Browsing by Author "Ozdede, Murat"

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Baseline Sodium-Glucose Cotransporter-2 Inhibitor Use Strongly Attenuates the Uric Acid-Elevating Effect of Thiazide Exposure
(2023) Guven, Alper Tuna; Ozdede, Murat; Sener, Yusuf Ziya; 0000-0002-6310-4240
Objective: Thiazide diuretics are among the major anti-hypertensive medications. However, their hyperuricemic effect restricts their use in patients with gout. Sodium glucose co-transporter 2 inhibitor (SGLT-2i) initiation lowers serum uric acid (SUA) levels. It is not known whether existing SGLT-2i use affects the SUA increasing effect of thiazides. Methods: Post-hoc data analysis of our published study was conducted. Hypertensive patients who were initiated on thiazide diuretics or whose dose escalated were included (thiazide exposure). Demographic, clinical, and laboratory data were acquired via an electronic database. Patients were grouped according to SGLT-2i presence at the time of thiazide exposure. Since the number of SGLT-2i users was low, bootstrapping via simple random sampling was performed. Results: 144 patients were included in the study, of whom 13 were on SGLT-2i. Initial sample analysis revealed that while baseline SUA levels were similar between groups, SUA change was significantly lower after thiazide exposure among patients receiving SGLT-2i (0.6 vs. 0.2, p = 0.039). Similarly, baseline SUA levels were similar, but SUA change after thiazide exposure was significantly lower among patients receiving SGLT-2 on bootstrapped data (0.13 [-0.25 - 0.57, 95%CI], vs. 0.61 [0.45 - 0.78, 95%CI], mean difference = 0.48, [0.04 - 0.91, 95%CI], p = 0.029). Conclusion: This study revealed that thiazide diuretics may be a safe anti-hypertensive medication in terms of hyperuricemia among patients using SGLT-2i. Further studies with similar outcomes may result in the elimination of restrictive recommendations for the use of thiazides in patients with hyperuricemia or gout, provided patients are on SGLT-2i.
Commentary and Critique to "VTE Prophylaxis Therapy: Clinical Practice vs Clinical Guidelines" by Abukhalil et al
(2022) Guven, Alper Tuna; Ozdede, Murat; https://orcid.org/0000-0002-6310-4240; 36276227
Evaluation of Machine Learning Algorithms for Renin-Angiotensin-Aldosterone System Inhibitors Associated Renal Adverse Event Prediction
(2023) Guven, Alper Tuna; Ozdede, Murat; Sener, Yusuf Ziya; Yildirim, Ali Osman; Altintop, Sabri Engin; Yesilyurt, Berkay; Uyaroglu, Oguz Abdullah; Tanriover, Mine Durusu; 0000-0002-6310-4240; 37217407
Background: Renin-angiotensin-aldosterone system inhibitors (RAASi) are commonly used medications. Renal adverse events associated with RAASi are hyperkalemia and acute kidney injury. We aimed to evaluate the performance of machine learning (ML) algorithms in order to define event associated features and predict RAASi associated renal adverse events.Materials and Methods: Data of patients recruited from five internal medicine and cardiology outpatient clinics were evaluated retrospectively. Clinical, laboratory, and medication data were acquired via electronic medical records. Dataset balancing and feature selection for machine learning algorithms were performed. Random forest (RF), k-nearest neighbor (kNN), naive Bayes (NB), extreme gradient boosting (xGB), support vector machine (SVM), neural network (NN), and logistic regression (LR) were used to create a prediction model.Results: 409 patients were included, and 50 renal adverse events occurred. The most important features predicting the renal adverse events were the index K and glucose levels, as well as having uncontrolled diabetes mellitus. Thiazides reduced RAASi associated hyperkalemia. kNN, RF, xGB and NN algorithms have the highest and similar AUC (> 98%), recall (> 94%), specifity (> 97%), precision (> 92%), accuracy (> 96%) and F1 statistics (> 94%) performance metrics for prediction.Conclusion: RAASi associated renal adverse events can be predicted prior to medication initiation by machine learning algorithms. Further prospective studies with large patient numbers are needed to create scoring systems as well as for their validation.
Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification
(2023) Ozdede, Murat; Guven, Alper T.; 38274913
Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease, hypertension, and heart failure, 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 estimated Glomerular Filtration Rate (eGFR) level, absence of insulin, action of indapamide, and absence of statin treatment, with absence of Sodium -glucose cotransporter 2 inhibitors (SGLT2i), 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 Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (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 are adept at accurately identifying the factors linked to uric acid fluctuations caused by THZ. This proficiency aids in customizing treatments more effectively, reducing the need to unnecessarily avoid THZ, and providing guidance on its use to prevent instances where uric acid levels could become problematic.