Wrong Blood In Tube Research Articles

Transfusion sample mislabelling and wrong blood in tube in the UK: Insights from the national comparative audits of blood transfusion in 2012 and 2022.

Samples for transfusion rejected due to mislabelling can lead to harm when a patient has to be re-bled or has a transfusion or procedure delayed. Electronic labelling systems which scan the patient's identification band and generate a label at their side aim to reduce mislabelling and misidentification leading to wrong blood in tube (WBIT) errors. The 2022 National Comparative audit of sample collection aimed to compare national rates of sample mislabelling and WBIT to the 2012 audit and to examine the impact of electronic systems. All UK hospitals were invited to provide data on rejected transfusion samples and WBIT incidents in 1 month (October 2022) and were asked if they had electronic labelling. Twenty-three thousand five hundred and eighty-four rejected samples were reported by 179 sites in 1 month. The rejection rate of 4.4% represents a 47% increase compared to 2012 (2.99%). There were 92 WBIT incidents, an incidence of 1 in 5882 samples-a 45% increase compared to 1 in 8547 in 2012. Twenty-three percent of sites can print a sample label at the patient's side, up by 224%. The six sites using only electronic sample labelling had a 46.9% lower rejection rate than sites using only hand-labelling but still reported WBIT. The increase in sample rejection and WBIT may reflect pressures facing clinical staff, zero tolerance policies and the two-sample rule. A human factors approach to understanding and tackling underlying reasons locally is recommended. Electronic systems are associated with fewer labelling errors, but careful implementation and training is needed to maximise their safety benefits.

Weakening of A Antigen in Myelodysplastic Syndrome-Mimicking a Case of Wrong Blood in Tube.

Weakening of A Antigen in Myelodysplastic Syndrome-Mimicking a Case of Wrong Blood in Tube.

A Machine Learning Model for the Routine Detection of "Wrong Blood in Complete Blood Count Tube" Errors.

Current laboratory procedures may fail to detect wrong blood in tube (WBIT) errors. Machine learning models have the potential to improve WBIT error detection, as demonstrated by proof-of-concept studies. The models developed so far, however, are not appropriate for routine use because they are unable to handle missing values and have low positive predictive value (PPV). In this study, a machine learning model suitable for routine use was developed. A model was trained and a preliminary evaluation performed on a retrospective data set of 135 128 current and previous patient complete blood count (CBC) results. The model was then applied prospectively to routine samples tested in a public hospital laboratory over a period of 22 weeks. Each week, the 5 samples identified by the model as most likely to be WBIT errors underwent further investigation by testing blood group and red cell phenotype. The study assessed the number of WBIT errors that were missed by current procedures but detected by the model, as well as the PPV of the model. The model was applied prospectively to 38 187 CBC results that had passed routine laboratory checks. One hundred and ten samples were identified for further testing and 12 WBIT errors were detected. The PPV of the model was 10.9%. A machine learning model suitable for routine use was able to identify WBIT errors missed by the laboratory's current procedures. Machine learning models are valuable for the identification of WBIT errors, and their validation and deployment in clinical laboratories would improve patient safety.

Explainability does not improve biochemistry staff trust in artificial intelligence-based decision support.

Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user trust, yet this has not been established in healthcare contexts. For advanced algorithms such as artificial neural networks, the generation of explanations is not trivial, but requires the use of a second algorithm. The assumption of improved user trust should therefore be investigated to determine if it justifies the additional complexity. Biochemistry staff completed a wrong blood in tube (WBIT) error identification task with the help of an ADS system. One-half of the volunteers were provided with both ADS predictions and explanations for those predictions, while the other half received predictions alone. The two groups were compared in terms of their rate of agreement with ADS predictions, as an index of user trust, and WBIT error detection performance. Since the AI model used to generate predictions was known to out-perform laboratory staff, increased trust was expected to improve user performance. Volunteers reviewed 1590 sets of results. The volunteers provided with explanations demonstrated no difference in their rate of agreement with the ADS system compared to volunteers receiving predictions alone (83.3% versus 81.8%, p = 0.46). The two volunteer groups were also equivalent in accuracy, sensitivity and specificity for WBIT error identification (p-values >0.78). For a WBIT error identification task, there was no evidence to justify the additional complexity of explainability on the grounds of increased user trust.

Adding Automation and Independent Dual Verification to Reduce Wrong Blood in Tube (WBIT) Events.

