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Enregistrement W2550933501 · doi:10.1182/blood.v126.23.4740.4740

Significant Increase in Reporting of Transfusion Reactions with the Implementation of an Electronic Reporting System

2015· article· en· W2550933501 sur OpenAlexaffabout
Rosanne St. Bernard, Matthew Yan, Shuoyan Ning, Alioska Escorcia, Jacob Pendergrast, Christine Cserti‐Gazdewich

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBlood donation and transfusion practices
Établissements canadiensUniversity Health NetworkUniversity of TorontoWestern University
Organismes subventionnairesnon disponible
Mots-clésMedicineMedical emergencyTransfusion medicineEmergency medicineDocumentationBlood transfusionSurgeryComputer science

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction In robust hospital transfusion services, transfusion reaction reporting triggers a structured response to the assessment, diagnosis, and clinical management of the individual. At a population level, the feedback loop of hemovigilance permits the perception of signals applicable to donors, material production, patterns of use, infusion care, and recipient vulnerabilities. Transfusion reaction reporting therefore aims to improve the quality of patient care and safety in transfusion, which remains one of the most commonly performed procedures in medicine today. Large variations between passive/retrospective or active/prospective systems imply underreporting. Reasons for this may lie in unawareness of, nihilism on, or obstacles to this duty. In 2009, our center transitioned from a paper-based to an electronic reporting system (ERS) for suspected patient reaction events (PREs). This study sought to determine the impact of this change on PRE reporting rates. Methods This study was conducted in Toronto, Canada at the University Health Network, a 4-site, 767-bed, ternary care hospital with high transfusion activity (2014: 60,000 component and 30,000 derivative dispensations). In 05/2009, hardcopy mountsheets for transfusion labels were revised to provide space for recording corresponding vital signs, with instructions on PRE reporting. Medical director PRE review followed with event documentation in a transfusion laboratory database (recording imputability, reaction type, severity, and implicated product(s)). An Acute Transfusion Reaction policy was also developed to protocolize and further streamline the approach to various reactions, but was not implemented across all sites until 11/2009. At this time, electronic PRE reporting went live in the existing electronic medical record, with medical director review hereafter culminating in uploaded case conclusions. Technical tutorials on healthcare worker reporting spanned several months before implementation, without emphasizing the theory or evidence-based value of hemovigilance. The quantity and characteristics of reactions pre-/post-ERS implementation were compared. Results Prior to the ERS option (5/2009-11/2009), the reported PRE rate was 0.26/day. Subsequent to launch (11/2009-12/2009), the reported PRE rate was 0.66/day, representing a 2.54 fold increase (p<0.05) (Figure 1). This nearly-trebled rate has been sustained throughout subsequent years: 01/2010-12/2010: 0.88/day; 01/2011-12/2011: 0.87/day; 01/2012-12/2012: 0.87/day; 01/2013-12/2013: 0.88/day; 01/2014-12/2014: 0.93/day. The distribution of PRE conclusions pre-ERS was: febrile non-hemolytic transfusion reaction (FNHTR) 40%; unrelated to transfusion (UTR) 34%; allergic transfusion reaction (ATR) 7.3%; query bacterial contamination (BaCON) 4.9%; transfusion related acute lung injury (TRALI) 3.6% and transfusion related circulatory overload (TACO) 2.4%. The distribution of PRE conclusions after ERS (11/2009-12/2014) was: ATR 28.8%; UTR 28.7%; FNHTR 19.9%; TACO 8.6%; transfusion associated dyspnea 4.7%; pain 3.2%; query BaCON 2.3% and TRALI 1.7%. Conclusions Our data demonstrate that PRE reporting significantly increased and was sustained after the implementation of an ERS. This finding suggests that despite a dearth of strategies to address underreporting, the solution may lie in removing disincentives while facilitating action in familiar practice platforms. Two other studies investigated the implementation of an ERS for transfusion reaction reporting (Fujihara H. et al. 2015; Yeh, S et al. 2011), with one confounded by a significant increase in transfusion rates in the post-ERS period. In contrast, our denominator of blood utilization has been stable or decreasing across sites over the last five years, with 3% of product recipients nevertheless experiencing a PRE. Despite the significant increase in reported PREs, we did not see an increase in UTRs (34% vs 25%) to account for the difference, arguing against "junk inflations," while rather suggesting that reporter suspicions generally concur with specialist conclusions on transfusion imputability. Given the importance of accurate transfusion reaction reporting for patient safety, we suggest that this strategy be considered by other centers to improve reporting activity with its potential downstream benefits. 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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,219
Score d'incertitude au seuil0,991

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,027
Tête enseignante GPT0,281
Écart entre enseignants0,254 · 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 tête enseignante, 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

Citations2
Publié2015
Routes d'admission2
Résumé présentoui

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