Blood products and liver transplantation: A strategy to balance optimal preparation with effective blood stewardship

BACKGROUND

Unanticipated transfusion requirements during liver transplantation can delay lifesaving intraoperative resuscitation and strain blood bank resources. Risk-stratified preoperative blood preparation can mitigate these deleterious outcomes.

STUDY DESIGN AND METHODS

A two-tiered blood preparation protocol for liver transplantation was retrospectively evaluated. Eleven binary variables served as criteria for high-risk (HR) allocation. Primary outcomes included red blood cell (RBC), plasma (FFP), and platelet (Plt) utilization. Secondary outcomes included product under- and overpreparation. Contingency tables for transfusion requirements above the population means were generated using 15 clinical variables. Modified protocols were developed and retrospectively optimized using the study population.

RESULTS

Of 225 recipients, 102 received HR preoperative orders, which correlated to higher intraoperative transfusion requirements. However, univariate analysis identified only two statistical risk factors per product: Hgb ≤7.8 g/dl (p < .001) and MELD ≥38 (p = .035) for RBCs, Hgb ≤7.8 g/dl (p = .002) and acute alcoholic hepatitis (p = 0.015) for FFP, and Hgb ≤7.8 g/dl (p = .001) and normothermic liver preservation (p = .037) for Plts. Based on these findings, we developed modified protocols for individual products, which were evaluated retrospectively for their effectiveness at reducing under-preparatory events while limiting product overpreparation. Cohort statistics were used to define the preparation strategy for each protocol. Retrospective comparative analysis demonstrated the superiority of the modified protocols by improving the under-preparation rate from 24% to <10% for each product, which required a 1.56-fold and 1.44-fold increase in RBC and FFP overpreparation, respectively. Importantly, there was no difference in Plt overpreparation.

DISCUSSION

We report translatable data-driven blood bank preparation protocols for liver transplantation.

Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).

A data-driven patient blood management strategy in liver transplantation

BACKGROUND AND OBJECTIVES

Blood utilization during liver transplant has decreased, but remains highly variable due to many complex surgical and physiologic factors. Previous models attempted to predict utilization using preoperative variables to stratify cases into two usage groups, usually using entire blood units for measurement. We sought to develop a practical predictive model using specific transfusion volumes (in ml) to develop a data-driven patient blood management strategy.

MATERIALS AND METHODS

This is a retrospective evaluation of primary liver transplants at a single institution from 2013 to 2015. Multivariable analysis of preoperative recipient and donor factors was used to develop a model predictive of intraoperative red-blood-cell (pRBC) use.

RESULTS

Of 256 adult liver transplants, 207 patients had complete transfusion volume data for analysis. The median intraoperative allogeneic pRBC transfusion volume was 1250 ml, and the average was 1563 ± 1543 ml. Preoperative haemoglobin, spontaneous bacterial peritonitis, preoperative haemodialysis and preoperative international normalized ratio together yielded the strongest model predicting pRBC usage. When it predicted <1250 ml of pRBCs, all cases with 0 ml transfused were captured and only 8·6% of the time >1250 ml were used. This prediction had a sensitivity of 0·91 and a specificity of 0·89. If predicted usage was >2000 ml, 75% of the time blood loss exceeded 2000 ml.

CONCLUSION

Patients likely to require low or high pRBC transfusion volumes were identified with excellent accuracy using this predictive model at our institution. This model may help predict bleeding risk for each patient and facilitate optimized blood ordering.