Computerized Physician Order Entry With Decision Support Decreases Blood Transfusions in Children

OBJECTIVE: Timely provision of evidence-based recommendations through computerized physician order entry with clinical decision support may improve use of red blood cell transfusions (RBCTs). METHODS: We performed a cohort study with historical controls including inpatients admitted between February 1, 2008, and January 31, 2010. A clinical decision-support alert for RBCTs was constructed by using current evidence. RBCT orders resulted in assessment of the patient's medical record with prescriber notification if parameters were not within recommended ranges. Primary end points included the average pretransfusion hemoglobin level and the rate of RBCTs per patient-day. RESULTS: In total, 3293 control discharges and 3492 study discharges were evaluated. The mean (SD) control pretransfusion hemoglobin level in the PICU was 9.83 (2.63) g/dL (95% confidence interval [CI]: 9.65–10.01) compared with the study value of 8.75 (2.05) g/dL (95% CI: 8.59–8.90) (P < .0001). The wards' control value was 7.56 (0.93) g/dL (95% CI: 7.47–7.65), the study value was 7.14 (1.01) g/dL (95% CI: 6.99–7.28) (P < .0001). The control PICU rate of RBCTs per patient-day was 0.20 (0.11) (95% CI: 0.13–0.27), the study rate was 0.14 (0.04) (95% CI: 0.11–0.17) (P = .12). The PICU's control rate was 0.033 (0.01) (95% CI: 0.02–0.04), and the study rate was 0.017 (0.007) (95% CI: 0.01–0.02) (P < .0001). There was no difference in mortality rates across all cohorts. CONCLUSIONS: Implementation of clinical decision-support alerts was associated with a decrease in RBCTs, which suggests improved adoption of evidence-based recommendations. This strategy might be widely applied to promote timely adoption of scientific evidence.

[1]  C. Longhurst,et al.  Decrease in Hospital-wide Mortality Rate After Implementation of a Commercially Sold Computerized Physician Order Entry System , 2010, Pediatrics.

[2]  J. Stamler,et al.  S-nitrosohemoglobin deficiency: A mechanism for loss of physiological activity in banked blood , 2007, Proceedings of the National Academy of Sciences.

[3]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[4]  Peter J. Pronovost,et al.  Barriers to translating evidence into practice , 2003, Current opinion in critical care.

[5]  P. Sperryn,et al.  Blood. , 1989, British journal of sports medicine.

[6]  M. Cabana,et al.  Why don't physicians follow clinical practice guidelines? A framework for improvement. , 1999, JAMA.

[7]  J. Lacroix,et al.  Length of storage and in vitro immunomodulation induced by prestorage leukoreduced red blood cells , 2009, Transfusion.

[8]  Melissa M. Honour,et al.  Assessment of education and computerized decision support interventions for improving transfusion practice , 2007, Transfusion.

[9]  T. Goss,et al.  Frequency and Outcomes of Blood Products Transfusion Across Procedures and Clinical Conditions Warranting Inpatient Care: An Analysis of the 2004 Healthcare Cost and Utilization Project Nationwide Inpatient Sample Database , 2010, American journal of medical quality : the official journal of the American College of Medical Quality.

[10]  Alastair Baker,et al.  Crossing the Quality Chasm: A New Health System for the 21st Century , 2001, BMJ : British Medical Journal.

[11]  R B Haynes,et al.  Bridges between health care research evidence and clinical practice. , 1995, Journal of the American Medical Informatics Association : JAMIA.

[12]  G. Nuttall,et al.  Evidence-based red cell transfusion in the critically ill: Quality improvement using computerized physician order entry* , 2006, Critical care medicine.

[13]  J. Collet,et al.  Transfusion strategies for patients in pediatric intensive care units. , 2007, The New England journal of medicine.

[14]  K Dave,et al.  A CRITICAL APPRAISAL , 2002 .

[15]  E. Balas,et al.  Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success , 2005, BMJ : British Medical Journal.

[16]  Robert W. Taylor,et al.  Red blood cell transfusions and nosocomial infections in critically ill patients* , 2006, Critical care medicine.

[17]  M. Pollack,et al.  Pediatric red blood cell transfusions increase resource use. , 2003, The Journal of pediatrics.

[18]  O. Gajic,et al.  The addition of decision support into computerized physician order entry reduces red blood cell transfusion resource utilization in the intensive care unit , 2007, American journal of hematology.

[19]  B. Reeves,et al.  Increased mortality, morbidity, and cost associated with red blood cell transfusion after cardiac surgery , 2008, Current opinion in cardiology.

[20]  L. Hayden,et al.  Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality , 2011 .

[21]  V. Fraser,et al.  Risk Factors for Nosocomial Primary Bloodstream Infection in Pediatric Intensive Care Unit Patients: A 2-Year Prospective Cohort Study , 2006, Infection Control &#x0026; Hospital Epidemiology.

[22]  M. Hazinski,et al.  PALS provider manual , 2006 .

[23]  C. Infante-Rivard,et al.  Survey on transfusion practices of pediatric intensivists* , 2002, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.

[24]  J. Lacroix,et al.  Determinants of red blood cell transfusions in a pediatric critical care unit: A prospective, descriptive epidemiological study* , 2005, Critical care medicine.

[25]  Increased Mortality, Postoperative Morbidity, and Cost After Red Blood Cell Transfusion in Patients Having Cardiac Surgery , 2007 .

[26]  A. Haines,et al.  Implementing findings of research , 1994, BMJ.

[27]  A. Randolph,et al.  Anemia, blood loss, and blood transfusions in North American children in the intensive care unit. , 2008, American journal of respiratory and critical care medicine.

[28]  B. Boulanger,et al.  Red blood cell arginase suppresses Jurkat (T cell) proliferation by depleting arginine. , 2008, Surgery.

[29]  Ellen Schwalenstocker,et al.  Use of Health Information Technology by Children's Hospitals in the United States , 2009, Pediatrics.

[30]  D. Fergusson,et al.  Clinical consequences of red cell storage in the critically ill , 2006, Transfusion.

[31]  B. Boulanger,et al.  Packed red blood cell-associated arginine depletion is mediated by arginase. , 2007, The Journal of trauma.

[32]  Chris A Rogers,et al.  Increased Mortality, Postoperative Morbidity, and Cost After Red Blood Cell Transfusion in Patients Having Cardiac Surgery , 2007, Circulation.

[33]  A. Slonim,et al.  Blood transfusions in children: a multi‐institutional analysis of practices and complications , 2007, Transfusion.

[34]  William L. Galanter,et al.  Application of Information Technology: A Trial of Automated Decision Support Alerts for Contraindicated Medications Using Computerized Physician Order Entry , 2005, J. Am. Medical Informatics Assoc..

[35]  C. Hollenbeak,et al.  Attributable Cost of Nosocomial Primary Bloodstream Infection in Pediatric Intensive Care Unit Patients , 2005, Pediatrics.

[36]  Eloa S. Adams A critical appraisal of “transfusion strategies for patients in pediatric intensive care units” by Lacroix J, Hebert PC, Hutchison, et al (N Engl J Med 2007; 356:1609–1619) , 2009, Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.