Transfusions remain a complicated procedure involving many disciplines performing various steps. Pretransfusion specimen identification errors remain a concern. Over the past two decades, system changes have been made and minimal improvements in the error rates have been seen. Wrong blood in tube (WBIT) events may lead to mistransfusions of components with life-threatening complications. A continuous quality improvement effort involving the introduction of electronic patient identification at the point of pretransfusion specimen collection (an automated system improvement), manual independent dual verification, and periodic education (human process system improvements) were implemented. Both automated and human system process improvements resulted in greater than 10-fold reduction in WBIT events and a 47% reduction in mislabeled specimens. Diligent improvement and implementation of combination automated system processes and human protocols with continuous monitoring led to great reductions in WBIT error rates and labeling discrepancies, leading to an increase in system safety. These combinations of improvement can lead to more decreased error rates if applied to other critical process steps in the transfusion process.

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood count (CBC) results against the benchmark of manual review of results by laboratory staff. De-identified current and previous (within seven days) CBC results were used in the computer simulation of WBIT errors. 101015sets of samples were used to develop machine learning models using artificial neural network, extreme gradient boosting, support vector machine, random forest, logistic regression, decision trees (one complex and one simple) and k-nearest neighbours algorithms. The performance of these models, and of manual review by laboratory staff, was assessed on a separate data set of 1940samples. Volunteers manually reviewing results identified WBIT errors with an accuracy of 85.7%, sensitivity of 80.1% and specificity of 92.1%. All machine learning models exceeded human-level performance (p-values for all metrics were <.001). The artificial neural network model was the most accurate (99.1%), and the simple decision tree was the least accurate (96.8%). Sensitivity for the machine learning models varied from 95.7% to 99.3%, and specificity varied from 96.3% to 98.9%. This study provides preliminary evidence supporting the value of machine learning for detecting WBIT errors affecting CBC results. Although further work addressing practical issues is required, substantial patient-safety benefits await the successful deployment of machine learning models for WBIT error detection.

Machine learning for detecting wrong blood in tube errors

Wrong blood in tube (WBIT) errors are a persisting patient safety issue in clinical laboratory practice. Post-analytically, these errors are identified by a combination of automated delta checks and manual review of results by laboratory staff. However, machine learning may offer a superior approach. Evidence is emerging that machine learning models outperform both delta checks and review of results by laboratory staff. This finding challenges the assumption of human superiority on the task and highlights the need to establish new laboratory workflows that optimise contributions from both machine learning and staff.

Implementation of a blood bank generated tube for second blood group determination: Challenges, yield, and cost.

BackgroundThe second blood group determination or group check sample is a process of verifying the ABO group with a second blood sample prior to transfusion. It has been used to detect errors related to wrong blood in tube (WBIT) events and reduce the risk of ABO‐incompatible transfusions. To prevent the clinical team from collecting the group check sample at the same time as the first sample, a tan top tube only available from the blood bank was introduced for second blood group determinations if drawn within 24 h of the first group and screen.Study design and methodsThis is a retrospective study analyzing data from 2005 to 2020 before and after the implementation of the blood bank supplied tan top tube for group check. The number of WBIT events, transfusion delays, and health care costs were determined.ResultsThe number of WBIT events remained unchanged throughout the time period. No delays in transfusion or procedure were reported due to the tan top tube group check. There was no increase in group O transfusions over time. In comparison to using an ethylenediaminetetraacetic acid (EDTA) tube, the tan top tube was estimated to add an extra yearly cost of $790.79 Canadian dollars.ConclusionSecond blood group determination using the blood bank supplied tan top tube did not increase the number of WBIT events detected but ensured an independent sample draw. A minimal incremental cost of implementing the tan top tube was noted with no delay in transfusions or procedures.

Proficiency-based progression intern training to reduce critical blood sampling errors including ‘wrong blood in tube’

