Substantial Variability in Platelet Transfusion Practice in a Patient-Level Audit of 56,204 Transfusions across 22 Hospitals
Notice bibliographique
Résumé
Introduction: Platelet transfusions are the second most commonly transfused blood product after red blood cells, with over 2 million units transfused annually in the United States. Despite randomized trials and evidence-based guidelines, recent audits have found high rates of unnecessary transfusion, ranging from 22% to 42%, driven primarily by prophylactic transfusion in non-bleeding patients at a threshold over 10,000/uL. Our multicenter, retrospective observational study sought to characterize transfused patients, assess time trends, and describe variability in pre-transfusion platelet counts across 22 hospital sites. Methods: We conducted a retrospective, multicenter observation study of general medicine wards, subspecialty wards (including hematology-oncology), and critical care areas at 32 hospitals from January 1, 2017 to June 30, 2022 participating in the GEMINI data platform (https://geminimedicine.ca/). Ten sites were excluded for invalid platelet transfusion data. A platelet unit was defined as any platelet type, including apheresis units and pooled whole blood derived platelets. Any platelet units issued within 60 minutes of one another without repeat platelet count between units was considered part of one transfusion event. A transfusion event was tied to the closest platelet count that occurred within the preceding 24 hours. Our primary analysis examined variability in pre-transfusion platelet count across clinical diagnosis, patient subgroups, hospital sites, and clinician characteristics. Results: Across 804,067 admissions over 22 hospitals, 17,777 (2.2%) involved at least one platelet transfusion. The analysis included 56,204 platelet transfusion events. The most common primary diagnoses of transfused patients were malignancy (25.1%), cardiovascular disease (19%), gastrointestinal disease (10.2%) and traumatic injury (8.9%). In an analysis of platelet units per transfusion event, 93.6% were given as 1 unit, 5.2% as 2 units and 1.2% as >2 units. No pre-transfusion platelet count was performed in the preceding 24 hours for 7.2% of transfusion events. Most platelet transfusions were prescribed by Hematology-Oncology (43.2%) and Internal Medicine (20.6%) with median pre-transfusion platelet count being 9,000/uL [IQR 7,000-17,000] and 18,000/uL [9,000-38,000] respectively. Specialties transfusing at the highest median thresholds were Cardiothoracic Surgery (108,000/uL [66,000-174,000]), other surgical specialties (65,000/uL [40,000-100,000]) and Cardiology (53,000/uL [27,000-127,000]). After adjusting for prescriber subspecialty, transfusion threshold was significantly higher with increasing years out of practice, but there was no statistically significant association with prescriber sex. The proportion of platelet transfusion events with pre-transfusion platelet count above 50,000/uL ranged widely from 1.6% to 55.4% across hospital sites. Patients with a diagnosis of hematologic malignancy or other malignancy were transfused at a lower threshold (10,000/uL [7,000-18,000] and 16,000/uL [8,000-34,000] respectively) than patients without known malignancy (42,000/uL [20,000-84,000]). Pre-transfusion platelet count thresholds were stable across the 5-year study period. Conclusion: Our study highlights substantial variability in pre-transfusion platelet counts across different hospital sites, patient diagnoses, and prescriber characteristics. Highlighting practice variation may allow for targeted change interventions to promote guideline adherence and reduce unnecessary transfusion and associated harms.
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 | 0,008 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».