Evidence-based red cell transfusion in the critically ill: quality improvement using computerized physician order entry.
OBJECTIVE
The implementation of evidence-based practice poses a significant challenge in the intensive care unit. In this quality improvement intervention we assessed the effect of an institutional protocol and computerized decision support for red cell transfusion in the critically ill.


METHODS
We compared processes of care and outcomes during the two 3-month periods before and after the introduction of a multidisciplinary quality improvement intervention.


METHODS
Multidisciplinary intensive care units--medical, surgical, and mixed--in a tertiary academic center.


METHODS
Consecutive critically ill patients with anemia (hemoglobin of <10 g/dL).


METHODS
Using the computerized provider order entry, we developed an evidence-based decision algorithm for red cell transfusion in adult intensive care units.


RESULTS
We collected information on demographics, diagnosis, severity of illness, transfusion complications, and laboratory values. The main outcome measures were number of transfusions, proportion of patients who were transfused outside evidence-based indications, transfusion complications, and adjusted hospital mortality. The mean number of red cell transfusions per intensive care unit admission decreased from 1.08 +/- 2.3 units before to 0.86 +/- 2.3 units after the protocol (p<.001). We observed a marked decrease in the percentage of patients receiving inappropriate transfusions (17.7% vs. 4.5%, p< .001). The rate of transfusion complications was also lower in the period after the protocol (6.1% vs. 2.7%, p = .015). In the multivariate analysis, protocol introduction was associated with decreased likelihood of red cell transfusion (odds ratio, 0.43; 95% confidence interval, 0.30 to 0.62). Adjusted hospital mortality did not differ before and after protocol implementation (odds ratio, 1.12; 95% confidence interval, 0.69 to 1.8).


CONCLUSIONS
The implementation of an institutional protocol and decision support through a computerized provider order entry effectively decreased inappropriate red cell transfusions.
Rana
Rimki
R
Family Medicine Residency, Mercy Medical Center-North Iowa, Mason City, IA, USA.
Afessa
Bekele
B
Keegan
Mark T
MT
Whalen
Francis X
FX
Nuttall
Gregory A
GA
Evenson
Laura K
LK
Peters
Steve G
SG
Winters
Jeffrey L
JL
Hubmayr
Rolf D
RD
Moore
S Breanndan
SB
Gajic
Ognjen
O
D000465:Algorithms; D000740:Anemia / Q000628:therapy; D016638:Critical Illness / Q000628:therapy*; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D017707:Erythrocyte Transfusion / Q000592:standards*; D019317:Evidence-Based Medicine; D006801:Humans; D007362:Intensive Care Units; D010348:Patient Care Team; D011787:Quality of Health Care / Q000592:standards