Predicting massive blood transfusions in liver transplantation: the McCluskey Risk Index revised 20 years later
Jacopo Belfiore1, Gabriella Licitra2, Alessandro Cardu1
1Transplant Anesthesia and Critical Care, Azienda Ospedaliera Universitaria Pisana, University School of Medicine, Pisa, Italy.
Background:
Liver transplantation (LT) is a life-saving procedure for end-stage liver disease (ESLD), yet massive perioperative transfusions (MT) remain a significant concern. The McCluskey Risk Index (McRI), a widely used tool for predicting MT, was developed nearly two decades ago and does not fully account for recent advancements in LT practices and patient demographics. This study aims to evaluate the validity of the McRI in a contemporary LT cohort and propose a revised McRI (revMcRI) incorporating additional predictors.
Materials And Methods:
A retrospective observational cohort study was conducted on 604 LT patients at a high-volume national center in Italy over five years. Patient demographic, clinical, and laboratory data were analyzed to assess MT risk, defined as transfusion of ≥6 packed red blood cell (PRBC) units within 24 hours of LT. Univariable and multivariable logistic regression analyses identified significant predictors, which were incorporated into the revMcRI. The discriminatory power of the revMcRI was evaluated using the area under the receiver operating characteristic curve (AUROC).
Results:
Among the cohort, 24.7% of patients required MT. The original McRI demonstrated predictive validity but did not fully capture contemporary risk factors. The revised model, revMcRI, incorporated MELD score, donation after circulatory death (DCD), and additional laboratory parameters. The revMcRI showed superior predictive performance compared to the original McRI, with AUROC values of 0.8121 and 0.8117 for MT defined as ≥6 and ≥5 PRBC units, respectively (p=0.02 and p=0.01 for AUROC comparison).
Discussion:
The revMcRI enhances the predictive accuracy of MT in LT by integrating modern risk factors. This refined model enables improved preoperative risk stratification and perioperative blood management, with potential implications for transfusion medicine, anesthesiology, and transplant surgery. Further validation in multicenter cohorts is warranted.
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