inhibit uric acid excretion, leading to hyperuricemia. To decrease hyperuricemia-related CVD, this study identified the risk of hyperuricemia in people taking aspirin.
The data for this study were collected from the NHANES between 2011 and 2018. The analysis included participants who answered the “Preventive Aspirin Use” questionnaire with positive answers. The study used six machine learning algorithms, and the eXtreme Gradient Boosting (XGBoost) model was selected to predict the risk of hyperuricemia.
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