Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, ShenZhen, 518116, China.
Administrative Department of Nurse, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, ShenZhen, 518116, China.
Sci Rep. 2020 Jun 22;10(1):10090. doi: 10.1038/s41598-020-67038-x.
To analyze the incidence of PICC associated venous thrombosis. To predict the risk factors of thrombosis. To validate the best predictive model in predicting PICC associated thrombosis. Consecutive oncology cases in 341 who initially naive intended to be inserted central catheter for chemotherapy, were recruited to our dedicated intravenous lab. All patients used the same gauge catheter, Primary endpoint was thrombosis formation, the secondary endpoint was infusion termination without thrombosis. Two patients were excluded. 339 patients were divided into thrombosis group in 59 (17.4%) and non-thrombosis Group in 280 (82.6%), retrospectively. Tumor, Sex, Age, Weight, Height, BMI, BSA, PS, WBC, BPC, PT, D-dimer, APTT, FIB, Smoking history, Location, Catheter length, Ratio and Number as independent variables were analyzed by Fisher's scoring, then Logistic risk regression, ROC analysis and nomogram was introduced. Total incidence was 17.4%. Venous mural thrombosis in 2 (3.4%), "fibrin sleeves" in 55 (93.2%), mixed thrombus in 2 (3.4%), symptomatic thrombosis in 2 (3.4%), asymptomatic thrombosis in 57 (96.6%), respectively. Height (χ² = 4.48, P = 0.03), D-dimer (χ² = 37.81, P < 0.001), Location (χ² = 7.56, P = 0.006), Number (χ² = 43.64, P < 0.001), Ratio (χ² = 4.38, P = 0.04), and PS (χ² = 58.78, P < 0.001), were statistical differences between the two groups analyzed by Fisher's scoring. Logistic risk regression revealed that Height (β = -0.05, HR = 0.95, 95%CI: 0.911-0.997, P = 0.038), PS (β = 1.07, HR = 2.91, 95%CI: 1.98-4.27, P < 0.001), D-dimer (β0.11, HR = 1.12, 95%CI: 1.045-1.200, P < 0.001), Number (β = 0.87, HR = 2.38, 95% CI: 1.619-3.512, P < 0.001) was independently associated with PICC associated thrombosis. The best prediction model, D-dimer + Number as a novel co-variable was validated in diagnosing PICC associated thrombosis before PICC. Our research revealed that variables PS, Number, D-dimer and Height were risk factors for PICC associated thrombosis, which were slightly associated with PICC related thrombosis, in which, PS was the relatively strongest independent risk factor of PICC related thrombosis.
分析 PICC 相关静脉血栓形成的发生率。预测血栓形成的危险因素。验证预测 PICC 相关血栓形成的最佳预测模型。
连续纳入 341 例初治拟行化疗的肿瘤患者，入组我院静脉置管专科实验室。所有患者均使用相同规格的导管。主要终点为血栓形成，次要终点为无血栓形成的输液终止。排除 2 例患者。回顾性分析 339 例患者，血栓组 59 例（17.4%），非血栓组 280 例（82.6%）。采用 Fisher 评分法分析肿瘤、性别、年龄、体重、身高、BMI、BSA、PS、WBC、BPC、PT、D-二聚体、APTT、FIB、吸烟史、置管部位、导管长度、比例和数量等独立变量，然后进行 Logistic 风险回归、ROC 分析和列线图分析。
总发生率为 17.4%。静脉壁血栓形成 2 例（3.4%），“纤维袖套”55 例（93.2%），混合血栓 2 例（3.4%），症状性血栓形成 2 例（3.4%），无症状性血栓形成 57 例（96.6%）。身高（χ²=4.48，P=0.03）、D-二聚体（χ²=37.81，P<0.001）、置管部位（χ²=7.56，P=0.006）、数量（χ²=43.64，P<0.001）、比例（χ²=4.38，P=0.04）和 PS（χ²=58.78，P<0.001）在 Fisher 评分分析中两组间存在统计学差异。Logistic 风险回归显示，身高（β=-0.05，HR=0.95，95%CI：0.911-0.997，P=0.038）、PS（β=1.07，HR=2.91，95%CI：1.98-4.27，P<0.001）、D-二聚体（β0.11，HR=1.12，95%CI：1.045-1.200，P<0.001）、数量（β=0.87，HR=2.38，95%CI：1.619-3.512，P<0.001）与 PICC 相关血栓形成独立相关。在 PICC 置管前，以 D-二聚体+数量为新协变量的最佳预测模型被验证可用于诊断 PICC 相关血栓形成。
PS、数量、D-二聚体和身高是 PICC 相关血栓形成的危险因素，与 PICC 相关血栓形成有轻微相关性，其中 PS 是 PICC 相关血栓形成最强的独立危险因素。
