Catheter spatial configuration and biological factors predict fibrin sheath–related dysfunction in totally implantable venous access ports

Background Fibrin sheath formation is a major cause of totally implantable venous access port (TIVAP) dysfunction, but tools for early risk assessment remain limited. Objectives To develop and validate an interpretable model for early post-implantation risk stratification and explore the hemodynamic context of catheter spatial predictors. Methods This single-center retrospective study included 507 patients divided into training, internal testing, and temporal validation cohorts. Demographic, laboratory, and catheter spatial variables were assessed. Nine predictors were selected using univariable logistic regression, Boruta, and least absolute shrinkage and selection operator (LASSO) regression. Eight machine-learning models were compared for discrimination, calibration, and clinical utility. The final model was interpreted using SHapley Additive Explanations (SHAP), and computational fluid dynamics (CFD) was performed in representative cases. Results XGBoost-linear showed stable discrimination, with areas under the curve of 0.891 in the internal testing cohort and 0.904 in the temporal validation cohort. SHAP analysis identified catheter spatial parameters as the leading predictors, while coagulation and inflammatory indices provided complementary information. In the two representative cases, hemodynamic disturbances were observed near venous curvature, confluence, and catheter–wall contact regions, with heterogeneous wall-based indices in the lower superior vena cava (SVC) above the right atrium (RA). A simplified six-variable model retained comparable performance in temporal validation (area under the curve, 0.893) and was implemented as an interactive web-based tool. Conclusion The model may support interpretable early risk stratification and closer catheter-function monitoring, subject to prospective multicenter validation. Exploratory CFD analysis in two representative cases provided preliminary hemodynamic context for the model-identified spatial predictors.