Factors Affecting Physicians’ Decisions to Transfuse Red Blood Cells to Patients Having Coronary Artery Bypass Graft Surgery: A National Survey.
Bibliographic record
Abstract
Abstract Decisions about when to transfuse patients having coronary artery disease may impact on patient morbidity and mortality. There is no published data about the factors that influence physicians’ decisions to transfuse red blood cells (RBCs) to patients having coronary artery bypass graft surgeries (CABGs). The objective of this study was to determine the hemoglobin transfusion thresholds and the factors that influence physicians’ decisions when transfusing CABG patients. Methods: This was a cross-sectional study using self-administered mailed questionnaires sent to all anesthesiologists and cardiovascular surgeons in Canada. The survey included a series of patient scenarios for which respondents were to indicate the Hb concentration below which they would transfuse the patient. Factors assessed in the survey included patient age, sex, cardiac index (CI), and myocardial ischemia (MI). Each factor was introduced consecutively in the case scenarios to allow for analysis of each factor separately and the interaction of factors. Data on physicians’ characteristics were also collected. Univariate analysis and mixed effects regression modelling were used to analyze the data. Results: The overall response rate was a 69.3%(n=339/489); 66.8% of all anesthesiologists and 75.7% of all cardiac surgeons responded. Responses were received from anesthesiologists in all 32 cardiac centres in Canada and from cardiac surgeons in 31/32 centres. The mean age of physicians was 46 years (standard deviation (sd)=8.6 years), years of practice was 14 years (sd=8.7 years), CABG cases/centre was 940 (sd=479) and CABG cases/individual was 121 (sd=77). The Hb transfusion thresholds for 6/24 case scenarios are illustrated in the Table. The Hb thresholds were similar for male patients. Univariate analysis revealed that for the base case scenario (i.e. 55 year old male/female), Hb thresholds did not differ significantly according to physician age, sex, years in practice, specialty, academic centre, the number of CABGs/centre/year, or patient sex but differed according to the number of CABGs/individual physician/year (p=0.009 for female case scenario and p=0.02 for the male case scenario), patient age (p<0.001), CI (p<0.001) and MI (p<0.001). Physicians selected the Hb concentration (51%), blood loss (21%), and MI (13%) as the most significant factor affecting their decision to transfuse. Conclusion: Patient age, CI, MI and the number of CABGs/individual physician were found to influence physicians’ transfusion decisions. Future studies are required to elucidate whether transfusions based on these variables affect patient morbidity and mortality. Table: Mean Hemoglobin Transfusion Thresholds For 6 Case Scenarios Case Scenario Mean Hemoglobin (g/L) Standard Deviation (g/L) yo=year old, CI=cardiac index, MI=myocardial ischemia 55 yo female 70 8 55 yo female, CI<2 79 10 55 yo female with MI 78 10 75 yo female 74 8 75 yo female, CI<2 82 10 75 yo female with MI 81 10
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".