Annals of Operations Research

, Volume 221, Issue 1, pp 377–406 | Cite as

Red Blood Cell Transfusion Safety: Probabilistic Risk Assessment and Cost/ Benefits of Risk Reduction Strategies

  • Anthony D. Slonim
  • Ebru K. Bish
  • Ryan S. Xie


While transfusion safety, particularly with respect to transfusion-transmitted infectious diseases, has improved dramatically over the past several decades, progress in other clinical processes of blood product transfusion continue with highly variable practices and human errors that contribute to adverse outcomes. In this paper, we study the adverse outcome risk in red blood cell (RBC) transfusion in the United States using Probabilistic Risk Assessment (PRA). PRA allows us to map, in a comprehensive manner, the various types of events that may contribute to an adverse outcome, including socio-technical factors such as the risk coming from human error; and allows us to formalize the logical relationships among those events and the adverse outcome risk. We utilize the PRA model to assess the risk to the patient from RBC transfusion in the United States, to identify the major risk points in the transfusion process, and to evaluate the costs and benefits of several risk reduction strategies. Our data come from published studies in the medical literature.

We find that the risk of a potentially severe outcome (e.g., mortality, major injury or other serious long-term consequences, a life threatening incident) from RBC transfusion lies in the interval [10.4327,511.2] per 100,000 units of RBC transfused, with a point estimate of 25.4527. The leading causes of severe outcomes include circulatory overload and bacterial infection. Acute hemolytic reactions, which are mainly caused by erroneous administration of the blood, also contribute significantly to severe outcomes of transfusion. Interestingly, our analysis indicates that an intervention that is targeted at reducing the risk of the erroneous administration of blood (through training programs or technology investments) has a higher potential impact in reducing the severe outcome risk from RBC transfusion than additional screening to further reduce the risk of transfusion-transmitted viral infections, of HIV 1-2, hepatitis B, and hepatitis C, which the lay public fears most. Furthermore, such an error reduction program will be more cost-effective than the additional screening of donated blood. Our study provides guidelines for public policy to improve the safety of RBC transfusion in the United States.


