To explore the risk factors of mesh infection after incisional hernia repair, and to develop a predictive nomogram as visualized tool for clinical prediction of mesh infection.

475 patients with incisional hernias were retrospectively selected from Beijing Chaoyang Hospital of Capital Medical University from January of 2016 to December of 2018. Patients' basic information, surgery information and recovery information after surgery were collected, and mesh infection information was followed up. Predictors of mesh infection were screened by Lasso regression analysis, and further analyzed by multivariate logistic regression analysis. Then the final determined ones were applied to develop a predictive nomogram. The area under the ROC curve (AUC) was used to evaluate the predictive utility of the nomogram.

Among the 475 cases, 11 developed a mesh infection; the incidence was 2.3%. The findings of Lasso regression with multivariate logistic analysis demonstrated that body mass index (BMI) [OR=1.206, 95% CI (1.034, 1.407)]、diabetes [OR=6.484, 95% CI (1.233, 34.108)] and surgical site infection (SSI) [OR=37.095, 95% CI (4.253, 323.532)] were associated with mesh infection. The predictive nomogram was established using the above-mentioned variables. The AUC of the nomogram was 0.880 [95% CI (0.785, 0.975)].

We successfully established a predictive nomogram with high accuracy, which may be used to improve the early identification of mesh infection and the clinical outcome of patients with incisional hernias.