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. 2020 Nov;60(11):2565-2580.
doi: 10.1111/trf.16019. Epub 2020 Sep 13.

Validation of predictive models identifying patients at risk for massive transfusion during liver transplantation and their potential impact on blood bank resource utilization

Aliaksei Pustavoitau  1 , Nicole A Rizkalla  1 , Brooke Perlstein  2 , Promise Ariyo  1 , Asad Latif  3 , April J Villamayor  1 , Steven M Frank  1 , William T Merritt  1 , Andrew M Cameron  4 , Benjamin Philosophe  4 , Shane Ottmann  4 , Jacqueline M Garonzik Wang  4 , Russell N Wesson  4 , Ahmet Gurakar  5 , Allan Gottschalk  1
Affiliations

Validation of predictive models identifying patients at risk for massive transfusion during liver transplantation and their potential impact on blood bank resource utilization

Aliaksei Pustavoitau et al. Transfusion. 2020 Nov.

Abstract

Background: Intraoperative massive transfusion (MT) is common during liver transplantation (LT). A predictive model of MT has the potential to improve use of blood bank resources.

Study design and methods: Development and validation cohorts were identified among deceased-donor LT recipients from 2010 to 2016. A multivariable model of MT generated from the development cohort was validated with the validation cohort and refined using both cohorts. The combined cohort also validated the previously reported McCluskey risk index (McRI). A simple modified risk index (ModRI) was then created from the combined cohort. Finally, a method to translate model predictions to a population-specific blood allocation strategy was described and demonstrated for the study population.

Results: Of the 403 patients, 60 (29.6%) in the development and 51 (25.5%) in the validation cohort met the definition for MT. The ModRI, derived from variables incorporated into multivariable model, ranged from 0 to 5, where 1 point each was assigned for hemoglobin level of less than 10 g/dL, platelet count of less than 100 × 109 /dL, thromboelastography R interval of more than 6 minutes, simultaneous liver and kidney transplant and retransplantation, and a ModRI of more than 2 defined recipients at risk for MT. The multivariable model, McRI, and ModRI demonstrated good discrimination (c statistic [95% CI], 0.77 [0.70-0.84]; 0.69 [0.62-0.76]; and 0.72 [0.65-0.79], respectively, after correction for optimism). For blood allocation of 6 or 15 units of red blood cells (RBCs) based on risk of MT, the ModRI would prevent unnecessary crossmatching of 300 units of RBCs/100 transplants.

Conclusions: Risk indices of MT in LT can be effective for risk stratification and reducing unnecessary blood bank resource utilization.

Keywords: blood allocation; liver transplantation; massive transfusion; predictive modeling.

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References

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