Department of Joint Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Department of Orthopaedics and Traumatology, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.
BMC Musculoskelet Disord. 2024 Jun 10;25(1):459. doi: 10.1186/s12891-024-07570-3.
The risk factors for excessive blood loss and transfusion during total knee arthroplasty (TKA) remain unclear. The present study aimed to determine the risk factors for excessive blood loss and establish a predictive model for postoperative blood transfusion.
This retrospective study included 329 patients received TKA, who were randomly assigned to a training set (n = 229) or a test set (n = 100). Univariate and multivariate linear regression analyses were used to determine risk factors for excessive blood loss. Univariate and multivariate logistic regression analyses were used to determine risk factors for blood transfusion. R software was used to establish the prediction model. The accuracy and stability of the models were evaluated using calibration curves, consistency indices, and receiver operating characteristic (ROC) curve analysis.
Risk factors for excessive blood loss included timing of using a tourniquet, the use of drainage, preoperative ESR, fibrinogen, HCT, ALB, and free fatty acid levels. Predictors in the nomogram included timing of using a tourniquet, the use of drainage, the use of TXA, preoperative ESR, HCT, and albumin levels. The area under the ROC curve was 0.855 (95% CI, 0.800 to 0.910) for the training set and 0.824 (95% CI, 0.740 to 0.909) for the test set. The consistency index values for the training and test sets were 0.855 and 0.824, respectively.
Risk factors for excessive blood loss during and after TKA were determined, and a satisfactory and reliable nomogram model was designed to predict the risk for postoperative blood transfusion.
全膝关节置换术（TKA）过程中失血过多和输血的风险因素仍不清楚。本研究旨在确定失血过多的风险因素，并建立术后输血的预测模型。
本回顾性研究纳入了 329 例接受 TKA 的患者，他们被随机分配到训练集（n=229）或测试集（n=100）。采用单因素和多因素线性回归分析确定失血过多的风险因素。采用单因素和多因素逻辑回归分析确定输血的风险因素。使用 R 软件建立预测模型。通过校准曲线、一致性指数和受试者工作特征（ROC）曲线分析评估模型的准确性和稳定性。
失血过多的风险因素包括使用止血带的时机、引流的使用、术前 ESR、纤维蛋白原、HCT、ALB 和游离脂肪酸水平。列线图中的预测因素包括使用止血带的时机、引流的使用、TXA 的使用、术前 ESR、HCT 和白蛋白水平。训练集和测试集的 ROC 曲线下面积分别为 0.855（95%CI，0.800 至 0.910）和 0.824（95%CI，0.740 至 0.909）。训练集和测试集的一致性指数值分别为 0.855 和 0.824。
确定了 TKA 期间和之后失血过多的风险因素，并设计了一个令人满意且可靠的列线图模型，以预测术后输血的风险。
