Analysis of a large dataset to identify predictors of blood transfusion in primary total hip and knee arthroplasty
ZeYu Huang,1,† Cheng Huang,2,† JinWei Xie,1,† Jun Ma,1,† GuoRui Cao,1 Qiang Huang,1 Bin Shen,1,** Virginia Byers Kraus,3,4 and FuXing Pei1,*Hip Knee
Background
The aim of this study was to identify the predictors of need for allogenic blood transfusion (ALBT) in primary lower limb total joint arthroplasty (TJA).
Study design and Methods
This study utilized a large dataset of 15,187 patients undergoing primary unilateral TJA. Risk factors and demographic information were extracted from the electronic health record. A predictive model was developed by both a random forest (RF) algorithm and logistic regression (LR). The area under the receiver operating characteristic curve (AUC-ROC) was used to compare the accuracy of the two methods.
Results
The rate of ALBT was 18.9% in total. Patient-related factors associated with higher risk of an ALBT included female sex (OR=1.26, p<0.001), American Society of Anesthesiologists (ASA) II (OR=1.32, p<0.001), ASAIII (OR=1.65, p<0.001) and ASAIV (OR=2.92, p<0.001). Surgery-related risk factors for ALBT were operative time (OR=1.00, p<0.001), drain use (OR=2.48, p<0.001) and amount of intraoperative blood loss (OR=1.003, p<0.001). Higher preoperative Hb (OR=0.99, p<0.001) and tranexamic acid (TXA) use (OR=0.43, p<0.001) were associated with decreased risk for ALBT. The RF model had a better predictive accuracy (AUC 0.84) than the LR model (AUC 0.77) (p<0.001).
Conclusion
The risk factors identified in the current study can provide specific, personalized perioperative ALBT risk assessment for a patient considering lower limb TJA. Furthermore, the predictive accuracy of the RF algorithm was significantly higher than that of LR, making it a potential tool for future personalized preoperative prediction of risk for perioperative ALBT.
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