BACKGROUND 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. STUDY DESIGN AND METHODS 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. RESULTS 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). CONCLUSION 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.

Although overall RBC distributions have decreased over time, the proportion of O units has increased substantially.

Background and Purpose— Thrombolysis for acute ischemic stroke is strikingly time sensitive. Current guidelines require confirmation of a platelet count ≥100 000 before thrombolysis; however, obtaining this laboratory test may delay treatment. Methods— We queried our hospital database to identify patients with ICD-9 codes consistent with acute ischemic stroke from 2000 to 2005 and to determine platelet counts in these patients. Medical charts of patients with platelet counts <100 000 were reviewed to determine whether the patient had a known history of thrombocytopenia or conditions associated with thrombocytopenia. Results— A total of 1752 patients were identified, and 82 (4.7%) had a platelet count <100 000 at stroke onset. Only 6/1752 (0.3%) had a platelet count <100 000 which was not suspected based on initial history. All of these 6 patients had only mildly decreased platelet counts. Conclusions— An unsuspected platelet count <100 000 was found in 0.3% of patients at stroke presentation. In patients without a history of thrombocytopenia or predisposing factors, the benefit of earlier thrombolysis may outweigh the bleeding risk of inadvertently treating a patient with thrombocytopenia.

Background Evidence indicates the life‐saving benefits of early blood product transfusion in severe trauma resuscitation. Many of these products will be RhD‐positive, so understanding the D‐alloimmunization rate is important. Methods This was a multicenter, retrospective study whereby injured RhD‐negative patients between 18–50 years of age who received at least one unit of RhD‐positive red blood cells (RBC) or low titer group O whole blood (LTOWB) during their resuscitation between 1 January, 2010 through 31 December, 2019 were identified. If an antibody detection test was performed ≥14 days after the index RhD‐positive transfusion then basic demographic information was collected, including whether the patient became D‐alloimmunized. The overall D‐alloimmunization rate, and the rate stratified by the number of units transfused, were calculated. Results Data were collected from nine institutions. Five institutions reported fewer than 10 eligible patients each and were excluded. From the remaining four institutions, all from the USA, there were 235 eligible patients; 77 (random effects estimate: 32.7%; 95% CI: 19.1–50.1%) became D‐alloimmunized. Three of the institutions reported D‐alloimmunization rates ≥38.6%, while the remaining institution's rate was 12.2%. In both random and fixed‐effects models, the rate of D‐alloimmunization was not significantly different between those who received one RhD‐positive unit and those who received multiple RhD‐positive units. Conclusion In this large, multicenter study of injured patients, the overall rate of D‐alloimmunization fell within the range previously reported. The rate of D‐alloimmunization did not increase as the number of transfused RhD‐positive units increased. These data can help to inform RhD type selection decisions.

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