Background: Blood sampling errors including ‘wrong blood in tube’ (WBIT) may have adverse effects on clinical outcomes. WBIT errors occur when the blood sample in the tube is not that of the patient identified on the label. This study aims to determine the effect of proficiency-based progression (PBP) training in phlebotomy on the rate of blood sampling errors (including WBIT). Methods: A non-randomised controlled trial compared the blood sampling error rate of 43 historical controls who had not undergone PBP training in 2016 to 44 PBP trained interventional groups in 2017. In 2018, the PBP training programme was implemented and the blood sampling error rate of 46 interns was compared to the 43 historical controls in 2016. Data analysis was performed using logistic regression analysis adjusting for sample timing. Results: In 2016, 43 interns had a total blood sample error rate of 2.4%, compared to 44 interns in 2017, who had error rate of 1.2% (adjusted OR=0.50, 95% CI 0.36-0.70; &lt;0.01). In 2018, 46 interns had an error rate of 1.9% (adjusted OR=0.89, 95% CI 0.65-1.21; p=0.46) when compared to the 2016 historical controls. There were three WBITs in 2016, three WBITs in 2017 and five WBITs in 2018. Conclusions: The study demonstrates that PBP training in phlebotomy has the potential to reduce blood sampling errors. Trial registration number: NCT03577561

Factors associated with wrong blood in tube errors: An international case series – The BEST collaborative study

A wrong blood in tube (WBIT) error signifies a blood sample that does not match the patient identified on the sample label. WBIT errors can result in ABO mistransfusions. In this international, multicenter, descriptive study, healthcare facilities provided detailed information on WBIT errors occurring from 1/1/2019 to 12/31/2020. Factors contributing to WBIT errors were classified as protocol violations, knowledge gaps, and slips/lapses. 331 WBIT errors were compiled from 36 centers in 11 countries. WBIT errors were most frequently detected through pretransfusion sample testing (191, 58%), with 38 (20%) detected by a second ("check") sample. WBIT errors were divided almost evenly between intended patient drawn/wrong label applied (166, 50%) and wrong patient drawn/intended label applied (158, 48%). Information on contributing factors was available for 260 WBIT errors; most involved a combination of protocol violations and slips/lapses (139, 53%). The most frequent contributing factor was another patient's sample labels or tubes being available during phlebotomy (61%). Protocol violations were more likely to result in wrong patient being drawn (p=.0007). In 43 WBIT errors, electronic positive patient identification (ePPID) was not used when available or was used incorrectly. Protocol violations and slips/lapses frequently contribute to WBIT errors. Sample collection processes should be designed to minimize error opportunities; staff should be educated on why protocol compliance is critical for patient safety. Using ePPID does not eliminate all WBIT errors. Institutions using ePPID may elect to require check sample verification as an added safety measure.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors.

Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model's predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

Proficiency-based progression intern training to reduce critical blood sampling errors including ‘wrong blood in tube’

Background: Blood sampling errors including ‘wrong blood in tube’ (WBIT) may have adverse effects on clinical outcomes. WBIT errors occur when the blood sample in the tube is not that of the patient identified on the label. This study aims to determine the effect of proficiency-based progression (PBP) training in phlebotomy on the rate of blood sampling errors (including WBIT). Methods: A non-randomised controlled trial compared the blood sampling error rate of 43 historical controls who had not undergone PBP training in 2016 to 44 PBP trained interventional groups in 2017. In 2018, the PBP training programme was implemented and the blood sampling error rate of 46 interns was compared to the 43 historical controls in 2016. Data analysis was performed using logistic regression analysis adjusting for sample timing. Results: In 2016, 43 interns had a total blood sample error rate of 2.4%, compared to 44 interns in 2017, who had error rate of 1.2% (adjusted OR=0.50, 95% CI 0.36-0.70; &lt;0.01). In 2018, 46 interns had an error rate of 1.9% (adjusted OR=0.89, 95% CI 0.65-1.21; p=0.46) when compared to the 2016 historical controls. There were three WBITs in 2016, three WBITs in 2017 and five WBITs in 2018. Conclusions: The study demonstrates that PBP training in phlebotomy has the potential to reduce blood sampling errors. Trial registration number: NCT03577561

Emergency departments are higher-risk locations for wrong blood in tube errors.

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. 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. 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). 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 outpatient clinics. EDs represent higher-risk clinical locations for WBIT errors, and electronic positive patient identification (ePPID) may provide a greater impact on safety in EDs relative to other clinical areas.

