Differentiating pulmonary transfusion reactions using recipient and transfusion factors. Issue 7 (3rd May 2017)

• Record Type: Journal Article
• Authors: Roubinian, Nareg H.; Looney, Mark R.; Keating, Sheila; Kor, Daryl J.; Lowell, Clifford A.; Gajic, Ognjen; Hubmayr, Rolf; Gropper, Michael; Koenigsberg, Monique; Wilson, Gregory A.; A. Matthay, Michael; Toy, Pearl; Murphy, Edward L.
• Is Part Of: Transfusion. Volume 57:Issue 7(2017)
• Page Start: 1684
• Page End: 1690
• Publication Date: 2017-05-03
• Subjects: Hematology -- Periodicals; Blood -- Transfusion -- Periodicals; Blood Group Antigens -- Periodicals; Blood Preservation -- Periodicals; Blood Transfusion -- Periodicals; 615
• DOI: 10.1111/trf.14118
• Languages: English
• ISSNs: 0041-1132

Abstract

Abstract : BACKGROUND: It is increasingly recognized that recipient risk factors play a prominent role in possible transfusion‐related acute lung injury (pTRALI) and transfusion‐associated circulatory overload (TACO). We hypothesized that both transfusion and recipient factors including natriuretic peptides could be used to distinguish TRALI from TACO and pTRALI. STUDY DESIGN AND METHODS: We performed a post hoc analysis of a case‐control study of pulmonary transfusion reactions conducted at the University of California at San Francisco and Mayo Clinic, Rochester. We evaluated clinical data and brain natriuretic peptides (BNP) levels drawn after transfusion in patients with TRALI (n = 21), pTRALI (n = 26), TACO (n = 22), and controls (n = 24). Logistic regression and receiver operating characteristics curve analyses were used to determine the accuracy of clinical and biomarker predictors in differentiating TRALI from TACO and pTRALI. RESULTS: We found that pTRALI and TACO were associated with older age, higher fluid balance, and elevated BNP levels relative to those of controls and TRALI. The following variables were useful in distinguishing cases of pTRALI and TACO from TRALI: age more than 70 years, BNP levels more than 1000 pg/mL, 24‐hour fluid balance of more than 3 L, and a lower number of transfused blood components. Using the above variables, our logistic model had a 91% negative predictive value in the differential diagnosis of TRALI. CONCLUSIONS: Models incorporating readily available clinical and biomarker data can be used to differentiate transfusion‐related respiratory complications. Additional studies examining recipient risk factors and the likelihood of TRALI may be useful in decision making regarding donor white blood cell antibody testing.