Transfusion Patient safety Risk Probabilistic risk assessment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alter, H. J., & Bradley, D. W. (1995). Non-A, non-B hepatitis unrelated to hepatitis C virus (non-ABC). Seminars in Liver Disease, 15, 110–120. CrossRefGoogle Scholar
  2. Alter, H. J., & Seeff, L. B. (2000). Recovery, persistence, and sequelae in HCV infection: A perspective on long-term outcome. Seminars in Liver Disease, 20, 17–35. CrossRefGoogle Scholar
  3. Bierbaum, B. E., Callaghan, J. J., & Galante, J. O. (1999). An analysis of blood management in patients having a total hip or knee arthroplasty. Journal of Bone and Joint Surgery. American Volume, 81A, 2–10. Google Scholar
  4. Biggerstaff, B. J., & Petersen, L. R. (2003). Estimated risk of transmission of the West Nile virus through blood transfusion in the US. Transfusion, 43, 1007–1017. CrossRefGoogle Scholar
  5. Brower, W. A., Nainan, O. V., & Han, X. (2000). Duration of viremia in hepatitis A virus infection. The Journal of Infectious Diseases, 182, 12–17. CrossRefGoogle Scholar
  6. Burgmeier, J. (2002). Failure mode and effect analysis: An application in reducing risk in blood transfusion. Journal of Quality Improvement, 28, 331–339. Google Scholar
  7. Busch, M. P. (2001). Insights into the epidemiology, natural history, and pathogenesis of hepatitis C infection from studies of infected donors and blood-product recipients. Transfusion Clinique Et Biologique, 8, 200–206. CrossRefGoogle Scholar
  8. Chen, B., Avrunin, G. S., Clarke, L. A., & Osterweil, L. J. (2006). Automatic fault tree derivation from little-JIL process definitions. In Q. Wang, D. Pfahl, D. Raffo, & P. Wernick (Eds.), Lecture notes in computer science: Vol. 3966. Software process change (pp. 150–158). Berlin: Springer. CrossRefGoogle Scholar
  9. Davies, A., Staves, J., Kay, J., Casbard, A., & Murphy, M. F. (2006). End-to-end electronic control of the hospital transfusion process to increase the safety of blood transfusion: Strengths and weaknesses. Transfusion, 46, 352–364. CrossRefGoogle Scholar
  10. Despotis, G., Eby, C., & Lublin, D. M. (2008). A review of tansfusion risks and optimal management of perioperative bleeding with cardiac surgery. Transfusion, 48, 1S–30S. CrossRefGoogle Scholar
  11. Dodd, R. Y. (1994). Adverse Consequences of blood transfusion: Quantitative risk estimates. In S. T. Nance (Ed.), Blood supply: Risks perceptions, and prospects for the future (pp. 1–24). Bethesda: American Association of Blood Banks. Google Scholar
  12. Dodd, R. Y., Notari, E. P., & Stramer, S. L. (2002). Current prevalence and incidence of infectious disease markers and estimated window-period risk in the American Red Cross blood donor population. Transfusion, 42, 975–979. CrossRefGoogle Scholar
  13. Dzik, W. H. (2005). Technology for enhanced transfusion safety. American Society of Hematology (pp. 476–482). Google Scholar
  14. Dzik, W. H., & Cooley, E. (2003). Lecture 2002: Transfusion safety in the hospital. Transfusion, 1190–1198. Google Scholar
  15. Glynn, S. A., Kleinman, S., & Wright, D. J. (2002). International application of the incidence rate/window period model. Transfusion, 42, 966–972. CrossRefGoogle Scholar
  16. Goodnough, L. T. (2003). Risk of blood transfusion. Critical Care Medicine, 31(12 (Suppl.)), 680–686. Google Scholar
  17. Goodnough, L. T., Shander, A., & Brecher, M. E. (2003). Transfusion medicine: Looking to the future. Lancet, 361, 161–169. CrossRefGoogle Scholar
  18. Greenwalt, T. J. (1997). A short history of transfusion medicine. Transfusion, 37, 550–563. CrossRefGoogle Scholar
  19. Guerrero, I. C., Weniger, B. C., & Schultz, M. G. (1983). Transfusion malaria in the United States, 1972–1981. Annals of Internal Medicine, 99, 221–226. CrossRefGoogle Scholar
  20. Haimes, Y. Y. (1998). Risk modeling, assessment, and management. New York: Wiley. Google Scholar
  21. Jackson, B. R., Busch, M. P., Stramer, S. L., & AuBuchon, J. P. (2003). The cost-effectiveness of NAT for HIV, HCV, and HBV in whole-blood donations. Transfusion, 43, 721–729. CrossRefGoogle Scholar
  22. Klein, H. G., Spahn, D. R., & Carson, J. L. (2007). Red blood cell transfusion in clinical practice. Lancet, 370, 415–426. CrossRefGoogle Scholar
  23. Kleinman, S. H., & Busch, M. (2001). Hepatitis B virus amplified and back in the blood safety spotlight. Transfusion, 41, 1081–1085. CrossRefGoogle Scholar
  24. Kleinman, S., Busch, M. P., & Korelitz, J. J. (1997). The incidence/window period model and its use to assess the risk of transfusion-transmitted HIV and HCV infection. Transfusion Medicine Reviews, 11, 155–172. CrossRefGoogle Scholar
  25. Kleinman, S., Chan, P., & Robillard, P. (2003a). Risks associated with transfusion of cellular blood components in Canada. Transfusion Medicine Reviews, 17(2), 120–162. CrossRefGoogle Scholar
  26. Kleinman, S. H., Kuhns, M. C., & Todd, D. S. (2003b). Frequency of HBV DNA detection in US blood donors positive for anti-HBC: Implications for transfusion transmission and donor screening. Transfusion, 43(6), 696–704. CrossRefGoogle Scholar
