Imagine you’re designing a futuristic airplane that needs to anticipate changes in weather patterns to ensure safe and efficient flights. In a similar way, researchers have developed a model using machine learning algorithms to predict the need for massive blood transfusions during liver transplantation surgery. By analyzing data from over 1,200 patients who underwent liver transplants, the researchers identified 15 key variables that contribute to the risk of massive blood transfusion. These variables include factors such as age, weight, hemoglobin levels, and various blood test results. The CatBoost algorithm emerged as the best performing model, demonstrating its ability to accurately predict the need for blood transfusions. This predictive model could significantly reduce the waste of blood resources and improve patient outcomes by guiding clinical decision-making. For those curious about the scientific details and potential applications of this research, it’s worth exploring the full article!

BackgroundLiver 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.ObjectiveTo develop a model for predicting intraoperative massive blood transfusion in liver transplantation surgery based on machine learning algorithms.MethodsA 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.ResultsFifteen 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.ConclusionA 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.

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