背景:全膝关节置换术(TKA)具有明显的出血风险,术后输血率不可忽视。通过术前确定患者的危险因素,节血策略已经发展到减少TKA后的失血量。在实践中,通常在术后进行血细胞计数,但很少改变患者的后续管理。本研究旨在确定与出血风险相关的术前变量,能够创建预测全膝关节置换术后输血风险和全血细胞计数需求的机器学习模型.
目的:根据术前资料,可以构建强大的机器学习预测模型来估计全膝关节置换术后的输血风险.
方法:这项回顾性单中心研究包括2020年1月至2023年3月期间进行的774例全膝关节置换术(TKA)。将25个术前变量集成到机器学习模型中,并通过递归特征消除算法进行过滤。选择最具预测性的变量并用于构建梯度增强机算法以定义总体术后输血风险模型。两组由TKA后输血和未输血的患者组成。确定了赔率比,曲线下的面积评估了模型的性能。
结果:在774名TKA手术患者中,术后输血100例(12.9%)。机器学习预测模型包括五个变量:年龄,身体质量指数,氨甲环酸给药,术前血红蛋白水平,和血小板计数。总体性能良好,曲线下面积为0.97[95%CI0.921-1],灵敏度为94.4%[95%CI91.2-97.6],特异性为85.4%[95%CI80.6-90.2]。用于评估TKA后输血风险的工具可在https://arthrorisk.com上获得。
结论:全膝关节置换术后输血的风险可以通过一个模型来预测,中度,或基于五个术前变量的高风险。该机器学习工具可在所有人都可以访问的网络平台上使用,易于使用,并具有较高的预测性能。该模型旨在限制例行检查的需要,取决于患者的风险。
方法:II;诊断研究。
BACKGROUND: Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient\'s risk factors preoperatively. In practice, a blood count is often performed postoperatively but rarely altering the patient\'s subsequent management. This study aimed to identify the preoperative variables associated with hemorrhagic risk, enabling the creation of a machine-learning model predictive of
transfusion risk after total knee arthroplasty and the need for a complete blood count.
OBJECTIVE: Based on preoperative data, a powerful machine learning predictive model can be constructed to estimate the risk of
transfusion after total knee arthroplasty.
METHODS: This retrospective single-centre study included 774 total knee arthroplasties (TKA) operated between January 2020 and March 2023. Twenty-five preoperative variables were integrated into the machine learning model and filtered by a recursive feature elimination algorithm. The most predictive variables were selected and used to construct a gradient-boosting machine algorithm to define the overall postoperative
transfusion risk model. Two groups were formed of patients transfused and not transfused after TKA. Odds ratios were determined, and the area under the curve evaluated the model\'s performance.
RESULTS: Of the 774 TKA surgery patients, 100 were transfused postoperatively (12.9%). The machine learning predictive model included five variables: age, body mass index, tranexamic acid administration, preoperative hemoglobin level, and platelet count. The overall performance was good with an area under the curve of 0.97 [95% CI 0.921 - 1], sensitivity of 94.4% [95% CI 91.2 - 97.6], and specificity of 85.4% [95% CI 80.6 - 90.2]. The tool developed to assess the risk of blood
transfusion after TKA is available at https://arthrorisk.com.
CONCLUSIONS: The risk of postoperative
transfusion after total knee arthroplasty can be predicted by a model that identifies patients at low, moderate, or high risk based on five preoperative variables. This machine learning tool is available on a web platform that is accessible to all, easy to use, and has a high prediction performance. The model aims to limit the need for routine check-ups, depending on the risk presented by the patient.
METHODS: II; diagnostic study.