Current literature has yet to evaluate the implementation of transfusion guidelines or the use of a decision support tool on intraoperative transfusion behavior. In the accompanying article, Picton et al1 analyze the effect of an intraoperative transfusion guideline on clinical behavior. The authors found that the implementation of a guideline was associated with both an increase in intraoperative hematocrit assessment and an increased use of restrictive transfusion practices. The use of a complementary software decision support tool had no increased effect on clinical practice. This editorial will discuss pretransfusion hematocrit assessment, transfusion guidelines, and clinical decision support tools as they relate to this study.
In the United States, more than 20 million blood components are transfused each year. Evidence has accumulated in recent decades describing both infectious and noninfectious risks associated with blood transfusions, leading many to advocate for hemovigilance strategies and best-practice guidelines with regard to blood product utilization. As a result, efforts have been made to standardize transfusion practices through the use of transfusion guidelines to minimize both morbidity and mortality and reduce the unnecessary use of a limited biological resource.
Beginning with the study of Hébert et al2 and continuing to present, the optimal transfusion threshold has been debated. Multiple studies have aimed to develop a definite hemoglobin or hematocrit target to incorporate into an evidence-based transfusion guideline. Many of the published blood transfusion guidelines and randomized control trials have focused on the distinction between liberal and restrictive transfusion thresholds in relation to patients’ pretransfusion hemoglobin or hematocrit. Liberal transfusion thresholds are generally considered 9–10 g/dL, while restrictive thresholds are considered 7–8 g/dL.3,4 In 2016, the AABB recommended that red blood cell (RBC) transfusion is not indicated until the hemoglobin is ≤7 g/dL in hospitalized hemodynamically stable adult patients.5 This recommendation reflects a push toward restrictive transfusion practices, such as the one incorporated into the study by Picton et al.1
A metanalysis conducted in 2014 suggests that restricting blood transfusion improves overall outcomes.6 However, concern is generated when restrictive transfusion guidelines are applied to specific populations. A 2016 Cochrane Database systematic review found no evidence that a restrictive transfusion strategy increased morbidity or mortality, although it did not comment on the transfusion safety in specific clinical subgroups.3 Another study found that mortality and morbidity improved with restrictive transfusion practices in patients with severe acute upper gastrointestinal bleeding.7 A 2015 study by de Almeida et al8 found that restrictive transfusion practices showed association with worse clinical outcomes in surgical oncology patients when compared to liberal transfusion practices. A suggestion of similar findings emerged from an analysis of patients undergoing cardiac surgery.9 However, it should be noted that mortality was a secondary outcome in what was an overall negative study that was not appropriately adjusted for multiple comparisons and had a P value of .045. While results reported by Picton et al1 show no change in 30-day mortality, myocardial infarction, or renal injury after the adoption of a restrictive transfusion practice, one must be careful not to apply these findings outside of the studied population.1 To account for these specific clinical subgroups, the AABB recommends that a threshold of 8 g/dL be used for those undergoing orthopedic and cardiac surgery, including those with preexisting cardiovascular disease.5 Clinicians must be cognizant of individual clinical subgroups when applying transfusion guidelines.
Limitations surrounding the use of a transfusion threshold may include a multitude of factors, such as failure to evaluate hemoglobin or hematocrit before the transfusion due to insufficient time between assessment and transfusion or the inability to rapidly access venous sites. Active bleeding and hypovolemia might appropriately preempt an intraoperative transfusion trigger. Nonetheless, when hemoglobin or hematocrit is unchecked before a transfusion, there is a risk that the transfusion may be clinically unnecessary. One study found that 9.2% of intraoperative transfusions failed to meet a physiological indication (mean arterial pressure or heart rate) or a hemoglobin threshold <10 g/dL10 and advocated for a decrease in extraneous intraoperative transfusions. The current article by Picton et al1 directly addresses this limitation by evaluating an educational intervention aimed at increasing the clinicians’ adherence to measure pretransfusion hemoglobin. The study shows an increase in compliance of pretransfusion hemoglobin assessment after implementation of their transfusion guideline.1
Electronic health systems increasingly utilize clinical decision support tools, including within the perioperative environment.11 This technology can be applied in the hopes of improving blood transfusion practices, with intraoperative decision support already proving effective in areas such as deep venous thrombosis and pulmonary embolus prophylaxis, among many others.12 Various studies reveal that implementation of a clinical decision support tool effectively reduces multiunit transfusions in a nonoperative setting,13,14 while use of these tools in the operative arena may have similar benefits. Razavi et al15 showed that reduced RBC transfusions after implementation of a novel clinical decision support tool in patients undergoing cardiothoracic surgery decreased RBC transfusions. In this study, incorporation of the clinical decision support tool led to lower pretransfusion hemoglobin levels, although the authors did not report overall changes in units transfused per patient—a measure of true blood utilization. Picton et al1 did not find that the use of a software tool influenced clinical behavior, likely due to its optional status and the timing of its rollout, but posits that a more intrusive alert system may have been more effective. Nevertheless, this area holds promise for ongoing research. Future analysis should be conducted to evaluate whether stratification by training status changes adoption rates. The hypothesis predicts that decision support tools are used more by trainees, who are more likely to adopt a new clinical behavior. Past literature has shown that the rate of adoption of clinical decision support tools is highest among entry-level trainees and lowest among attending physicians.16–19
Name: Daniel Hagaman.
Contribution: This author helped draft the initial manuscript.
Name: Michael A. Pilla, MD.
Contribution: This author helped edit and critically review the manuscript.
Name: Jesse M. Ehrenfeld, MD, MPH.
Contribution: This author helped edit and critically review the manuscript.
This manuscript was handled by: Marisa B. Marques, MD.
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