Sun Zhenmin, Yang Nan, Wang Lei, Zhou Jiansuo, Zhang Hua, Wang Jun
Department of Transfusion, Peking University Third Hospital, Beijing, China.
Beijing HealSci Technology, Beijing, China.
Clin Biochem. 2025 Jan;135:110856. doi: 10.1016/j.clinbiochem.2024.110856. Epub 2024 Dec 1.
DESIGN & METHODS: We collected patients who underwent two-segment and three-segment posterior lumbar decompression and fusion internal fixation surgery in our hospital from 2016 to 2021. A total of 24 preoperative indicators were analyzed, covering medical history, demographic characteristics, segment, operator and laboratory test results. We used a logistic regression model to optimize the model's feature selection. The predictive model was constructed using the multivariable logistic regression method with all included methods, and a nomogram was created to display the model. Activated partial thromboplastin time, surgeon volume, American Society of Anesthesiologists classification, body mass index, and the number of fusion and fixation lumbar segments were used to construct the predictive model. The predictive model's discrimination, calibration, clinical applicability, and rationality were evaluated.
The predictive model's area under the receiver operating characteristic curve is 0.723, with a 95% confidence interval of (0.685-0.760). The training set's decision curve analysis demonstrates that applying this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 40%.
This study developed a novel nomogram with relatively good accuracy to assist clinical doctors in assessing the high intraoperative bleeding risk in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits. By evaluating individual risk, surgeons can develop an individualized treatment plan to reduce the risk of intraoperative bleeding for each patient.
收集2016年至2021年在我院行两节段和三节段后路腰椎减压融合内固定手术的患者。共分析24项术前指标，涵盖病史、人口统计学特征、节段、手术医生及实验室检查结果。采用逻辑回归模型优化模型的特征选择。使用多变量逻辑回归方法及所有纳入方法构建预测模型，并创建列线图展示该模型。采用活化部分凝血活酶时间、手术医生手术量、美国麻醉医师协会分级、体重指数以及腰椎融合固定节段数构建预测模型。对预测模型的区分度、校准度、临床适用性及合理性进行评估。
预测模型的受试者操作特征曲线下面积为0.723，95%置信区间为（0.685 - 0.760）。训练集的决策曲线分析表明，当阈值概率在5%至40%之间时，应用此诊断曲线将增加净效益。
本研究开发了一种准确性相对较好的新型列线图，以协助临床医生评估门诊行后路腰椎减压融合内固定手术患者术中出血多的风险。通过评估个体风险，外科医生可制定个体化治疗方案，以降低每位患者术中出血的风险。
