Predisposing factors for allogeneic blood transfusion in patients with ankylosing spondylitis undergoing primary unilateral total hip arthroplasty: a retrospective study

Bian, Tao; Zhang, Liang; Man, Siliang; Li, Hongchao; Dou, Yong; Zhou, Yixin

doi:10.1186/s13018-022-03464-z

Cited by 1 publication

(2 citation statements)

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“…As for surgery-specific variables, it was well-accepted that patients with longer operation time were at significantly increased risk of blood transfusion. Our study was consistent with past findings [ 16 , 39 ]. Moreover, our data showed that the correlation between diagnosis and blood transfusion risk was important, which has been reported in previous studies.…”

Section: Discussionsupporting

confidence: 94%

“…The risk factors screened by the Ridge classifier were consistent with clinical practice and previous literature. In agreement with previous studies, lower preoperative hemoglobin and higher ASA Physical Status have been regarded as important risk factors for transfusion in this study [ 16 , 39 , 40 ]. Additionally, anemia was a strong factor that increased the risk of transfusion.…”

Section: Discussionsupporting

confidence: 92%

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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: Discussionsupporting

confidence: 94%