Two Sample Policy

Background and Objectives: Pretransfusion testing has undergone remarkable changes over the past few decades starting from manual methods to semi-automation to full automation, but sample collection from the right person is out of scope of blood center. Through this study, we tried to address this issue by introducing two-sample policy in our institute. The objective of this study is to emphasize the importance of using two different samples for type and screen and crossmatch. Methods: This is a retrospective study extending over a period of 3.5 years (January 2015–June 2018) in a tertiary care health center. Retrospective data collections were done for the period post implementation of two-sample policy in the institute. As per the two-sample policy, two samples of the same patient would be collected, one at the time of type and screen and other at the time of blood component arrangement request. In case of any discordance between the two samples, incident is generated. Results: Of total 110,949 requests, 85,326 blood/blood components were issued, 56879 of which were for packed red blood cells (PRBCs) and a total of 34,922 PRBC units were released in the study duration. A total of 22/(0.06%) errors were recorded as grouping discrepancy between type and screen sample and sample received for blood component arrangement request. Incidentally, all 22 cases were for the arrangement of PRBC. Out of 22 errors, 20 were near misses and were detected in immunohematology laboratory at the time of compatibility testing and 2/22 (9.09%) errors were actual no harm events. Conclusion: The problem of wrong blood in tube (WBIT) is still a challenge faced by many laboratory persons. The issue can be resolved by training dedicated staff for sample collection. Although this policy has many drawbacks, such as added costs of sample collection and double testing, inconvenience to the patient, delay in providing blood, and increased type O blood use, we would like to emphasize that it would be helpful in error reduction.

Trend in ABO-incompatible RBC transfusion-related fatalities reported to the FDA, 2000-2019.

ABO-incompatible red blood cell (RBC) transfusions and acute hemolytic reactions occur infrequently, yet resultant fatalities are reported to the US Food and Drug Administration (FDA) every year. We describe a 20-year retrospective study of reported mistransfusion cases to identify temporal trends, common causes, and corrective actions taken to prevent recurrence. ABO-incompatible RBC transfusion-related fatalities reported to the FDA in 2000-2019 were reviewed for patient demographics, primary attributed cause, contributing factors, and corrective actions. Eighty reported deaths after ABO-incompatible RBC transfusion occurred in the 20-year period. A decrease in the number of cases after 2008 was sustained through 2019 (mean 6 cases/y, 2000-2009 vs 2 cases/y, 2010-2019). The estimated rate of reported mistransfusion fatalities was 1 per 2 million RBC units transfused in 2000-2009 and 1 per 7.14 million RBC units in 2010-2019 (P < .0001). Administration errors (wrong patient or wrong unit transfused) and sample collection errors (wrong blood in tube [WBIT]) significantly decreased over time but remained the most common causes. In all WBIT cases, verification of patients' ABO type with a second sample or historical type was not performed before transfusion; 16 of 19 (84%) institutions that reported corrective actions subsequently instituted this requirement. In the other categories, 22 of 58 (38%) facilities reported plans for technological process improvements, such as electronic patient identification. The rate of reported fatalities from ABO-incompatible RBC transfusion has significantly decreased in the past decade. Still, about two cases are reported each year, highlighting gaps in best practices and areas for improvement.

Validation of phlebotomy performance metrics developed as part of a proficiency-based progression initiative to mitigate wrong blood in tube

AimsThe purpose of this study was to (1) characterise the procedure of phlebotomy, deconstruct it into its constituent parts and develop a performance metric for the purpose of training healthcare...

Evaluation of incompatible crossmatch

Background and Objectives: Crossmatching is one of the bases of pretransfusion testing. Resolving problems in crossmatching should be carried out after proper planning and following departmental guidelines, and hence that time is not wasted, or blood withheld from patient unnecessarily. The aim of this study was to find the prevalence and cause of incompatible crossmatch and to formulate root cause analysis to help ensure safe transfusion. Materials and Methods: This was a prospective study conducted at a tertiary care hospital in North India, from December 2018 to November 2019. Request for blood components was received along with 2 ml labeled sample in ethylenediaminetetraacetic acid and plain tube. Crossmatching was done by column agglutination method in polyspecific (IgG + C3d) bead cards by ortho clinical diagnostic using semi-automated biovue. In case of any incompatible result, it was resolved using appropriate steps. Results: During the study period from December 2018 to November 2019, only 67 (0.65%) of the 10,320 samples received were found to be crossmatch incompatible and evaluated and appropriate donor units issued. The crossmatch incompatibility was much higher in the females (46, 68.7%) than the males (21, 31.3%). The direct antiglobulin test (DAT) was positive in nine patient samples, and the indirect antiglobulin test was found positive in 37 incompatible crossmatch units. Eleven cases of incompatible crossmatch were due to wrong blood in tube, seven due to contamination of reagents, and three due to DAT-positive donor units. Conclusion: In this study, alloimmunization (55.2%) was the most prevalent cause of incompatible crossmatch, and the most common alloantibody identified was anti-E. Incompatible crossmatch poses a challenge in the field of transfusion medicine. Root cause analysis is a systemic method for identifying all the contributing factors to a problem, so that the corrective action can be taken. A logical stepwise approach will enable the provision of safe transfusion.

