Analysis of Risk Factors and Construction of a Nomogram Prediction Model for Surgical Site Infection after Modified Radical Mastoidectomy

Abstract

Background:

Modified radical mastoidectomy (MRM) is a common surgical procedure in otology. However, postoperative surgical site infection (SSI) will lengthen hospital stay, raise healthcare expenses, and even lead to the death of patients. At present, there is relatively little research on the risk factors of SSI after MRM, especially the lack of an established risk prediction model.

Patients and Methods:

Patients who underwent MRM at Jining NO.1 People’s Hospital from 2020 to 2024 were selected. Univariate analysis and multivariate logistic regression analysis were used to identify the risk factors for SSI after MRM. On the basis of these factors, a Nomogram prediction model was constructed. The predictive value of the model was evaluated by constructing receiver operating characteristic (ROC) curve, calibration curve, and decision curve.

Results:

A total of 278 MRM patients met the inclusion criteria, 19 (6.83%) had developed SSI, and 259 (93.17%) had not. Multivariate logistic regression analysis confirmed diabetes, hypoproteinemia, neutrophil-to-lymphocyte ratio, antibiotic prophylaxis administered 0.5–1 h preoperatively, and operative time as independent factors (all p <0.05). The prediction model demonstrated excellent discriminative ability. Area under the curve of the ROC curve was 0.856, validated by Hosmer–Lemeshow testing (χ² = 6.265, p = 0.618), calibration curve, and decision curve analysis. These findings highlight the model’s robust accuracy and clinical utility in stratifying the risk of SSI after MRM.

Conclusions:

The Nomogram prediction model constructed based on logistic regression can effectively predict the risk of SSI after MRM, which is helpful for early clinical intervention and reducing the occurrence of nosocomial infection.