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Enregistrement W4405054375 · doi:10.1182/blood-2024-203534

Exploring Physician-Related Transfusion Errors Reported to the Transfusion Error Surveillance System (TESS) from 2016-2023: A Single Centre Study

2024· article· en· W4405054375 sur OpenAlexaffabout
Julia Lou, Widad Abdulwahab, Matilda Cheung, Connie Colavecchia, H. G. Downie, Heather VanderMeulen, Jami‐Lynn Viveiros, Jane Yang, Yulia Lin, Akash Gupta

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood transfusion and management
Établissements canadiensCanadian Blood ServicesUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineBlood transfusionTransfusion reactionEmergency medicineSurgery

Résumé

récupéré en direct d'OpenAlex

Introduction: Blood transfusions involve a multi-step process with an interdisciplinary team. Errors, defined as any deviation from established policies and standard operating procedures, can occur in any step of this process and may lead to adverse transfusion events. The web-based Transfusion Error Surveillance System (TESS) was implemented in Canada in 2005 and funded by the Public Health Agency of Canada to anonymously report and track error events. Previous studies have examined transfusion-related error types and frequency; however, an analysis of physician-related transfusion errors has not been performed. The purpose of this study was to describe physician-related transfusion errors in a tertiary care academic center, their harms and consequences, and how the COVID-19 pandemic may have influenced error trends. Methods: All transfusion error events are investigated, detailed and reported through TESS by trained blood bank laboratory technologists. These errors are categorized as those performed by the clinical or transfusion service. This was a retrospective study of errors reported to TESS from 2016 to 2023, focusing on clinical service transfusion errors and errors where the primary individual involved was a physician. Clinical service errors were categorized as involving sample collection (SC), sample handling (SH), product request (PR), request for pickup (RP), or unit transfusion (UT). SC errors are events related to sample collection; SH errors occur during the collection process but do not involve the sample itself; PR errors involve the incorrect ordering of blood or blood products for transfusion; RP errors involve the request to pick up blood or blood products from the transfusion service; and UT errors occur outside of the transfusion service and involve the storage, selection and administration of blood or blood product. The consequence of an error is classified as harm if the error leads to an adverse event including a transfusion reaction, delayed transfusion or under-transfusion. For error rates, the following denominators were used: SC and SH errors used total number of specimens received; PR errors used total number of blood components (BC) and fractionated products (FP) requested; and RP and UT errors used total number of BC and FP issued. Error rates were reported per 1000 of their respective denominator. Descriptive statistics were used to determine overall error rate trends across the years, types and consequences of errors, and details of patient harm cases. Results: Between January 2016 and December 2023, 29852 total errors were reported to TESS. 16155 (54%) were clinical service errors while 13697 (46%) were transfusion service errors. 2448 (8%) of the events reached the patient level, with 32 (0.1%) of these resulting in patient harm. SC had the highest error rate across all 8 years, ranging from 23 to 37 per 1000. In 2020, error rates for SC, SH, and PR increased. SC error rates decreased in 2021, while SH and PR decreased in 2022. Error rates for UT remained steady. RP errors increased in 2021 and remained consistently higher thereafter, increasing from 6 to 13 per 1000. While physician-related errors made up 9% (n=2672) of total errors, they accounted for 91% (29/32) of patient harm cases. Of the 29 patient harm cases, 27 (93%) were PR errors and 2 (7%) were UT errors. The consequences included 20 (69%) patients who had transfusion-associated circulatory overload (TACO), 3 (10%) who had febrile non-hemolytic transfusion reactions, 1 (3%) who had an allergic reaction, 1 (3%) who was hypotensive and 4 (14%) who had other adverse outcomes. Three patients died within 30 days of discovering the error; two deaths were not deemed attributable to the error. Conclusion: Physician-related transfusion errors have serious consequences. Despite making up only 9% of total errors, physician-related transfusion errors are responsible for 91% of cases that resulted in patient harm. Preliminary analysis suggests that harmful transfusion events such as TACO, could have been prevented (ex. by ordering diuretics or decreasing the rate of transfusion to high-risk patients). Further exploration into the patient harm cases will be conducted to identify areas of improvement for physicians. Using the TESS database to examine physician-related transfusion errors highlights the potential harm and preventable nature of these errors and offers opportunities to improve patient care.

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)0,008
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,992
Score d'incertitude au seuil0,082

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,030
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,004
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,055
Tête enseignante GPT0,259
Écart entre enseignants0,203 · 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.

Devis d'étudeObservationnel
DomaineMéthodes
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

Citations1
Publié2024
Routes d'admission2
Résumé présentoui

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