What question did this study set out to answer?

To identify critical risk factors for surgical site infection (SSI) in type 2 diabetic patients undergoing oral surgery and develop a predictive model.

June 12, 2026Annali Italiani di Chirurgia0 citationsOpen Access

Evaluation of Critical Risk Factors and Development of a Predictive Model for Surgical Site Infection in Type 2 Diabetic Patients Undergoing Oral and Maxillofacial Surgery

TSTing SunShandong University JGJunxia GongNanjing Medical University WZWenqing ZhuNanjing Traditional Chinese Medicine Hospital

Key Points

To identify critical risk factors for surgical site infection (SSI) in type 2 diabetic patients undergoing oral surgery and develop a predictive model.
Retrospective case-control study with 110 diabetic patients with SSI and 110 matched controls.
Data collected included demographics, surgical details, blood glucose control, and antibiotic usage.
Nomogram developed and validated via bootstrap resampling and ROC curve analysis.
Predominant pathogens in SSI cases were Gram-negative bacteria, mainly Pseudomonas aeruginosa (18.18%) and Klebsiella pneumoniae (14.55%).
Key risk factors included incision type (OR = 3.789), severe periodontal calculus (OR = 4.092), poor glycemic control (OR = 3.347), and elevated CRP levels (OR = 1.627).
The nomogram yielded an AUC of 0.897, indicating excellent predictive performance for SSI.

Abstract

AIM: This study aims to explore the critical risk factors associated with surgical site infection (SSI) in patients with type 2 diabetes mellitus (T2DM) undergoing oral and maxillofacial surgery, and to develop a predictive model to support early risk stratification and guide targeted preventive approaches.METHODS: This retrospective, case-control study enrolled patients with T2DM who underwent oral surgery at Nanjing Stomatological Hospital, Nanjing University between June 2022 and June 2025. A total of 110 patients who developed postoperative SSI were included in the infection group. A total of 110 patients without SSI were selected as the control group, matched 1:1 according to age and sex. Detailed demographic and clinical data, including patient history, perioperative blood glucose control levels, surgical type, oral environment, and antibiotic usage, were collected from both groups. Risk factors associated with SSI were analyzed and compared between groups, and a nomogram prediction model was developed. Internal validation was performed using 5000 bootstrap resamples, and model performance was assessed via the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plots.RESULTS: Among the 110 patients who developed SSI after oral surgery, microbiological assessment identified Gram-negative bacteria as the predominant pathogens (62.73%), with Pseudomonas aeruginosa accounting for 18.18% and Klebsiella pneumoniae for 14.55% of the isolates. This was followed by Gram-positive organisms, which account for 34.55% of the pathogens, predominantly Staphylococcus aureus (10.91%). Multivariable logistic regression analysis showed that a surgical incision classified as Type II or III (vs Type I; Odds Ratio OR = 3.789), severe periodontal calculus in the oral environment (Grade III vs Grade I–II; OR = 4.092), poor blood glucose control (vs good; OR = 3.347), and elevated serum C‑reactive protein (CRP) levels (per unit increase; OR = 1.627) were independently associated with postoperative surgical site infection. A nomogram was constructed based on the equation: Logit (P) = 1.402 + 1.332 × (incision type) + 1.409 × (oral environment) + 1.208 × (blood glucose control) + 0.487 × (CRP). The maximum total score on the nomogram was 225 points, corresponding to a 90% predicted probability of postoperative SSI. The Hosmer-Lemeshow test (χ2 = 2.088, p = 0.230 > 0.05) demonstrated no significant difference between the observed and predicted outcomes of the nomogram model. The nomogram demonstrated excellent predictive performance, with an AUC of 0.897 (95% Confidence Interval CI: 0.855 to 0.939). CONCLUSIONS: The oral environment, perioperative glycemic control, CRP levels, and surgical incision type are independent risk factors associated with postoperative SSI. Establishing a prediction model based on these factors and implementing targeted interventions can effectively reduce infection in this high-risk cohort.

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