We read with great interest a recently published work by Raphael et al.1 Their primary hypothesis was to establish independent causality of intraoperative transfusion of red blood cells (RBCs) on venous thromboembolism (VTE) after coronary bypass grafting surgery (n = 751,893) using the database of the Society of Thoracic Surgeons. In their secondary analyses, they further explored the types of VTE, mortality, readmission rate, and length of hospital stay. Their analyses of these data were extensive; however, there are issues that warrant further discussion. First, the overall exposure to RBCs was reported as 17.8% in the current study, but it is important to note the authors handled missing data on intraoperative/postoperative blood products as “no transfusion.” Previous studies in similar patients have shown that intraoperative transfusion rates varied between 12.8% and 60.3% due to variations in institutional practice.2 Missing data labeled as “no transfusion” and omission of any postoperative transfusion before any VTE might have skewed the relationship between intraoperative RBCs and VTE. Second, 37% of women versus 12% of men received blood transfusion. The women were 3 times more likely to receive more than 3–4 units of transfusion than men; however, the adjusted odds ratio for VTE was 0.90 for women. Others also demonstrated that women were more likely to receive >4 U RBC transfusion.3 Women tend to have a lower body mass index (BMI) compared with men, and thus they are more prone to dilutional anemia, and subsequently RBC transfusion. A causality relationship between RBCs and VTE was not supported by the lower odds of VTE in women. As a related issue, we would like to discuss their use of conditional imputation for handling missing data. The extent or presumed pattern of missing data was not specified, but BMI was mentioned as an example of conditional imputation. BMI is known to have a wide variation, particularly by geographic location. Conditional imputation without consideration of regional clustering might have distorted the relationship of BMI (and other predictors) and transfusion in development of the propensity score.4,5 Third, the authors attempted to isolate the effect of intraoperative RBCs by addressing or adjusting all the other risk factors for VTE. From Table 3, the exposures to non-RBC components, recombinant activated factor VII, and factor VIII inhibitor bypassing agent were increased in those with increased RBC transfusion. Hemostatic components were treated as a confounder in the model, but there may be causal associations with both acute and subacute VTE and arterial thromboses.6,7 Patients with severe postoperative bleeding tend to have prolonged hospital stay. The authors showed that cohorts with ≥3 units of RBC had longer hospital stay (mean, 9.8–11.2 days) compared with nontransfused patients (mean, 6.6 days). Although not captured in the database, delayed thromboprophylaxis during a prolonged hospital stay may also exacerbate VTE risk. Lastly, the authors utilized inverse probability treatment weighting method using inverse probability of treatment weighting based on the matching as shown in their Table 2; however, the authors did not mention the handling of extreme weights, which may lead to undue influence of otherwise less common groups (ie, patients with 3 or 4+ units of RBCs) (Tables S1–S10, https://links.lww.com/SLA/E307). While inverse probability treatment weighting adequately accounted for measured confounders pertinent to transfusion (eg, cardiopulmonary bypass [CPB] time), it is speculated that there are residual confounders that impact VTE rates.8 For example, extensive tissue injury and CPB are well known to trigger activations of endothelial cells and leukocytes. Cross-talks between coagulation and immune systems are referred to as “thrombo-inflammation,” and it plays a pivotal role in the pathogenesis of VTE.9 Patient-level inflammation parameters are neither routinely measured nor captured in the database. The statistical adjustment for CPB time does not account for inter-individual variability in inflammation and VTE risk. Given the modest area under the curve statistics they reported for these models, it seems clear there remains an unmet need to improve precision of VTE prediction by including patient-level biomarkers with sufficient sensitivity and specificity. ACKNOWLEDGMENTS A.L.B. conceptualized the letter, and writing up of the first draft of the letter. E.C.W., K.A.T., and K.S. have helped write the letter, and approved the final article. All authors have written and reviewed the article, and approved the final article.
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May 1, 2024
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