Lack of Cost-Effectiveness of Preoperative Erythropoiesis-Stimulating Agents and/or Iron Therapy in Anaemic, Elective Surgery Patients: A Systematic Review and Updated Analysis
Bibliographic record
Abstract
OBJECTIVES: For anaemic elective surgery patients, current clinical practice guidelines weakly recommend the routine use of iron, but not erythrocyte-stimulating agents (ESAs), except for short-acting ESAs in major orthopaedic surgery. This recommendation is, however, not based on any cost-effectiveness studies. The aim of this research was to (1) systematically review the literature regarding cost effectiveness of preoperative iron and/or ESAs in anaemic, elective surgery patients and (2) update existing economic evaluations (EEs) with recent data. METHODS: Eight databases and registries were searched for EEs and randomized controlled trials (RCTs) reporting cost-effectiveness data on November 11, 2020. Data were extracted, narratively synthesized and critically appraised using the Philips reporting checklist. Pre-existing full EEs were updated with effectiveness data from a recent systematic review and current cost data. Incremental cost-effectiveness ratios were expressed as cost per (quality-adjusted) life-year [(QA)LY] gained. RESULTS: Only five studies (4 EEs and 1 RCT) were included, one on intravenous iron and four on ESAs + oral iron. The EE on intravenous iron only had an in-hospital time horizon. Therefore, cost effectiveness of preoperative iron remains uncertain. The three EEs on ESAs had a lifetime time horizon, but reported cost per (QA)LY gained of 20-65 million (GBP or CAD). Updating these analyses with current data confirmed ESAs to have a cost per (QA)LY gained of 3.5-120 million (GBP or CAD). CONCLUSIONS: Cost effectiveness of preoperative iron is unproven, whereas routine preoperative ESA therapy cannot be considered cost effective in elective surgery, based on the limited available data. Future guidelines should reflect these findings.
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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) | 0.008 | 0.001 |
| 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) | 0.000 | 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".