B H Newman1, A J Roth. 1. American Red Cross Blood Services, Southeastern Michigan Region, Detroit, 48201, USA. newmanb@usa.redross.org
Abstract
BACKGROUND: Estimating the probability that a donor will have or not have an adverse event is useful for staff knowledge to give blood donors reassurance upon request. STUDY DESIGN AND METHODS: One-thousand donors from the general donor pool were interviewed for seven potential adverse events 3 weeks after a 525-mL whole-blood phlebotomy. The four most common adverse events were bruise (22.7%), sore arm (10.0%), fatigue (7.8%), and donor reaction (7.0%). A stepwise logistic regression analysis was performed based on five donor characteristics that were studied: age, weight, sex, race, and first-time donor status. The contribution of each significant or marginally significant factor to each adverse event was quantified. RESULTS: For donor reaction, weight (p < 0.0001) and age (p = 0.015) were significant contributors, and first-time donor status (p = 0.054) was a marginally significant contributor. An equation was derived, and the donor reaction rate can be estimated for a group based on the donor's weight, age, and first-time donor status. Similar analyses were performed for fatigue, sore arm, and bruise. CONCLUSION: Based on the derived formulas and with the use of a spreadsheet, data can be entered and the probability that a donor will have (or not have) a donor reaction, fatigue, sore arm, or bruise can be estimated.
BACKGROUND: Estimating the probability that a donor will have or not have an adverse event is useful for staff knowledge to give blood donors reassurance upon request. STUDY DESIGN AND METHODS: One-thousand donors from the general donor pool were interviewed for seven potential adverse events 3 weeks after a 525-mL whole-blood phlebotomy. The four most common adverse events were bruise (22.7%), sore arm (10.0%), fatigue (7.8%), and donor reaction (7.0%). A stepwise logistic regression analysis was performed based on five donor characteristics that were studied: age, weight, sex, race, and first-time donor status. The contribution of each significant or marginally significant factor to each adverse event was quantified. RESULTS: For donor reaction, weight (p < 0.0001) and age (p = 0.015) were significant contributors, and first-time donor status (p = 0.054) was a marginally significant contributor. An equation was derived, and the donor reaction rate can be estimated for a group based on the donor's weight, age, and first-time donor status. Similar analyses were performed for fatigue, sore arm, and bruise. CONCLUSION: Based on the derived formulas and with the use of a spreadsheet, data can be entered and the probability that a donor will have (or not have) a donor reaction, fatigue, sore arm, or bruise can be estimated.
Authors: Thelma T Gonçalez; Ester C Sabino; Karen S Schlumpf; David J Wright; Silvana Leao; Divaldo Sampaio; Pedro L Takecian; Anna B Proietti; Anna B Proitetti; Edward Murphy; Michael Busch; Brian Custer Journal: Transfusion Date: 2011-11-11 Impact factor: 3.157
Authors: Maurits D Hoogerwerf; Ingrid J T Veldhuizen; Wim L A M De Kort; Monique H W Frings-Dresen; Judith K Sluiter Journal: Blood Transfus Date: 2015-01-29 Impact factor: 3.443
Authors: Katja Van Den Hurk; Karlijn Peffer; Karin Habets; Femke Atsma; Pieternel C M Pasker-de Jong; Paulus A H Van Noord; Ingrid J T Veldhuizen; Wim L A M De Kort Journal: Blood Transfus Date: 2016-07-12 Impact factor: 3.443