An acute drop in highly dialyzable antihypertensive drug levels is considered to be one of the pathophysiological mechanisms of blood pressure rise during hemodialysis (HD). The study aimed to assess… Click to show full abstract
An acute drop in highly dialyzable antihypertensive drug levels is considered to be one of the pathophysiological mechanisms of blood pressure rise during hemodialysis (HD). The study aimed to assess the prevalence of intradialytic hypertension (ID-HTN) and identify the most significant risk factors of its development. We performed a retrospective case-control single-center study of HD patients from January 1st, 2014 to December 30th, 2016. Baseline evaluation included recording of antihypertensive medications with a focus on dialyzability of drugs. ID-HTN was defined as an increase in systolic blood pressure more than 10 mmHg after HD session. We enrolled 131 HD patients (52% males, median age 55.7 [53.5; 58.0] years, dialysis vintage 59.3 [51.8; 66.8] months). 79 patients suffered from ID-HTN. Highly dialyzable drugs were used in 61% of patients, most often – in 68% of cases – in the group of beta-blockers, less often among inhibitors of angiotensin-converting enzyme (32%). ID-HTN was associated with use of beta-blockers (Spearman's rank correlation coefficient (r)=0.212; p=0.015), moxonidine (r=0.313; p=0.001) and highly dialyzable drugs (r=0.440; p<0.0001). Using the identified risk factors, a prediction model for ID-HTN based on logistic regression was constructed: y = −1.015 + 1.720 × highly dialyzed drugs + 0.993 × moxonidine; p = exp (y) / (1 + exp (y)). Table 1 displays actual and predicted values on the sample of HD patients. In present study 60% of dialysis patients suffered from ID-HTN. Drugs with high dialysis clearance were widely used (61%) in dialysis population. Highly dialyzable drugs were associated with increased risk of ID-HTN (OR 5.585; 95% CI 2.49–12.54; p<0.0001). The low specificity (65.4%) of the prediction model limits its use in clinical practice. Figure 1 shows the covariate-adjusted ROC curve by logistic regression model. Type of funding source: None
               
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