The incidence and adverse events of postoperative blood transfusion in spinal tuberculosis (TB) have attracted increasing attention. Our purpose was to develop a prediction model to evaluate blood transfusion risk after spinal fusion (SF) for spinal TB. Nomogram and machine learning algorithms, support vector machine (SVM), decision tree (DT), multi‑ layer perceptron (MLP), Naive Bayesian (NB), k-nearest neighbors (K-NN) and random forest (RF), were constructed to identifed predictors of blood transfusion from all spinal TB cases treated by SF in our department between May 2010 and April 2020. |
