To analyze the risk factors of PICC catheter-associated venous thrombosis in the upper limb of lung cancer patients after PICC catheterization, and to construct a preliminary risk prediction model presented in a visual graph.

A total of 453 patients with lung cancer who received PICC intravenous chemotherapy in the Department of Oncology of two third-class A hospitals in Beijing were selected from May 2019 to May 2022. According to whether the patients had PICC-associated venous thrombosis in half a year, they were divided into thrombe group(48 cases) and non-thrombe group(405 cases). The clinical data of the two groups of patients were collected, and R. 4.2.0 software was used for univariate analysis to screen the risk factors of thrombosis. Variables with P<0.05 were included in multivariate Logistic regression analysis to determine the independent risk factors of thrombosis. A line diagram of the prediction model was established, and the Bootstrap method was used to verify the model internally. The ROC curve was used to evaluate the differentiation of the prediction model, and the calibration curve was used to evaluate the calibration degree of the model.

The independent risk factors of PICC-associated venous thrombosis in patients with lung cancer were determined by Logistic regression analysis, including cancer stage, gender, pre-catheterization VTE risk assessment, pre-catheterization mobility assessment, history of cerebral infarction, history of cancer metastasis, and D-dimer level. The above risk factors were constructed by R software. The ROC curve analysis results showed that the area under the curve was 0.823 (95%CI: 0.767, 0.879), the optimal cut-off value was 0.087, the sensitivity was 0.792, and the specificity was 0.699. The model had good distinguishing ability and calibration ability.

Advanced cancer, male, high risk of VTE assessment, low mobility assessment, history of cerebral infarction, history of cancer metastasis, and high D-dimer level are independent risk factors for PICC-associated venous thrombosis in lung cancer patients. The line graph model built based on the above risk factors has a good ability to distinguish and calibrate.