Abstract: Objective To construct a nomogram model to predict the risk of postoperative incisional infection in patients with cervical cancer combined with diabetes mellitus (DM)undergoing radical hysterectomy and to evaluate the model. 
Methods A total of 267 patients with cervical cancer and DM who received radical hysterectomy in our hospital were selected as the research subjects, including 97 patients with postoperative incision infection (infection group) and 170 patients without infection (uninfection group). Univariate and multivariate logistic regression analyses were used to screen for independent influencing factors related to postoperative incision infection. A nomogram model was plotted using R software and related packages. 
Results Compared with the uninfection group, the proportion of patients with age ≥ 55 years, body mass index (BMI) ≥ 25, retention time of drainage tube ≥ 7 days, length of hospital stay ≥ 14 days, and serum albumin<30 g/L in the infection group was significantly increased (P＜0.05). Compared with the uninfection group, the infection group had significantly increased levels of serum interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), C-reactive protein (CRP) and procalcitonin (PCT) (P＜0.05). Multivariate logistic regression analysis showed that age ≥ 55 years, BMI ≥ 25, retention time of drainage tube ≥ 7 days, length of hospital stay ≥ 14 days, as well as elevated levels of serum IL-6, TNF-α, CRP and PCT were independent risk factors for postoperative incision infection (P＜0.05). The nomogram used to assess the risk of postoperative incision infection in patients with cervical cancer and DM had good prediction accuracy (C index was 0.947), discrimination ［area under the receiver operating characteristic(ROC) curve (AUC)was 0.947］, consistency (mean absolute error of Hosmer Lemeshow goodness of fit test was 0.011), and clinical efficacy. 
Conclusion In this study, a nomogram model for predicting postoperative incisional infection in patients with cervical cancer combined with DM was constructed based on perioperative characteristics. The model may help to enhance awareness of infection control and provide a reference for the management of high-risk patients after radical hysterectomy.

Key words: uterine cervical neoplasms, diabetes mellitus, nomogram model