Construction and validation of a prediction model for postoperative infection within one week of totally implantable venous access port surgery

Abstract:

Objective To explore the risk factors for the infection within one week after totally implantable venous access port(TIVAP)surgery, and establish a prediction model. Methods 650 patients undergoing TIVAP surgery were included in the analysis. 480 cases from January 2017 to January 2022 were as the modeling set, and 170 cases from February 2022 to March 2023 were as the validation set. The modeling set was further divided into the infection group (n=17) and the non-infection group (n=463) according to the occurrence of postoperative infection. General condition and clinical characteristics of patients in two groups were compared using t-test and χ² test, and risk factors for postoperative infection were analyzed with binary logistic regression. Visualization was realized through nomogram. Results Among the 17 patients in the infection group of the modeling set, 6 had incisional infection, 10 had pocket infection, and only 1 had blood stream infection. Logistic regression analysis showed that age ≥65 years old (OR=3.093, 95%CI: 1.048-9.128, P=0.041), duration of surgery ≥2 hours (OR=3.286, 95%CI: 1.008-10.716, P=0.049), and pocket hematoma (OR=20.200, 95%CI: 6.750-60.455, P < 0.001) were independent risk factors for TIVAP postoperative infection. The receiver operating characteristic (ROC) curve showed an area under the curve of 0.833, sensitivity of 70.6%, and specificity of 88.1%, respectively. Calibration curve analysis showed a good consistency of the predictive model with the actual risk occurrence. Decision curve analysis (DCA) showed that the model had clinical benefits with risk threshold probability between 1% and 60%. Conclusion Age ≥65 years old, duration of surgery ≥2 hours, and pocket hematoma are independent risk factors for the infection within one week after TIVAP surgery. Prediction model constructed based on these factors has a good accuracy and calibration. Healthcare workers should keep in mind the high risk factors of infection and postope- rative infection care procedures, and strengthen postoperative observation on high risk patients.