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Enregistrement W2564332170 · doi:10.1182/blood.v126.23.3569.3569

Post-Transfusion Fevers and Post-Reaction Culture Practices at a Large Academic Hospital Transfusion Service: Quality of Information and Calculated Bacterial Contamination Event Rates

2015· article· en· W2564332170 sur OpenAlexaffabout
Christine Cserti‐Gazdewich, Jacob Pendergrast, Yulia Lin, Jeannie Callum, Lani Lieberman, Alioska Escorcia, Sandra Ramírez‐Arcos

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood transfusion and management
Établissements canadiensSunnybrook Health Science CentreCanadian Blood ServicesUniversity of TorontoHealth Sciences CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineCryoprecipitateBlood productTransfusion medicineAdverse effectEmergency medicineBlood transfusionFresh frozen plasmaIntensive care medicineMedical emergencyPediatricsSurgeryInternal medicinePlatelet

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: The extent to which febrile transfusion reactions (FTRs) are investigated is the extent to which bacterial contamination (BaCon) may be ascertained; FTR rates in turn vary with policies concerned with their recognition and approach. Microbiology and serology aim to rule out contamination or incompatibility, both potentially fatal. BaCon and acute hemolytic transfusion reactions (AHTR) followed transfusion related lung injury (TRALI) as the leading causes of transfusion-related death in the US in 2013 (FDA: 59 fatalities: 38% TRALI, 15% AHTR, 10% BaCon). Timely BaCon recognition enables interdiction/examination of sister products, while highlighting contamination points in the work sequence. The quality and quantity of hemovigilance data from a large hospital transfusion service were reviewed with respect to overall component utilization, adverse events, and patient/product microbiology, so as to gain a contemporary estimate of BaCon. Methods: The blood transfusion laboratory (BTL) of the tertiary care, 767-bed university hospital is supplied by Canadian Blood Services and managed by a team of technologists, a transfusion safety officer (TSO), and transfusion medicine specialists (TM MDs). Patient Reaction Events (PRE) reported to the BTL were logged over a 5 year period alongside components transfused (red cell units [pRBC], adult dose platelet concentrates [APC], frozen plasma [FP], and cryoprecipitate [crpt]). By policy, PRE are reported and formally investigated, with quarterly analyses. Roughly 3% of product recipients experience a PRE, and 40% are febrile in nature. Patient sampling is discouraged for "lower risk" fevers (asymptomatic Tmax <39C), whereas "high risk" fevers (Tmax >39C or major symptoms and/or vital sign disturbances) call for cultures of the patient and implicated product(s), as well as AHTR testing. TSO and TM MD review ensue to conclude product imputability, event severity, and final diagnosis. Definite BaCon (Def-BaCon) is defined as product and patient positive (+) for the same microbe, Probable BaCon (Prob-BaCon) as product (+) [but patient negative or untested], and Possible BaCon (Poss-BaCon) as patient (+) [but product negative or untested]. Poss-BaCon was re-classified to high-imputability (Hi-Imp Poss-BaCon) if case review failed to discover a more likely pre-existing source. Results: Between 1/1/2010 to 31/12/2014, 1,624 PRE occurred through 290,044 components dispensed (175,542 pRBC, 43,187 APC, 58,235 FP, 13,080 crpt). Patient cultures occurred in 617 (38%) of PRE, and product cultures occurred in 406 (25%) of PRE. BaCon rates varied significantly according to concluded certainty, with significant re-scaling of poss-BaCon after careful case review (Table).Table 1.rate (95% confidence interval):rate per culturerate per patient reaction event (PRE)rate per component dispensedDef-BaCon (4 cases)0.65% (0.26-1.6)0.25% (0.10-0.63)1.4 x 10^-5 (0.6-3.5) or 1 in 72,511Prob-BaCon (13 cases)3.2% (1.9-5.4) (products)0.80% (0.47-1.4)4.5 x 10^-5 (2.6-7.7) or 1 in 22,311Poss-BaCon (96 cases)15.6% (12.9-18.6)5.9% (4.9-7.2)3.3 x 10^-4 (2.7-4.0) or 1 in 3,021Hi-Imp Poss-BaCon (14 cases)2.3% (1.4-3.8)0.86% (0.52-1.4)4.8 x 10^-5 (2.9-8.1) or 1 in 20,717 Discussion/Conclusions: These data illustrate practical limits to deducing BaCon rates, despite robust hemovigilance. Def-BaCon was rare (1 in 72,511), while Prob-BaCon and Hi-Imp Poss-BaCon were more frequent at ~1 in 20,000. Current as-practiced tools in FTR/BaCon investigation are flawed at various levels. Underestimates stem from under-culturing and test sensitivity, and overestimates occur with incomplete case review for true sources of bacteremia, with Poss-BaCon as high as 1 in 3000. The MD Anderson Cancer Center (Ricci, et al 2014) reported on 999 reactions, with 738 (74%) in 642 central venous catheter (CVC) patients; 606 were cultured within a week of reaction, and 60 (9.9%) were bacteremic. Fevers were concluded to more likely represent the unmasking of CVC colonization rather than BaCon. Systematically incorporating (and adjusting for) CVC data may thus help to reduce inflationary poss-BaCon rates. On the other hand, more rigorous product testing (with biofilm studies) may scale BaCon rates upwards. Clinicolaboratory studies are needed to clarify the true relationship between febrile reactions, bacterial sources, and their significance. 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,021
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,091

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,021
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,006
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,300
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission2
Résumé présentoui

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