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Can a comparative database study help to develop an effective risk index system?

A dvances in surgical techniques have improved survival of musculoskeletal tumors. These procedures are usually extensive, especially if they are combined with reconstructive procedures, and can be accompanied by major blood loss. The use of an index system to predict blood loss during these procedures would be a useful tool to ensure that adequate blood products are available before the need rather than on an emergency basis. In this issue of TRANSFUSION, Thompson and colleagues present a retrospective analysis of risk factors associated with large-volume transfusion during surgeries for musculoskeletal tumors. The authors identified malignant tumors, bone tumors, sacral tumors, pelvic tumors, tumor size of more than 5 cm, and high ASA grade as risk factors for large-volume transfusion. The results of this study were more or less in agreement with a previous study by Kwai and coworkers. However, in contrast to the findings of Kwai and coworkers, the current study did not identify an association between the use of chemotherapeutic or radiotherapeutic agents and transfusion volume. Thompson and colleagues suggest that preoperative use of nonsteroidal anti-inflammatory drugs (NSAIDs) could have influenced the intraoperative bleeding and volume of the blood transfusion, although they do not provide supportive data. However, it may also be the case that the use of preoperative NSAIDs reduced the bleeding and blood transfusion. NSAIDs inhibit cyclooxygenase-2, which is responsible for the production of prostaglandin E2 and thromboxane A2, which are procarcinogenic, contribute to new vessel formation, and facilitate tumor growth. Thompson and colleagues avoided the use of hydroxyethyl starch solutions in the intraoperative fluid management as they have adverse effects on the coagulation process. However, excessive use of intraoperative crystalloids is not without adverse effects on patient outcome. The excessive use of crystalloids could result in dilution of the coagulation factors and consequently enhance the volume of blood transfusion. In addition, the excessive use of intraoperative crystalloids would result in disruption of the renal glycocalyx and the endothelial surface layer. The endothelial surface layer plays an important role in maintaining the proper functions of coagulation and immune systems. The use of albumin has been shown to maintain the integrity of endothelial surface layer, which could have effect on the volume of blood transfusion and the patients’ outcome. Maintaining intraoperative hemodynamic stability is very crucial as hemodynamic instability could contribute to coagulopathy and increase risk of blood transfusion. Thompson and colleagues set a 7 g/dL threshold for blood transfusion without consideration to the patients’ comorbidities. However, patients with preoperative cardiac or ischemic neurologic diseases may not be able to tolerate the low hemoglobin (Hb) threshold for blood transfusion set by the authors. Therefore, the increased mortality rate in patients with high ASA score noticed in the study could be attributed to low Hb threshold for transfusion. The goal of developing a successful risk index for the prediction of the volume of intraoperative blood transfusion during musculoskeletal surgeries should be considered separate and distinct from the goal of assessing association between various factors and transfusion volume. Along these lines, there have been great advances in the science of clinical prediction modeling (or data mining or machine learning). One such finding is that purposefully biasing multivariable regression coefficients toward zero can increase prediction accuracy—a phenomenon known in the literature as “shrinkage” or “regularization.” It is for this reason that it is impossible to simultaneously achieve both the goal of minimizing prediction error for the outcome and the goal of removing as much bias as possible in estimating degrees of association between various exposures and the outcome. Other types of prediction models may be better than standard linear regression analyses to predict outcome more accurately, for instance, by capturing complex interactions among predictors. That being said, the inherent difficulty in modeling an outcome such as number of units transfused should be recognized. Most patients receive no transfusions, while those who do vary with respect to transfusion volume. Predicting whether or not patients are transfused allows for application of the modern techniques already discussed at the expense of removing transfusion volume from consideration. Predicting transfusion volume requirements in the presence of low overall transfusion incidence is much more complex and further research is needed. © 2014 AABB