Design of a tool to predict the risk of postoperative infection in patients with dental implants based on the Nomogram model

TAO Xue; FENG Feiyan; JI Yingying; ZHU Hong; ZHANG Yiwen; TANG Peipei

doi:10.12102/j.issn.2095-8668.2025.05.007

Abstract：Objective:To construct a tool to predict the risk of postoperative infection in patients with dental implants based on the Nomogram model,and to verify its predictive value.Methods:A total of 150 patients with dental implants admitted to the Special Clinic Department of the Affiliated Stomatological Hospital of Nanjing Medical University from January 2022 to January 2024 were selected as the survey subjects.The general data questionnaire was used to collect data.The patients were divided into the occurrence group and the control group according to whether they had postoperative infection.The clinical data of the two groups were compared.And the indicators with statistical significance were included in the Logistic regression analysis to clarify the independent influencing factors.The risk prediction chart of the Nomogram model was designed using R software.And its predictive performance was verified.Results:Among the 150 patients,25 patients developed postoperative infection,with an infection rate of 16.67%.Logistic regression analysis showed that smoking and drinking history,comorbidities,use of antibiotics during surgery,brushing frequency,and preoperative education were independent influencing factors for postoperative infection of dental implants.The area under the receiver operating characteristic curve of the model was 0.782(95%CI 0.725-0.832).The sensitivity was 0.834.The specificity was 0.746.Tthe critical value probability was 0.329.The C-index was 0.741.And the prediction accuracy was 83.33%.Conclusions:In this study,the constructed risk prediction chart for postoperative infection of patients with dental implants based on the Nomogram model provides a set of scientific,intuitive,and effective risk assessment tool for clinical practice.Which is helpful to improve the quality of preventive nursing management of postoperative infection.