Risk factors for perioperative blood transfusion in total hip arthroplasty: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The present study assessed and synthesized the potential risk factors for perioperative blood transfusion in total hip arthroplasty from various studies through Meta-analysis. METHODS: We systematically searched for relevant studies in databases including Web of Science, PubMed, Embase, and Cochrane Library from the time of database creation to 1 February 2025 and included all observational studies exploring perioperative transfusion risk factors in patients undergoing total hip arthroplasty. All included studies were assessed for quality using the Newcastle-Ottawa Scale (NOS) scale. Data were analyzed using Stata 15 software. RESULTS: A total of 18 articles (n = 424,158) were included, meta-analysis results suggest that increased intraoperative bleeding [OR = 1.13, 95%CI (1.02, 1.24)], increased postoperative drainage [OR = 2.24, 95%CI (1.24, 4.83)], body mass index ≤ 18.5 [OR = 1.10, 95%CI (1.02, 1.20)], preoperative anemia [OR = 1.82, 95%CI (1.62, 2.03)], age ≥ 80 [OR = 1.49 95%CI(1.21, 1.83)], female [OR = 1.92, 95%CI (1.71, 2.15)], ASA class ≥ 3 [OR = 2.06, 95%CI (1.63, 2.61)] in patients with total hip arthroplasty (THA) increases the incidence of perioperative blood transfusion. CONCLUSION: The results of the current study suggest that increased intraoperative bleeding, increased postoperative drainage, low body mass index (≤ 18.5), preoperative anemia, advanced age (≥ 80 years), female gender, and high ASA classification (≥ 3) were significantly associated with the likelihood of needing blood transfusion. These findings highlight the importance of preoperative risk assessment and perioperative management strategies to reduce the need for blood transfusion and improve patient outcomes.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.070 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".