Statin use among Veterans with dialysis‐dependent chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: The benefits of statin medications in patients receiving maintenance dialysis remains controversial and clinical trials overall have shown no benefit. Potential side effects of statin medications include myalgias, myopathy, and memory loss and risk of side effects associated with statin medications increase with higher statin doses. We examined statin use and statin dose among Veterans with dialysis dependent CKD. Such information may help clinicians modulate medication use and reduce pill burden in appropriate patients. METHODS: This cross-sectional analysis ascertained medication utilization by linking records from the U.S. Department of Veteran's Affairs (VA) Managerial Cost Accounting Pharmacy National Data Extracts and Medicare Part D during calendar year 2013 for Veterans with dialysis-dependent CKD enrolled in and/or using VA healthcare. The venue of dialysis and patient characteristics were ascertained by linking VA Medical SAS datasets, VA Fee Basis datasets (for non-VA care paid for by VA), Medicare claims and the United States Renal Data Systems patient core files. FINDINGS: We identified 18,494 Veterans with dialysis-dependent CKD who were enrolled in and/or used VA healthcare, had no history of kidney transplantation, and were alive on January 1, 2014. More than half (58.1%) of Veterans with dialysis-dependent CKD used statins and 35.7% of statin utilization was high dose. Statins were the third most commonly prescribed medication after beta blockers (64.8%) and phosphate binders (64.5%). DISCUSSION: Statins are a commonly prescribed medication among Veterans receiving maintenance dialysis and approximately one-third of statin utilization is high dose in this population. Future studies should examine patient preferences, comorbidities, and dialysis characteristics that impact the risks and benefits of statin use in order to identify those patients who will or will not benefit from continued statin use.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".