The true risk of blood transfusion after nephrectomy for renal masses: a population‐based study
Bibliographic record
Abstract
What's known on the subject? and What does the study add? There is a paucity of population‐based analyses of expected outcomes after renal surgery for kidney cancer. Reported blood transfusion rates after nephrectomy show considerable variability, probably as a result of the referral patterns that influence reports from tertiary academic medical centres. With emerging data on the inferior outcomes in patients undergoing allogeneic blood transfusion, we aimed to evaluate the patient, surgeon and hospital factors that influence the receipt of a blood transfusion after nephrectomy. A more detailed understanding of these factors may help in preoperative patient counselling and informed consent. Objective To examine blood transfusion rates after nephrectomy for renal masses at the population‐level. Patients and Methods We performed a population‐based, retrospective observational study using a national discharge abstract database. The study cohort consisted of 10 902 patients who were treated by radical nephrectomy ( RN ) or partial nephrectomy ( PN ) for a renal mass between 1 A pril 2003 and 31 M arch 2008. The association between blood transfusion and various explanatory variables was examined using the chi‐squared test and multivariable logistic regression. Results The overall blood transfusion rate was 18.1%. Transfusions occurred after 28.2%, 12.7%, 9.2% and 8.6% of open RN , open PN , laparoscopic RN and laparoscopic PN , respectively ( P < 0.001). Transfusion rates were found to be strongly associated with age and comorbidity, such that patients aged <50 years with C harlson scores of 0 were transfused 11.2% and 14.5% of the time compared to 28.2% and 40.7% in patients aged ≥80 years with C harlson scores of ≥3, respectively ( P < 0.001). On multivariable logistic regression, age ( P < 0.001), C harlson score ( P < 0.001), procedure type ( P < 0.001), surgeon ( P < 0.001) and hospital volume quartile ( P < 0.001) were all found to be associated with the rate of blood transfusions, whereas year of surgery, sex and income quintile were not. Conclusions The transfusion rate after nephrectomy in general clinical practice is higher than that reported in the urological literature. Patient and provider factors appear to contribute to the considerable variability that exists in the observed transfusion rate. A more detailed understanding of these factors may help with respect to preoperative patient counselling and informed consent.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".