Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery

Chen, Xiao; Pan, Junpeng; Li, Yi; Tang, Ruixin

doi:10.1007/s40520-023-02550-4

Cited by 2 publications

(2 citation statements)

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“…The Ridge Classifier had the best comprehensive performance and provided good net benefit for predicting perioperative blood transfusion. Unlike most previous studies, we focused on perioperative blood transfusion rather than intraoperative or postoperative transfusion, which decreased complexity and inconvenience of routine practice [ 33 , 34 ]. Moreover, existing prediction tools have typically specialized in modeling a limited subset of surgeries or population and did not incorporate diagnoses to develop models [ 16 , 26 ].…”

Section: Discussionmentioning

confidence: 99%

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Development of machine learning models to predict perioperative blood transfusion in hip surgery

Zang,

Hu,

et al. 2024

BMC Med Inform Decis Mak

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Background Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to develop machine learning models for predicting perioperative blood transfusion in hip surgery and identify significant risk factors. Methods Data of patients undergoing hip surgery between January 2013 and October 2021 in the Peking Union Medical College Hospital were collected to train and test predictive models. The primary outcome was perioperative red blood cell (RBC) transfusion within 72 h of surgery. Fourteen machine learning algorithms were established to predict blood transfusion risk incorporating patient demographic characteristics, preoperative laboratory tests, and surgical information. Discrimination, calibration, and decision curve analysis were used to evaluate machine learning models. SHapley Additive exPlanations (SHAP) was performed to interpret models. Results In this study, 2431 hip surgeries were included. The Ridge Classifier performed the best with an AUC = 0.85 (95% CI, 0.81 to 0.88) and a Brier score = 0.21. Patient-related risk factors included lower preoperative hemoglobin, American Society of Anesthesiologists (ASA) Physical Status > 2, anemia, lower preoperative fibrinogen, and lower preoperative albumin. Surgery-related risk factors included longer operation time, total hip arthroplasty, and autotransfusion. Conclusions The machine learning model developed in this study achieved high predictive performance using available variables for perioperative blood transfusion in hip surgery. The predictors identified could be helpful for risk stratification, preoperative optimization, and outcomes improvement.

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Section: Discussionmentioning

confidence: 99%

“…Fifth, the period of data was relatively large and there may be some unknown time-related effects. Finally, it is necessary to acknowledge that the odds of transfusion could not be represented based on present analysis, which is the common limitation in the studies of prediction models [ 16 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Development of machine learning models to predict perioperative blood transfusion in hip surgery

Zang,

Hu,

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

Khalifa,

Albadawy

2024

Computer Methods and Programs in Biomedicine Update

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Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery

Cited by 2 publications

References 33 publications

Development of machine learning models to predict perioperative blood transfusion in hip surgery

Development of machine learning models to predict perioperative blood transfusion in hip surgery

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions

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