Factors Affecting Physicians’ Decisions to Transfuse Red Blood Cells to Patients Having Coronary Artery Bypass Graft Surgery: A National Survey.
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».