Machine Learning Insights into Uric Acid Elevation with Thiazide Therapy Commencement and Intensification

Background: Serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease and hypertension, can be elevated by thiazide or thiazide-like drugs (THZ), essential in hypertension management. Identifying clinical determinants affecting THZ-related uric acid elevation is critical. Methods: In this retrospective cross-sectional study, we explored the clinical determinants influencing uric acid elevation related to THZ, focusing on patients where THZ was initiated or the dose escalated. A cohort of 143 patients was analyzed, collecting baseline and control uric acid levels, alongside basic biochemical studies and clinical data. Feature selection was conducted utilizing criteria based on mean squared error increase and enhancement in node purity. Four machine learning algorithms—Random Forest, Neural Network, Support Vector Machine, and Gradient Boosting regressions—were applied to pinpoint clinical influencers. Results: : Significant features include uncontrolled diabetes, index eGFR level, absence of insulin, action of indapamide, and absence of statin treatment, with absence of SGLT2 inhibitors, low dose aspirin exposure, and older age also being noteworthy. Among the applied models, the Gradient Boosting regression model outperformed the others, exhibiting the lowest MAE, MSE, RMSE values, and the highest R2 value (0.779). While Random Forest and Neural Network regression models were able to fit the data adequately, the Support Vector Machine demonstrated inferior metrics. Conclusions: Machine Learning Algorithms can precisely predict THZ-related uric acid changes, facilitating optimized therapy tailoring, minimizing unnecessary THZ abstinence, and guiding to prevent usage in cases where uric acid levels might reach undesirable levels.