Real-world evaluation of patiromer utilization and its effects on serum potassium in veterans with end stage kidney disease
Bibliographic record
Abstract
Hyperkalemia (serum potassium [K+] ≥5.1) is life-threatening in patients diagnosed with end stage kidney disease (ESKD). Patiromer is approved for the treatment of hyperkalemia, although its role in hyperkalemic patients with ESKD is not well understood. This study describes real-world patiromer utilization in an ESKD population and its corresponding association with serum K+ level changes. The study population was comprised of US veterans with an outpatient dispensing of patiromer and 2 or more International Classification of Diseases diagnostic codes for ESKD. A treatment course of patiromer was defined by serial dispensing events without a 30-day gap. Patiromer utilization was described by duration, average dose, persistence, and proportion of days covered during patiromer course. Mean serum K+ values were described for baseline and 3 follow-up intervals during the 180-day follow-up period. There were 458 patients with ESKD included in the study. On average, patients had 1.24 (95% CI: 1.20-1.29) patiromer courses. Half of the population discontinued their first patiromer course within 30 days, while approximately 10% of patients remained persistent at the end of the 180-day period and 102 (22.3%) patients started a second course during the 180-day follow up period. Average serum K+ concentrations during baseline and the 3 evaluation intervals during the 180-day follow-up were 5.91 mEq/L (5.85-5.97), 4.94 mEq/L (4.86-5.03), 4.89 mEq/L (4.8-4.98) and 4.88 mEq/L (4.8-4.96). Few patients remained persistent on their initial course of patiromer at the end of follow-up, but approximately 20% of patients initiated a second treatment episode after a 30-day gap in treatment during the 180-day follow-up period. Nonetheless, average serum K+ in ESKD patients were sustainably reduced by approximately 1 mEq/L during follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".