Cost Savings Using a Protocol Approach to Manage Anemia in a Hemodialysis Unit
Bibliographic record
Abstract
BACKGROUND: National guidelines recommend using anemia management protocols to guide treatment. The objective of this study was to determine if an anemia management protocol would improve hemoglobin (Hgb) indices in hemodialysis patients and to measure whether the protocol would reduce the use and cost of darbepoetin alfa (DBO) and intravenous (IV) iron in hemodialysis patients. METHODS: An anemia management protocol was created and implemented for hemodialysis patients at our institution. A retrospective observational review of the use of DBO and IV iron as well as changes in Hgb, transferrin saturation and ferritin in 174 patients was conducted 6 months before and after implementation of the anemia protocol. RESULTS: The number of Hgb measurements in the target range increased from 44.3 to 46.0% (p = 0.48) after protocol implementation. The mean weekly dose of DBO was reduced from 34.56 ± 31.12 to 31.11 ± 28.64 μg post-protocol implementation (p = 0.011), which translated to a cost savings of USD 41,649 over 6 months. The mean monthly IV iron dose also decreased from 139.56 ± 98.83 to 97.65 ± 79.05 mg (p < 0.005), a cost savings of USD 18,594 over the same time period. CONCLUSION: The use of an anemia management protocol resulted in the deprescribing of DBO and iron agents while increasing the number of patients in the target Hgb range, which led to significant cost savings in the treatment of anemia.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".