Predicting the possibility of blood transfusion after total knee arthroplasty based on machine learning algorithm

doi:10.12307/2021.344

Abstract

Abstract: BACKGROUND: To maintain the hemodynamic stability of patients with total knee replacement, blood transfusion is necessary, but this is often accompanied by adverse reactions. Studying the risk factors of blood transfusion after total knee replacement can help determine which patients need blood transfusion, which is conducive to preoperative evaluation and clinical decision-making.
OBJECTIVE: To establish a prediction model based on a machine learning algorithm and explore its predictive value in predicting the possibility of blood transfusion after total knee replacement.
METHODS: The clinical data after total knee arthroplasty in the panyu hospital of chinese medicine from January 2012 to December 2019 were retrospectively analyzed, and divided the patients into a non-transfusion group and a blood transfusion group according to whether blood transfusion was performed after the operation. The data of sex, age, body mass index, preoperative hemoglobin, ASA anesthesia score, anesthesia mode, operation duration, operation type, smoking history, past medical history, and the use of insulin were compared between the two groups. The above-mentioned potential influencing factors were incorporated into logistic regression, support vector machine, random forest and XGBoost algorithm to establish four kinds of prediction model, obtain the importance of predictive variables and draw receiver working curve, and test the predictive value of the model.
RESULTS AND CONCLUSION: (1) We included a total of 634 samples, including 527 untransfused total knee arthroplasty patients and 107 total knee arthroplasty patients requiring transfusion. (2) Combining the four models, the top five prediction importance scores were hemoglobin, age, operation length, body mass index and operation type were the top five variables with the highest correlation. (3) The areas under the curve of logistic regression, support vector machine, random forest and XGBoost algorithm were 0.816, 0.864, 0.773 and 0.888, respectively. By comparison, the XGBoost algorithm performed best. (4) It is concluded that the machine learning model based on XGBoost algorithm can accurately predict the risk of blood transfusion in patients after total knee arthroplasty, which is conducive to preoperative evaluation and clinical decision-making. Hemoglobin, age, length of surgery, body mass index, and type of surgery may be important predictors of the risk of transfusion after total knee arthroplasty.

Key words: machine learning, total knee arthroplasty, blood transfusion, risk factors, logistic regression, forecast model, retrospective analysis, influencing factors

CLC Number:

Chen Chaofeng, He Dadong, Liang Jincheng, He Zhijun. Predicting the possibility of blood transfusion after total knee arthroplasty based on machine learning algorithm[J]. Chinese Journal of Tissue Engineering Research, 2021, 25(36): 5792-5797.

Figures/Tables 4

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