Literature DB >> 16691136

Evidence-based red cell transfusion in the critically ill: quality improvement using computerized physician order entry.

Rimki Rana1, Bekele Afessa, Mark T Keegan, Francis X Whalen, Gregory A Nuttall, Laura K Evenson, Steve G Peters, Jeffrey L Winters, Rolf D Hubmayr, S Breanndan Moore, Ognjen Gajic.   

Abstract

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.
DESIGN: We compared processes of care and outcomes during the two 3-month periods before and after the introduction of a multidisciplinary quality improvement intervention.
SETTING: Multidisciplinary intensive care units--medical, surgical, and mixed--in a tertiary academic center. PATIENTS: Consecutive critically ill patients with anemia (hemoglobin of <10 g/dL). INTERVENTION: Using the computerized provider order entry, we developed an evidence-based decision algorithm for red cell transfusion in adult intensive care units.
MEASUREMENTS AND MAIN 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.

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Year:  2006        PMID: 16691136     DOI: 10.1097/01.CCM.0000220766.13623.FE

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  15 in total

1.  Evaluation of RBC Transfusion Practice in Adult ICUs and the Effect of Restrictive Transfusion Protocols on Routine Care.

Authors:  Kevin P Seitz; Jonathan E Sevransky; Greg S Martin; John D Roback; David J Murphy
Journal:  Crit Care Med       Date:  2017-02       Impact factor: 7.598

2.  Blood transfusion practice: a nationwide survey in Italy.

Authors:  Franco Verlicchi; Giuseppina Facco; Michela Macrì; Stefano Antoncecchi; Pietro Bonomo
Journal:  Blood Transfus       Date:  2011-05-12       Impact factor: 3.443

3.  Eight-year trend of acute respiratory distress syndrome: a population-based study in Olmsted County, Minnesota.

Authors:  Guangxi Li; Michael Malinchoc; Rodrigo Cartin-Ceba; Chakradhar V Venkata; Daryl J Kor; Steve G Peters; Rolf D Hubmayr; Ognjen Gajic
Journal:  Am J Respir Crit Care Med       Date:  2010-08-06       Impact factor: 21.405

4.  Performance of a computerized protocol for trauma shock resuscitation.

Authors:  Joseph F Sucher; Frederick A Moore; R Matthew Sailors; Ernest A Gonzalez; Bruce A McKinley
Journal:  World J Surg       Date:  2010-02       Impact factor: 3.352

5.  Red blood cell transfusion practices in acute lung injury: what do patient factors contribute?

Authors:  David J Murphy; David Howard; Angela Muriithi; Pedro Mendez-Tellez; Jonathan Sevransky; Carl Shanholtz; Giora Netzer; Peter J Pronovost; Dale M Needham
Journal:  Crit Care Med       Date:  2009-06       Impact factor: 7.598

Review 6.  Impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay, and mortality in intensive care units: a systematic review and meta-analysis.

Authors:  Mirela Prgomet; Ling Li; Zahra Niazkhani; Andrew Georgiou; Johanna I Westbrook
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

7.  Computerized provider order entry in the clinical laboratory.

Authors:  Jason M Baron; Anand S Dighe
Journal:  J Pathol Inform       Date:  2011-08-13

8.  Impact of computerized physician order entry (CPOE) system on the outcome of critically ill adult patients: a before-after study.

Authors:  Hasan M Al-Dorzi; Hani M Tamim; Antoine Cherfan; Mohamad A Hassan; Saadi Taher; Yaseen M Arabi
Journal:  BMC Med Inform Decis Mak       Date:  2011-11-19       Impact factor: 2.796

Review 9.  ICU staffing and patient outcomes: more work remains.

Authors:  David J Murphy; Eddy Fan; Dale M Needham
Journal:  Crit Care       Date:  2009       Impact factor: 9.097

10.  Prolonged acute mechanical ventilation and hospital bed utilization in 2020 in the United States: implications for budgets, plant and personnel planning.

Authors:  Marya D Zilberberg; Andrew F Shorr
Journal:  BMC Health Serv Res       Date:  2008-11-25       Impact factor: 2.655

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