Emergency departments are higher-risk locations for wrong blood in tube errors
Abstract
BACKGROUND: Wrong blood in tube (WBIT) errors can lead to ABO mistransfusions. It is unknown if WBIT errors are more likely in specific healthcare locations or if specific collection practices influence the commission of WBIT errors.
STUDY DESIGN AND METHODS: Data on pretransfusion samples from calendar year 2019 were collected retrospectively by 39 transfusion services in nine countries. We compared the proportion of WBIT errors made in emergency departments (EDs), inpatient wards, and outpatient clinics.
RESULTS: In total, 143 WBIT errors were detected among 1,394,862 samples for an unadjusted aggregate WBIT proportion of 1.03/10,000 samples. Using a pooled random effects model, the WBIT proportion was estimated to be significantly higher in EDs (1.23/10,000 samples, 95% CI 0.62-2.43) than inpatient wards (0.71/10,000, 95% CI 0.44-1.14; p < .001) or outpatient clinics (0.24/10,000, 95% CI 0.08-0.65; p < .001) and significantly higher in inpatient wards than outpatient clinics (p = .043). The use of electronic positive patient identification (ePPID) systems was associated with a significantly lower WBIT proportion in the ED (odds ratio, OR: 0.32, 95% CI: 0.11-0.96, p = .041), but not in inpatient wards (OR: 0.45, 95% CI: 0.20-1.01, p = .054) or outpatient clinics (OR: 1.95, 95% CI: 0.39-9.74, p = .415).
DISCUSSION: Normalized for the number of samples drawn per location, the WBIT proportion in EDs was 1.7 times higher than inpatient wards and 5.1 times higher than…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.