  27. Kumamoto, H., & Henley, E. J. (2000). Probabilistic risk assessment and management for engineers and scientists (2nd ed.). New York: Wiley-IEEE Press. CrossRefGoogle Scholar
  28. Laupacis, A., Brown, J., & Costello, B. (2001). Prevention of posttransfusion CMV in the era of universal leukoreduction: A consensus statement. Transfusion, 41, 560–569. CrossRefGoogle Scholar
  29. Leiby, D. A. (2001). Parasites and other emergent infectious agents. In S. Stramer (Ed.), Blood safety in the new millenium (pp. 55–78). Bethesda: American Association of Blood Banks. Google Scholar
  30. Linden, J. V., Wagner, K., & Voytovich, A. E. (2000). Transfusion errors in New York State: An analysis of 10 years’ experience. Transfusion, 40, 1207–1213. CrossRefGoogle Scholar
  31. Lyons, M., Adams, S., Woloshynowych, M., & Vincent, C. (2004). Human reliability analysis in healthcare: A review of techniques. The International Journal of Risk and Safety in Medicine 16, 223–237. Google Scholar
  32. Mayer, K. (1982). A Different view of transfusion safety—Type and screen, transfusion of Coombs incompatible cells, and fatal transfusion induced graft versus host disease. In H. F. Polesky & R. H. Walker (Eds.), Safety in transfusion practices. Skokie: College of American Pathologists. Google Scholar
  33. Mazzei, C. A., Popovsky, M. A., & Kopko, P. M. (2002). Noninfectious complications of blood transfusion. In M. E. Brecher (Ed.), AABB technical manual (14th ed.) (pp. 586–587). Bethesda: American Association of Blood Banks. Google Scholar
  34. Mungai, M., Tegtmeier, G., & Chamberland, M. (2001). Transfusion-transmitted malaria in the United States from 1963 through 1999. The New England Journal of Medicine, 344, 1973–1978. CrossRefGoogle Scholar
  35. Pineda, A. A., Vamvakas, E. C., & Gorden, L. D. (1999). Trends in the incidence of delayed hemolytic and delayed serologic transfusion reactions. Transfusion, 39, 1097–1103. CrossRefGoogle Scholar
  36. Popovsky, M. A., & Moore, S. B. (1985). Diagnostic and pathogenetic considerations in transfusion-related acute lung injury. Transfusion, 25, 573–577. CrossRefGoogle Scholar
  37. Popovsky, M. A., & Taswell, H. F. (1985). Circulatory overload: An underdiagnosed consequence of transfusion. Transfusion, 25, 469. Google Scholar
  38. Preiksaitis, J. K. (2000). The cytomegalovirus “safe” blood product: Is leukoreduction equivalent to antibody screening? Transfusion Medicine Reviews, 14, 112–136. CrossRefGoogle Scholar
  39. Rausand, M., & Høyland, A. (2004). Wiley series in probability and statistics. System reliability theory: Models, statistical methods, and applications (2nd ed.). Hoboken: Wiley-Interscience. Google Scholar
  40. Ross, S. M. (2007). Introduction to probability models. Amsterdam: Academic Press. Google Scholar
  41. Saxena, S., Ramer, L., & Shulman, I. A. (2004). A comprehensive assessment program to improve blood-administering practices Using the FOCUS–PDCA model. Transfusion, 44, 1350–1356. CrossRefGoogle Scholar
  42. Schaefer, A. J., Bailey, M. D., Shechter, S. M., & Roberts, M. S. (2004). Modeling medical treatment using Markov decision processes. In M. L. Brandeau, F. Sainfort, & W. P. Pierskalla (Eds.), Kluwer’s International Series Operations research and health care (pp. 593–612). Berlin: Springer. Google Scholar
  43. Schreiber, G. B., Busch, M. P., & Kleinman, S. H. (1996). The risk of transfusion-transmitted viral infection. The New England Journal of Medicine, 334, 1685–1690. CrossRefGoogle Scholar
  44. Stockwell, D. C., & Slonim, A. D. (2006). Quality and safety in the intensive care unit. Journal of Intensive Care Medicine, 21(4), 199–210. CrossRefGoogle Scholar
  45. Stramer, S. L. (2007). Current risks of transfusion-transmitted agents: A review. Archives of Pathology & Laboratory Medicine, 131, 702–707. Google Scholar
  46. Stramer, S. L., Dodd, R. Y., & Leiby, D. A. (2007). Blood donor screening for Chagas disease—United States, 2006–2007. Morbidity and Mortality Weekly Report, 56, 141–143. Google Scholar
  47. Trammell, S. R., & Wright, R. D. (1999). Integrating risk assessment into management systems. In Electronics manufacturing technology symposium twenty-fourth IEEE/CPMT (pp. 156–159). CrossRefGoogle Scholar
  48. Turner, C. L., Casbard, A. C., & Murphy, M. F. (2003). Barcode technology: Its role in increasing the safety of blood transfusion. Transfusion, 43, 1200–1209. CrossRefGoogle Scholar
  49. Williamson, L. M., & Warwick, R. M. (1995). Transfusion-associated graft-versus-host disease and its prevention. The Blood Review, 9, 251–261. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Anthony D. Slonim
    • 1
  • Ebru K. Bish
    • 2
  • Ryan S. Xie
    • 2
  1. 1.Carilion Medical CenterRoanokeUSA
  2. 2.Grado Department of Industrial and Systems EngineeringVirginia Polytechnic Institute and State UniversityBlacksburgUSA

Personalised recommendations