Incidence of hyperkalemia in anuric hemodialysis patients treated with sacubitril/valsartan
Bibliographic record
Abstract
INTRODUCTION: Sacubitril/valsartan is increasingly used in hemodialysis patients due to its cardioprotective benefits. However, its impact on serum potassium levels in anuric patients undergoing hemodialysis remains controversial. METHODS: We conducted a retrospective data from patients undergoing hemodialysis at two dialysis centers. A total of 71 out of 332 patients receiving hemodialysis treatment were enrolled. Mean serum potassium (mean value of 6-8 determinations), peak serum potassium (maximum K value observed during follow-up observations), and other biochemical parameters were recorded at baseline and during the follow-up period. FINDINGS: After 6 months of follow-up, mean serum potassium increased from 4.84 ± 0.45 mmol/L at baseline to 5.07 ± 0.46 mmol/L at 3 months and 5.04 ± 0.46 mmol/L at 6 months (p < 0.001). Notably, no significant group differences were found in peak serum potassium concentrations between baseline and 6 months after sacubitril/valsartan therapy (5.69 ± 0.56 vs. 5.75 ± 0.41, p = 0.419). Prior to starting sacubitril/valsartan treatment, none of the patients had severe hyperkalemia; however, after 3 and 6 months of sacubitril/valsartan therapy, two (2.80%) and three (4.20%) patients experienced severe hyperkalemia, respectively; however, this difference was not statistically significant. Additionally, there was a significant reduction in blood pressure; however, serum sodium, bicarbonate, and Kt/V values did not change significantly during either period. DISCUSSION: Sacubitril/valsartan therapy is associated with an increase in serum potassium levels in anuric hemodialysis patients. Nevertheless, the proportion of patients with severe hyperkalemia did not increase significantly. This suggests that the use of sacubitril/valsartan in anuric patients on hemodialysis is relatively safe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".