MétaCan
Menu
Back to cohort

Institution-Wide Quantification of Iatrogenic Blood Loss Using a Novel Informatics-Driven Methodology

2010· article· en· W2550150059 on OpenAlexaff
D L Ledingham, Don Doiron, Bryan Crocker, Calvino Cheng

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsQueen Elizabeth II Health Sciences CentreCapital District Health AuthorityDalhousie University
Fundersnot available
KeywordsPhlebotomyMedicinePopulationEmergency medicineBlood transfusionBlood lossAnemiaIntensive care medicineSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.295
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicBlood donation and transfusion practicesFrench-language works237,207