Characteristics and Outcomes of Patients Receiving Massive Transfusions at a Tertiary Care Centre
Notice bibliographique
Résumé
Abstract Abstract 4412 Purpose: To determine the characteristics and outcomes of patients who had been massively transfused at a university affiliated trauma centre. Method: Consecutive patients who received ≥ 10 RBC units in ≤ 24 hours at Sunnybrook Health Sciences Centre and Women's College Hospital from January 2007 to December 2008 were identified. Demographic and outcome data were extracted through the blood bank database and retrospective chart analysis. Result: 6,326 patients were transfused a total of 19,845 RBC units in 2007 and 2008 at Sunnybrook Health Sciences Centre and Women's College Hospital. 134 consecutive massive transfusions among 131 patients were identified. Patients who were massively transfused received a total of 2,284 RBC units, represented 2.1% of the total number of patients transfused and used 11.5% of the total RBC units transfused. Their median age was 66 years and 63% were men. 50% survived to discharge. Of those who survived, 50% were discharged home, 41 % were discharged to a rehabilitation facility, and 9% was discharged to another acute care facility. A single patient had poor neurological function and was ventilator dependent at discharge. The median number of units transfused in ≤ 24 hrs among patients who received massive transfusions was 18.5 RBC units, 2 pools of platelets, 8 FFP and 8 cryoprecipitate. At our institution, 1 pool of platelet equals 4 individual platelets or a single apheresis platelet. Recombinant Factor 7a was utilized in 19% of the massive transfusions. Median length of stay in hospital was 17 days. Median time to death (TTD) from the massive transfusion event was 1 day. Overall survival (OS) among patients who were transfused 10–19 units (n=107), 20–29 (n=16), ≥30 (n=11) RBC units in ≤ 24 hours were: 56%, 31%, and 55% respectively. Indications and overall survival for massive transfusion were: trauma (49%, OS 51%, TTD 0 days), cardiac surgery (22%, OS 41%, TTD 12 days), oncologic surgery (12%, OS 63%, TTD 12 days), vascular surgery (6%,OS 50%, TTD 8.5 days), GI bleed (3%, OS 50%, TTD 18 days) and other (8%, OS 55%, TTD 19 days). Conclusion: Patients who have been massively transfused (≥10 RBC units in ≤ 24 hours) use a disproportionate amount of blood products. Trauma, cardiac and oncologic surgeries are the most common indications for massive transfusion at Sunnybrook Health Sciences Centre and Women's College Hospital. All causes of massive transfusion had similar mortality of approximately 50% and the majority of the survivors were discharged home or to rehabilitation with preserved neurological function. Transfusions of ≥ 30 RBC units in 24 hours were not associated with worse survival outcomes. A noticeable difference between the groups was in trauma where death occurred within hours of admission, while the majority of deaths for other causes occurred between 8 and 18 days later. These findings suggest that bleeding may have been directly responsible for most trauma deaths, while complications related to bleeding may have caused the late deaths observed in the other groups. Measures for defining medical futility, other than volume of blood products transfused, need to be sought. Disclosures: No relevant conflicts of interest to declare.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,003 | 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 ».