The Impact of Preoperative Intravenous Iron Therapy on Perioperative Outcomes in Cardiac Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Anemia is common in cardiac surgery affecting 25-40% of patients and associated with increased blood transfusions, morbidity, mortality, and higher hospital costs. Higher rates of stroke, acute renal injury, and total number of adverse postoperative outcomes have also been reported to be associated with preoperative anemia. This systematic review assessed the current evidence for preoperative intravenous iron on major outcomes following cardiac surgery. METHODS: Outcome measures included postoperative hemoglobin, transfusion rates, major adverse events, and mortality. The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and articles were identified using PubMed, Cochrane, CLINAHL, WOS, and EMBASE databases. Articles were included if they compared patients with and without preoperative anemia based on treatment with intravenous iron. Quality was assessed using Cochrane Risk of Bias Tool and Newcastle-Ottawa scale, and strength of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. RESULTS: Of the articles reviewed, six met inclusion criteria. These included four randomized double-blind prospective cohort studies, one randomized non-blinded prospective study, and one non-randomized non-blinded prospective study with historical control. Across studies, 1,038 patients were enrolled. Two studies showed higher hemoglobin with iron therapy, and only one study showed significant differences in multiple outcomes such as transfusion and morbidity. CONCLUSIONS: Given the paucity of studies and biases within them, the current evidence for treatment with intravenous iron prior to cardiac surgery is weak. More evidence is needed to support the administration of preoperative intravenous iron in cardiac surgery patients.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".