Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction

Güven, Alper Tuna; Özdede, Murat; Şener, Yusuf Ziya; Yıldırım, Ali Osman; Altıntop, Sabri Engin; Yesilyurt, Berkay; Uyaroğlu, Oğuz Abdullah; Tanrıöver, Mine Durusu

doi:10.1016/j.ejim.2023.05.021

Cited by 6 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study employed a subset of patients derived from the dataset of our earlier research, acting as the foundation for this study [ 16 ]. This cohort comprises cases from four distinct clinics, both secondary and tertiary care centers, spanning from October 1st, 2020, to October 30th, 2021.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification

Özdede,

Guven

2023

Cureus

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…As a subgroup, uncontrolled DM was arbitrarily defined as patients with an A1c over 8%. Detailed definitions of specific diseases are available from our previous research [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification

Özdede,

Guven

2023

Cureus

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors contrasted various classifiers to determine which performed the best across the various representations. The study in [13] aimed to use ML algorithms to identify risk factors for renal adverse events caused by renin-angiotensinaldosterone system inhibitor (RAASi) medications. The researchers will use these findings to develop predictive models that can be used to identify patients at high risk of developing these adverse events so that they can be monitored and treated more closely.…”

Section: Related Workmentioning

confidence: 99%

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Aldughayfiq,

Allahem,

Mostafa

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In this study, we introduce a novel combination of layer-static-weighted attention and ascending feature selection techniques to predict the seriousness level of adverse drug events using the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized natural language processing (NLP) to analyze the terms in the active substance field, in addition to considering demographic and event information such as patient sex, healthcare provider qualification, and drug characterization. Our ascending feature selection method, which progressively incorporates additional features based on their importance, demonstrated continuous enhancements in prediction performance. Simultaneously, we employed a layer-static-weighted attention technique, which dynamically adjusts the model’s focus between natural language processing (NLP) and demographic features. This technique achieved its best performance at a balanced weight of 50%, yielding an average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included, indicating a compelling advantage to include a larger volume of meaningful features. By integrating these methodologies, we constructed a robust model capable of effectively predicting seriousness levels, offering significant potential for improving pharmacovigilance and enhancing drug safety monitoring. The results underscore the value of NLP and demographic data in predicting drug event seriousness and demonstrate the effectiveness of our combined techniques. We encourage further research to refine these methods and evaluate their application to other clinical datasets.

show abstract

“…This study was designedas a post-hoc analysis of our previously published study (10). All patient data was acquired using electronic medical records (EMR).…”

Section: Design Settings and The Study Populationmentioning

confidence: 99%

Uric acid elevations differ between equivalent hydrochlorothiazide and indapamide doses

GÜVEN,

ÖZDEDE,

ŞENER

et al. 2024

Osmangazi̇ Journal of Medicine

View full text Add to dashboard Cite

Introduction: Thiazide diuretics are among the major anti-hypertensive medications. Hydrochlorothiazide and indapamide are among the most commonly used thiazides. Common side effects include impaired renal function and electrolyte disturbances. Whether hydrochlorothiazide and indapamide cause the same amount of disturbance at equivalent doses is uncertain. Methodology: Patient data from four different clinics was analyzed. Patients whose thiazide diuretic was initiated or dose escalated were included if they met the inclusion criteria. Patient characteristics, including demographics, comorbidities, medications, renal function, and electrolyte values at the initial visit and control visit, and hydrochlorothiazide or indapamide exposure per milligram, were acquired. Indapamide doses were multiplied by ten to convert into equivalent thiazide doses. Changes in renal function and electrolyte values, and renal function and electrolyte changes per equivalent thiazide exposure, were calculated. Results: The uric acid increase and potassium decrease were different for the indapamide and hydrochlorothiazide groups. However, when these changes were analyzed per equivalent thiazide exposure, potassium was not different, but the uric acid increase was still significantly different between indapamide and hydrochlorothiazide (.05(.08) vs .03 (.08) mg/dL per equivalent mg thiazide, p = .049). Discussion: This study demonstrated that indapamide’s hyperuricemic effect is more profound than that of hydrochlorothiazide. Choosing hydrochlorothiazide instead of indapamide may be more appropriate in patients with higher initial uric acid levels.

show abstract

Evaluation of machine learning algorithms for renin-angiotensin-aldosterone system inhibitors associated renal adverse event prediction

Cited by 6 publications

References 31 publications

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification

Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

Uric acid elevations differ between equivalent hydrochlorothiazide and indapamide doses

Contact Info

Product

Resources

About