Drug-Drug Interactions and Their Association With Quality of Life in Patients With Hypertension.

Introduction This study aimed to evaluate drug-drug interactions (DDIs) and their association with the quality of life in patients with hypertension. Materials and methods This cross-sectional study included 123 patients with hypertension. DDIs were evaluated using the Medscape Drug Interaction Checker Database (Medscape, New York, NY). The EuroQol-5D (EQ-5D) Quality of Life Scale was used for each patient. Results The overall blood pressure control rate (systolic/diastolic blood pressure levels, <140/90 mmHg) was 43% (53/123). The age of the patients with uncontrolled hypertension was higher than the patients with controlled hypertension (63.67 ± 11.00 vs. 58.42 ± 10.07 years; p = 0.007). The number of DDIs showed significant correlations, positively with age (r = 0.303, p = 0.001), total number of drugs (r = 0.784, p < 0.001), number of antihypertensive drugs (r = 0.640, p < 0.001), and body mass index (BMI) (r = 0.321, p < 0.001) and inversely with EQ-5D index score (r = -0.247, p = 0.006). The EQ-5D index and visual analog score were inversely correlated with age and BMI. Additional significant linear correlations between age and the total number of drugs, age and number of the antihypertensive drugs, the number of antihypertensive drugs and BMI, and the number of total drugs and BMI were detected. Of a total of 511 identified DDIs, 14 interactions in 12 patients were considered serious, 402 interactions in 82 patients were considered significant, and 95 interactions in 39 patients were considered minor. Conclusions This study supports that DDIs have important associations with antihypertensive treatment and the quality of life of patients. Higher age and BMI values were associated with a higher risk of DDIs and lower quality of life in patients with hypertension.

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques.

OBJECTIVE: This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success. METHODS: Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints. RESULTS: The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (-0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (-0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (-0.12), glucose (-0.12), hemoglobin A1c (-0.12), uric acid (-0.09) and creatinine (-0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients. CONCLUSION: Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.

Evaluating the relationship of blood pressure, plasma angiotensin peptides and aldosterone with cognitive functions in patients with hypertension.

Renin Angiotensin Aldosterone System (RAAS) plays an important role in the development of hypertension. On the other hand, hypertension is a well-known and independent risk factor for cognitive impairment. The aim of the present study was to evaluate the relationship of blood pressure control, plasma angiotensin peptides and aldosterone with cognitive functions. Forty-one patients who were under treatment with the same antihypertensive medications for at least three months were included in the study. Plasma angiotensin II, angiotensin 1-7, angiotensin IV, and aldosterone concentrations were analyzed using an enzyme-linked immunosorbent assay (ELISA). Standardized Mini Mental State Examination (SMMSE) was used to evaluate cognitive functions. When the participants were grouped according to their SMMSE scores (cut-off value: 26 points), we determined significant differences between systolic blood pressure (SBP) levels, diastolic blood pressure levels, plasma angiotensin II and angiotensin 1-7 concentrations of the groups. When the participants were stratified according to their SBP levels (cut-off value: 140 mm Hg), we found significant differences in SMMSE scores and plasma angiotensin IV concentrations of the groups. A negative correlation between SBP and SMMSE scores and strong linear correlations among angiotensin peptides levels were determined. The relationship found between SBP and SMMSE in the present study was compatible with the literature. Our 33 patients were using at least one angiotensin II receptor blocker (ARB). Regarding AT1 receptor blockage, the significant association between higher SMMSE scores and increased angiotensin peptides may support a finding that ARBs prevent dementia and improve cognitive function. Further larger studies are needed to confirm and prove the relation of RAAS biochemical parameters with cognitive function.