Richard M Kaufman1, Anh Dinh2, Claudia S Cohn3, Mark K Fung4, Jed Gorlin5, Stacy Melanson1, Michael F Murphy6, Alyssa Ziman7, Allahna L Elahie8, Danielle Chasse9, Lynsi Degree4, Nancy M Dunbar9, Walter H Dzik10, Peter Flanagan11, Kimberly Gabert12, Tina S Ipe13, Bryon Jackson14, Debra Lane15, Elisabetta Raspollini16, Charles Ray9, Yudit Sharon17, Martin Ellis17, Kathleen Selleng18, Julie Staves6, Philip Yu19, Michelle Zeller8, Mark Yazer12. 1. Department of Pathology, Brigham and Women's Hospital, Boston, MA. 2. Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA. 3. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN. 4. Department of Pathology, University of Vermont, Burlington, VT. 5. Memorial Blood Centers, St. Paul, MN. 6. Oxford University Hospitals Foundation Trust, Oxford, United Kingdom. 7. Department of Pathology and Laboratory Medicine, UCLA Health, Los Angeles, CA. 8. Department of Medicine, McMaster University, Hamilton, Canada. 9. Dartmouth-Hitchcock Medical Center, Department of Pathology and Laboratory Medicine, Lebanon, NH. 10. Department of Pathology, Massachusetts General Hospital, Boston, MA. 11. New Zealand Blood Service, Auckland, New Zealand. 12. Department of Pathology and the Institute for Transfusion Medicine, University of Pittsburgh, Pittsburgh, PA. 13. Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX. 14. Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA. 15. Canadian Blood Services, Winnipeg, Canada. 16. Ospedale Maggiore Policlinico, Milan, Italy. 17. Meir Medical Center, Kfar Saba, Israel. 18. University Medicine Greifswald, Institute for Immunology and Transfusion Medicine, Greifswald, Germany. 19. St. Paul's Hospital, Vancouver, Canada.
Abstract
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.
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.
Authors: Anas Haroun; Majeda A Al-Ruzzieh; Najah Hussien; Abdelrahman Masa'ad; Rateb Hassoneh; Ghada Abu Alrub; Omar Ayaad Journal: Asian Pac J Cancer Prev Date: 2021-04-01
Authors: Evan M Bloch; Aaron A R Tobian; Shmuel Shoham; Daniel F Hanley; Thomas J Gniadek; Edward R Cachay; Barry R Meisenberg; Kimberly Kafka; Christi Marshall; Sonya L Heath; Aarthi Shenoy; James H Paxton; Adam Levine; Donald Forthal; Yuriko Fukuta; Moises A Huaman; Alyssa Ziman; Jill Adamski; Jonathan Gerber; Daniel Cruser; Seble G Kassaye; Giselle S Mosnaim; Bela Patel; Ryan A Metcalf; Shweta Anjan; Ronald B Reisler; Anusha Yarava; Karen Lane; Nichol McBee; Amy Gawad; Jay S Raval; Martin Zand; Matthew Abinante; Patrick B Broderick; Arturo Casadevall; David Sullivan; Kelly A Gebo Journal: Transfusion Date: 2022-04-04 Impact factor: 3.337