| 引用本文: | 麦吾菊丹·阿力甫,徐蕊,张瑞,胡玉,陈瑶,孙璇,孙荣鑫.骨科术后低分子量肝素抗Xa因子活性达标率的影响因素分析及其列线图预测模型的建立[J].中国现代应用药学,2026,43(14):143-154. |
| maiwujudanalifu,XU Rui,ZHANG Rui,HU Yu,CHE Yao,SUN Xuan,sunrongxin.Analysis of Factors Influencing the Compliance Rate of Anti-Factor Xa Activity with Low-Molecular-Weight Heparin in Orthopedic Surgery and the Establishment of a Nomogram Prediction Model[J].Chin J Mod Appl Pharm(中国现代应用药学),2026,43(14):143-154. |
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| 骨科术后低分子量肝素抗Xa因子活性达标率的影响因素分析及其列线图预测模型的建立 |
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麦吾菊丹·阿力甫, 徐蕊, 张瑞, 胡玉, 陈瑶, 孙璇, 孙荣鑫
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新疆医科大学第六附属医院
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| 摘要: |
| 目的 本研究旨在建立预测骨科术后低分子量肝素预防下肢DVT的抗Xa因子活性达标率模型，并探明其核心影响因素。方法 回顾性地收集就诊于新疆医科大学第六附属医院、骨科术后使用低分子量肝素预防下肢深静脉血栓患者228例。根据抗Xa因子活性，将患者分为达标组（156例，≥0.2 U?mL?1）与未达标组（72例，＜0.2 U?mL?1）。按7:3的比例分为训练集（160例）与验证集（68例）。收集患者人口学资料、生化指标、出血事件及抗Xa因子活性检测结果。在比较两组患者一般资料并进行相关系数热图分析的基础上，运用二元Logistic回归、LASSO回归与随机森林等机器学习方法，评估并筛选影响因素。进一步采用多因素Logistic回归构建列线图预测模型。通过受试者操作特征(ROC)曲线的曲线下面积(AUC)、校准曲线及决策曲线评估预测模型的效能。结果 本研究基于Logistic回归筛选出了AT-III活性、日剂量、PLT及Ccr等4个变量；经LASSO回归筛选出了日剂量、AT-III、PLT、Ccr及PT等5个变量；而基于Random Forest则筛选出了日剂量、AT-III、年龄、RBC、CRP等因素。并通过多因素Logistic回归分析以三种筛选中均得以保留的核心危险因素构建模型A，回归方程为YA= 0.052×日剂量 + 0.095×AT-Ⅲ，列线图综合分数为109，最终预测抗Xa活性达标概率为0.794。训练集AUC为0.756，敏感度98.2%，特异度41.7%，在验证集中AUC为0.722、敏感度81.8％及特异度58.3%。仅在两种筛选中得以保留的次要危险因素联合核心危险因素建模型B，回归方程为：YB=0.04日剂量 + 0.12 AT-III - 0.005PLT - 0.01Ccr，列线图综合分数为213，最终预测抗Xa活性达标概率为0.805。训练集中AUC为0.794，敏感度61.6%，特异度83.3%；在验证集中AUC为0.726、敏感度68.2％及特异度79.2%。以三种方法中筛选出的所有因素构建全变量模型C，回归方程为：YC=0.036日剂量 + 0.115AT-III - 0.004PLT -0.01 Ccr - 0.003年龄- 0.206PT + 0.251RBC - 0.004CRP；列线图综合分数为430，最终预测抗Xa活性达标概率为0.885。在训练集中AUC为0.805，敏感度69.6%，特异度79.2%；在验证集中AUC为0.775、敏感度77.3％及特异度66.7%。校准曲线与理想曲线贴合良好，模型有较好的预测准确性。结论 本研究发现日剂量与AT-III活性是影响骨科术后患者低分子量肝素抗Xa因子活性达标率的两个核心影响因素。所开发的列线图模型具有较高的预测价值与临床决策辅助价值。该系列模型有望为骨科患者制定个体化抗凝方案提供实用工具。 |
| 关键词: 抗Xa因子活性 低分子量肝素 骨科患者 深静脉血栓 |
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| Analysis of Factors Influencing the Compliance Rate of Anti-Factor Xa Activity with Low-Molecular-Weight Heparin in Orthopedic Surgery and the Establishment of a Nomogram Prediction Model |
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maiwujudanalifu, XU Rui, ZHANG Rui, HU Yu, CHE Yao, SUN Xuan, sunrongxin
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Department of Pharmacy,The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang
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| Abstract: |
| OBJECTIVE This study aimed to establish a model for predicting the target achievement rate of anti-Xa factor activity in the prevention of lower extremity deep vein thrombosis (DVT) with low-molecular-weight heparin (LMWH) after orthopedic surgery and to identify its core influencing factors. METHODS A retrospective analysis was conducted on 228 patients who received LMWH for lower extremity DVT prophylaxis after orthopedic surgery at the Sixth Affiliated Hospital of Xinjiang Medical University. Based on anti-Xa factor activity, patients were divided into a target-achievement group (156 cases, ≥0.2 U?mL?1）and a