肺癌化疗患者PICC置管后血栓风险因素及预测模型研究-现代医学

肺癌化疗患者PICC置管后血栓风险因素及预测模型研究

单位：1. 康复大学青岛中心医院 PICC门诊, 山东青岛 266000;
2. 康复大学青岛中心医院门诊部, 山东青岛 266000;
3. 康复大学青岛中心医院心理科, 山东青岛 266000

分类号：R734.2

出版年·卷·期（页码）：2025·53·第十二期（1927-1933）

摘要：

目的：探讨肺癌化疗患者外周静脉穿刺中心静脉导管(PICC)置管后导管相关性血栓的独立危险因素，并构建列线图预测模型。方法：回顾性分析2022年1月至2024年12月期间在本院行PICC置管的200例肺癌化疗患者资料，依据PICC置管后是否发生血栓分为血栓组(85例)和非血栓组(115例)。比较两组患者的临床资料与穿刺置管特征，采用LASSO回归筛选变量，并通过多因素Logistic回归分析确定独立危险因素。根据危险因素构建列线图预测模型，采用受试者工作特征(ROC)曲线进行内部验证。结果：两组患者病理类型、临床分期、穿刺次数、导管末端位置、导管材质、置管后负重比较，差异均有统计学意义(均P<0.05)。多因素Logistic回归分析显示,临床分期(OR=3.764,95%CI 1.215~11.654)、穿刺次数(OR=47.673, 95％CI 9.710~234.063)、导管末端位置(OR=11.972, 95％CI 3.389~42.297)、导管材质(OR=20.401, 95％CI 5.164~80.594)、置管后负重(OR=43.786, 95％CI9.462~202.614)是肺癌化疗患者PICC置管后发生血栓的独立危险因素(均P<0.05)。ROC曲线显示，该模型预测肺癌患者发生血栓的曲线下面积(AUC)为0.966(95%CI 0.944~0.988)，敏感度0.904，特异度0.926，高于各单一因素的预测效能。内部验证经偏差校正后C-index=0.903,预测曲线贴近理想对角线；在阈值概率0.00~1.00范围内均获得正净收益,优于两条无效线。结论：临床分期、穿刺次数、导管末端位置、导管材质、置管后负重均是肺癌化疗患者PICC置管后发生导管相关性血栓风险的独立危险因素，基于上述因素构建的列线图预测模型有利于为肺癌化疗患者提供个性化血栓风险评估。

Objective: To identify independent risk factors for peripherally inserted central catheter(PICC)-related thrombosis in lung cancer patients undergoing chemotherapy, and to develop and validate a nomogram for predicting this complication. Methods: A retrospective analysis was conducted on clinical data from 200 lung cancer patients who received PICC placement for chemotherapy at our hospital between January 2022 and December 2024. Patients were divided into a thrombosis group(n=85) and a non-thrombosis group(n=115) based on the occurrence of PICC-related thrombosis. Clinical and catheterization characteristics were compared between the two groups. Variables were selected using LASSO regression, and independent risk factors were identified via multivariate Logistic regression analysis. A nomogram prediction model was then constructed based on these factors and internally validated using the receiver operating characteristic(ROC) curve and the bootstrap method. Results: There were significant differences between the two groups in pathological type, clinical stage, puncture times, catheter end position, catheter material, and weight-bearing after catheterization(all P<0.05). Logistic regression analysis showed that clinical stage(OR=3.764, 95%CI 1.215-11.654), puncture times (OR=47.673, 95%CI 9.710-234.063), catheter end position(OR=11.972, 95% CI 3.389-42.297), catheter material(OR=20.401, 95%CI 5.164-80.594), and post catheter weight(OR =43.786, 95%CI 9.462-202.614) were independent risk factors for thrombosis in patients with lung cancer after PICC(all P<0.05).ROC curve showed that the area under the curve(AUC) of the model for predicting thrombosis in lung cancer patients was 0.966, the sensitivity was 0.904, and the specificity was 0.926, which was higher than the prediction efficiency of each single factor. After internal verification and deviation correction, C-index=0.903, and the prediction curve was close to the ideal diagonal. Within the threshold probability range of 0.00-1.00, positive net income was obtained, which was better than the two invalid lines. Conclusion: The clinical stage, number of punctures, catheter tip location, catheter composition, and post-catheterization body weight are individual predictors for thrombosis in the lung cancer patients undergoing PICC catheterization. A model incorporating these factors can facilitate tailored assessments of thrombosis risk for lung cancer patients undergoing post-chemotherapy.

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