Treatment Response and Dosing of Patiromer in Veterans With Dialysis-Dependent ESKD and Hyperkalemia
Bibliographic record
Abstract
Background: Hyperkalemia (HK) is a common, potentially life-threatening metabolic disorder that presents a challenge for clinicians caring for dialysis patients with end-stage kidney disease (ESKD). Patiromer is a non-absorbed, sodium-free potassium (K+)-binding polymer approved for HK treatment. This historical cohort study aimed to describe patiromer utilization and associated serum K+ (sK+) changes in veterans with ESKD and HK on dialysis. Methods: Patiromer utilization and sK+ changes were evaluated using the National VA Corporate Data Warehouse between 1/1/16 and 2/28/21. Inclusion criteria were adults aged ≥18 years old, receiving patiromer, with at least 2 ICD codes for ESKD, on dialysis, and a sK+ ≥5.1 mEq/L recorded within 91 days of the index date (date of first patiromer dispensing). sK+ was assessed at baseline (BL) and 1-, 3-, and 6-months follow-up (FU) from index date. sK+ change from BL to each FU timepoint was assessed by paired t-test. Results: 1,267 patiromer users were identified with ESKD based on ICD code during the BL period; 458 meet the inclusion criteria and had a sK+ available for evaluation during the 3 months pre-index. BL characteristics included mean age 66 years old, 97% male, 45% African American, and mean sK+ 5.91 mEq/L. Comorbidities of interest included 72% diabetes, 50% heart failure, and 45% coronary artery disease. Dosing of patiromer was daily in 87% of cases with an average daily dose of 8.4 g. A dose increase was observed in 11% (n=52) and dose decrease in 5% (n=24) during the FU period. Following patiromer initiation, significant reductions (P<0.01) in mean sK+ concentrations from BL were observed within 1 month (-1.02 mEq/L; n=307), 3 months (-1.04 mEq/L; n=351), and 6 months (-1.05 mEq/L; n=351; Figure). Conclusions: Among US veterans with ESKD and HK on dialysis, patiromer use was associated with clinically relevant reductions in sK+ concentrations at all study timepoints. Funding: Commercial Support - Otsuka Pharmaceuticals Canada
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".