Objective To explore the influencing factors of catheter blockage in patients with implantable venous access ports (IVAP) and to construct a risk prediction model.

Methods A total of 563 patients who underwent IVAP implantation from January 2022 to March 2024 were retrospectively enrolled and randomly divided into a modeling group (n = 394) and a validation group (n = 169) at a 7∶3 ratio. Univariate analysis and multivariate logistic regression (bidirectional stepwise regression) were used to identify independent influencing factors for catheter blockage. A nomogram prediction model was then constructed and evaluated in terms of discrimination, calibration, and clinical utility.

Results The incidence of catheter blockage was 12.79% (72/563). Multivariate Logistic regression showed that body mass index (BMI) ≥ 24 kg/m² (OR = 3.535, 95%CI: 1.500–8.329), diabetes mellitus (OR = 3.723, 95%CI: 1.540–8.999), malignancy (OR = 3.161, 95%CI: 1.327–7.526), history of thrombosis (OR = 3.324, 95%CI: 1.425–7.755), elevated D-dimer (OR = 547.088, 95%CI: 46.093–649.462), and catheter retention time ≥ 6 months (OR = 2.633, 95%CI: 1.060–6.543) were independent risk factors, while regular maintenance (OR = 0.200, 95%CI: 0.080–0.499) was an independent protective factor (all P<0.05). The area under the receiver operating characteristic curve (AUC) of the nomogram model was 0.91 (95%CI: 0.87–0.96) in the modeling group and 0.94 (95%CI: 0.88–1.00) in the validation group. The Hosmer-Lemeshow test showed good calibration for both the modeling group (χ² = 10.782, P = 0.214) and the validation group (χ² = 3.744, P = 0.879). Decision curve analysis indicated a positive net clinical benefit.

Conclusion The IVAP catheter blockage risk prediction model constructed in this study demonstrates good predictive performance and clinical utility. It can assist healthcare providers in early identification of high-risk patients and implementation of individualized preventive interventions, thereby reducing the incidence of catheter blockage.