Insun Park , Jae Hyon Park , Jongjin Yoon , Chang-Hoon Koo , Ah-Young Oh , Jin-Hee Kim , Jung-Hee Ryu
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引用次数: 0
Abstract
Background
This study aimed to develop a machine learning classifier for predicting intraoperative blood transfusion in non-cardiac surgeries.
Methods
Preoperative data from 6255 patients were extracted from the VitalDB database, an open-source registry. The primary outcome was the area under the receiver operating characteristic (AUROC) curve of ML classifiers in predicting intraoperative blood transfusion, defined as the receipt of at least one unit of packed red blood cells. Five different machine learning algorithms including logistic regression, random forest, adaptive boosting, gradient boosting, and the extremely gradient boosting classifiers were used to construct a binary classifier for intraoperative blood transfusion, and their predictive abilities were compared.
Results
337 (5%) patients received intraoperative blood transfusion. In the test-set, the logistic regression classifier demonstrated the highest AUROC (0.836, 95% CI, 0.795–0.876), followed by the gradient boosting classifier (0.810, 95% CI, 0.750–0.868), AdaBoost classifier (0.776, 95% CI, 0.722–0.829), random forest classifier (0.735, 95% CI, 0.698–0.771), and XGBoost classifier (0.721, 95% CI, 0.695–0.747). The logistic regression classifier showed a higher AUROC compared to that of a multivariable logistic regression model (0.836 vs. 0.623, P < 0.001). Among various parameters used to construct the logistic regression classifier, the top three most important features were operation time (0.999), preoperative serum hemoglobin level (0.785), and open surgery (0.530).
Conclusion
We successfully developed various ML classifiers using readily available preoperative data to predict intraoperative transfusion in patients undergoing non-cardiac surgeries. In particular, the logistic regression classifier demonstrated the best performance in predicting intraoperative transfusion.
期刊介绍:
Transfusion Clinique et Biologique, the official journal of the French Society of Blood Transfusion (SFTS):
- an aid to training, at a European level
- the only French journal indexed in the hematology and immunology sections of Current Contents
Transfusion Clinique et Biologique spans fundamental research and everyday practice, with articles coming from both sides. Articles, reviews, case reports, letters to the editor and editorials are published in 4 editions a year, in French or in English, covering all scientific and medical aspects of transfusion: immunology, hematology, infectious diseases, genetics, molecular biology, etc. And finally, a convivial cross-disciplinary section on training and information offers practical updates.
Readership:
"Transfusers" are many and various: anesthetists, biologists, hematologists, and blood-bank, ICU and mobile emergency specialists...