Institution-Wide Quantification of Iatrogenic Blood Loss Using a Novel Informatics-Driven Methodology
Notice bibliographique
Résumé
Abstract Abstract 1530 Rationale: Anemia has been shown to have an adverse impact on patient outcomes. In the transfusion literature, various blood conservation and patient blood management systems have been proposed as a way to reduce the burden of anemia. An important component of limiting blood loss is the reduction of iatrogenic blood loss through diagnostic phlebotomy. Studies in the phlebotomy and transfusion literature largely focus on small patient populations on critical care units. Such research provides a great depth of information about those settings, but the impact of diagnostic phlebotomy on the broader inpatient population is unknown. We present a novel method, not previously described in the literature, characterising the extent of iatrogenic blood loss in inpatients at our institution. Methods and results: Following a pilot project, data from September 1 to December 1, 2009 were queried from the institution's laboratory information system. This comprehensive dataset included records of tests conducted during 7503 admissions of patients (n=6733) at twelve individual facilities within Capital District Health Authority (CDHA). There were 70,790 unique laboratory orders, for which a total of 397,770 individual tests were performed. This required a total of 120,398 tubes of blood drawn for a cumulative volume of 648,350 mL from the entire population. The majority of tests were done on a “routine” basis (44,820/ 70,790 orders, 63%); most testing was also done after the first day of admission (59,051/ 70,790 orders, 83%). Patient demographics and testing burden are contrasted by gender in Table 1; males appear to experience a higher testing burden than females, despite similar mean length of stay. There were 618 (9%) of 6733 inpatients having ≥250mL (approximately 1 unit of packed red cells) phlebotomised (Table 1). Phlebotomy volumes are unevenly distributed across the age range, with patients in the two youngest age groups demonstrating lower mean cumulative volumes than older patients (Table 2). When individual admissions are examined, phlebotomy volume per patient is greater in hospitals providing tertiary care, as contrasted to other facilities. At the nursing unit level, the cumulative phlebotomy volume exceeded the population average on patients admitted to critical care units, long term care units and medical wards. This trend was also reflected in the testing performance of service providers, where patients cared for by critical care physicians and internal medicine teams had greater than average phlebotomy volumes. Conclusions: The study demonstrates consistent findings with the critical care literature and identifies a patient population – elderly males – who may be at risk for greater phlebotomy volumes. This study also demonstrates that informatics-based methods can be used to quantify phlebotomy-related blood loss across a broad range of facilities, and identify patient and institution-related variables associated with higher total blood loss. This data set will also provide the ability to model the impact of interventions such as small-volume tubes, direct clinician education initiatives, and could be the basis for a feedback tool in the future. Given the widespread use of laboratory information systems throughout the industrialized world, this approach is readily transferable to other institutions, where it may be used to help reduce iatrogenic blood loss, reduce testing costs and improve patient outcomes. Disclosures: No relevant conflicts of interest to declare.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».