Blood Transfusion: a Guide to Clinical Decision Making

Nowadays, management of hemotherapy is regulated in Germany by the transfusion act and several guidelines while the transfusing physician is responsible for correct implementation at the bedside. Indications for blood products have to be carefully adapted to the patient's current clinical situation and pre-existing diseases have to be considered as well. Today, for most perioperative elective surgeries, evidence-based transfusion thresholds for packed red blood cell concentrates (RBC) have been defined and should be considered. Platelet concentrates (PC) have to be transfused in bleeding patients with thrombocytopenia or -pathy. In addition, PC are indicated prophylactically in clinically stable, not bleeding patients with a platelet trough count of less than 10/nl. Therapeutic plasma or fresh frozen plasma (FFP) is indicated for balanced substitution of coagulation factors, coagulation inhibitors and fibrinolysis factors in massive transfusion settings, where dilution of coagulation factors takes place. Plasma exchange in adults with thrombotic-thrombocytopenic purpura (TTP) or adult hemolytic-uremic syndrome (HUS) also requires FFP. In addition, FFP might be indicated, if no coagulation factor concentrate is available (e.g. FV deficiency). Adverse transfusion reactions are rare nowadays; however, hemolytic and allergic reactions are not too uncommon. For severe hemolytic transfusion reactions, in almost all cases, wrong blood in tube (WBIT) at the diagnostic bedside blood withdrawal or a mix-up of blood components before transfusion is causative. Massive transfusion situations require a proactive management, which includes RBC, FFP and potentially also PC and coagulation factor concentrates. Prior to elective surgery, anemic patients should be diagnosed and treated for the cause of their anemia, if possible.

Electronic patient identification for sample labeling reduces wrong blood in tube errors.

Wrong blood in tube (WBIT) errors are a preventable cause of ABO-mismatched RBC transfusions. Electronic patient identification systems (e.g., scanning a patient's wristband barcode before pretransfusion sample collection) are thought to reduce WBIT errors, but the effectiveness of these systems is unclear. Part 1: Using retrospective data, we compared pretransfusion sample WBIT rates at hospitals using manual patient identification (n = 16 sites; >1.6 million samples) with WBIT rates at hospitals using electronic patient identification for some or all sample collections (n = 4 sites; >0.5 million samples). Also, we compared WBIT rates after implementation of electronic patient identification with preimplementation WBIT rates. Causes and frequencies of WBIT errors were evaluated at each site. Part 2: Transfusion service laboratories (n = 18) prospectively typed mislabeled (rejected) samples (n = 2844) to determine WBIT rates among samples with minor labeling errors. Part 1: The overall unadjusted WBIT rate at sites using manual patient identification was 1:10,110 versus 1:35,806 for sites using electronic identification (p < 0.0001). Correcting for repeat samples and silent WBIT errors yielded overall adjusted WBIT rates of 1:3046 for sites using manual identification and 1:14,606 for sites using electronic identification (p < 0.0001), with wide variation among individual sites. Part 2: The unadjusted WBIT rate among mislabeled (rejected) samples was 1:71 (adjusted WBIT rate, 1:28). In this study, using electronic patient identification at the time of pretransfusion sample collection was associated with approximately fivefold fewer WBIT errors compared with using manual patient identification. WBIT rates were high among mislabeled (rejected) samples, confirming that rejecting samples with even minor labeling errors helps mitigate the risk of ABO-incompatible transfusions.

Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors.

An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called "wrong blood in tube" (WBIT) errors and potential patient harm. Here, we sought to develop a machine learning-based, multianalyte delta check algorithm to detect WBIT errors and mitigate patient harm. We simulated WBIT errors within sets of routine inpatient chemistry test results to develop, train, and evaluate five machine learning-based WBIT detection algorithms. The best-performing WBIT detection algorithm we developed was based on a support vector machine and incorporated changes in test results between consecutive collections across 11 analytes. This algorithm achieved an area under the curve of 0.97 and considerably outperformed traditional single-analyte delta checks. Machine learning-based multianalyte delta checks may offer a practical strategy to identify WBIT errors prior to test reporting and improve patient safety.