non-achievement group (72 cases, ＜0.2 U?mL?1）. They were randomly split into a training set (160 cases) and a validation set (68 cases) in a 7:3 ratio. Patient demographic data, biochemical indices, bleeding events, and anti-Xa activity results were collected. After comparing general data between the two groups and performing correlation heatmap analysis, machine learning methods including Binary Logistic Regression, LASSO Regression, and Random Forest were employed to evaluate and screen for influencing factors. Subsequently, a multivariable Logistic Regression was used to construct a nomogram prediction model. The model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS Based on Logistic Regression, four variables were identified: AT-III activity, daily dose, platelet count (PLT), and creatinine clearance (Ccr). LASSO Regression selected five variables: daily dose, AT-III, PLT, Ccr, and prothrombin time (PT). Random Forest identified factors including daily dose, AT-III, age, red blood cell count (RBC), and C-reactive protein (CRP). Using the core risk factors consistently retained across all three screening methods, Model A was constructed via multivariable Logistic Regression. The regression equation is YA = 0.052 × daily dose + 0.095 × AT-III. The nomogram's total score was 109, and the final predicted probability of achieving target anti-Xa activity was 0.794. The AUC in the training set was 0.756 (sensitivity 98.2%, specificity 41.7%), and in the validation set, it was 0.722 (sensitivity 81.8%, specificity 58.3%). Model B was built by combining the core risk factors with secondary risk factors retained in only two screening methods. Its regression equation is YB = 0.04 × daily dose + 0.12 × AT-III - 0.005 × PLT - 0.01 × Ccr. The nomogram's total score was 213, with a final predicted probability of 0.805. The training set AUC was 0.794 (sensitivity 61.6%, specificity 83.3%), and the validation set AUC was 0.726 (sensitivity 68.2%, specificity 79.2%). A full-variable Model C incorporated all factors selected by the three methods. Its regression equation is YC = 0.036 × daily dose + 0.115 × AT-III - 0.004 × PLT - 0.01 × Ccr - 0.003 × age - 0.206 × PT + 0.251 × RBC - 0.004 × CRP. The nomogram's total score was 430, and the final predicted probability was 0.885. The training set AUC was 0.805 (sensitivity 69.6%, specificity 79.2%), and the validation set AUC was 0.775 (sensitivity 77.3%, specificity 66.7%). Calibration curves showed good alignment with the ideal curve, indicating satisfactory predictive accuracy for the models. CONCLUSION This study identified daily LMWH dose and AT-III activity as the two core factors influencing the target achievement rate of anti-Xa activity in orthopedic postoperative patients. The developed nomogram models demonstrate high predictive value and potential for aiding clinical decision-making. This series of models is expected to provide practical tools for formulating personalized anticoagulation regimens for orthopedic patients. |
| Key words: anti-xa factor activity low-molecular-weight heparin orthopedic patients deep vein thrombosis |
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