Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, 116021, China.
Department of Orthopedics, Xianyang Central Hospital, Xianyang, 712000, China.
BMC Musculoskelet Disord. 2021 Sep 25;22(1):825. doi: 10.1186/s12891-021-04715-6.
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), multilayer perceptron (MLP), Naive Bayesian (NB), k-nearest neighbors (K-NN) and random forest (RF), were constructed to identified predictors of blood transfusion from all spinal TB cases treated by SF in our department between May 2010 and April 2020. The prediction performance of the models was evaluated by 10-fold cross-validation. We calculated the average AUC and the maximum AUC, then demonstrated the ROC curve with maximum AUC.
The collected cohort ultimately was consisted of 152 patients, where 56 required allogeneic blood transfusions. The predictors were surgical duration, preoperative Hb, preoperative ABL, preoperative MCHC, number of fused vertebrae, IBL, and anticoagulant history. We obtained the average AUC of nomogram (0.75), SVM (0.62), k-NM (0.65), DT (0.56), NB (0.74), MLP (0.56) and RF (0.72). An interactive web calculator based on this model has been provided ( https://drwenleli.shinyapps.io/STTapp/ ).
We confirmed seven independent risk factors affecting blood transfusion and diagramed them with the nomogram and web calculator.
脊柱结核（TB）术后输血的发生率和不良事件受到越来越多的关注。我们的目的是建立一个预测模型，以评估脊柱融合（SF）治疗脊柱 TB 后的输血风险。
从 2010 年 5 月至 2020 年 4 月期间在我科接受 SF 治疗的所有脊柱 TB 病例中，构建了列线图和机器学习算法，包括支持向量机（SVM）、决策树（DT）、多层感知机（MLP）、朴素贝叶斯（NB）、k-最近邻（K-NN）和随机森林（RF），以确定输血的预测因子。模型的预测性能通过 10 倍交叉验证进行评估。我们计算了平均 AUC 和最大 AUC，然后展示了具有最大 AUC 的 ROC 曲线。
最终收集的队列由 152 例患者组成，其中 56 例需要异体输血。预测因子包括手术时间、术前 Hb、术前 ABL、术前 MCHC、融合椎体数、IBL 和抗凝史。我们得到了列线图（0.75）、SVM（0.62）、K-NM（0.65）、DT（0.56）、NB（0.74）、MLP（0.56）和 RF（0.72）的平均 AUC。基于该模型的交互式网络计算器已经提供（https://drwenleli.shinyapps.io/STTapp/）。
我们确认了 7 个独立的影响输血的危险因素，并通过列线图和网络计算器进行了图示。
