Abstract:
Objective: To explore the risk factors of the peripherally inserted central catheter (PICC)-related venous thrombosis and correspondingly construct a nomogram risk prediction model.
Methods: The clinical data of 401 patients receiving PICC catheterization in our hospital from June 2019 to June 2022 were retrospectively analyzed. The independent influencing factors for venous thrombosis were predicted using logistic regression analysis, and significant indicators were screened to construct a nomogram for predicting PICC-related venous thrombosis. The difference in predictive efficacy between simple clinical data and nomogram was analyzed using a receiver operating characteristic (ROC) curve, and the nomogram was internally validated.
Results: Single-factor analysis showed that catheter tip position, plasma D-dimer concentration, venous compression, malignant tumor, diabetes, history of thrombosis, history of chemotherapy, and history of PICC/CVC catheterization were correlated with PICC-related venous thrombosis. Further multi-factor analysis revealed that catheter tip position, plasma D-dimer elevation, venous compression, history of thrombosis and history of PICC/CVC catheterization were the risk factors for PICC-related venous thrombosis. Based on binary logistic regression analysis, a nomogram prediction model for PICC-related venous thrombosis was constructed. The area under the curve (AUC) was 0.876 (95%CI: 0.818-0.925), with a statistically significant difference (P < 0.01).
Conclusion: The independent risk factors for PICC-related venous thrombosis are screened out, including catheter tip position, plasma D-dimer elevation, venous compression, history of thrombosis and history of PICC/CVC catheterization, and a nomogram prediction model with good effect is constructed to predict the risk of PICC-related venous thrombosis.
Reference:Chen L, Lu Y, Wang L, Pan Y, Zhou X. Construction of a nomogram risk prediction model for PICC-related venous thrombosis and its application. Asian J Surg. 2023 Jun 9:S1015-9584(23)00713-3. doi: 10.1016/j.asjsur.2023.05.043. Epub ahead of print. PMID: 37302889.