Validation of McCluskey Index for Massive Blood Transfusion Prediction in Liver Transplantation

2023 Clinical Update in Liver Transplantation

Liver transplantation continues to provide life-saving treatment for patients with end-stage liver disease. Advances in the field of transplant anesthesia continue to support the care of more complex patients. The use of extracorporeal membrane oxygenation has been described in critical care settings and cardiac surgery but may be a valuable option for specific conditions for patients undergoing liver transplantation. Changes to the allocation process for liver grafts now focus on acuity circles to reduce regional disparities. As the number of life-saving transplant surgeries increases, so does the need for specialty knowledge in the anesthetic considerations of these procedures. The specialty of transplant anesthesia continues to grow and develop to meet the demands of complex patients and the increased number of transplants performed. Liver transplantation can be a resource-demanding procedure, and predicting the need for massive transfusion can aid in planning and preparing for significant blood loss.

Risk factors of massive blood transfusion in liver transplantation: consequences and a new index for prediction including the donor

BACKGROUND

Massive blood transfusion (MBT) is a common occurrence in liver transplant (LT) patients. Recipient-related risk factors include cirrhosis, history of multiple surgeries and suboptimal donors. Despite advances in surgical techniques, anesthetic management and graft preservation have decreased the need for transfusions, this complication has not been completely eliminated.

METHODS

One thousand four hundred and sixty-nine LT were performed at our institution between May 2003 and December 2020, and data was available regarding transfusion for 1198 of them. We divided the patients into two groups, with regards to transfusion of 6 or more units of packed red blood cells in the first 24 h posttransplant, and we analyzed the differences between the groups.

RESULTS

Out of the 1198 patients, 607 (50.7%) met criteria for MBT. Survival was statistically lower at 1, 3, and 5 years when comparing the groups that had MBT to those that did not (92.6%, 85.2% and 79.7%, respectively, in the non MBT group, vs. 78.1%, 71.6% y 66.8%, respectively, in the MBT group). MBT was associated with a 1.5 mortality risk as opposed to non-MBT patients. Logistical regression analysis of our variables yielded the following results for a new model, including serum creatinine (OR 1.97), sodium (OR 1.73), hemoglobin (OR 1.99), platelets (OR 1.37), INR (OR 1.4), uDCD (OR 2.13) and split liver donation.

CONCLUSION

Massive blood transfusion impacts patient survival in a statistically significant way. The most significant risk factors are preoperative hemoglobin, INR and serum creatinine.

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data

Importance

Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management.

Objective

To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data.

Design, Setting, and Participants

This prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021.

Exposures

A deep learning-based real-time prediction model for massive transfusion.

Main Outcomes and Measures

Massive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time.

Results

Among 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P < .001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC).

Conclusions and Relevance

The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.

Advancing Prediction of Risk of Intraoperative Massive Blood Transfusion in Liver Transplantation With Machine Learning Models. A Multicenter Retrospective Study

Background

Liver transplantation surgery is often accompanied by massive blood loss and massive transfusion (MT), while MT can cause many serious complications related to high mortality. Therefore, there is an urgent need for a model that can predict the demand for MT to reduce the waste of blood resources and improve the prognosis of patients.

Objective

To develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms.

Methods

A total of 1,239 patients who underwent liver transplantation surgery in three large grade lll-A general hospitals of China from March 2014 to November 2021 were included and analyzed. A total of 1193 cases were randomly divided into the training set (70%) and test set (30%), and 46 cases were prospectively collected as a validation set. The outcome of this study was an intraoperative massive blood transfusion. A total of 27 candidate risk factors were collected, and recursive feature elimination (RFE) was used to select key features based on the Categorical Boosting (CatBoost) model. A total of ten machine learning models were built, among which the three best performing models and the traditional logistic regression (LR) method were prospectively verified in the validation set. The Area Under the Receiver Operating Characteristic Curve (AUROC) was used for model performance evaluation. The Shapley additive explanation value was applied to explain the complex ensemble learning models.

Results

Fifteen key variables were screened out, including age, weight, hemoglobin, platelets, white blood cells count, activated partial thromboplastin time, prothrombin time, thrombin time, direct bilirubin, aspartate aminotransferase, total protein, albumin, globulin, creatinine, urea. Among all algorithms, the predictive performance of the CatBoost model (AUROC: 0.810) was the best. In the prospective validation cohort, LR performed far less well than other algorithms.

Conclusion

A prediction model for massive blood transfusion in liver transplantation surgery was successfully established based on the CatBoost algorithm, and a certain degree of generalization verification is carried out in the validation set. The model may be superior to the traditional LR model and other algorithms, and it can more accurately predict the risk of massive blood transfusions and guide clinical decision-making.