79 THE TRUE RISK OF BLOOD TRANSFUSION AFTER NEPHRECTOMY FOR RENAL MASSES
Bibliographic record
Abstract
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Evidence-based Medicine & Outcomes II1 Apr 201279 THE TRUE RISK OF BLOOD TRANSFUSION AFTER NEPHRECTOMY FOR RENAL MASSES Gino Vricella, Antonio Finelli, Shabbir Alibhai, Lee Ponsky, Rabii Madi, and Robert Abouassaly Gino VricellaGino Vricella Cleveland, OH More articles by this author , Antonio FinelliAntonio Finelli Toronto, Canada More articles by this author , Shabbir AlibhaiShabbir Alibhai Toronto, Canada More articles by this author , Lee PonskyLee Ponsky Cleveland, OH More articles by this author , Rabii MadiRabii Madi Cleveland, OH More articles by this author , and Robert AbouassalyRobert Abouassaly Cleveland, OH More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.125AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Perioperative blood transfusions are costly and have attendant risks and safety concerns. Reported blood transfusion rates after nephrectomy (radical and partial) demonstrate considerable variability, likely due to the referral patterns and selection biases that influence reports from tertiary academic centers. To our knowledge, the transfusion rate in general practice has not been previously reported. The aim of our study was to examine the actual transfusion rate and risk factors for blood transfusion after nephrectomy for renal masses on a population-level. METHODS We performed a population-based, retrospective observational study using a national discharge abstract database. Our cohort consisted of 10,902 patients treated by radical (RN) or partial nephrectomy (PN) for a renal mass between April 1, 2003 and March 31, 2008. Patient demographics and treatment approach (i.e. open vs. laparoscopic) were available for all patients. Surgeon and institution volume quartiles for kidney surgery were created. Adjustment for comorbidity was performed using the Charlson-Deyo Index. The association between blood transfusion and various explanatory variables was examined using the Chi-square test, as well as with multivariable logistic regression. RESULTS The overall blood transfusion rate for patients in our study 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<.0001). Transfusion rate was found to be strongly associated with age and comorbidity, such that patients <50 years old and having Charlson scores of 0 were transfused 11.2% and 14.5% of the time, compared with 28.2% and 40.7% in patients ≥80 years old and with Charlson scores of ≥3, respectively (p<.0001). On multivariable logistic regression, age (p<.0001), Charlson score (p<.0001), procedure type (p<.0001), surgeon (p<.0001) and hospital volume quartile (p<.0001) were found to be associated with the rate of blood transfusions, whereas year of surgery, gender and income quintile were not. CONCLUSIONS The transfusion rate after nephrectomy in general clinical practice is higher than that reported in the urologic 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 in preoperative patient counseling and informed consent. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e33-e34 Peer Review Report Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gino Vricella Cleveland, OH More articles by this author Antonio Finelli Toronto, Canada More articles by this author Shabbir Alibhai Toronto, Canada More articles by this author Lee Ponsky Cleveland, OH More articles by this author Rabii Madi Cleveland, OH More articles by this author Robert Abouassaly Cleveland, OH More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".