Justification for initiating patiromer when restricted by prior authorization and clinical guidance in a US health care system
Bibliographic record
Abstract
BACKGROUND: In the Veterans Health Administration, patiromer is a formulary medication restricted by prior authorization and criteria-for-use (CFU). Historically, patiromer approval was restricted by step therapy, requiring prescribers to trial sodium polystyrene sulfonate (SPS). OBJECTIVE: To describe clinical scenarios leading to patiromer initiation by characterizing patient experience with primary hyperkalemia treatment modalities, especially SPS. METHODS: All veterans who initiated patiromer between January 1, 2016, and February 28, 2021, with chronic kidney disease and dispensed SPS during the 3 months preceding patiromer were included. A structured chart-review process was used to abstract prior authorization drug request notes to characterize patiromer approval and patient experience with primary pharmacotherapy. Results were reported with descriptive frequencies and proportions. RESULTS: Three hundred thirty-one veterans met inclusion criteria. Primary justification for patiromer initiation included continuation of patiromer initiated outside the Veterans Health Administration or during inpatient stay (5.7%) and SPS inventory shortage (25.4%). CFU justification was mentioned in 83.7% of notes and, among those with CFU justification, SPS treatment was documented in 68.7%. Clinician statements indicating that SPS was ineffective occurred in 65 (41.7%) and statements of safety concerns (either observed or potential) in 37 (23.7%) veterans. CONCLUSIONS: Patiromer approval is multifactorial, and clinicians often opted to avoid long-term SPS use because of safety concerns, lack of consistent availability, and concerns about its appropriateness for longterm hyperkalemia management. DISCLOSURES: Drs Patel and Sauer received funding from Otsuka Canada Pharmaceutical Inc. to study the use of patiromer in the VHA. This material is the result of work supported with resources and the use of facilities at the VA Salt Lake City Health Care System. The views expressed in this manuscript are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the US government. Drs Qualls and Pinnell are supported by the VA Advanced Fellowship Program in Medical Informatics with the Office of Academic Affiliations. This work was funded through a partnered research mechanism. Otsuka Canada Pharmaceutical Inc. was not involved in development or review of this manuscript.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".