Transfusion in the ICU Interest Group: Evidence-based red cell transfusion in the critically ill: Quality improvement using computerized physician order entry

Mayo Clinic - Rochester, Рочестер, Minnesota, United States
Critical Care Medicine (Impact Factor: 6.31). 08/2006; 34(7):1892-7. DOI: 10.1097/01.CCM.0000220766.13623.FE
Source: PubMed

ABSTRACT 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.
We compared processes of care and outcomes during the two 3-month periods before and after the introduction of a multidisciplinary quality improvement intervention.
Multidisciplinary intensive care units--medical, surgical, and mixed--in a tertiary academic center.
Consecutive critically ill patients with anemia (hemoglobin of <10 g/dL).
Using the computerized provider order entry, we developed an evidence-based decision algorithm for red cell transfusion in adult intensive care units.
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).
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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    • "Variation in clinical transfusion practice has long been recognized, particularly among acutely ill hospitalized patients [1-4]. Several well-powered randomized controlled clinical trials of certain groups of adult medical and surgical patients support the notion that restrictive red blood cell (RBC) transfusion strategies result in similar or better patient outcomes compared to a more liberal strategy [5-10]. "
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    ABSTRACT: Background Randomized controlled trial evidence supports a restrictive strategy of red blood cell (RBC) transfusion, but significant variation in clinical transfusion practice persists. Patient characteristics other than hemoglobin levels may influence the decision to transfuse RBCs and explain some of this variation. Our objective was to evaluate the role of patient comorbidities and severity of illness in predicting inpatient red blood cell transfusion events. Methods We developed a predictive model of inpatient RBC transfusion using comprehensive electronic medical record (EMR) data from 21 hospitals over a four year period (2008-2011). Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization. Model predictors included administrative data (age, sex, comorbid conditions, admission type, and admission diagnosis), admission hemoglobin, severity of illness, prior inpatient RBC transfusion, admission ward, and hospital. Results The study cohort included 275,874 patients who experienced 444,969 hospitalizations. The 24 hour and overall inpatient RBC transfusion rates were 7.2% and 13.9%, respectively. A predictive model for transfusion within 24 hours of hospital admission had a C-statistic of 0.928 and pseudo-R2 of 0.542; corresponding values for the model examining transfusion through the entire hospitalization were 0.872 and 0.437. Inclusion of the admission hemoglobin resulted in the greatest improvement in model performance relative to patient comorbidities and severity of illness. Conclusions Data from electronic medical records at the time of admission predicts with very high likelihood the incidence of red blood transfusion events in the first 24 hours and throughout hospitalization. Patient comorbidities and severity of illness on admission play a small role in predicting the likelihood of RBC transfusion relative to the admission hemoglobin.
    BMC Health Services Research 05/2014; 14(1):213. DOI:10.1186/1472-6963-14-213 · 1.71 Impact Factor
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    • "Another study showed that CPOE in the ICU was associated with significant reduction in the time between ordering and reporting stat tests [26]. Development of evidence-based decision algorithm for red cell transfusion facilitated by CPOE led to significant reduction in inappropriate transfusions and decrease in blood transfusion from 1.08 ± 2.3 units 0.86 ± 2.3 units (p < 0.001)[27]. In a before-after study at a 20-bed tertiary pediatric ICU, Del Beccaro, et al observed a statistically insignificant decrease in mortality from 4.2% to 3.46% in the 13-month pre- and post-CPOE implementation respectively [9]. "
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    ABSTRACT: Computerized physician order entry (CPOE) systems are recommended to improve patient safety and outcomes. However, their effectiveness has been questioned. Our objective was to evaluate the impact of CPOE implementation on the outcome of critically ill patients. This was an observational before-after study carried out in a 21-bed medical and surgical intensive care unit (ICU) of a tertiary care center. It included all patients admitted to the ICU in the 24 months pre- and 12 months post-CPOE (Misys®) implementation. Data were extracted from a prospectively collected ICU database and included: demographics, Acute Physiology and Chronic Health Evaluation (APACHE) II score, admission diagnosis and comorbid conditions. Outcomes compared in different pre- and post-CPOE periods included: ICU and hospital mortality, duration of mechanical ventilation, and ICU and hospital length of stay. These outcomes were also compared in selected high risk subgroups of patients (age 12-17 years, traumatic brain injury, admission diagnosis of sepsis and admission APACHE II > 23). Multivariate analysis was used to adjust for imbalances in baseline characteristics and selected clinically relevant variables. There were 1638 and 898 patients admitted to the ICU in the specified pre- and post-CPOE periods, respectively (age = 52 ± 22 vs. 52 ± 21 years, p = 0.74; APACHE II = 24 ± 9 vs. 24 ± 10, p = 0.83). During these periods, there were no differences in ICU (adjusted odds ratio (aOR) 0.98, 95% confidence interval [CI] 0.7-1.3) and in hospital mortality (aOR 1.00, 95% CI 0.8-1.3). CPOE implementation was associated with similar duration of mechanical ventilation and of stay in the ICU and hospital. There was no increased mortality or stay in the high risk subgroups after CPOE implementation. The implementation of CPOE in an adult medical surgical ICU resulted in no improvement in patient outcomes in the immediate phase and up to 12 months after implementation.
    BMC Medical Informatics and Decision Making 11/2011; 11(1):71. DOI:10.1186/1472-6947-11-71 · 1.83 Impact Factor
    • "Orders for RBCs not meeting guidelines are flagged by the computer system and transfusion service staff are electronically alerted to review flagged orders with the ordering clinician. In another example from transfusion medicine, Rana et al., demonstrated a significantly decreased rate of inappropriate transfusion upon integration of a transfusion algorithm into CPOE.[25] In a cardiac intensive care unit setting, Wang et al., found that integration of practice guidelines into standard admissions order templates significantly decreased the use of laboratory tests without compromising care.[26] "
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    ABSTRACT: Clinicians have traditionally ordered laboratory tests using paper-based orders and requisitions. However, paper orders are becoming increasingly incompatible with the complexities, challenges, and resource constraints of our modern healthcare systems and are being replaced by electronic order entry systems. Electronic systems that allow direct provider input of diagnostic testing or medication orders into a computer system are known as Computerized Provider Order Entry (CPOE) systems. Adoption of laboratory CPOE systems may offer institutions many benefits, including reduced test turnaround time, improved test utilization, and better adherence to practice guidelines. In this review, we outline the functionality of various CPOE implementations, review the reported benefits, and discuss strategies for using CPOE to improve the test ordering process. Further, we discuss barriers to the implementation of CPOE systems that have prevented their more widespread adoption.
    08/2011; 2(1):35. DOI:10.4103/2153-3539.83740
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